In 1876, John Wanamaker, who had built one of America’s first department stores in Philadelphia, made the observation that would follow the advertising industry for the next 150 years: half of his advertising spend was wasted, and he could not figure out which half. The line became the most quoted complaint in the history of marketing not because it was clever but because it remained accurate for so long. You placed an ad in a newspaper and you hoped. You bought a television commercial and trusted a panel of 5,000 households to represent 330 million viewers. You ran a billboard and assumed that location, repeated exposure, and human memory were doing something useful. The whole expensive enterprise rested on inference.

Digital advertising was supposed to end all of that. The internet created, for the first time, a mechanism that could theoretically follow a consumer from the moment they encountered an advertisement to the moment of purchase, producing the causal proof that print and television could never provide. The industry built impressions and click-through rates and last-click attribution models, and spent a decade discovering that what it had built was good at measuring what happened after an ad and poor at measuring whether the ad had actually caused anything. A consumer who sees a sponsored link for shoes they already planned to buy and then buys them is not evidence that the ad sold the shoes. But the model said it was, and the budgets grew accordingly.

Retail media was the industry’s most compelling answer to this problem, and the argument for it deserves to be stated in its strongest form before being examined. A retailer occupies a position that no other advertising medium has ever occupied: it owns the transaction. When a shopper buys a product at Kroger, Kroger knows the shopper’s identity, purchase history, basket composition, frequency, price sensitivity, and the precise moment of conversion. That is not an inference about consumer intent. That is a receipt. If you can connect the ad to the receipt, you have finally given John Wanamaker his answer. The loop, the industry announced, was closed.

US advertisers spent $60.32 billion on retail media in 2025, according to EMARKETER’s December 2025 forecast, and will spend $71.09 billion in 2026, the fastest-growing major advertising channel in the world, expanding at roughly 18 percent annually against a broader market growing at 4.3 percent. The promise is on every slide deck and in every conference keynote: first-party data, purchase-intent audiences, closed-loop attribution, proof of ROI. The channel has attracted more than 200 retail media networks globally, with over 80 operating in the United States alone, ranging from Amazon Ads and Walmart Connect at the top to grocery chains including Albertsons Media Collective, Kroger Precision Marketing, and CVS Media Exchange; specialty retailers like Home Depot’s Orange Apron Media, Best Buy Ads, Lowe’s One Roof Media Network, and Sephora Media Collective; and smaller but notable networks including Sam’s Club’s Member Access Platform, Walgreens Advertising Group, Macy’s Media Network, Kohl’s Media Network, 7-Eleven’s Gulp Media, Ace Hardware’s RedVest Media launched in 2025, and Petco’s emerging network. Every retailer with a loyalty programme and a website is building one, and the commercial logic is obvious: advertising generates 50 to 70 percent operating margins against the 3 percent that the underlying retail business produces.

This Tuesday, April 14, the Interactive Advertising Bureau (IAB) convenes its Connected Commerce Summit in New York, the industry’s most focused annual gathering on the specific business of retail media; and the session titles that appear on the agenda are more honest than anything that tends to appear in a keynote at a retail media launch event. Executives from Colgate-Palmolive, Mondelēz, Bayer, Monster Energy, and Mars Wrigley will appear alongside platform leaders from Target Roundel, Best Buy Ads, Grocery TV, Dollar General, 7-Eleven, and Ace Hardware’s newly launched RedVest Media network to discuss sessions titled, with a directness that the industry has usually reserved for private conversations: ‘Making Omnichannel Measurement Real’, ‘Building the Transparent Commerce Media Tech Stack’, and ‘Connecting Off-Site Media Signals to Sales Outcomes’. The final panel of the day is titled ‘The Great Debate: Will Retail Media Be the Casualty of AI-Driven Commerce?’ — a question that, as this article will attempt to demonstrate, would have been considered professionally hazardous to ask in public as recently as two years ago. The right questions are finally being asked. The fact that they are being asked at the IAB’s industry summit rather than answered by the retailers selling the product is the point.

Bain and Company surveyed advertisers to determine how many trust the measurement numbers they receive from retail media networks. The answer is 6 percent. Not 60. Not 16. Six. The industry running on the most data-rich advertising proposition in the history of the medium is being trusted, by the people spending the money, at about the rate you would trust a stranger you met ten minutes ago. Wanamaker’s problem has not been solved. It has been monetised at scale.

“Only 6% of advertisers fully trust retailers’ reported media metrics.” — Bain and Company, 2025

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The measurement crisis begins with a methodology called last-touch attribution, and understanding it is the key to understanding everything that follows. An analysis of the top sixteen ad types across the six largest retail media networks in the US (Amazon Ads, Target Roundel, Walmart Connect, Kroger Precision Marketing, Criteo’s commerce media platform, and Instacart Ads) found that 80 percent of them assign 100 percent of the credit for a purchase to the final advertisement the buyer encountered before completing the transaction. Every other touchpoint in that buyer’s journey, the television commercial they saw six months ago that introduced the brand, the social media post that kept the brand salient, the display ad that appeared when they were considering switching to a competitor, is credited with zero.

The critical flaw in this methodology is not that it measures the wrong ad. It is that it frequently credits the ad for a sale that was already happening. Consider a consumer who has bought the same protein bars every three weeks for two years. On Thursday, Kroger Precision Marketing serves them a sponsored product for those bars. On Saturday, they buy the bars, as they were always going to. KPM’s attribution model records a successful campaign. The actual question, did the ad change this consumer’s behaviour in any way, was never asked, because answering it would require comparing the consumer’s behaviour against a control group that never saw the ad. That methodology has a name: incrementality. And it is precisely what the industry has been avoiding.

This avoidance is not accidental. Incrementality testing produces lower ROAS numbers than last-touch attribution, because it strips away the organic demand that the last-touch model was claiming as advertising success. If a brand’s loyal customers would have repurchased without any advertising at all, which many of them would, then the ROAS attributable to genuinely incremental sales is substantially lower than what the platform dashboard shows. Skai and Stratably surveyed 166 retail media advertisers for their 2026 State of Retail Media report and found that while 71 percent of brands now consider incrementality their most important KPI, only 20 percent are good at both measuring it and acting on the results. The remaining 80 percent are making budget decisions against numbers they cannot independently verify and privately do not trust.

The distinction between retention and acquisition campaigns makes this problem even starker, and it is a distinction that retail media’s standard measurement frameworks have almost entirely ignored. Retention campaigns reaching existing loyal buyers to maintain their purchase frequency look outstanding on a ROAS metric because the buyer was going to purchase anyway, and the ad gets full credit. Acquisition campaigns reaching consumers who have never bought the brand, or who are actively considering switching from a competitor require proving genuinely incremental demand, because there is no baseline purchase pattern to misattribute. They look worse on ROAS not because they are worse advertising but because they are doing harder work. The 2025 ANA study on retail media found that 62 percent of retail media budgets go to audiences who are already brand buyers. The brands spending $71 billion on retail media are using more than half of it to advertise to people who were already their customers, measuring the effectiveness of that spend with a methodology that guarantees a favourable result, and calling the combination a closed loop.

PepsiCo’s test with Skai capabilities across Amazon DSP, where the company specifically optimised for new-to-brand customers rather than existing buyers and measured results using incremental ROAS, unlocked over 80 percent new-to-brand ROAS, a metric that looks different from, and less flattering than, overall ROAS precisely because it is honest. That test is instructive not because PepsiCo discovered some breakthrough but because it demonstrated how different the numbers look when the measurement methodology is designed to find the truth rather than confirm the budget allocation. Jason Wescott of WPP Media has said the overreliance on ROAS as the benchmark of value is over, and independent, transparent measurement is the baseline. CJ Pendleton, Chief Strategy Officer at Matrixx CPG, has named the commercial consequence without diplomatic softening: the platforms that solve for incrementality will earn the lion’s share of CPG investment while those that don’t will likely die on the vine.

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Understanding why the measurement crisis persists requires understanding what retail media’s first-party data actually is, and what it is not, across the spectrum of networks that are selling it. At the top of the market, the data proposition is real. Amazon’s $68.63 billion in global advertising revenue in 2025, confirmed by the company’s Q4 earnings release, with $21.32 billion in that quarter alone is built on direct transaction data from hundreds of millions of consumers who have bought on Amazon’s platform. Amazon knows your purchase frequency, your basket composition, your price sensitivity, your device preferences, and the moment your repurchase window opens. Walmart, which generated $6.4 billion in global advertising revenue in 2025, up 46 percent year-over-year per its February 2026 earnings, is building toward comparable depth: CFO John David Rainey noted that advertising and membership together accounted for a full third of Q4 2025 operating income, describing a business where advertising has become structurally important to profitability rather than incidental to it.

But Amazon and Walmart together hold approximately 87.7 percent of US digital retail media advertising spend. Amazon at roughly 79.7 percent and Walmart at about 8 percent, per EMARKETER’s December 2025 platform analysis. EMARKETER further projects that approximately 89 percent of the $10.77 billion in incremental retail media growth in 2026 will go to those same two companies. That leaves the other 80-plus US retail media networks competing for a slice of the market that, in incremental terms, is barely growing for anyone else. They are competing for smaller budgets with weaker data infrastructure, using the same first-party data marketing language as Amazon, and delivering a product that Georgia-Pacific, the consumer goods company, described in terms that should give every CPG brand media buyer pause.

Georgia-Pacific evaluated approximately 40 retail media networks over several years as it shifted more than 20 percent of its total media budget to the channel. Its Digital Marketing Director’s conclusion, reported in 2024, was precise and devastating: they told you they had first-party data, but they were not always giving first-party data to activate against them. The company was not describing an experience with a single bad actor. It was describing a pattern across a systematic evaluation of 25 networks it trialled. The data being sold as first-party was, in a material number of cases, something else: modelled segments, third-party augmentation, or loyalty data with coverage gaps so significant that the targeting precision implied in the pitch bore little relation to what was actually available.

EMARKETER identified the structural reason in February 2026: even genuinely robust first-party datasets reflect only shoppers who have engaged with the retailer’s loyalty or login system. That excludes irregular buyers, lapsed customers who still influence category dynamics, high-value prospects who are in-market but have never used that retailer’s card, and entirely new-to-category consumers who represent the actual growth opportunity for most CPG brands. A brand trying to find new customers which is the primary purpose of brand marketing investment is using targeting data that systematically excludes the people it most needs to find. The closed loop that retail media promises is real for the customers who are already inside it. For everyone outside it, retail media is programmatic display with better demographic labels.

The way retailers build audience segments compounds this problem in ways that rarely surface in a sales meeting. The standard taxonomy available across most retail media networks (heavy category buyers, lapsed purchasers, competitive brand switchers) consists of demographic buckets with transaction labels attached, and those constructs were designed for programmatic display targeting in 2010. A retailer with years of weekly purchase history across tens of millions of loyalty members has, in principle, the raw material to build something genuinely different: individual-level purchase cycle models that predict when a specific household is entering its consideration window, price elasticity signals that identify the moment a loyal customer is vulnerable to a competitor’s promotion, declining frequency patterns that flag at-risk customers before they switch. The most sophisticated networks like Kroger Precision Marketing through its 84.51 analytics arm, which works with purchase data from over 60 million loyalty households, are beginning to build in this direction. Most networks are selling the same five segments to every brand that will buy them.

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The audience segmentation problem becomes more severe, not less, when retail data is used to target audiences outside the retailer’s own properties. Off-site retail media, the practice of using a retailer’s first-party data to serve ads on external publisher websites, connected television, programmatic display, and audio is the fastest-growing component of the channel. In Q4 2025, 60 percent of Walmart Connect’s self-serve display spend went to offsite inventory, per Tinuiti’s Digital Ads Benchmark Report. Target’s Roundel reports that more than 30 percent of partner media spend now happens off its owned platforms. The retailers’ audiences are being used to buy ads everywhere. The question of whether the measurement travels with them has a troubling answer.

CTV is where the audience segmentation problem is most visible, and most consequential. Retail media’s extension into connected television is one of the industry’s most celebrated developments: Amazon’s Prime Video ad inventory, which delivered ads to an average of 315 million viewers globally in Q4 2025; Walmart Connect’s CTV capability through the $2.3 billion Vizio acquisition; Kroger Precision Marketing’s partnership with Magnite to extend into CTV. The proposition is genuinely compelling. Use purchase-based audience data to reach household-level audiences in the most engaging media environment available. The measurement reality is described, with characteristic bluntness, by Advertising Week’s 2026 analysis of CTV performance: measurement is still stuck two or three years behind the channel’s growth, and most CTV campaign reporting is comparing apples and oranges.

Comscore’s 2026 State of Programmatic Report, based on more than 200 media buyer respondents, found that 87 percent say cross-channel performance metrics inside programmatic platforms are critical or valuable for decision-making, and simultaneously that CTV measurement challenges like inconsistent reporting windows, limited cross-device visibility, and fragmented clean room integrations, remain significant barriers. The problem is not that CTV is unmeasurable. It is that the audience segment that entered the CTV buy as a retail-data-defined group of heavy category buyers cannot be tracked through the living room, through the consideration period, and back to a verified purchase at a retail location, without a data infrastructure connecting all three environments that most networks have not yet built. Kroger’s partnership with Magnite is a step toward it. The measurement framework that closes that loop does not yet exist at scale.

Audio faces the same fundamental challenge, with an additional layer: the format is inherently harder to connect to a purchase outcome because a consumer listening to a podcast while cooking dinner is not in a purchase moment the way a consumer on a search results page is. Programmatic audio is growing as Comscore’s 2026 report projects audio will capture 10 percent of programmatic budgets on average, with 21 percent of marketers reallocating from linear radio. Comscore itself launched audio targeting and measurement capabilities with The Trade Desk in January 2026, providing contextual targeting across 4.6 million podcasts and campaign measurement without relying on identity signals. The measurement progress is real. The gap between a retail-defined audience segment applied to a streaming audio buy and a verified purchase outcome at a physical or digital retail location remains wide enough that most brands treating audio as a performance channel are doing so on attribution windows that cannot distinguish advertising causation from coincidence.

Programmatic display, the channel that retail media was supposed to supersede with its superior first-party data, has quietly absorbed the segmentation problem rather than solved it. When a retailer’s audience segments are activated through The Trade Desk, DV360, or any other major DSP and served as display impressions across the open web, the impression appears on a publisher’s page, the cookie or identifier connects to the retail audience profile, and the attribution model waits for a subsequent purchase on the retailer’s platform. If the purchase occurs on a different retailer like the consumer saw Kroger’s display ad, considered the product, and bought it on Amazon because the price was lower, Kroger’s model counts a miss while the sale happened regardless. If the consumer was already planning to buy, Kroger’s model potentially counts a hit for a behaviour the ad did not influence. The closed loop that retail media promised was closed at the transaction point. It remains open everywhere the ad ran before the transaction.

Topsort, the commerce media platform that partnered with Skai in September 2025 to enable unified access to retail media networks across 40 countries through a single API integration, is building toward the cross-channel measurement consolidation that these problems require. Its architecture treats the retailer audience as a portable targeting signal rather than a siloed network property, which is the structural shift that cross-channel retail media measurement needs. Pentaleap, which in July 2025 launched real-time bidding for sponsored product ads through its Teads partnership enabling, for the first time, fully programmatic sponsored products on retailer sites is creating the supply-side transparency that makes cross-channel attribution auditable. These are meaningful technical contributions. They are building components of the infrastructure that the measurement problem requires. The full infrastructure does not yet exist anywhere except at Amazon, and only partially at Walmart.

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The most interesting and least honestly discussed extension of retail media is the physical store, and the timing of its emergence as an industry priority reveals something important about the online channel’s maturity. When onsite inventory runs out, when every available sponsored product slot on a search results page is sold and the user experience of adding more would drive shoppers away, the channel’s next move is either offline or inside someone else’s inventory. Both have happened simultaneously. The offsite expansion into CTV and programmatic display has been widely discussed. The in-store screen expansion has received less scrutiny, which is unfortunate, because it is where the measurement crisis is most acute and where the parallel with a $200 million failure in a Dallas warehouse is most directly relevant.

Retail stores were advertising’s original closed-loop environment. The shopper arrives with a purchase intent, encounters a display, makes a decision, and the transaction happens within the same four walls. Brand managers have understood this since the 1950s, and the physical store, the endcap, the in-aisle display, the checkout lane has always been the most valuable last-mile advertising surface available to CPG brands. What retail media has now added is the possibility of making that surface programmatic: digital screens in the aisle that can be targeted, measured, and optimised in near-real time, connected to the same loyalty data infrastructure that powers online retail media. This is the promise of Digital Out-of-Home within the retail environment, a $7.4 billion market in North America in 2026, according to Fortune Business Insights, and it represents a genuine opportunity that traditional DOOH advertising has historically left uncaptured because it was not connected to transaction data.

Kroger committed in June 2025 to a nationwide expansion of in-store digital screens through its partnership with Barrows Connected Stores, with Christine Foster, SVP of Kroger Precision Marketing, noting that the grocer sees the physical store as one of the most underutilised platforms for brand storytelling. Albertsons launched a digital display network in 80 stores in July 2025 through Stratacache. Hy-Vee deployed more than 10,000 screens across 400-plus locations with Grocery TV. Best Buy announced full-store takeover packages. Even Save-a-Lot is plotting an in-store rollout across 900 locations. Paul Brenner, SVP of retail media at In-Store Marketplace, reported in January 2026 that his firm had 20 active RFPs from retailers for 2026 screen deployments, some requesting thousands of units per retailer. The investment thesis is clear: over 91 percent of food and beverage purchases still happen in physical stores, yet 99 percent of retail media advertising has been online. The physical store is the single largest measurement gap in the entire retail media landscape.

The DOOH connection matters here because it is the technical framework that makes in-store screens programmable and measurable at scale. A screen in the frozen food aisle, running on DOOH infrastructure, can in principle be served to a shopper whose loyalty profile indicates they have not purchased a particular brand for six weeks — a lapsed buyer, targeted at the precise moment they are standing in front of the product. That is a targeting capability that outdoor DOOH on a highway billboard can never match, and it is the reason that the smart money in advertising infrastructure is watching in-store retail media very carefully. When a screen at the Kroger dairy case can trigger a sponsored display based on the household loyalty profile of the shopper scanning their app at the same time, and when the subsequent basket scan closes the attribution loop at the same transaction, the closed-loop measurement that online retail media has been claiming becomes something it actually can deliver.

The reason it has not delivered this yet is the Cooler Screens lesson, made permanent in a Dallas warehouse. Cooler Screens installed 10,300 smart refrigerator door screens across 700 Walgreens locations, claiming 100 million monthly impressions from sensors that turned out to be counting bathroom trips, shopping carts, and shadows. The screens generated $215 per door annually against a contractual minimum of roughly double that. When CEO Arsen Avakian cut data feeds to 100 stores during the December 2023 holiday season in a contract dispute, screens went white across refrigerator aisles at the industry’s most commercially sensitive moment. The $200 million lawsuit followed. The hardware, nearly $50 million worth, is sitting in Texas. The measurement system had been counting the wrong things, calling them impressions, and reporting the result as evidence of a closed loop. In-store retail media, run correctly, could finally deliver what the DOOH industry has always promised and what grocery advertising has always needed. Run without genuine measurement infrastructure, it produces lawsuits and warehouses.

Christine Foster’s comment about not wanting the screens to feel like Times Square is the tension that the industry has not resolved. A screen designed to be invisible is not a high-attention advertising placement. A screen designed to attract attention is a disruption to the shopping experience the retailer depends on. These two goals are not reconcilable without measurement that tells you which placement intensity produces the best combination of advertising effectiveness and shopper retention which is precisely the measurement that the IAB’s December 2025 in-store standards framework, which defines store zones and impression metrics but does not address individual-level attribution through loyalty data or cross-channel purchase journeys, does not yet require.

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Criteo, the independent commerce media company that is probably the industry’s most instructive case study, is not a retailer. It is a technology platform operating both as a demand-side platform helping brands buy across retail media inventory and as a supply-side platform helping retailers monetise their audiences and it built the technical infrastructure on which a significant share of the industry runs. Criteo is not Amazon or Walmart. It is the middleware. That distinction matters enormously for understanding its measurement position, because Criteo sits in the measurement gap itself: it processes transactions between retailers and brands, which means it sees both the ad delivery and the attributed purchase signal, but it does not own either the retailer’s closed-loop data or the brand’s independent measurement. In September 2025, Criteo became Google’s first onsite retail media partner through Search Ads 360. The announcement positioned this as a significant expansion. It also reveals that an independent infrastructure company, after building integrations with more than 200 retailers globally and completing a $380 million acquisition of IPONWEB in 2022, needed Google’s advertiser demand to make its retail media infrastructure commercially viable. That is not a failure. It is a structural reality about where the leverage in retail media actually sits.

The agencies that manage the advertising investment of the brands spending these billions are navigating the same structural terrain, with varying degrees of conflict. Publicis Groupe, which reported 5.9 percent organic growth in Q2 2025 and maintained its position as the industry’s growth leader, owns both CitrusAd, a retail media technology platform powering multiple retailer networks and Epsilon’s data infrastructure, which powers audience segmentation for clients including Kroger. Publicis is the agency advising brands on which retail media networks to trust, while simultaneously operating the technology those networks run on and the data infrastructure those networks use. CEO Arthur Sadoun’s framing of the company’s 2026 strategy as building agentic solutions that deliver business outcomes at a moment when 95 percent of AI projects fail is directionally accurate. The structural position his company occupies in the retail media ecosystem is worth noting alongside it. WPP’s $60 billion annual media investment operation, operating under the WPP Media brand after retiring GroupM in 2025, is in a different position: managing at massive scale without the same proprietary data infrastructure, which creates both a cleaner measurement posture and a more exposed commercial position in a world where data ownership is becoming the primary competitive variable.

The $60 million FTC settlement that Instacart reached in December 2025 is, on the surface, a consumer protection story: the commission alleged that the company’s advertising and billing practices misled consumers with promises of free delivery that masked mandatory fees adding up to roughly 15 percent of order value. But it connects to the retail media measurement story in a specific way. Instacart is one of the larger retail media networks outside the Amazon-Walmart duopoly, generating close to $1 billion in annual US advertising revenue according to EMARKETER projections. Its advertising proposition to CPG brands rests on the same closed-loop claim as every other network: reach high-intent grocery shoppers at the moment of purchase, measure results through Instacart’s transaction data. The FTC’s finding that Instacart’s representations to consumers were not accurate about its own fees, the basic facts of how much the platform costs, raises a question about whose methodology is being used to measure everything else the platform claims about the effectiveness of the advertising it sells. A platform that was found to have misrepresented its cost structure to consumers was simultaneously providing CPG brands with closed-loop performance data about the advertising those same consumers were shown. The FTC settlement is about consumer protection. The measurement implication is about advertiser protection.

The Instacart case is the Cooler Screens pattern at a different level of the ecosystem. Both companies built their retail media products on a specific measurement claim. Cooler Screens on impression counting that was measuring bathroom trips; Instacart on a consumer transparency standard that the FTC determined was not being met. And both cases ultimately revealed that the numbers being used to justify advertising investment were produced by parties with competing interests in what those numbers showed. This is the structural condition that produces the 6 percent trust figure. It is not that retailers and platforms are deliberately falsifying data. It is that the entity producing the measurement has a financial interest in the measurement being favourable, and the entity receiving the measurement has no independent mechanism to verify it.

This is precisely where demand-side and supply-side platforms, the DSPs and SSPs that sit between advertisers and retail media networks, have an underutilized and under-appreciated role to play. Because independent DSPs are not retailers and do not own the inventory they are buying, they occupy a structurally different measurement position than the retail networks themselves. When The Trade Desk runs a campaign across Walmart Connect, Kroger Precision Marketing, and Instacart simultaneously, it has cross-network impression data that none of those three networks can see independently, data that, in principle, allows a brand to detect frequency overlap, audience duplication, and attribution double-counting that individual network dashboards systematically obscure. The problem is that The Trade Desk’s Retail Index marketed as a neutral benchmark for cross-network performance is produced by the same DSP routing spend through those networks, with financial agreements in place with the data providers whose segments are being scored. Grading your own homework is a problem at the retailer level. Outsourcing the grading to your DSP, whose revenue depends on routing your spend efficiently, does not fully solve it. Andrew Casale of Index Exchange has said publicly that some supply-side vendors extract more value than they create is a notable observation from the CEO of an SSP, and one that applies with equal force to some DSPs. What the independent DSP ecosystem could provide, if it chose to build the capability, is cross-network attribution that no single retail media network can produce: a unified view of where an advertiser’s spend went, what audiences it actually reached in deduplicated terms, and which network attributed the same consumer’s purchase as its own success. Rajeev Goel of PubMatic, whose AgenticOS platform is building toward agent-driven media buying, has described the value chain compression that agentic AI enables as an opportunity to create more transparent, direct connections between buyer and seller. The measurement opportunity in that compression is real. The DSPs and SSPs that build toward it, rather than routing around the transparency question, will earn a position in the measurement ecosystem that the retailers and their networks cannot credibly occupy.

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Adobe Analytics reported that AI-driven traffic to US retail sites increased 805 percent year-over-year on Black Friday 2025 and 670 percent on Cyber Monday. ChatGPT now drives more than 20 percent of referral traffic to Walmart.com, approximately 15 percent to Target, and 10 percent to eBay, per a December 2025 MetaRouter analysis. Salesforce reports that 39 percent of consumers, and more than half of Gen Z, already use AI for product discovery. These numbers are still small in absolute terms relative to human-browsed retail traffic. They are not small in directional terms.

The sponsored product being the foundational revenue unit of retail media, projected to account for $38 billion in advertiser spend in 2025 is a format built for human browsing. A consumer loads a search results page, sees a placement, processes the creative, and decides. An AI agent managing a household’s weekly grocery order does not load a search results page. It queries the inventory API, checks price and availability, cross-references reviews and nutritional data against stated preferences, and completes the transaction without ever rendering a sponsored product or registering an impression in any attribution model. The ad never ran. The attribution window opened and closed around a transaction it could not see.

This is where Generative Engine Optimisation connects directly to the measurement problem, and the connection is not a trend analogy. It is a causal relationship. GEO as the practice of ensuring that product data is complete, structured, and machine-readable so that AI agents can confidently discover and recommend it addresses exactly the data quality gap that has made retail media measurement unreliable from the beginning. The reason last-touch attribution dominates retail media is partly technical inertia, but it is also a symptom of insufficient data discipline: if the signal connecting an ad impression to a purchase outcome were as clean and structured as a product API response, incrementality testing would be computationally trivial. GEO forces brands to build the data infrastructure that makes products findable by agents. That same infrastructure, applied to the advertising side, makes ad exposures linkable to purchase outcomes with the kind of precision that closed-loop measurement has promised and not delivered. The brands building GEO discipline are, almost without realising it, building the foundation for measurement that actually works.

Walmart’s Sparky, the agentic shopping assistant that reached half of all Walmart app users and generated 35 percent higher average order values than standard search, has already begun testing sponsored prompts inside its conversational interface that an advertiser can pay to be the recommendation Sparky surfaces when a consumer asks for a product category. Walmart CEO Dave Guggina said on the Q4 2025 earnings call that the company is learning as it goes on how advertising works in agentic commerce. That honesty is more valuable than any measurement dashboard Walmart could have published instead. Amazon’s Rufus assistant, with 300 million users and $12 billion in attributed sales in 2025, is facing the same question. The retailer that invented retail media and the retailer that followed it most closely are both trying to figure out what an ad looks like when the shopper cannot see it.

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The measurement problem is solvable. This is the critical point, because the failure to solve it is commercial rather than technical, and commercial barriers move when the financial incentives change. The infrastructure that would genuinely close the loop has been described accurately by the industry’s own standards bodies, the IAB and MRC published retail media measurement guidelines in January 2024 but guidelines describe what should happen, not what does. What does happen is that the entity producing the measurement has a financial interest in that measurement being large, and the entity receiving the measurement has no systematic mechanism for independent verification. There is a phrase for this in education: grading your own homework. In advertising, it has been the norm for thirty years. In retail media, which was supposed to solve the problem, it has become the structural foundation.

A small ecosystem of independent measurement companies has built the tools that could break this cycle, and the most interesting thing about them is how small they remain relative to the problem they are trying to solve. Haus, founded by former Google and Facebook data scientists and backed by meaningful venture capital, runs incrementality measurement through geo-based holdout experiments: it divides a brand’s geographic markets into exposed and unexposed groups, measures the difference in sales outcomes, and produces an estimate of genuinely incremental lift that is not filtered through the retailer’s attribution model. Measured, which raised $21 million in Series B funding and works with hundreds of brands across their full media portfolio, offers multi-touch attribution combined with incrementality testing, allowing brands to evaluate not just whether retail media is working but how it compares against other channels competing for the same budget dollar. Northbeam applies machine learning to attribution modeling across channels, helping brands understand which combination of touchpoints actually contributed to a sale rather than which single touchpoint the last-touch model credited. NCSolutions, which connects advertiser campaign data to actual purchase outcomes using purchase-based measurement drawn from over 150 million US households via retail loyalty programmes, operates at the intersection of panel-based measurement and first-party retail data — providing a verification layer that does not require the retailer’s own attribution system to be trusted. Neustar, now a TransUnion company, offers campaign measurement and media mix modelling that treats retail media as one input among many rather than a closed system that defines its own success.

These companies exist. They work. The brands that use them describe, consistently, a gap between platform-reported ROAS and independently measured incrementality that is large enough to change budget allocation decisions. Ryan Verklin, Retail Media and Paid Media Senior Lead at Bayer — who will participate in the IAB Connected Commerce Summit’s ‘Great Debate’ panel next week — won AdExchanger’s Best In-House Media Operation award specifically by pioneering self-serve retail media with incremental measurement at the centre of the strategy rather than the margin. The capability exists. It is not scaling. And the reason it is not scaling is a cascade of commercial barriers that are worth naming precisely.

The first barrier is data access. An independent measurement firm running a holdout experiment needs impression-level exposure data from the retail media network, specifically, a list of which consumers were served the ad and when, linked to purchase outcomes after the campaign. Most retail media networks do not provide this data to third parties. Amazon Marketing Cloud offers impression-level data within its clean room environment, which means Haus or Measured can run holdout analysis there but only within AMC’s infrastructure, under AMC’s terms, using AMC’s identity graph. This is meaningful progress and simultaneously a measurement system that still relies, at the data layer, on the entity being measured. Kroger Precision Marketing, to its credit, has been more forthcoming with clean room access for external measurement partners than most networks its size. Most networks below the top tier provide aggregate reporting with limited granularity and no access to individual impression data that would support true holdout analysis.

The second barrier is commercial inertia. A brand that is receiving ROAS numbers of 5 or 6 from a retail media network dashboard, and has not run an independent incrementality test, has no empirical reason to doubt those numbers except for the intuition, increasingly widespread, that they seem too good to be consistently true. Running an incrementality test requires budget, time, internal analytical resources, and the willingness to accept a result that might require going back to the CFO and explaining why the advertising that was reporting excellent returns has been producing lower incremental sales than the dashboard suggested. The brands that have run these tests tend not to publicise the results, because the results frequently require awkward conversations with retail media network account teams. The brands that have not run these tests tend to remain comfortable with the dashboard numbers, because the alternative is uncomfortable. This is not irrational behaviour. It is rational behaviour in an environment where the measurement system was designed by a party with an interest in large numbers, and where the cost of discovering the truth includes the cost of revising a budget allocation that current stakeholders have committed to.

The third barrier is scale. Haus, Measured, and Northbeam are formidable technical products serving sophisticated brands. They are not The Trade Desk, which reported nearly $3 billion in annual revenue. They are not IAS or DoubleVerify, which have achieved the market position and MRC accreditation to be included in standard campaign specifications at major agencies. IAS earned its first MRC accreditation for retail media measurement specifically viewability within Amazon’s ecosystem in November 2025, nearly a decade after IAS was founded. MRC accreditation for incrementality measurement within retail media networks, which is the certification that would actually address the 6 percent trust problem, does not exist. The MRC accreditation process takes approximately two years and costs hundreds of thousands of dollars per network. An independent measurement firm that wanted to achieve MRC-accredited incrementality certification across the ten largest US retail media networks would be looking at a multi-year, multi-million-dollar credentialing exercise before any agency could include it in a standard measurement specification. The economics of that path are not attractive for a venture-backed startup competing with platforms that have unlimited marketing budgets and brand recognition built over decades. The measurement ecosystem needs these companies to be significantly larger than they are, and the path to being significantly larger runs through advertiser mandates that do not yet exist, which requires advertiser sophistication that is still being built, which requires campaign results that are still too often treated as proprietary, which means the ecosystem stays smaller than the problem requires.

Closing the loop on this cascade requires, first, genuine holdout testing: a portion of the target audience is held back from seeing the ad, and the difference in purchase rates between exposed and unexposed groups is measured. Second, attribution windows must be set empirically based on documented purchase cycles for specific product categories, not selected by the platform to maximise attributed conversions, and disclosed before the campaign runs. Third, and this is the requirement that the industry’s most powerful players have the least incentive to adopt, the measurement itself must be certified by parties with no financial relationship to the platforms being measured. Integral Ad Science’s November 2025 MRC accreditation for viewability measurement within Amazon’s ecosystem is genuine progress. It measures viewability, which is the floor of advertising standards: it tells you the ad was technically capable of being seen by a human. It does not measure incrementality, which is the ceiling: it does not tell you whether seeing the ad changed what that human did. The gap between viewability and incrementality is the gap between the measurement the industry has accredited and the measurement it actually needs.

Fourth, and most foundationally, the audience data being sold as first-party must actually be first-party. This means standardised data quality audits making the kind of systematic evaluation Georgia-Pacific conducted over years available to every brand without requiring a multi-year self-funded programme. The startups building the next generation of retail media infrastructure, Pentaleap with RTB for sponsored products, Koddi with unified commerce media, Kevel with API-first ad serving built for retail constraints rather than adapted from display advertising are creating technical conditions under which data provenance becomes auditable as a structural feature of the platform rather than a voluntary disclosure. The question is whether the incumbent networks follow. The answer will depend on whether the brands writing the cheques start requiring data quality audits the way they started requiring brand safety controls a decade ago. They started requiring brand safety controls when a YouTube adjacency scandal made not requiring them commercially embarrassing. The retail media equivalent of that moment has not yet arrived. The Instacart FTC settlement and the Cooler Screens warehouse in Texas are early drafts of it.

86 percent of commerce media decision-makers in North America and Europe identified strengthening measurement and attribution as a high or critical priority for 2026, according to the November 2025 Koddi and Forrester survey. Only 12 percent had reached an advanced state of full-funnel measurement capability. The gap between the priority and the capability is 74 percentage points. That gap is the measurement crisis expressed in the industry’s own numbers about itself, and it is the most honest data point that the $71 billion market has produced.

· · ·

Possible 2026 in Miami this month, and every industry conference before and after it, will feature a version of the retail media keynote that describes a closed-loop advertising channel with unmatched first-party data and measurable ROI. The presentation will be compelling, and some of it will be accurate. At Amazon, the loop genuinely approaches something close. At Walmart, it is being built with intention and honesty about the gaps. At the other 80-plus US retail media networks competing for a market that is barely growing for anyone outside the top two, the closed loop is a marketing claim running on measurement infrastructure that a Fortune 500 consumer goods company, after years of systematic evaluation, found did not consistently deliver what was promised.

The corridor conversations at Possible 2026, between sessions and over dinner, are more instructive than the keynotes. Brand marketers who have run holdout experiments describe a gap between platform-reported ROAS and independently measured incrementality that, once seen, cannot be unseen. Media buyers who have managed campaigns across seven or eight networks simultaneously describe the operational overhead of managing seven or eight different attribution windows, seven or eight different data definitions, and seven or eight different dashboards, none of which speaks to any other, as something that makes budget allocation feel more like administrative management than strategic investment.

The measurement crisis is not a secret. Everyone in the industry knows it exists. The 6 percent trust figure is not a surprise to the people spending the $71 billion. It is a number they recognise. The crisis persists because the current system, whatever its limitations, produces ROAS numbers that are large enough to justify the next budget request, and because the retailers providing those numbers have structured their measurement methodology to ensure that outcome. The change will come not from a technical breakthrough but from brands refusing to accept measurement that the platform producing it also defines and certifies, and from AI agents routing around the sponsored product in ways that force the industry to build data infrastructure that makes products findable by machines and outcomes attributable to the advertising that preceded them.

John Wanamaker complained in 1876 that half of his advertising was wasted and he could not tell which half. Retail media raised $71 billion on the promise that it had finally answered his question. Six percent of the people spending that $71 billion believe the answer. The other 94 percent are still waiting, not for better technology, but for measurement that belongs to them rather than to the platform selling them the inventory.

Somewhere in a warehouse outside Dallas, $50 million worth of refrigerator door screens sits in the dark, counting nothing, measuring nothing, attributing nothing. The sensors that were supposed to close the loop were counting shadows. In retail media, they still are.

ABOUT THE AUTHOR

Amit Goel writes longform articles about digital media and advertising technology, careers, technology and product management at blog.careerplot.com. Previous articles in adtech domain covered demand-side adtech economics, programmatic supply chain margins, AI’s disruption of publisher economics, and the conflict between The Trade Desk and the agency holding companies. All financial data in this article is drawn from primary sources including company earnings releases and SEC filings. All quotes are attributed to named individuals and on record.

THE OBLIGATORY DISCLAIMER

Written in a personal capacity. All data comes from publicly available, verified sources. No employer input. No confidential sources. Claude assisted with research and structure. The analysis, the frustrations, and the sarcasm are entirely mine. If you find a factual error, say so in the comments and I will correct it.

On Thursday, March 26, 2026, OpenAI’s communications team sent out a press release that was, depending on your point of view, either a milestone or an indictment. The company’s advertising pilot, which had launched on February 9 with a careful announcement and explicit promises of restraint, had crossed $100 million in annualized revenue in under six weeks. More than 600 advertisers and agencies had joined the program. Target, Ford, Adobe, WPP Media, Omnicom and Dentsu were among them. Criteo had become the first formal advertising technology partner, announced on March 2. David Dugan, who had spent the previous decade helping build Meta’s advertising infrastructure into one of the most profitable machines in commercial history, had been named to lead OpenAI’s global advertising solutions team earlier that same week. Self-serve advertiser tools were scheduled for April. International expansion to Canada, Australia and New Zealand was already underway.

The reaction in the publishing industry was muted in the way that the reaction to bad news often is when the bad news has been expected for so long that the actual arrival of it produces mainly silence. The OpenAI advertising business was not a surprise. The timing and scale of it were.

Six weeks. One hundred million dollars in annualized revenue. From a standing start, reaching fewer than twenty percent of eligible users daily, with a product that places ads at the bottom of responses rather than integrated into the conversation. What that number implies about the eventual ceiling, when self-serve tools open the market in April, when international expansion adds hundreds of millions of additional users, when ad load increases from its deliberately restrained pilot level, is a question the publishing industry is now collectively trying not to think about too carefully.

To understand why the publishing industry finds this particular press release so difficult to absorb, you need to understand a chain of causation that stretches back at least three years, and possibly much longer. It begins with a simple fact: the content that powers OpenAI’s models, the reporting, the analysis, the commentary, the wikis, the forums, the reference works, the decades of archived journalism and scholarship and creative writing that gave the models the vocabulary and knowledge and context to produce fluent, accurate, useful answers, was produced by publishers. Not by OpenAI. Not by any technology company. By the journalists, editors, researchers, bloggers, academics and writers who built the open internet’s information infrastructure, piece by piece, over the past three decades.

The models consumed that infrastructure. The platforms that trained on it are now running advertising businesses. The publishers who produced it are, in many cases, fighting for survival. This article is about how that situation came to exist, what it is specifically costing the industry, what a very small number of publishers are doing about it that actually works, and what the publishing economy will look like if the structural problems are not addressed in time.

The Extraction, and How It Actually Worked

In August 2025, a document leaked that the internet’s AI economy would have preferred to keep private. Drop Site News published what appeared to be an internal Meta list of websites that the company had been systematically scraping to train its AI models. The list covered approximately six million unique websites. It included publishers of every kind: national newspapers, specialist magazines, academic journals, forums, recipe sites, legal databases, fan wikis, and personal blogs. It included content published behind robots.txt restrictions that Meta’s crawlers had bypassed. The scraping was not incidental. It was systematic, ongoing, and apparently indifferent to the commercial or legal preferences of the people who had produced what was being taken.

Meta is not an outlier here. It is, if anything, the only company for whom the documentation became public. OpenAI, Google, Anthropic, Mistral and virtually every other major AI developer built their models on the same open internet, using largely the same approach. The distinction between the companies lies in their legal strategies afterward, not in whether they scraped. Some scraped and then offered licensing deals to defuse legal risk. Some scraped and insisted it constituted fair use. Some scraped, got sued, and started negotiating. None of them asked first. The data was there. The legal framework was unclear. The commercial opportunity was enormous. The decision to wait for permission was not made.

The mechanism by which AI companies converted that scraped content into a threat to publishers operates in three stages, each of which would be damaging in isolation. Together they constitute something genuinely new in the history of platform disruption.

The first stage is training. When a large language model is trained on the text of the New York Times, the Financial Times, Reuters, Wikipedia and several hundred thousand other publications, it absorbs not just factual information but editorial judgment, contextual awareness, analytical frameworks, and writing style. The model does not merely learn facts. It learns how to think about and explain facts in ways that readers find credible and useful. That learned credibility is not the model’s. It was lent to the model by the institutions whose work formed its training data.

The second stage is inference. When a user asks ChatGPT a question, the model does not retrieve an article from a publisher’s database. It generates an answer by drawing on patterns it learned during training, patterns that were derived from publisher content. The publisher who produced the underlying reporting receives no payment for this use, no attribution in the answer, and no traffic in the form of a click. The Cloudflare data from June 2025 makes the asymmetry visible with unusual precision: Google, the search engine that at least sends some users back to publishers, generates fourteen crawls for every one referral it eventually delivers. OpenAI, which trained on publishers’ content and now answers questions based on that training, generates 1,700 crawls per single referral sent back. Anthropic, whose Claude product is built on the same content economy, generates 73,000 crawls for every referral. These numbers are not fees for content licensing. They are the ratio at which AI systems extract content versus the rate at which they return traffic value to its producers.

The third stage is advertising. This is the stage that the March 26 announcement represents. Having trained on publisher content, having built a user base of ~900 million weekly active users as of February 2026, OpenAI is now running advertisements against the audience that gravitates to its product partly because that product absorbed the knowledge and credibility of the institutions that produced the content it was trained on. The advertisers buying those ads are, in many cases, the same advertisers who used to buy from publishers. The budget that flows to OpenAI’s ad inventory is budget that does not flow to a publisher’s ad inventory. The loop is closed.

Kint’s data from the nineteen publishers in the DCN network, spanning national newsrooms and global entertainment brands, showed median Google Search referral traffic down almost every week across eight weeks in May and June 2025, with losses outpacing gains two to one. The worst single weeks were particularly stark: news publishers plunged sixteen percent in the week of May 25th. Non-news publishers fell seventeen percent in the week of June 22nd. These are not minor fluctuations or seasonal patterns. They are sustained, structural losses from content categories that Google’s AI specifically summarises at the top of the search results page.

What makes Kint’s framing more penetrating than the typical publisher complaint is that he identifies the mechanism precisely. Google is not simply building a better product that happens to reduce traffic. It is taking publisher content, using it to build an AI answer system, placing that system at the top of the search results it controls through a monopoly position that Judge Amit Mehta is currently adjudicating in the US Department of Justice antitrust case, and thereby channelling the advertising revenue that publisher traffic would have generated into Google’s own products. The content is still producing value. It is just that Google is capturing that value rather than the publisher.

What the Numbers Actually Show

The problem with how the publishing industry discusses its AI-related decline is that it frames everything as a traffic problem. Traffic is a symptom. The disease runs deeper, and it is attacking publisher revenue from multiple directions at the same time, each through a different mechanism, each at a different speed, and each compounding the others in ways that make the aggregate impact considerably worse than any individual measure suggests.

Start with the traffic. Google search traffic to publishers declined globally by thirty-three percent in the twelve months to November 2025, according to Chartbeat data covering 2,500 publisher websites. The US figure was thirty-eight percent. Google Discover, which feeds mobile users through Android and Google’s own apps and which publishers had been building supplementary strategies around, fell twenty-one percent over the same period. Pew Research Center’s study of sixty-eight thousand real user search queries, published in July 2025, found that users clicked on results eight percent of the time when AI Overviews were present, compared to fifteen percent without them. That is a forty-seven percent relative reduction in click-through rate from the same ranking position, simply because Google placed its AI summary above the publisher’s result. Advanced Web Ranking research found that AI Overviews average around 169 words and include approximately seven links when expanded. Once expanded, the first organic result often appears around 1,674 pixels down the page. The publisher is still technically present in the search results. They are just considerably less visible, considerably less likely to receive a click, and considerably less able to do anything about it.

The financial arithmetic that connects traffic decline to revenue collapse is where most external commentary misses the severity of what is happening. A twenty-five percent drop in traffic does not produce a twenty-five percent drop in programmatic revenue. It produces a forty to fifty percent drop, because publisher cost structures are largely fixed while revenue is variable. AdMonsters’s analysis from October 2025 made this precise: the infrastructure, the editorial staff, the technology stack, the licensing fees, the operational overhead, all of these continue at approximately the same level whether the site receives ten million monthly visitors or seven million. When advertising revenue falls faster than costs, the math becomes existential at a speed that quarterly earnings reports can disguise for only so long.

The BuzzFeed number in that table deserves a moment of separate attention. BuzzFeed, which once represented the future of digital media with sufficient conviction that it filed for a billion-dollar IPO, disclosed in its March 12, 2026 earnings release that it lost fifty-seven million dollars in revenue in 2025, that it does not currently have sufficient assets to fund its 2026 cash obligations, and that executives have substantial doubts about the company’s ability to continue operating as a going concern. This is a company that employed Pulitzer Prize-winning journalists, that genuinely pioneered the study of how content spreads on the internet, that at various points in its history was studied as a model for what a modern media company could be. It did not fail because it produced bad journalism. It failed because the business model underpinning its journalism depended on a discovery and distribution layer that has been systematically redirected by platforms that used its content to train the systems that replaced it.

BuzzFeed is the most visible casualty but not the only one. The Washington Post cut approximately one hundred staff in January 2026, with seventy-three positions in the advertising department alone. Dotdash Meredith, now renamed People Inc., cut 226 positions in October 2025 following 143 layoffs in January. HuffPost eliminated its editor-in-chief position. Travel blog The Planet D, founded in 2008, lost half its traffic when Google’s AI Overviews launched in May 2024, laid off staff in response, then lost ninety percent more traffic following those layoffs, and ceased publication entirely. Music blog Stereogum lost seventy percent of its advertising revenue in 2025, its founder Scott Lapatine attributing most of it to AI Overviews and the rest to Facebook and X deprioritising external links. Press Gazette tracked more than three thousand journalism job cuts across the UK and United States during 2025, a figure that does not include the smaller publications that closed entirely without filing public notices.

The CPM numbers in the table require equal attention, because they are the mechanism connecting traffic decline to financial distress in publishers that have not yet collapsed. Display CPMs fell thirty-three percent year-on-year in January 2025. Video CPMs fell thirty-nine percent. These are not modest corrections. They are the market pricing the fact that there are fewer verified audiences to reach, that cookie deprecation is degrading targeting precision, and that programmatic buyers increasingly prefer the authenticated, identifiable audiences in walled gardens and private marketplace deals over the anonymous, algorithmically-assembled audiences in the open programmatic exchange. A publisher who lost thirty percent of their traffic and also saw their CPMs fall thirty-three percent did not suffer a combined thirty percent revenue reduction. They suffered something approaching a fifty to sixty percent revenue reduction, before accounting for the fixed costs that did not fall with either number.

The Deals, the Lawsuits, and the Distance Between Them

Publishers have pursued two distinct strategies in response to the situation described above, and the tension between them tells you more about how the industry sees itself than any amount of conference panel discussion. Some have negotiated. Some have litigated. Most have done both, sequentially or simultaneously, with varying degrees of enthusiasm. Neither approach has solved the underlying structural problem, and both have produced outcomes whose adequacy is a matter of legitimate disagreement.

The licensing deals represent the clearest acknowledgment that publisher content has quantifiable commercial value in the AI economy. News Corp’s five-year agreement with OpenAI, reportedly worth more than $250 million according to the Wall Street Journal’s reporting from May 2024, covers the Wall Street Journal, Barron’s, MarketWatch, the New York Post, The Times and The Sunday Times of London, and a range of Australian titles. Robert Thomson, News Corp’s CEO, described it as historic and said it acknowledged that there is a premium for premium journalism. The Financial Times struck a deal worth between five and ten million dollars annually. Dotdash Meredith received at least sixteen million dollars, confirmed by IAC’s chief operating officer Chris Halpin on a Q3 2024 earnings call, who said the lion’s share of a $4.1 million year-on-year licensing revenue increase was driven by the OpenAI deal. Axel Springer agreed to a deal worth twenty-five million dollars plus variable fees. John Wiley and Sons received a one-time twenty-three million dollar payment for its academic book archive, confirmed by interim CEO Matthew Kissner on an earnings call. Curiosity Stream projected $19.6 million in AI licensing revenue from its 210,000-hour factual video library for 2025, revenue that contributed to the company’s first profitable quarter.

These deals are real money. They are also strikingly modest when measured against what the companies paying for them are spending to build the infrastructure that requires them. OpenAI committed to infrastructure partnerships with Microsoft and Amazon that total nearly three hundred billion dollars. The entire market for AI content licensing, across all publishers, all deals, all platforms, represents a fraction of a percent of what the AI companies are investing in the technology that consumes publisher content. The publishers are the essential raw material supplier for an industry that is spending hundreds of billions of dollars on the factory, and they are negotiating with considerably less leverage than that description would suggest they should have.

The Axios deal is the one that points most clearly toward what a well-structured licensing arrangement could look like. Axios signed a three-year agreement with OpenAI in 2025 under which ChatGPT answers user questions using Axios content, with clear attribution and links back to Axios’s site. What made the deal structurally different from a simple content license was that OpenAI also provided funding for Axios to open four new local newsrooms, in Pittsburgh, Kansas City, Boulder and Huntsville. The deal was not just a revenue payment for past content. It was an investment in future content production, structured around the recognition that OpenAI’s models need fresh, authoritative, locally-reported material to remain genuinely useful, and that this material requires funded journalism organizations to produce it. This is not philanthropy. It is supply chain management.

The litigants have pursued a different calculation. The New York Times filed suit against Microsoft and OpenAI in December 2023, alleging copyright infringement in the training of the models. The suit remains active. Penske Media Corporation, the owner of Rolling Stone, Variety, Deadline and The Hollywood Reporter, sued Google in September 2025, specifically arguing that AI Overviews had reduced affiliate revenue from shopping links by more than a third compared to the end of 2024, and that the summaries appear on approximately fifteen to twenty percent of search results related to PMC’s content. This suit is important because it tests a legal theory that the licensing deals cannot: that the harm occurs not in training but in inference, in the moment when Google’s AI system generates a summary that substitutes for a click that would otherwise have reached the publisher. Chegg filed an antitrust complaint in February 2025 after its non-subscriber traffic fell forty-nine percent in a single year, alleging that Google used Chegg’s educational content to train systems that now compete directly with Chegg’s core product. The argument is not just about compensation for use. It is about the use of publisher content to build a competing service.

The distance between the deals and the lawsuits is the distance between two readings of the same situation. In one reading, the AI companies are building new products that benefit users and happen to reduce publisher traffic as a side effect, and the correct response is to negotiate fair compensation for the content that enables those products. In the other reading, the AI companies are using publisher content to build products that directly substitute for what publishers provide, redistributing publisher revenue to themselves, and the correct response is to treat this as the anticompetitive conduct it arguably is. Both readings can be simultaneously true. The industry is living with that ambiguity while the revenue declines continue at a pace that is not waiting for the courts to resolve it.

The Publishers Who Are Not Dying, and the Specific Decisions That Explain Why

The publishing industry’s failures have been better documented than its successes, partly because failure is more dramatic and partly because the publishers who are navigating this period well tend not to discuss their strategies in public. There is, however, a clear and consistent pattern across the case studies that are available, and the pattern does not support the conclusion that AI disruption is uniformly destroying the industry. It supports the conclusion that publishers who built direct, authenticated relationships with their audiences are surviving, and publishers who built their economics on anonymous, algorithmically-discovered traffic are not.

The Wall Street Journal sells ninety percent of its inventory direct, against the authenticated first-party data of its paid subscriber base. Advertisers who use WSJ’s first-party data in their targeting experience a thirty-seven percent higher renewal rate than those who do not, according to a Google Ad Manager case study. That renewal rate premium is the commercial expression of something the advertising industry has always known in principle but rarely quantified: reaching the right audience in the right context produces results that justify the premium, and the publisher who can verify the audience has the leverage to price accordingly. BCG’s global digital marketing maturity survey confirms the structural advantage: first-party data delivers twice the advertising revenue per impression of non-first-party inventory. Newsweek implemented a first-party data strategy that produced average eCPM increases of fifty-two percent, with peaks reaching two hundred and twenty-four percent. These are not marginal improvements. They are the outcome of a single foundational decision: treat the subscriber relationship as a data asset, not just a revenue line.

That quote, from the person responsible for revenue at the Wall Street Journal’s parent company, is the most compressed description available of what actually works. Logged-in consumption. Knowledge of the impression. Precision targeting that produces outcomes advertisers pay a premium for and renew because they can verify the outcomes. Everything in that sentence depends on one prior decision: requiring the audience to identify itself in exchange for access to the content. The publisher who has not made that decision is not merely leaving a premium on the table. They are operating without the foundational data asset that makes premium advertising possible.

Apartment Therapy Media, the parent company of the lifestyle brand Kitchn, provides a useful case study of what the transition looks like in practice. The company’s president Riva Syrop told Digiday that after traffic declines in the first half of 2025, the team spent the following three months intensively building newsletter offerings and improving its membership and retention programs. The specific goal was to increase the quantity of monetizable known visitors relative to anonymous ones. The Kitchn brand was up fifteen to twenty-five percent year-on-year every month in the period that followed. The mechanism Syrop described is worth quoting: the purpose of getting people as members is first-party data. Enabling them to have a personal experience. And then bringing that data back to the advertisers. This is not a sophisticated technological intervention. It is a commercial decision to invest in a direct audience relationship, and it is producing measurable returns in a period when publishers whose equivalent strategy is “wait for Google traffic to come back” are watching their numbers continue to deteriorate.

The WAN-IFRA sixth AI report, based on a survey of more than one hundred media leaders conducted in the second quarter of 2025, found that seventy-five percent of publishers report efficiency improvements from AI in their operations, sixty-four percent report better content production, and fifty-five percent report faster publishing times. Only nine percent can point to direct revenue gains from AI at this stage. That gap, between operational savings and commercial improvement, is the space where the decisions being made right now will determine which publishers are viable in 2028. The operational savings are real and immediate. AsiaOne in Singapore deployed an AI classification model for advertising targeting and saw revenue rise twenty percent while cutting sales staff forty percent, freeing the remaining team to focus on higher-value commercial activity. The South China Morning Post saves more than three hundred hours monthly through AI-assisted summarization, translation and editorial workflow tools. Legit.ng in Nigeria used AI to halve translation times, expanding Hausa-language coverage without proportional staff increases. Oliver Wyman’s 2025 analysis projects production cost reductions of twenty to thirty percent and labour cost savings of up to forty percent for publishers that implement AI at the operational level without using it to replace the editorial judgment that makes content worth producing.

The publishers using those savings to hire more reporters, assign more original investigations, and produce the kind of journalism that AI cannot summarize because AI cannot conduct it are the ones building the content advantage that will matter in a world where commodity content is free and distinguished content commands the premium. The publishers using those savings to produce more commodity content faster are accelerating toward a market where they will be competing against systems that produce equivalent output at literally zero marginal cost. This is a strategic decision, not a technical one, and it needs to be made consciously rather than by default.

The SSP’s Last Chance

The supply-side platform was a genuine innovation in 2008. Publishers with unsold inventory and no way to efficiently access multiple buyers simultaneously needed exactly what the SSP provided: aggregated demand, real-time auction mechanics, and the technological infrastructure to monetize remnant inventory that was otherwise worthless. That problem was real, and the companies that solved it created genuine value and were appropriately rewarded for doing so. The problem is that fifteen years later, the SSP market is largely still selling the same solution to a problem that has been transformed beyond recognition.

The Jounce Media 2025 State of the Open Internet report establishes the operational reality with unusual clarity. Publishers currently average 24.5 SSP partner relationships. Those 24.5 relationships generate bid requests of which nine out of ten go unprocessed because the system is congested with duplicate signals. Rebroadcasting, the practice of routing the same impression through multiple supply paths to generate the appearance of additional scale, accounts for thirty-seven percent of all display auctions and thirty-two percent of all video auctions. The publisher maintaining 24.5 SSP relationships is paying the operational, latency and complexity costs of those relationships in exchange for a bidstream that is mostly noise. The model that was designed to maximize demand for publisher inventory has, through proliferation and duplication, created a system where the majority of that demand is theoretical rather than real.

In August 2025, The Trade Desk made a change to its Kokai buying platform that received considerably less attention than it warranted. The company reclassified supply-side platforms from direct supply chain participants to resellers. Under Kokai’s scoring and prioritization system, SSP bid requests now receive lower priority than The Trade Desk’s own OpenPath direct connections. The implication, which Jeff Green had forecast in an earnings call months earlier, is that publishers who connect directly to The Trade Desk through OpenPath are delivering their inventory through a higher-priority path than publishers who route through even the largest SSP. Green said in that call that he expected the biggest content owners to integrate directly through OpenPath and essentially be their own SSP, particularly in connected television and audio. OpenPath’s volume grew what Green described as “many hundreds of percentage points” in 2025. OpenAds launched in January 2026 with nine publishers including the Guardian, Hearst Magazines, Newsweek and BuzzFeed, creating a transparent auction environment where publishers see bid-level data that has historically been visible only to the SSP.

The Ventura Ecosystem, announced in February 2026, integrates OpenPath, UID2 and EUID, OpenAds and OpenPass into a unified architecture that allows premium publishers to connect authenticated audience data directly to advertiser demand without SSP intermediation. OpenTTD, launched in March 2026 as a unified developer portal, is the infrastructure layer that makes this architecture accessible to publishers who want to build on it. What The Trade Desk is assembling, piece by piece and with considerable tactical patience, is a direct-to-demand infrastructure for publishers who have authenticated audiences and the commercial sophistication to operate within it.

The SSPs that are growing in this environment share a common characteristic: they have moved from being inventory aggregators to being audience intelligence platforms. PubMatic launched its AI-powered monetization platform in September 2025, integrating natural language deal setup through PubMatic Assistant, which allows publishers to configure PMP and programmatic guaranteed deals by describing them in ordinary language rather than navigating a complex interface. PubMatic Connect helps publishers package and activate their first-party audience data for buyers at the deal level. CEO Rajeev Goel’s statement at launch, that the myth of the passive publisher is over, describes the direction correctly: the SSP’s value proposition for the next decade is helping publishers communicate the specific, differentiated value of their authenticated audiences to buyers who will pay a premium for them, not aggregating anonymous inventory through an automated auction.

Magnite’s April 2025 unification of the SpringServe ad server with its programmatic SSP capabilities, deployed initially with Disney Advertising, Paramount, Roku, Samsung and Warner Bros. Discovery as clients, represents the other viable architectural direction: combining ad serving and yield management in a single system so that the publisher controls every dimension of their monetization strategy in one place. The Jounce Media March 2025 Supply Path Benchmarking Report verified that the unified platform connects buyers to ninety-nine percent of US streaming supply on a dollar-weighted basis. A publisher using this system knows what their inventory is worth, controls how it is accessed, and does not route their audience intelligence through a third party that takes a fee in exchange for providing less information than the publisher could access directly.

Onetag, the European exchange and curation platform, took a different approach when it acquired Aryel in March 2026. Aryel specializes in immersive and interactive advertising formats, including augmented reality and rich interactive experiences, that generate creative performance data alongside placement decisions. By integrating Aryel’s creative technology with Onetag’s global exchange connecting more than 2,000 publishers and 5,000 advertisers, the combined entity is building something that neither a standard SSP nor a standard creative platform can offer: a system where the quality of the creative experience, the quality of the editorial environment, and the AI-driven optimization of campaign outcomes are integrated at the infrastructure level. This matters because it addresses the question that the SSP market has never adequately answered, which is: beyond demand aggregation, what specific and measurable value does the intermediary add to the transaction? Onetag’s answer is: better outcomes, verified by creative performance data and audience quality signals, optimized in real time by AI decisioning that has access to all three elements simultaneously.

The publisher choosing SSP partners in 2026 is making a decision with long-term structural consequences. The partners who are building intelligence layers around authenticated publisher audiences, who are investing in tools that help publishers communicate audience value rather than aggregate anonymous inventory, who can demonstrate in log-level data that their participation improves yield rather than merely adding a fee to a transaction that would occur anyway, those are the relationships worth maintaining. The partners who cannot answer the question of what specific value they add to a publisher’s authenticated first-party audience inventory are the ones that The Trade Desk’s Kokai algorithm is already beginning to route around, and that AI-powered supply path optimization will route around with increasing efficiency over the next two years.

The Infrastructure Being Built Around the Problem

While the publishing industry debates its response to AI, a small number of companies has been quietly building the payment infrastructure for a world that the existing advertising economy has no mechanism to address: a world where AI systems are significant consumers of publisher content, but consume it in ways that generate no page views, no sessions, no impressions, and no advertising revenue. The problem these companies are solving is new in kind, not just in scale. The advertiser-publisher-audience triangle that has underpinned digital advertising for twenty years has a structural blind spot: it has no mechanism to capture value when the “reader” is not a human being.

TollBit, co-founded by Toshit Panigrahi and Olivia Joslin, has raised thirty-one million dollars and serves more than three thousand publisher clients. The company starts from a fact that most publishers have not fully processed: they are visited by AI crawlers at a scale that dwarfs their human readership, by systems that extract content value without generating any of the commercial signals on which advertising revenue depends. According to Cloudflare’s June 2025 data, Anthropic’s crawlers generated 73,000 crawls per single referral sent back to publishers, OpenAI’s crawlers generated 1,700 crawls per referral, and Google’s crawlers generated fourteen per referral. The publisher with a million monthly human visitors and typical crawler ratios is receiving something in the range of fifty million AI crawler visits monthly. Not one of those visits generates an impression, a session, or a commercial signal. TollBit’s bot paywall creates a payment infrastructure for exactly this category of non-human visitor, allowing publishers to set differentiated rates by purpose: the price for AI training access is different from real-time inference access, which is different from search enrichment access, because the commercial value generated by each use case is different.

TollBit’s recent addition of per-query pricing, using Microsoft’s NLWeb protocol, addresses a point that IAB Tech Lab CEO Anthony Katsur made explicitly: one crawl can feed ten thousand queries, fifty thousand queries, and the publisher has been paid only once for the crawl. Per-query pricing converts the relationship between a publisher’s content and an AI system’s output from a one-time transaction to an ongoing royalty structure. It is, in commercial principle, the same model that music rights holders use when they licence recordings for streaming: not a one-time payment for a file, but a per-play fee that accumulates over time relative to actual usage.

ProRata took a different route to the same destination. The company, which raised forty million dollars in Series B funding in September 2025 and has more than five hundred publisher partners, built Gist.ai, an AI search engine that uses only licensed publisher content and distributes fifty percent of all revenue generated by that search engine to publishers based on how often their content powers the responses. The Atlantic, Time, Fortune, ADWEEK, BuzzFeed and Lee Enterprises are among the participants. The model’s structural advantage over Cloudflare’s and TollBit’s bot-paywall approaches is that it does not require AI companies to opt into payment before the model generates revenue. ProRata creates a separate commercial ecosystem where licensed content is the only content, advertising runs against the answers that licensed content powers, and revenue flows monthly to the publishers whose content was cited. It is a parallel economy rather than a reformed version of the existing one.

Cloudflare’s Pay Per Crawl marketplace, launched on July 1, 2025, operates at a different scale than either TollBit or ProRata because Cloudflare sits in front of approximately sixteen percent of all global internet traffic. The company blocks AI crawlers by default for new websites and has created a market where publishers can set micropayment rates per page access. The publisher participants at launch read like a partial inventory of what the open web’s premium content looks like: Conde Nast, Dotdash Meredith, the Associated Press, The Atlantic, TIME, Fortune, BuzzFeed, Gannett, Reddit and Universal Music Group. Conde Nast CEO Roger Lynch: when AI companies can no longer take anything they want for free, it opens the door to sustainable innovation built on permission and partnership. Neil Vogel, CEO of Dotdash Meredith: we can now limit access to our content to those AI partners willing to engage in fair arrangements. These statements describe a commercial reality that is only beginning to materialize. The Cloudflare marketplace does not yet represent the majority of publisher-to-AI company transactions. It represents the architecture for what those transactions could become once the legal and commercial frameworks catch up with the technical infrastructure.

Microsoft’s pay-per-use AI content marketplace, which signed both the Associated Press and USA Today in 2025, is the first attempt by a major AI company itself to build systematic market infrastructure rather than negotiating individual bilateral deals. The significance of this is not merely commercial. It is a signal that at least one major AI platform has concluded that the sustainable model for content access involves a functioning market rather than a series of separately negotiated agreements at different prices, with different terms, signed by publishers who have no way to benchmark what anyone else received. A market is better for publishers than bilateral negotiations, because a market creates pricing transparency and allows any publisher to understand the fair value of their content relative to comparable titles. Markets are generally better for buyers too, because markets are more efficient than individual negotiations. The fact that Microsoft is building one suggests that the current state of bilateral dealing is recognized as unsatisfactory even by the companies that have benefited from it.

The Dependency Nobody Wants to Discuss

There is a structural dependency embedded in the AI economy that the AI companies prefer not to emphasize and the publishing industry has been slow to recognize as leverage. The large language models that power every major AI product on the market were trained on human-produced content. Not on synthetic data. Not on AI-generated text. On the journalism, analysis, fiction, scholarship, instruction, commentary and conversation that human beings produced and published over several decades. The models learned to write well because they read writing that was good. They learned to reason about complex topics because they absorbed reasoning about complex topics. They learned which facts are reliable because they trained on sources with editorial standards and accountability structures. When the training data degrades, which it will if the institutions producing it can no longer afford to operate, so do the models.

This dependency is already visible in the way AI companies are competing for content. News Corp’s $250 million deal with OpenAI was not the price of a favour. It was the market rate for an archive that provides exactly the kind of verified, sourced, dated, contextually-rich content that makes models more accurate and trustworthy. Google’s first AI content deal, struck with the Associated Press in 2025, gives Gemini access to AP’s real-time news production. Washington Post joined OpenAI in April 2025. The New York Times struck a deal with Amazon for Alexa and Amazon’s AI products. These companies are not paying for content because Sam Altman woke up one morning with a conscience about journalism. They are paying because the models need it and the alternative is litigation that could set precedents substantially more expensive than the licensing fees.

Axios understood this dependency more clearly than most when it negotiated not just payment but investment: OpenAI funding four new local newsrooms in exchange for content access is a recognition that the model’s value depends on the continued production of authoritative local reporting, and that this reporting requires funded organizations to produce it. The deal creates a direct commercial incentive for OpenAI to ensure that Axios has the resources to keep generating the content the model needs. This is a structural arrangement, not a charitable gesture, and it is the model that the rest of the licensing market should be working toward.

The publishers who have recognized this dependency as leverage are the ones building the most durable commercial positions. The Financial Times’s Storyfinding team uses AI to surface patterns in large datasets, which human reporters then investigate. The model’s ability to spot patterns in financial data only creates value if a reporter with decades of markets expertise can assess which patterns are significant and pursue the story. That reporting is what makes the FT worth licensing, worth subscribing to, and worth citing in AI answers. The AI improves the reporter’s efficiency. The reporter validates and extends what the AI found. The content that results is both more efficiently produced and more exclusively valuable than it would have been without either component. This is the correct division of labour, and it produces content that AI platforms will continue to need and pay for precisely because it cannot be replicated without the human component.

The publisher who has not made this calculation is still producing content in the old way, at the old cost, for an audience that the AI has redirected. The window in which they can use the operational savings that AI tools make possible to reinvest in the irreplaceable reporting that makes their content worth licensing is not infinite. The models are already training on a substantial volume of AI-generated text, and research suggests this degrades model quality over successive generations of training. The premium that authentic, human-reported, editorially-verified content commands in the AI training market will increase as synthetic content proliferates and the scarcity of genuine human expertise becomes more visible. The publishers who are still producing it in 2028 will be in a stronger negotiating position than those who have already cut the editorial functions that make their content distinctive.

The argument that publishers have genuine leverage in this situation is not naive optimism. It is grounded in the specific dependency the AI companies cannot route around. They need original, verified, recently-produced, contextually-rich human content to remain credible and useful. Without it, the models degrade, hallucinate more frequently, and lose the currency that makes them commercially valuable. Every major AI company knows this, which is why they are paying for licensing deals, funding newsrooms, and building compensation infrastructure rather than simply continuing to scrape freely and defend the practice in court. The leverage exists. The question is whether publishers are willing to price it like the asset it is, rather than continuing to treat access to their content as a default that any technology company is entitled to use.

What Needs to Happen, in Order

The publishing industry is at the point in a structural disruption where the companies that adapt early enough to rebuild their foundations before the revenue collapse reaches critical mass will survive, and those that delay will not. This is not a new observation about disrupted industries. It is, however, an observation with a specific urgency in this case because the pace of the structural change, as measured by the traffic and CPM numbers, is faster than the timeline on which most publishing companies make strategic decisions.

The first decision is architectural and cannot be deferred: publishers need to know who their audience is. Not the monthly uniques number in the analytics dashboard, which measures anonymous visits that have no commercial value in the world being built around them. The number of verified email addresses from people who have actively provided them. The count of logged-in sessions in the past thirty days. The active subscriber total. A publisher who cannot answer these questions with precision does not have an audience in the commercially meaningful sense. They have traffic, and traffic is what they are losing.

The second decision is about content investment. AI tools make it genuinely possible to produce the operational work of a newsroom, the formatting, the metadata, the headline testing, the distribution scheduling, the translation, the summarization, at significantly lower cost than before. The question every editorial leadership team needs to answer is whether those savings are being reinvested in the work that AI cannot do: the investigations that require a reporter in the room, the analysis that requires expertise built over years, the accountability journalism that requires the institutional weight to withstand legal and political pressure. If the answer is no, then the publication is using AI to accelerate its own commoditization.

The third decision is about the supply chain. A publisher running 24.5 SSP relationships while nine in ten bid requests go unprocessed is not operating a monetization strategy. They are operating the simulation of one, at the cost of latency, complexity and the fee that each intermediary extracts from each transaction that does complete. The publishers ahead of this curve are consolidating their SSP relationships to partners who can demonstrate, in log-level data and verifiable yield analysis, that their participation adds margin to the publisher’s inventory rather than extracting it. They are exploring direct connections through OpenPath and OpenAds for their premium authenticated inventory, where the buyer’s demand can reach the publisher’s audience without an intermediary taking a percentage of a transaction that the intermediary did not originate.

The fourth decision is about AI access pricing. Publishers who have not established a commercial position with AI crawlers, through Cloudflare’s marketplace, TollBit’s bot paywall, ProRata’s licensing pool, or bilateral negotiations with AI companies, have implicitly priced their content at zero for AI access. Zero is not a business decision. It is the absence of one. The price of content in the AI training market is in the process of being established right now, through the deals being signed and the court cases being argued. Publishers who participate in establishing that market price, even at modest initial levels, are building commercial relationships and legal frameworks that will matter considerably more once the regulatory environment catches up with the commercial one.

The fifth decision is one of framing, and it is the one the industry finds hardest to make: publishers need to stop thinking of their problem as a traffic problem and start thinking of it as an asset pricing problem. The content is an asset. The authenticated subscriber relationship is a more valuable asset. The editorial authority that makes a publication worth citing in an AI answer is an asset. The twenty-year archive of verified, dated, sourced, human-produced reporting is an asset that AI companies have demonstrated they will pay for when the alternative is litigation. None of these assets is worth its full potential value if the publisher has not built the commercial infrastructure to price and sell them. Building that infrastructure is the work of the next eighteen months, and for the publishers who do not start it, the next eighteen months may be the last period in which starting is an option.

The content that made the open internet’s information economy possible was produced by people who took professional and financial risks to report things that were difficult to report, to explain things that were difficult to explain, to hold accountable people and institutions that would have preferred not to be held accountable. The AI companies consumed that content to build products that are now generating hundreds of billions of dollars in enterprise value. The deal they are currently offering to publishers, modest per-article licensing fees and the promise of citation-with-attribution, is the deal that the only party with leverage in the negotiation should be able to improve. The leverage is real. The dependency is structural. The window is closing, but it has not closed yet.

Nobody, and I genuinely mean nobody on the planet, woke up this morning hoping to see an advertisement. Not one person set their alarm earlier than necessary to catch a pre-roll video. No one booked bandwidth for a programmatic display unit. The person reading the New York Times did not pay thirty dollars a month for a digital subscription because they wanted to be followed around the internet for six weeks by a mattress brand.

Advertising is a tax. Specifically, it is a tax that consumers pay in the currency of attention and time, in exchange for content they actually want. That is the deal. It is a reasonable deal. Premium journalism, streaming entertainment, newsletters, podcasts, sports coverage, specialist blogs across 190 countries, users get all of this either free or below production cost because advertisers pick up part of the bill in exchange for showing their products to the people consuming that content. The user tolerates the ad. The publisher gets paid. The advertiser reaches an audience. Everybody goes home, if not exactly thrilled, then at least functional.

Before we go any further, stop. Stop and ask yourself a genuinely simple question: what is adtech, really? Not what the industry calls it. Not what the conference circuit describes it as. What is it, from first principles? Strip away the acronyms. Strip away the trade press vocabulary. Strip away fifteen years of white papers and working groups. What you are left with is this: supply and demand. Publishers have content and audiences. Advertisers want to reach those audiences. That is the whole thing. Everything that exists between those two parties is, in plain old-fashioned business language, a middleman. That is not an insult. Middlemen exist in every industry, every market, every supply chain in human history. They can add genuine value — aggregating supply, reducing friction, creating liquidity. The question has never been whether middlemen exist. The question is whether they earn their position in the chain, or simply occupy it. That question is what this article is about. And it is a question the adtech industry has been remarkably reluctant to answer from the ground up.

The problem is not the deal between the publisher and advertiser. The problem is the supply chain executing the deal. Between the advertiser who hands over the money and the publisher who is supposed to receive it, there are approximately forty different companies, each with their own technology stack, their own fee structure, their own auction mechanics, and their own sincere belief that their layer of this arrangement is entirely indispensable. By the time the money completes its journey, the publisher receives about forty-four cents of every dollar the advertiser spent. In 2020, the ISBA and PwC tracked one hundred million pounds in UK advertising spend and found fifteen percent of it attributed to nobody at all. Not to fraud. Not to a specific fee. Just to a gap in the universe.

The user is watching a show on Paramount Plus. The advertiser is spending a hundred thousand dollars to reach them. The publisher is receiving forty-four cents of that dollar. The other fifty-six cents are funding the most elaborate toll road in the history of commerce. Nobody commissioned the toll road. Everybody is using it. Everybody in it is getting paid. Including, as it turns out, the people nobody specifically hired to be there.

This article is about how that toll road was built, how AI is about to install cameras on every stretch of it, why publishers outside the walled gardens have more power than they are exercising, and why the open internet, the part that has been writing transparency pledges since 2016 while running practices that made transparency commercially inconvenient, is heading toward a reckoning it cannot write its way out of.

But first, we need to go to India at five in the morning. And then to a trading floor in New York in 2007. Both are relevant. Both are the same story.

01

The Farmer, the Bond Trader, and the Programmatic Supply Chain

A farmer in India loads his harvest onto a truck. Five months of work: seeds, fertilizer, water, labour, and the specific anxiety of someone whose entire year depends on the weather making two correct decisions in a row. He knows his production cost to the rupee. What he cannot know, because the market structure prevents him from knowing, is the price his grain will fetch at the regulated marketplace down the road. That information belongs to the licensed traders who operate there. They know the prices. They know each other. And they are not in the habit of sharing either.

Academic researchers studying agricultural supply chains in India found that for every one dollar a consumer pays more for food, the farmer receives two extra cents. Two. Out of a hundred. The Reserve Bank of India confirmed in its 2024 research bulletin that farmer income as a share of consumer price ranges from 28 percent to 78 percent depending on the crop. The farmers at the 28 percent end have learned to live with this mathematics. They do not have much choice. The information that determines the price sits with someone else.

Now, picture a trading floor in New York in 2007. American banks are issuing mortgages to people who will not be able to repay them. These mortgages are rated BBB. Then the banks package bundles of BBB mortgages together, hire ratings agencies to certify the resulting product as AAA, and sell it to pension funds as a safe instrument. The pension fund cannot see inside the package. The ratings agency certifying the quality is being paid by the bank that built the package.

“It’s a big pile of bonds bundled together. The genius part is that the banks thought they could package them together and spread the risk. But actually they were just piling up the risk.”

-- Ryan Gosling as Jared Vennett, The Big Short, 2015

Michael Lewis documented what happened next in his 2010 book The Big Short. Adam McKay made it into a film five years later. The key scene is a Jenga tower. Each block is a mortgage. Stack enough uncertain blocks the right way, apply the right label, and what was individually BBB becomes collectively AAA. The ratings agency says so. The pension fund buys it. The bank collects its fee.

Now, here is the programmatic advertising supply chain.

An agency trading desk buys digital advertising inventory in bulk. It buys some premium inventory: genuine quality publishers, real engaged audiences, brand-safe environments. It also buys made-for-advertising inventory, which is the polite industry term for automatically generated websites that exist solely to host ads, with traffic assembled to satisfy a targeting algorithm rather than to actually read anything. This mixed inventory gets packaged together as ‘proprietary media’ and sold to clients at a uniform premium price. The client cannot see inside the package any more than the pension fund could see inside the CDO. The entity certifying the quality of the bundle is the same entity selling it.

The ANA found in its 2024 benchmark that 6.2 percent of US programmatic spend, roughly $3.2 billion at $52 billion total market size, still landed on made-for-advertising sites, with some marketers directing more than 25 percent of their budgets there. The BBB inventory, dressed as AAA, sold at the AAA price. The publisher who created genuine premium content receives forty-four cents of the advertiser’s dollar. The farmer gets two cents.

The farmer in India, the 2007 pension fund manager, and the 2026 brand manager approving a programmatic budget are all living inside the same structural problem. Someone in the middle controls the information about what the product is actually worth and what price it traded at. The intermediaries are different in every story. The mechanism is identical in all three.

What Happens When Someone Actually Measures the Gap -- ISBA/PwC 2020 vs 2022

2020: ISBA and PwC tracked approximately 100 million pounds in UK ad spend across 15 major advertisers including Tesco, Unilever, and BT. Publishers received approximately 51 percent of advertiser spend. A further 15 percent (the ‘unknown delta’) could not be attributed to any known supply chain participant. Not to fraud. Not to a stated fee. To nobody. The Guardian received 30 pence per brand pound in its own controlled test.

2022 follow-up: publishers’ share rose to 65 percent. Unknown delta fell to 3 percent. The 14 percentage point improvement in publisher share came entirely from measurement and sustained industry pressure. Nobody improved their practices out of virtue. They improved because the data made their margin visible and clients started asking questions. That is the most important data point in the transparency debate: accountability works when it is quantified. Sources: ISBA/AOP/PwC Programmatic Supply Chain Transparency Studies, 2020 and 2022.

From 51 cents to 65 cents in two years, just from making the measurement public. No regulation. No litigation. No conference panel. Just data, published, visible to the clients paying for all of it. This is also, if you think about it, a fairly devastating commentary on what fifteen years of transparency white papers without measurement achieved.

02

Nobody Cares About Open Internet vs. Walled Gardens, Except the People Whose Margins Depend on You Caring

Here is a confession the advertising technology industry finds uncomfortable: advertisers do not have a religious preference for where their ads run. They are not emotionally attached to Google or philosophically committed to the open internet. They want to reach the people they need to reach, in contexts where those people are paying attention, at a price that produces a return. That is the entire brief. Full stop.

A chief marketing officer at a consumer goods company does not lie awake agonizing over whether their campaign runs on YouTube or the Washington Post or a podcast or a Substack newsletter. They lie awake wondering whether the campaign sold the product. The CMO who tells their board they achieved ‘strong reach and frequency metrics’ while declining to report revenue contribution is not long for their job in 2026, which is a recent development in the profession and a healthy one.

The walled gardens have been winning the budget argument not because advertisers love them but because they made ROAS measurement relatively easy to report, even if the methodology is controlled entirely by the platforms measuring themselves. Meta says your ad produced these conversions. Google says this search campaign drove this revenue. Amazon says these sponsored products generated these purchases. The open internet’s equivalent for many years was: trust us, it worked, here is a third-party viewability report and an audience segment that may or may not have seen the ad. One of these is a harder sell to a CFO who is now, for the first time, actively present in the marketing budget conversation.

The users are not in walled gardens because they love Meta’s advertising infrastructure. They are on Instagram because their friends are on Instagram. They are watching Prime Video because Amazon spent $8.5 billion acquiring MGM in 2022 and has been commissioning premium content ever since. They are on YouTube because YouTube has more video content than any human could watch in several lifetimes. The content and the community are there. The advertiser follows the user. The money follows the advertiser. The walled garden wins not because it invented better advertising technology but because it controls where attention goes, and then built an advertising business on top of that control.

Here is a first-principles point the open internet conversation consistently refuses to make clearly: walled gardens are not a violation of natural order. From basic business logic, they make complete sense. A walled garden owns the technology. It owns the user base. It provides services those users actively choose. It bears the cost of that relationship — the infrastructure, the content, the moderation, the product development. In exchange, it reserves the right to monetise that relationship as it sees fit. That is not opacity. That is a vertically integrated business doing exactly what vertically integrated businesses do. The term “open internet” was not coined because it was philosophically superior. It was coined because not everyone can be a walled garden. Can Paramount? In theory, yes. In practice, it requires billions in technology investment, a global user base of sufficient scale, and the organisational appetite to bear the full cost of building and operating all of it. The open internet exists because most publishers cannot clear that bar — not because the open model is inherently more virtuous. Once you understand that from first principles, the transparency argument changes shape entirely. The question is not why walled gardens are opaque. The question is why the open internet built a supply chain so complex that even its own participants could not account for where fifteen percent of the money went.

Also here is what the walled garden debate obscures: the audiences exist everywhere. The New York Times crossed 10 million digital subscribers in February 2023. Spotify has approximately 600 million monthly active users across 180 markets. Paramount Plus, Disney Plus, Warner Bros. Discovery’s streaming services, Bloomberg, the Financial Times, The Economist, and thousands of specialist publishers across 190 countries, these are not leftover audiences that advertisers have to settle for. In many categories they are better audiences than the walled garden alternatives: more engaged, more logged in, more willing to pay for what they are consuming, which tells you something specific and useful about their disposable income and the quality of their attention.

The advertiser who needs to reach financially active adults in their thirties and forties has a stronger argument for Bloomberg or the Financial Times than for Instagram Reels adjacent to a recipe video. The advertiser trying to reach sports fans in connected television has a better case for ESPN or a league-owned streaming service than for generic YouTube pre-rolls. Advertisers know this. The problem is that the open internet’s supply chain has been too busy collecting its own toll to demonstrate the value of the inventory it sits on top of.

The Statistical Sleight of Hand: Both Numbers Are Real and They Contradict Each Other

The open internet received more advertising money in absolute dollar terms in 2024 than in 2016. Total global digital ad spend grew to approximately $650 billion in 2024. In absolute terms, the open internet is a larger business than it was. In share terms, it went from approximately 48 percent of global digital advertising in 2008 to approximately 20 to 21 percent in 2024. New walled gardens are being created constantly: TikTok built one of the fastest-growing ad platforms in history from essentially zero after 2019 and now has approximately one billion monthly active users. Retailers, from Walmart to Target to Kroger to Tesco, have turned their customer data into closed advertising ecosystems. The market is expanding and the walled garden share of that expanding market is growing faster than the open internet’s share. In the long race, the open internet is losing ground every year even as the cheque it receives gets slightly larger. Focusing on absolute growth to avoid discussing relative decline is precisely the kind of statistical framing that has allowed the industry to avoid the harder conversation for a decade.

03

How the Supply Chain Got Built, and Why It Stayed Complicated

Take a step back further. Adtech as a distinct industry started because the internet happened. With the internet came cookies — a mechanism for remembering who had visited what, which advertisers and publishers latched onto as the infrastructure for targeting and tracking. The rest of the tech world moved on from third-party cookies relatively quickly. Adtech did not. The entire programmatic ecosystem was built on, and remained dependent on, cookie-based identity for roughly thirty years. When Google announced its Privacy Sandbox project to phase out third-party cookies in Chrome, the industry went into a sustained state of anxiety. When Google eventually abandoned the project in 2024, the collective sigh of relief across the industry was audible. Think about what that tells you from first principles: an industry that had three decades to develop a durable, privacy-respecting identity infrastructure had instead spent those three decades hoping nobody would take away the crutch. That is not a technology problem. That is a structural incentive problem. Real-time bidding, when it arrived, was a genuine innovation. But if you look at it honestly, RTB in practice has never been true bidding in the classical economic sense. True bidding produces price discovery — a clearing price that reflects what the inventory is genuinely worth to the buyer who values it most. What RTB produced in practice was closer to a waterfall with extra steps: a structured sequence in which certain buyers get preferential access, floor prices are set opaquely, and the “winning bid” often reflects who had the best positioning in the queue rather than who had the highest genuine valuation. Header bidding emerged specifically to address this — to give publishers a mechanism to run a more genuinely simultaneous auction across multiple demand sources. It was the market’s own correction for the structural distortion that RTB had introduced. All of this, cookies, RTB, header bidding, the proliferation of SSPs and DSPs, happened not because someone designed a rational architecture from first principles, but because each layer solved the immediate problem created by the previous layer, while generating the conditions for the next layer to extract its own margin.

Programmatic advertising’s origin was legitimate. In 2007, publishers had unsold remnant inventory and no efficient way to reach multiple buyers simultaneously. DoubleClick was acquired by Google in 2007 for $3.1 billion because it had built infrastructure the market genuinely needed. The supply-side platform, the ad exchange, and the demand-side platform were real solutions to real market problems. The companies building them created genuine value and were correctly rewarded for it.

Then the market did what markets do when the original problem is solved and the money is still flowing. New entrants arrived. By 2014 the IAB had counted more than 1,000 advertising technology companies operating across the stack. Supply-side platforms proliferated. Supply path optimisation, which became a significant industry practice from 2019 onward, was the market’s first honest acknowledgment that the supply chain had developed a redundancy problem. When buyers started asking which of these hundred intermediary paths was actually necessary, the ISBA/PwC answer was: we cannot tell you where 15 percent of your money went, and the publisher at the end of the chain is receiving 51 cents of your pound.

Here is the core of the issue, stated from first principles: the entire adtech supply chain grew the way it did because publishers did not know how to price their own inventory and advertisers did not know their return on investment in any quantitative, verifiable sense. That is it. That is the whole explanation. When the seller does not know the value of what they are selling and the buyer cannot measure what they are getting, the middle fills with people who are very confident about the value of their own contribution. Advertisers burnt money on the advice of agencies, routed through DSPs, exchanges, SSPs, ad servers, chains of intermediaries stacked on top of each other, each extracting a fee at a point in the chain where neither the publisher upstream nor the advertiser downstream had full visibility. The result was that more than fifty cents of every programmatic dollar got absorbed before it reached the person who had created the content in the first place. The transparency debate — the white papers, the industry coalitions, the conference panels — was a downstream symptom of this upstream failure of price discovery. The Trade Desk built its entire commercial identity around transparency as a differentiator, and it worked, because nobody else was willing to make the same bet. That tells you everything about how the rest of the industry had chosen to compete.

Pritchard said that representing approximately $10 billion in annual advertising spend. The industry agreed, published white papers, and largely continued as before. Three years later, ISBA/PwC found 15 percent unattributable. The muck, it turned out, was load-bearing.

At $52 billion in US programmatic spend in 2024, with $439 per thousand reaching publishers, approximately $22.8 billion reached content creators. The other $29.2 billion went to fees, margins, MFA inventory, and a residual unknown category. In December 2024, the AdExchanger Transparency Benchmark found that 61 percent of media buyers said they did not fully trust reported auction fairness. After fifteen years of programmatic advertising, the majority of the people buying it do not trust how it works. That is not a measurement problem. That is a supply chain that has never been required to be fully honest about itself.

04

The 78 Percent Problem: The Open Internet Is Losing the Long Race

In 2008, walled gardens held approximately 52 percent of US digital advertising revenue. By 2020, per Skai’s analysis of eMarketer data, they held over 82 percent in the United States. Globally, Statista reported that walled gardens reached 78 percent of digital advertising revenue in 2022. The projected share for 2027 is 83 percent. Analyst consensus places the 2030 figure above 85 percent if the retail media expansion continues at its current pace.

Alphabet, Meta, and Amazon generated approximately $422 billion in advertising revenue in 2024, roughly 65 percent of total global digital ad spend outside China. Advertisers in the United States spent approximately $2.50 with walled gardens for every $1 on the open web. And the model is replicating. Amazon, Walmart, Target, Kroger, Tesco, JD.com, and dozens of other retailers have built closed advertising ecosystems around their own customer purchase data. TikTok reached approximately one billion monthly active users and built one of the fastest-growing ad platforms in history from essentially zero after 2019. Every new platform that achieves scale defaults to the walled garden model because the walled garden model is profitable, relatively defensible, and relatively easy to operate.

The open internet did not lose to walled gardens because it ran out of audiences, publishers, or content. It lost because a significant portion of its own intermediaries spent fifteen years competing on margin extraction rather than on demonstrable value creation. Google, Meta, and Amazon are opaque about their auction mechanics, targeting logic, and attribution methodology. The open internet’s claimed advantage over this was transparency. In practice, its own supply chain generated ‘unknown deltas’ of 15 percent, bundled made-for-advertising inventory with premium inventory, collected non-product-related income from client spend without client knowledge, and fought the audit tools that would have demonstrated its value. An industry that cannot account for 15 percent of a client’s spend does not win the transparency argument by stating it. Advertisers made a rational choice.

In Europe, the EU Digital Markets Act designated Google and Meta as gatekeepers in September 2023, imposing data sharing and interoperability obligations that created the first serious legal framework for challenging walled garden dominance. Early implementation has not changed market share but established the regulatory infrastructure that could eventually do so. In China, Alibaba, Tencent, ByteDance, and Baidu operate a parallel walled garden ecosystem entirely closed to Western programmatic infrastructure. In Southeast Asia, Grab and Shopee are building super-app media environments. The open programmatic internet as practised in the United States and Western Europe barely exists in most of Asia Pacific.

05

Why 2025 and 2026 Are Different: Three Reasons the Old Playbook Stops Working

The transparency debate is not new. The ANA and K2 Intelligence documented agency principal-media markups of 30 to 90 percent in 2016. The ISBA/PwC unknown delta was mapped in 2020. What is different now is that three forces are operating simultaneously, each making opacity more expensive to maintain than it was the year before.

The CFO Has Entered the Building

Between 2020 and 2022, cheap money and pandemic-driven digital growth made return-on-advertising-spend conversations aspirational rather than mandatory. US interest rates then rose to their highest level since 2007. Marketing budgets became the most visible discretionary cost on the P&L. CFOs who had been passive observers started requiring ROAS data rather than impressions reports. The question ‘where did the money go’ became a CFO question, and the answers that satisfied media operations teams for a decade did not satisfy CFOs.

AI Is Making the Invisible Visible

When a bidding algorithm manages campaign optimisation in real time at a fraction of the cost of a human trading desk, the question of what the trading desk was charging for becomes impossible to avoid. A 2025 Bannerflow survey found 83 percent of senior brand marketers already use AI to target digital ads. Basis Technologies found 92 percent of advertising agencies use AI in some capacity. The ANA’s 2024 benchmark found that private marketplace spend, where algorithmic curation replaced manual supply chain navigation, rose to 59 percent of programmatic, up from 41 percent in 2023. The manual work that was billed at margin is being automated. The billing structures have not changed at the same speed. That gap is where the new friction lives.

AI is also changing attribution measurement in ways that systematically disfavour opacity. Machine learning attribution models can now produce ROAS estimates with a precision that turns ‘did this campaign work’ from a narrative argument into a numbers argument. Numbers arguments are harder to win when the numbers show 43.9 cents reaching publishers and 6.2 percent going to made-for-advertising sites.

The Courts Produced What the Industry Would Not Volunteer

The US Department of Justice antitrust case against Google’s advertising technology, which went to trial in 2024, placed supply chain economics into a public court record with the specificity and authority that fifteen years of industry conferences could not. The WPP whistleblower case, which became public through WPP’s own court filings in November 2025, placed approximately $1 billion annually in what WPP’s internal documents called ‘non-product related income’ into evidence, with internal data showing 97.4 percent of the relevant clients were not using the inventory that generated that income. Neither disclosure was volunteered. Both were extracted by legal process.

Three Converging Forces: Why the Old Playbook Has a Shelf Life

CFO pressure: marketing must justify itself in revenue terms, not impressions. AI displacement: the manual work of trading desks is being automated, making the cost-for-value question impossible to ignore. Legal exposure: the DOJ antitrust trial and the WPP whistleblower filing put specific supply chain economics into public court records for the first time. Running an opaque supply chain was always expensive for the advertisers paying for it. In 2026, it is also becoming expensive for the intermediaries running it, because the information that justified the opacity is no longer available only to them.

06

Publishers Have More Power Than They Think, But Only If They Control the Technology

Here is the argument the publishing industry needs to make to itself, out loud, in 2026. The content is yours. The audience is yours. The first-party data, the logged-in user behaviour, the reading patterns, the subscription history, the content consumption signals, all of it is yours. The only thing that is not yours, and the only thing that determines what any of it is worth to an advertiser, is the technology sitting between your inventory and the advertiser’s demand. That technology, for most publishers outside the very largest, was built by someone else to serve someone else’s interests.

Consider the specific assets in play. The New York Times crossed 10 million digital subscribers in February 2023. Bloomberg has roughly 400,000 paid subscribers who are among the most financially active people on the planet. Spotify has approximately 600 million monthly active users across 180 markets. The Economist, the Financial Times, Paramount Plus, Disney Plus, Warner Bros. Discovery’s streaming services and thousands of specialist publishers in dozens of languages, these are not substitute audiences for Meta’s social graph. In many advertising categories they are superior audiences: more engaged, more verifiably identified, more contextually appropriate, and more willing to pay for what they are consuming, which is a reliable signal about disposable income.

The publisher whose logged-in subscriber audience receives a $3 CPM when that audience is worth $15 to the right advertiser does not have an audience problem. It has a supply chain problem. The supply chain between the publisher’s content and the advertiser’s budget has been too focused on its own margin to communicate the audience’s value clearly. The farmer’s wheat is better than the bulk trader claims. The bulk trader has never let the buyer see it without his label on it.

The SP500+: The Trade Desk Does What Nobody Else Would

In February 2024, The Trade Desk began beta testing a product called the Sellers and Publishers 500+, deliberately named to evoke the S&P 500. The concept: a curated, actively maintained list of premium open internet inventory sources, evaluated at the individual ad placement level rather than just the publisher level. Initial participants in the beta included the New York Times, Disney Plus, Hulu, ABC, and the Wall Street Journal. Spotify was added subsequently. The product is globally available across connected television, web, and digital audio.

The Trade Desk’s Marketplace Quality team, which does the thankless ongoing work of keeping made-for-advertising inventory and fraudulent traffic out of the platform, underpins the whole exercise. The SP500+ is essentially that quality work made visible and actionable for buyers: instead of maintaining their own exclusion lists, advertisers can select SP500+ inventory and outsource the quality management to The Trade Desk’s ongoing curation.

The SP500+ directly addresses the BBB-dressed-as-AAA problem. It creates a transparent, auditable definition of what ‘premium open internet’ actually means and makes it actionable with a single product choice. This is something an SSP, the IAB, a publishers’ trade body, or an industry consortium could have built years ago. None of them did, because defining quality means excluding inventory that someone is currently monetising, and nobody wanted to be the one who drew that line.

The legitimate concern the industry has expressed about this is worth stating plainly: if buyers route spend primarily through SP500+ inventory that The Trade Desk curates and controls the criteria for, the DSP has moved from being a neutral buy-side technology to being a gatekeeper of the open internet. Green’s structural argument, that TTD’s buy-side-only model prevents this from becoming a true walled garden because the company has no supply-side inventory to favour, is credible but not complete. The SP500+ is a significant expansion of what a DSP has historically been willing to decide unilaterally. You can acknowledge that the work was necessary and nobody else was doing it while also noting that it transfers considerable power to the entity curating the list.

The Ad Server: Where All the Truth Lives

The ad server is the technology that determines which ad appears on a publisher’s page, at what price, in what order, following what rules. It is the point in the supply chain where information asymmetry is smallest: the publisher knows what they received and, with direct access, the buyer knows what they paid. In display advertising, Google Ad Manager dominates publisher ad serving to a degree that forms a central argument in the DOJ antitrust case. The company selling publishers the technology that manages their inventory also operates the exchange that buys that inventory and the demand-side platform that bids on it. This is not a coincidence. It is a competitive strategy that the DOJ is currently examining in considerable detail.

In connected television, two significant alternatives have emerged. Freewheel, acquired by Comcast in 2014, is the primary ad server for broadcast and cable networks making the transition to streaming. SpringServe, acquired by Magnite in 2021, became the principal independent alternative in the streaming space. And in April 2025, Magnite did something the industry had been debating for years: it actually merged the two.

On April 23, 2025, Magnite combined the SpringServe ad server with the programmatic capabilities of the Magnite Streaming SSP into a single unified CTV and OTT platform, initially in closed beta. Initial clients: Disney Advertising, LG Ad Solutions, Paramount, Roku, Samsung, and Warner Bros. Discovery. Jounce Media’s March 2025 Supply Path Benchmarking Report verified the platform connects buyers to 99 percent of US streaming supply on a dollar-weighted basis. General availability was targeted for early to mid-summer 2025. Sean Buckley, Magnite’s President of Revenue: ‘By unifying the programmatic layer as a complementary step in the buying process, it gives buyers greater transparency, predictability, and control over their ad placements, and lays the foundation for more effective monetisation and yield management for media owners.’

A publisher using Magnite’s unified SpringServe platform controls their yield management, their demand access, their supply path decisions, and their deal management in one system. That is a publisher operating as a technology company rather than as a technology customer. Will Doherty, SVP Inventory Development at The Trade Desk, endorsed the move directly at launch: ‘Magnite helps fuel the premium, open internet. Combined with tools like OpenPath, the next generation of SpringServe is accretive to advertisers and publishers.’ Two sides of the same transaction, for once, pointing in the same direction.

The unified ad server and SSP model is the correct architectural direction. A publisher who controls both the technology managing their inventory and the platform connecting it to demand has closed the information gap that has defined their relationship with the supply chain since programmatic began. They know what their wheat is worth. They can see what it traded for. They can compare the two. That is the foundation on which everything else in this article is built.

07

Agentic AI: The Technology That Enforces What Contracts Cannot

On November 13, 2025, the IAB Tech Lab released the Agentic RTB Framework, version 1.0, for public comment. If you missed the announcement in a busy November news cycle, here is what you missed: the most consequential technical standard in programmatic advertising since OpenRTB standardized the auction protocol in 2010.

The ARTF uses containerized architecture to bring demand-side platforms, supply-side platforms, data vendors, and bidding algorithms into the same virtual processing environment, reducing bid request-response latency by up to 80 percent, from the current 600 to 800 milliseconds to approximately 100 milliseconds. Netflix, Paramount, The Trade Desk, Yahoo, Index Exchange, and Chalice were among the early supporters. Anthony Katsur, CEO of IAB Tech Lab: ‘ARTF establishes a true control plane for an agentic future, where autonomous agents and specialised software enhance the bidstream in real time with rigour, safety, and interoperability.’ Joshua Prismon of Index Exchange called it ‘production-tested’ and ‘transformational’ at the November launch.

What the ARTF actually does, in plain language, is allow AI agents to operate inside the auction process as active verifiers rather than passive rule-followers. Within the microseconds available before a bid is placed, an AI agent can check whether the supply path for this specific impression matches the contracted route, whether the floor price the SSP declared matches what the publisher actually set, whether the inventory type is classified correctly, and whether the fee being applied is what was agreed in the contract. The 80 percent latency reduction is not an engineering efficiency. It is the time budget that makes real-time contract enforcement commercially feasible for the first time.

What ARTF Enables and What It Does Not Do Automatically

What it enables: per-impression supply path verification; real-time floor price confirmation; automated fee discrepancy detection across millions of transactions per hour; MFA and inventory quality evaluation within the auction window; and impression-level transaction records creating an auditable supply chain trail. What it does NOT do automatically: enforce anything. The technology detects discrepancies. It acts on them only if the contract specifies what action to take. An AI agent can halt spend on a non-compliant supply path automatically, but only if the buying contract says it should. The technology is the enabler. The contract is the instrument. The buyer or publisher who writes the rules the agent follows decides what actually changes.

Start on the Publisher Side, Not the Demand Side

There is a strong argument, and it is the correct argument, that AI agents should be introduced on the publisher side before the demand side deploys its own. Here is the logic. A demand-side AI agent built by or for an agency that profits from principal buying will be optimized for outcomes that preserve that profit. An SSP’s AI agent will be optimized for the SSP’s revenue. The only participant whose AI agent has no structural conflict of interest is the publisher. The publisher’s agent, sitting at the ad server, has one job: maximize the value of this impression for this publisher. It has nothing to gain from hiding the price from itself.

A publisher who deploys an ARTF-compatible AI agent at their ad server can verify, at the impression level, whether the floor price declared to buyers matches what the publisher actually set. Whether the winning demand source paid what it said it would. Whether the supply path routing matches the contract. The publisher’s agent is not trying to extract margin from anyone. It is trying to verify that the publisher received what it was owed.

When the publisher publishes those verified transaction records at the campaign and supply path level, they become the independent audit the industry has never produced voluntarily. A buyer’s AI agent can compare the publisher’s transaction record to its own. Discrepancies surface automatically. Contracts that specify consequences for discrepancies become enforceable in real time rather than through a formal audit process that takes weeks and produces a report that gets disputed for months.

The ARTF is not a finished product. Version 1.0 is a foundation. Adoption is in closed beta with initial supporters. But the IAB Tech Lab’s track record with OpenRTB, which became the universal auction standard within three years of its introduction, suggests that adoption will accelerate significantly in 2026 and 2027. The agencies and intermediaries that believe the business model they have run for twenty years will survive this shift are making the same bet that Blockbuster made when Netflix started mailing DVDs. The technology exists. The standard exists. The question is how quickly the contracts require it.

And this is where the first-principles argument becomes urgent rather than merely instructive. With agentic AI entering the adtech stack — with the IAB Tech Lab’s ARTF and the emerging AdCP standards creating the infrastructure for autonomous agents to operate inside the auction process — the industry is at a fork in the road. One path: deploy AI as a smarter version of the existing architecture, automating the same intermediary layers, preserving the same structural information asymmetries, making the same toll road faster and harder to audit. The other path: use AI to actually go back to basics. Supply and demand. Publisher and advertiser. A clear answer to the question “what is this inventory worth, and did the money reach the person who created it?” If the industry does not choose the second path consciously and deliberately, agentic AI will default to optimizing the first one, because that is the architecture it will inherit. The fundamentals do not disappear because the technology becomes more sophisticated. They become more consequential. An industry that cannot explain its own supply chain from first principles to a first-year economics student has no business layering autonomous agents on top of it and calling that progress. Stop. Think from the basics. Then build.

The Publisher-Led Agentic Supply Chain: How It Works in Practice

Step 1: Publisher controls its own ad server. In CTV, that now means Magnite’s unified SpringServe or Freewheel. In display and every other channel, it means an independent alternative to Google Ad Manager.

Step 2: Publisher activates first-party audience data within the ad server to demonstrate audience quality to buyers at the impression level.

Step 3: Publisher deploys an ARTF-compatible AI agent that verifies every transaction against contracted terms in real time and maintains an impression-level transaction record.

Step 4: Publisher negotiates contracts with SSPs and DSPs specifying that floor prices are verifiable in the transaction record, supply path routing is disclosed, and discrepancies trigger automatic consequences rather than letters to lawyers.

Step 5: The publisher’s verified transaction records are accessible to buying partners, creating the real-time audit trail that quarterly formal audits have never consistently produced. Every component of this stack exists today.

08

SSPs: The Data Platform or the Toll Booth

The supply-side platform was built to aggregate demand for publishers who could not independently access multiple buyers simultaneously. In 2015, that function justified twenty-plus independent SSPs. In 2026, direct connectivity tools like the PubMatic’s Activate platform, which grew more than threefold in 2025 and allows advertisers to buy directly within the SSP bypassing DSP intermediation, are all testing the same question: how many SSP layers does the supply chain actually need?

AI supply path optimization is the automated version of the question buyers have been asking manually since 2019. An AI agent routing spend to the best-performing supply paths, verified at the impression level, does not favour an intermediary that cannot demonstrate its contribution. The SSPs that have a future are the ones controlling something that AI cannot route around.

The One Asset Worth Building: Aggregated Publisher First-Party Data

Individual publishers have rich first-party audience data. Logged-in users. Behavioural signals within the publisher’s ecosystem. Content consumption patterns. The problem is that individual publishers outside the very largest cannot make this commercially valuable at scale on their own. A regional news publisher with two million monthly readers has excellent audience data. It cannot sell that data to global advertisers the way Meta can.

An SSP that aggregates first-party data across hundreds of publisher partners, with consent frameworks and publisher agreements that make the arrangement transparent, creates something no DSP can replicate through direct publisher connections: a scaled, consented, cross-publisher audience graph that is genuinely differentiated from walled garden data. The SSP that moves a publisher CPM from $3 to $5 by demonstrating, through aggregated audience data, that an impression reaches a logged-in user whose behaviour matches an advertiser’s target, is adding transparent, measurable, auditable value. The SSP that adds a margin to transactions it did not improve is the one that AI routes around.

Magnite has not posted an annual net profit since 2019 and invests over $200 million annually in R&D. On the Q1 2025 earnings call, CEO Michael Barrett’s primary growth argument was that the Google antitrust ruling ‘could significantly increase our monetization opportunities and market share, possibly as soon as next year.’ That may be true. It is also a strategy whose timeline is entirely outside Magnite’s control. The SpringServe unification is a more durable bet because it is entirely within Magnite’s control and delivers something publishers demonstrably want: a single system for their entire monetization operation. Building on that is the right path forward.

09

DSPs and Agencies: The Clock Is Running on Both

The demand-side platform is structurally the correct model for transparent programmatic buying. Works exclusively for the buyer. Charges a disclosed technology fee. Has no supply-side inventory to secretly favour. This is the commission agent from the grain market: declaring its fee openly, competing on service quality, passing the rest through. The model is right. The metric DSPs have historically competed on is wrong.

Reach, frequency, and cost per thousand impressions are activity metrics. Return on advertising spend is the outcome metric that actually matters. The DSP that consistently delivers better ROAS than its competitors can charge a higher technology fee and justify it with a verifiable track record. The DSP competing on CPM against Amazon, which reportedly offers fees as low as 1 percent for major spending clients because its real margin is on Prime Video and Thursday Night Football, is not going to win that argument on price alone.

StackAdapt’s numbers are worth examining carefully. The Globe and Mail reported in February 2025 that StackAdapt generated nearly $400 million in revenue in FY2024 and was expected to surpass $500 million in FY2025 with more than $125 million in operating earnings, making it one of Canada’s most profitable private technology companies. Ontario Teachers’ Pension Plan led a $235 million funding round. Six consecutive years rated number one DSP on G2 by user satisfaction. Founded by former WPP employees who built the platform they wished existed when they were agency practitioners. That origin story tends to produce user-focused product development rather than billing-optimised product development, and the customer satisfaction ratings reflect it.

For agencies, the financial advisor parallel remains the clearest template. The UK Financial Conduct Authority’s Retail Distribution Review, effective January 2013, banned commission payments on retail investment advice and required advisors to charge explicit fees. The number of independent financial advisors fell from approximately 35,000 in 2011 to approximately 22,000 by 2014. The ones who left were the ones whose income depended on the commission structure rather than the quality of the advice. The ones who survived competed on demonstrable investment outcomes. WPP lost approximately 62 percent of its market value in 2025, saw WPP Media decline 10.8 percent in Q4 on a like-for-like basis, and lost the Mars global media account (approximately $1.7 billion) and the Coca-Cola North America media account (approximately $700 million) to Publicis. The financial advisor parallel does not require a regulator. The market is applying the same pressure on its own schedule.

10

The Supply Chain That Could Exist: A Dollar Comparison

The walled garden delivers 80 cents to the publisher because the publisher IS the walled garden. There is no external supply chain extracting a toll because the platform owns both sides of the transaction. The open internet’s answer to this cannot be another transparency pledge. It has to be 70 cents, delivered verifiably, confirmed by AI agents at the impression level, with ROAS outcomes that an independent firm can audit and compare to walled garden alternatives.

From 51 cents in 2020 to 65 cents in 2022 in the ISBA/PwC premium UK market study. From 36 cents in 2023 to 43.9 cents in 2024 in the broader ANA US benchmark. The trajectory is real. What accelerates it is publishers who control their own technology, advertisers who require log-level verification, SSPs that compete on publisher first-party data rather than inventory control, DSPs that publish ROAS benchmarks rather than reach metrics, agencies that separate advisory fees from execution margin, and AI agents whose rules are written by the people whose money is being spent.

11

The End: Or the End of the Beginning

Back to India for a moment. India’s e-NAM platform, the National Agriculture Market, launched in 2016 to give farmers access to the price information that licensed marketplace traders had held exclusively for decades. A farmer can now compare prices across multiple buyers, sell into distant markets, and verify that the weight recorded at the point of sale matches what was actually loaded. The system is imperfect. Implementation is uneven. Some traders have maintained advantages through side arrangements. But the structural condition that allowed two cents to the farmer and ninety-eight to the intermediary is being disrupted, slowly and unevenly, by the simple act of making price data publicly available.

The advertising technology industry’s e-NAM moment is arriving on multiple fronts simultaneously. The ARTF enables impression-level verification. The Magnite SpringServe unification gives CTV publishers integrated control over their ad serving and SSP in one system. The Trade Desk’s SP500+ creates the first actionable definition of premium open internet inventory. The DOJ antitrust case is extracting from Google the specific economic details of how publisher ad serving dominance translates into programmatic market power. AI attribution tools are making the revenue contribution of advertising spend measurable with a precision that did not exist three years ago. And the CFO is now in the room.

None of this requires virtue. None of it requires anyone to voluntarily give up income. The ISBA/PwC improvement from 51 cents to 65 cents reaching publishers came from measurement pressure, not moral awakening. The SP500+ exists because The Trade Desk calculated that cleaning up the open internet serves its own commercial interests. The Magnite SpringServe unification happened because publishers asked for it. Everything that is working is working because someone calculated that transparency was in their commercial interest. That is fine. That is how markets are supposed to work.

The open internet currently holds approximately 20 percent of global digital advertising. It is heading toward 17 percent by 2027 on current trajectory. Seventeen percent of an expanding market is still a large absolute number. But it is also a market that is losing competitive ground every year to platforms that are less diverse, less editorially independent, and less capable of reaching audiences across the full breadth of human content consumption. That matters for reasons beyond advertising economics. The publisher whose revenue falls cannot maintain the newsroom or the production budget or the creator community that made the audience worth advertising to in the first place.

The user watching a show on Paramount Plus, reading the Washington Post, listening to a podcast, scrolling a newsletter, all of them are tolerating the advertising because the advertising subsidizes the content they actually want. That is the deal. It is still a reasonable deal. It just requires the supply chain executing it to actually deliver the money to the people making the content, rather than treating the journey between advertiser and publisher as an opportunity for forty companies to clip the ticket.

The publishers sitting on those audiences have more leverage than they are currently exercising. The technology to exercise it exists today. The contracts to enforce it can be written this quarter. The AI agents to verify it in real time will be commercially widespread within two years. The only barrier is the institutional inertia of participants who benefit from the current arrangement and would prefer to keep discussing it at conferences rather than changing it at the contract level.

The farmers have smartphones now. The price data is available. The supply chain that built a business model on the farmer not having that information is going to have to find a different reason to exist.

Some of them will. The ones who add genuine value, who help publishers activate their audience data, who verify supply path integrity, who deliver ROAS outcomes that hold up under independent scrutiny, those intermediaries will thrive in the transparent supply chain because transparency confirms their value rather than threatening it.

The ones who got paid because nobody looked closely enough will find the next few years considerably more interesting than the last fifteen.

One last thing, and this is the reason this article exists. Every time adtech faces a genuine structural reckoning — privacy sandbox, the DOJ case, the ISBA/PwC findings, the arrival of AI — the industry’s first instinct is to reach for the vocabulary of the existing architecture. New technical standard? Build a working group. New privacy requirement? Launch a coalition. New technology? Retrofit it onto the current stack and give it a new acronym. That instinct is understandable. It is also the instinct that has kept the farmer at two cents and the pension fund manager holding AAA-rated BBB paper. The only way out is to stop, get off the bandwagon for a moment, and ask the genuinely simple questions. What is this industry, at its most basic level? Supply and demand. Publishers and advertisers. Anything in the middle needs to justify its existence by making that exchange more efficient and more valuable — not by making it more opaque. If your business model requires the publisher not to know what their inventory is worth, and the advertiser not to know where their money went, you are not adding value. You are making hay while the sun shines. And the sun, as this article has attempted to document in considerable empirical detail, is beginning to set on that particular field.

Everybody got paid. Including the people nobody hired. That part is about to get a lot harder to pull off.

Disclaimer: I am writing this to the best of my knowledge of whatever time I have spent in my professional life in the mediatech and adtech industry and whatever I could learn either from experience or from others or by media reports. In no way, it reflects on any insider information or anything that could be confidential. Also, it has no input from my current or any of the previous employers and is written in personal capacity with publicly available information. It is just my attempt to summarize the whole discourse that’s going on in recent times in the trade press about the adtech industry in general. This is my attempt to summarize everything as a knowledge base for people to learn more about adtech industry in general and how everyone has been operating for more than a decade. There are 100s of experts who know more than me and if you are one of them reading this, please comment or let me know about inaccuracies mentioned in any way. Like everyone else, Claude has helped me write this article in a journalistic tone to cover facts without adding my personal opinion. Lastly, if you are offended and are planning to sue me, please don’t. I am not so rich that you can make money of it.

Here is a story about a company that spent fifteen years building an industry on a simple, radical idea: tell the truth about what you charge, show buyers exactly what they are buying, and never work for both sides of a transaction at the same time. In an industry that had made opacity its art form and its business model, this was not just unusual. It was a genuine competitive threat — to everyone who had been making a comfortable living from the opacity.

Here is also a story about the very powerful companies that were built on the opposite principle — the idea that complexity, secrecy, and a creative relationship with client budgets could generate extraordinary income if advertisers never asked too many questions. These companies cheered The Trade Desk when it was useful to them. They championed it, partnered with it, named it their preferred technology. And then, when The Trade Desk’s growth began to illuminate exactly the practices they had been quietly running for years, some of them picked up a flag labelled ‘transparency’ and used it to beat the transparency company over the head.

This is a story with a genuine protagonist, a set of genuine conflicts of interest, and — in the spirit of honest journalism — one chapter where the protagonist also trips over its own shoelaces. In public. Loudly. More than once.

To understand all of it, you have to start not in 2026 but in 2009, in Ventura, California, where a man named Jeff Green decided to build something the advertising industry had never seen — and which certain parts of it were hoping it would never have to.

01

The Honest Broker — How Jeff Green Built the Company the Industry Needed

The programmatic advertising ecosystem that Jeff Green entered in 2009 was, to put it charitably, a confidence game with better branding. Advertisers handed enormous budgets to agencies. Agencies bought media on their behalf — or, increasingly, bought it for themselves at undisclosed prices and sold it to clients at a markup, keeping the difference. Supply-side platforms, ad exchanges, and demand-side platforms formed a chain of intermediaries each collecting a slice that was individually invisible but collectively vast. The entire system ran on a foundational assumption: that clients would not, or could not, look too closely.

Green and his co-founder Dave Pickles had both worked inside this system. They understood where the money went and why it went there. And they made a deliberate, calculated, and at the time quite contrarian bet: build a demand-side platform that was transparent about what it charged, worked exclusively for buyers and never for sellers, maintained no supply-side relationships that could create conflicts of interest, and showed advertisers precisely what they were buying, at what price, in what context.

This was not idealism. It was a business model built on a specific insight: the agencies whose clients were beginning to ask awkward questions about where all the money went would pay a premium to use a platform they could actually defend in a board meeting. The platform that made transparency its product would win in a market that was slowly, reluctantly, being forced to confront its own opacity.

The bet paid off beyond almost anyone’s expectations. The Trade Desk went public on September 21, 2016, priced at $18.00 per share. On that first day, it opened at $28.75 and closed at $30.10 — up 67% in a single session. Over the following years it delivered revenue growth above 25% annually with remarkable consistency. Customer retention stayed above 95% — a figure that speaks to something beyond contractual lock-in. It speaks to a platform that was, year after year, doing what it said it would do.

The company’s all-time high closing price was approximately $139–141, reached on December 4, 2024. At its market peak it was valued at tens of billions of dollars. And in 2021, in what was perhaps the most explicit public endorsement the company had ever received, Publicis Groupe — the world’s most valuable advertising holding company — announced that The Trade Desk would be the exclusive third-party DSP partner for Epsilon’s Core ID, Publicis’s flagship identity solution. The world’s biggest holdco naming TTD as its preferred technology partner. A formal declaration of trust in what Green had built.

File that one away carefully. We will need it in about six chapters.

The Trade Desk’s Structural Foundations — Why They Matter

Buy-side only: TTD has never operated a supply-side platform, never owned a media property, and never taken supply-side money. Its entire business model depends on advertisers getting good results — because that is the only reason they keep paying. No walled gardens: TTD does not own the inventory it trades and cannot secretly favour its own media. Transparent fees: TTD charges a disclosed technology fee on managed spend. No rebates, no principal mark-ups, no ‘non-product related income.’ These are structural facts about how the company is built, not marketing claims.

02

The Fire Alarm Nobody Wanted to Hear — 2016 and the Rebate Reckoning

In March 2015, a man named Jon Mandel stood up at the ANA Media Leadership Conference in Hollywood, Florida, and said the thing the entire room had been pretending was not true. Mandel, the former CEO of Mediacom, one of the world’s largest media agencies, alleged bluntly that media agencies were systematically collecting undisclosed rebates from media companies — payments for directing client budgets toward those media companies — and keeping the money rather than returning it to the clients whose spend had earned it.

The ANA commissioned a seven-month independent investigation by K2 Intelligence, one of the most reputable research firms in the field. The report landed in June 2016. One hundred and forty-three interviews. One hundred and fifty individual sources. The findings were unambiguous.

ANA / K2 Intelligence Report, June 2016 — Verified Public Document

Non-transparent practices including cash rebates were found to be ‘pervasive’ in the U.S. media ecosystem. Senior executives were aware of and had mandated some non-transparent practices. Markups on principal media transactions ranged from approximately 30% to 90%. Media buyers were ‘sometimes pressured or incentivised by their agency holding companies to direct client spend’ to principal media regardless of client best interests. Of 41 sources confirming rebate deals exist in the U.S., 34 confirmed the rebates were undisclosed to clients.

Five of the six major agency holding companies declined K2’s request to make current executives available for interview. The American Association of Advertising Agencies called the report ‘anonymous, inconclusive, and one-sided.’ The irony of an industry transparency report being called opaque by the companies who declined to participate in it was apparently lost on the people issuing the statement.

The industry moved on. Some contracts were updated. Some clients asked better questions for a while. The underlying practices did not disappear. They found new vocabulary — ‘principal-based buying,’ ‘inventory investment,’ ‘non-product related income’ — and in some cases simply grew. The Trade Desk kept building. Kept being transparent. Kept growing.

The 2016 ANA/K2 report is not ancient history. It is the context that makes the 2026 dispute comprehensible. Every executive at every agency holding company who is now raising transparency concerns about The Trade Desk was working in this industry when that report was published. Some of them had been interviewed for it.

03

The China Raid — When Allegation Became Criminal Investigation

In October 2023, Chinese law enforcement officers walked into GroupM’s offices in China. More than three employees were detained as part of a state investigation. The subject of the investigation was the systematic retention of client rebates — precisely the practice Jon Mandel had described at a conference in 2015, and precisely the practice documented in the ANA/K2 report in 2016. Seven years later, in one of the world’s largest advertising markets, employees of what was then the world’s largest media-buying network were being detained by police over it.

GroupM is the media-buying arm of WPP. Campaign Asia-Pacific had spent two years investigating rebate-driven practices in China linked to former GroupM employees. WPP confirmed the detentions publicly.

This Is Not an Allegation

A law enforcement raid is a documented legal event. Chinese authorities raided the offices of GroupM — WPP’s media-buying arm — and detained employees. The subject was the retention of client rebates. WPP confirmed the detentions. This is verified fact, not an accusation from a rival or a whistleblower.

The industry largely moved on. Some reporting, some concern, and then the news cycle continued. But the raid was a very loud data point: the practices documented as pervasive in the United States in 2016 were being treated as criminal violations in China in 2023. The same structural logic. A different jurisdiction’s patience.

04

OpenPath — When Transparency Becomes a Commercial Threat

In 2022, The Trade Desk launched OpenPath, its direct-to-publisher buying programme. The concept was both elegant and threatening to a large number of people who had been making money from the absence of what it offered. OpenPath allowed advertisers to buy publisher inventory directly, bypassing the chain of supply-side platforms and intermediaries that each took a cut before the money reached the publisher. More of the advertiser’s budget reaches actual content. A cleaner, more verifiable supply chain.

Jeff Green positioned it as exactly what it was: a transparency tool. Publishers liked it because they received more money. Advertisers liked it because they received better value. By 2025 it had more than 400 publisher partners integrated, and Green had publicly anticipated that year as the beginning of OpenPath’s ‘steep acceleration phase.’

The agencies did not like it, and the reason is not complicated. OpenPath performs, structurally, the function that agency trading desks sell to clients as a service. When a transparent DSP tool performs supply-path optimisation transparently, at cost, the agency’s ability to charge clients for that optimisation — and, more significantly, to execute those optimisations in ways that also happen to generate proprietary margin — becomes very hard to justify.

In late February 2026, AdWeek exclusively reported that Dentsu had quietly disabled OpenPath entirely, after using it since its 2022 launch. WPP had withdrawn shortly after the programme’s launch and had never used it in markets including Australia. Both agencies cited concerns about fee visibility and uncertainty about ad placement transparency.

The Trade Desk’s CMO Ian Colley responded directly and specifically on LinkedIn: ‘TTD doesn’t push spend to OpenPath. It’s not a marketplace or curated inventory. OpenPath is offered at cost to the ecosystem. We’ve been clear about that. There are no hidden fees beyond that. If OpenPath is selected by an advertiser, it is because it represents the cleanest and most cost-efficient path.’

Worth Noting on Both Sides

The agencies’ stated concerns about OpenPath fee transparency have not been independently verified by any published audit. However, the commercial incentive to raise those concerns — since OpenPath competes directly with services agencies charge for — exists entirely independently of whether the concerns are valid. Both things can be true simultaneously: TTD may have had imperfections in OpenPath’s communication, and the agencies may also have had financial motives for exiting that had nothing to do with their clients’ interests. The timing also matters: OpenPath launched in 2022. The agencies did not exit in 2022. They exited in late 2025 and early 2026, at a moment when TTD was at its most commercially vulnerable.

05

Richard Foster and the Report That Became a $100 Million Lawsuit

Richard Foster spent seventeen years at GroupM, WPP’s media-buying arm — now rebranded as WPP Media. He rose to become the Global CEO of Motion Content Group, GroupM’s entertainment investment division. His division co-produced more than 2,500 television series during his tenure, including Love Island, and managed roughly $500 million in annual GroupM entertainment investment. In his final year, his US operation reportedly posted 140% revenue growth. He established Motion Content Group in May 2017, specifically structured to operate independently from GroupM Trading’s media inventory practices, and focused on compliance with client contractual obligations. He was, by any measure, a successful and long-serving senior executive.

In December 2024, Brian Lesser — the incoming CEO of GroupM, which was in the process of being rebranded as WPP Media — asked Foster to prepare a strategic assessment of the division’s operations and potential. Foster produced what became known internally as Project Claridges: an approximately 35-36 page document that did two things simultaneously. It outlined a proposal for a new consolidated entertainment division projected to generate net sales exceeding $2 billion by 2029 at profit margins above 70%. And it raised serious internal concerns about GroupM’s trading practices, concerns Foster had first raised internally as far back as a 2016 meeting with former GroupM executives.

What the Lawsuit Alleges — With WPP’s Response

The following is what Foster’s lawsuit alleges. WPP has filed a motion to dismiss, disputes the material characterisation of all practices, and the case is before the courts with no ruling yet issued.

According to the complaint, GroupM used its approximately $60 billion in annual client advertising spend to negotiate volume-based discounts and rebates from media vendors. Rather than returning these benefits to clients, the complaint alleges GroupM reclassified the resulting inventory as ‘proprietary media,’ sold it back to clients through opt-in agreements, and booked the spread as what internal documents reportedly called ‘non-product related income.’ An internal presentation Foster submitted as part of the Project Claridges report allegedly estimated GroupM derived nearly $1 billion annually from this income stream, with an internal growth target of 15% per year. Foster estimated that over the period 2019 to 2024, GroupM generated $3 to $4 billion from such deals and improperly retained approximately $1.5 to $2 billion of it.

According to the complaint, when Foster submitted the report, Lesser initially expressed concern and said he would investigate. He then, unbeknownst to Foster, forwarded the original report to Mark Patterson — the executive responsible for GroupM’s trading activities. Patterson is currently WPP Media’s Global President of Markets and Business Operations. Hours after Lesser separately asked Foster via text to produce a ‘sanitised’ version of the report excluding criticism of GroupM, a restructure was announced placing Foster’s division under Patterson’s direct oversight. Foster alleges he was then excluded from key meetings, removed from deals he had built, and gradually isolated from decision-making. On July 10, 2025 — the day after WPP’s stock fell 18% on a trading update disclosing serious deterioration at WPP Media — Foster was fired.

In November 2025, Foster filed the lawsuit in the Supreme Court of New York, seeking more than $100 million in damages. He alleges wrongful termination, retaliation, and violations of whistleblower protection statutes in California and New York. The individual executives named as relevant non-parties in the complaint include Mark Patterson (WPP Media’s Global President of Markets and Business Operations), Andrew Meaden (Global Head of Investment at WPP Media), and Nicola McCormick (Global General Counsel for GroupM). The specific allegations against each are described in the complaint, which WPP disputes in full.

WPP’s Defence — In Full, as Published

WPP has filed a motion to dismiss. Its key arguments, as reported by Digiday based on court filings, are: (1) According to a sworn affirmation from Lesser, Foster’s counsel sent WPP a draft complaint on October 10, 2025 — more than two months before filing — and threatened to go public unless GroupM agreed to a large severance payment within 30 days. WPP argues offering to stay silent for a payout is incompatible with being a whistleblower. (2) WPP contends Project Claridges contains no mention of illegal activity and is a business proposal tied to Foster’s own ambitions, not a whistleblower document. (3) Foster was among hundreds of U.S. employees let go in a routine organisational restructuring. WPP states: ‘The court has not yet made any findings in relation to the allegations and we will defend them vigorously.’

The Number From WPP’s Own Filing

Among the internal documents that entered the public record through WPP’s own court filings was a piece of data that is striking on its face. Across GroupM’s top 30 U.S. clients — representing $13.4 billion in total billings — 97.4% of the proprietary inventory from which GroupM was allegedly generating income had not been used by the opted-in clients. Google, GroupM’s single largest U.S. client at $2.3 billion in annual billings, had utilised just 0.51% of the proprietary inventory its budget was helping to generate. This data was in WPP’s own filing, submitted as part of its motion to dismiss, and was first reported by The Times and subsequently by Digiday.

WPP’s purpose in submitting these documents was to characterise Project Claridges as a routine business proposal rather than a whistleblower disclosure. The commercial picture the documents created in the public record was not something WPP anticipated becoming the story. Ivan Fernandes, a former WPP executive now advising other groups, described the filing as ‘commercially significant’ in comments reported by The Times.

The case is ongoing. No court has made findings on any of the allegations. Both the specific claims and the defences deserve to be heard in full before conclusions are drawn. What is clear is that the financial picture described in the publicly filed documents has given the advertising industry’s clients a great deal to think about.

06

Agencies Take the High Ground — A Brief and Ironic History

It is worth documenting, clearly and chronologically, how enthusiastically the major agency holding companies supported The Trade Desk — before they found it commercially inconvenient to do so.

When TTD went public in 2016, holding company trading desks were among its most significant clients. GroupM, Publicis’s Starcom, IPG’s Mediabrands, Dentsu — all were buying through TTD, recommending it to clients, and in several cases publicly praising its approach. The model Green had built — powering the agencies rather than competing with them — was working exactly as intended. The agencies loved having a best-in-class transparent DSP they could point to when clients asked awkward questions about programmatic buying.

In 2021, Publicis named TTD the exclusive third-party DSP partner for its Epsilon Core ID. Not a quiet commercial arrangement. A loud, public endorsement from the world’s largest advertising holding company by market capitalisation, telling the market: we trust this platform above all others.

The dynamic began shifting after 2022, as The Trade Desk launched a series of initiatives that were simultaneously transparency tools and competitive threats to specific agency revenue lines. OpenPath (2022) competed with agency supply-path services. Kokai (2023-24) automated functions agencies traditionally charged for. OpenAds (2025) built an alternative auction infrastructure. Ventura (2025) put TTD into the CTV operating system business. Each was framed accurately as advancing the open internet. Each also happened to undermine a specific way agencies were extracting margin from client budgets.

The Progression — From Partner to Auditor: A Verified Timeline

2016-2021: Agencies enthusiastically use and recommend TTD. GroupM, Starcom, Mediabrands, Dentsu all buy through the platform.

2021: Publicis names TTD exclusive DSP partner for Epsilon Core ID — a flagship public endorsement.

2022: TTD launches OpenPath, directly competing with agency supply-path optimisation services.

2023-24: Kokai AI platform automates campaign functions agencies charge for. TTD and agencies begin to experience operational friction.

2025: WPP and Dentsu quietly exit OpenPath, citing fee and transparency concerns. TTD launches OpenAds and Ventura. WPP stock falls 62% in 2025. Foster lawsuit filed November 2025.

Early 2026: Publicis commissions FirmDecisions audit of TTD. Issues memo advising clients to stop using it. The relationship publicly celebrated in 2021 becomes, five years later, the subject of a client advisory.

Green’s frustration is understandable and his characterisation is, on the available facts, substantially supported. The companies now auditing TTD’s fee structure are the same companies whose practices were documented in the 2016 K2 report, whose Chinese offices were raided in 2023, and whose internal documents — submitted in their own court filings — describe a financial model whose beneficiaries, according to those same documents, were not primarily the clients whose budgets funded it.

The question is not whether the agencies are hypocritical. The question is whether their specific concerns about TTD are valid. Those are different questions, and conflating them is a mistake both sides have made.

07

The Epsilon SSP — Transparency Concerns, Apply Elsewhere

In September 2025, AdWeek exclusively reported that over the prior 18 months, the media-buying arms of WPP, IPG, Dentsu, Havas, and at least four independent agencies had been purchasing ad inventory indirectly through Publicis’s own Epsilon supply-side platform. In some cases, apparently without full visibility into where their supply chain was routing client money.

Publicis, through its 2019 acquisition of Epsilon for $4.4 billion, had built an SSP that was operating as a competitor to other agency holding companies. The agencies buying through it were, at least in some cases, routing client budgets through a direct rival’s infrastructure, potentially giving that rival access to buying patterns, audience data, and campaign intelligence.

No one commissioned an independent audit of this situation. No holding company issued a client memo advising its clients to pause using the relevant supply chain until the conflict was reviewed. The trade press covered it, the industry discussed it, and life continued. The contrast with the formal audit process subsequently applied to The Trade Desk is a question worth sitting with.

08

The Trade Desk’s Difficult Year — In the Spirit of Honest Reporting

A story told fairly requires honesty about the protagonist’s stumbles alongside its strengths. The Trade Desk had a difficult 2025 that was not entirely other people’s fault, and deserves to be reported as such.

The First Earnings Miss in Company History

In the fourth quarter of 2024, The Trade Desk missed its quarterly earnings expectations for the first time in its history as a public company. Bank of America’s analysts cited one primary factor in their note: poor execution on the rollout of Kokai, TTD’s AI-powered campaign management platform. Agencies that had used TTD’s familiar interface for years were asked to adopt a new system, and TTD’s onboarding support was not adequate to the scale of the transition. Clients experienced frustration. Campaigns ran less efficiently during the migration. Revenue guidance came in below expectations. CEO Jeff Green issued a rare public apology to investors. The stock fell approximately 25% on the news.

Two CFOs in Under Twelve Months

In August 2025, TTD announced that CFO Laura Schenkein — a long-serving company veteran — was departing, to be replaced by Alex Kayyal, a board member from Lightspeed Ventures with no prior experience as a public-company CFO. The announcement was paired with a revenue miss and conservative guidance. The stock fell approximately 39% in a single trading session — the worst day in TTD’s history as a public company. Green issued a public apology to investors at the time.

Kayyal was then terminated effective January 24, 2026. He had been CFO for approximately five months. Tahnil Davis, the Chief Accounting Officer and an 11-year company veteran, was named interim CFO. Two CFOs in under twelve months at a company whose brand identity rests substantially on stability and trustworthiness is a self-inflicted wound. The agencies did not cause it. Good governance requires better.

Revenue Deceleration — Context Matters Both Ways

The Trade Desk — Revenue Growth Deceleration, 2025 (Source: TTD Q4 2025 Earnings Release, February 25, 2026)

The deceleration is real and material. Amazon Ads reported 23% year-on-year growth in Q4 2025 — compared to TTD’s 14% — and was reportedly offering DSP fees as low as 1% to major spending clients in direct competition. The broader programmatic market grew; TTD’s relative position weakened. These are real competitive dynamics, not simply the result of agency pressure.

Operational Friction

Digiday’s February 2026 reporting documented operational friction beyond the CFO situation. A media director at one mid-sized agency described cycling through three separate TTD account teams in under a year — three rounds of introductions, three attempts to rebuild working context on a platform complex enough that continuity is operationally critical. A separate executive described an incident where TTD threatened mid-contract rate increases when spending was pacing below an agreed annual target, then backed down when the agency threatened to move spend. Customer retention above 95% over eleven consecutive years means the vast majority of clients do not have these experiences. But the experiences are real, and a platform that claims the transparency high ground has less tolerance for this kind of inconsistency than one that never made that claim.

What TTD Gets Right Despite Everything

Full-year 2025 revenue was $2.9 billion, up 18% from 2024. Q4 2025 revenue was $847 million, up 14% year-on-year. Net income for 2025 was $443 million. Adjusted EBITDA margins were approximately 41%. The company carries no long-term debt and holds approximately $1.5 billion in cash. Customer retention has been above 95% for eleven consecutive years. Between March 2 and 4, 2026, Jeff Green personally purchased 6 million shares of TTD stock at weighted average prices between $23.49 and $25.08, totalling approximately $148 million — the largest insider stock purchase in the company’s history, per insider trading tracker Secform4, confirmed by an SEC Form 4 filing. These are the characteristics of a company with genuine structural strength navigating a difficult transition period.

09

The Allegedly Failed Audit — The Main Event

On March 17 and 18, 2026, Publicis Groupe — the company that had publicly named The Trade Desk its exclusive DSP partner just five years earlier — sent an email to select clients advising them it would no longer recommend TTD as a preferred DSP. The memo was based on an audit conducted by FirmDecisions, a unit of the Ebiquity Group. FirmDecisions is a credible, well-established media compliance auditor that had collaborated with the ANA on the K2 transparency report a decade earlier. Its institutional credibility is not in question.

The audit examined Publicis’s Master Services Agreement with TTD and, according to Publicis’s account of its findings, identified three specific concerns: that TTD had ‘improperly applied their DSP fee to other fees’ charged to Publicis and its clients; that Publicis and its clients had been billed for tools they were automatically opted into without authorisation; and that TTD had not provided the auditor with information necessary to validate that media and data costs were invoiced at cost without mark-up, as the agreement reportedly required.

Publicis stated it had engaged TTD’s senior leadership without reaching a satisfactory resolution. Stifel analyst Mark Kelley confirmed the same day that Publicis represents more than 10% of TTD’s gross billings — making it TTD’s largest holding company client. The stock fell approximately 5.7% on the news. Stifel characterised the move as likely a negotiating tactic, while downgrading the stock to reflect the financial risk.

The Word ‘Allegedly’ Is Doing Real Work Here

TTD ‘failing’ the audit is Publicis’s characterisation of FirmDecisions’ conclusions. The Trade Desk flatly disputes it. No court has ruled. No regulatory body has found a violation. What exists is a contractual dispute between a platform and its largest holding company client, mediated by an auditor whose findings one party accepts and the other specifically rejects. TTD states it has ‘never failed any audit ever’ in its history as a public company. This article uses ‘allegedly’ deliberately and accurately throughout.

The Trade Desk’s denial was specific and direct. In a company statement: ‘Any notion that TTD failed an audit is not true.’ The company argued that the auditor had requested data that would violate customer and partner confidentiality agreements — framing the refusal to provide certain information as a contractual necessity, not concealment.

Jeff Green took to LinkedIn with the broader argument about agency opacity. He is not wrong about the hypocrisy of agencies criticising TTD’s transparency while operating principal-based buying models, collecting rebates, and routing client spend through rival holding companies’ SSPs. He is also not, in addressing those arguments, specifically addressing the audit allegations. Those are two different things. Being more transparent than WPP is factually true, but it is not what resolving a FirmDecisions audit finding looks like.

The specific dispute — fee stacking, auto-opt-ins, refusal to validate cost claims — remains unresolved. TTD denies them. Publicis asserts them. FirmDecisions concluded them. No independent adjudicator has yet ruled. The word ‘failed’ is Publicis’s word, not a settled fact, and should be read as such.

10

Amazon, the Walled Gardens, and the Selective Transparency Standard

There is a third major actor in this story that rarely receives adequate scrutiny: Amazon Ads, which reported 23% year-on-year revenue growth in Q4 2025 while The Trade Desk reported 14%, and which was in 2025 reportedly offering DSP fees as low as 1% for major spending clients in a direct effort to attract agency budgets away from TTD.

Amazon’s structural advantages over any independent DSP are genuine and growing. It owns Prime Video, which launched ad-supported streaming in January 2024. It owns Thursday Night Football. It has the world’s largest e-commerce intent dataset. And it can trace an advertising impression to a purchase on Amazon.com in a way no independent DSP can replicate. At the 2025 Cannes Lions festival, Amazon Ads and Roku announced a partnership giving Amazon DSP exclusive authenticated access to logged-in Roku user data across more than 80 million U.S. households — the largest authenticated CTV footprint in the United States. In September 2025, Netflix announced advertisers could purchase Netflix inventory directly through Amazon DSP.

Green has argued, not entirely without basis, that Amazon’s ad business is approximately 90% Sponsored Listings competing with Google Search, not open-web programmatic display. Several agency buyers confirmed to Digiday in 2025 that Amazon DSP spend was ‘additive’ — from retail media budgets rather than trade desk budgets. This is plausible in the near term. Its durability as Amazon’s CTV ambitions mature is a different question.

The Transparency Standard Applied Selectively

The agencies auditing TTD’s fee structure route enormous budgets to Meta (ad revenue up approximately 21% in 2025), Google, and Amazon — platforms where advertiser transparency into auction mechanics, targeting logic, and fee structures is essentially zero. There are no FirmDecisions audits of Google’s ad exchange. There are no client memos about Meta’s auction opacity. There are no formal reviews of Amazon DSP fees when Amazon also owns the media. The Trade Desk — the one platform that publishes its fee structure and operates no owned media — is the one that received the formal audit. This observation does not resolve the audit findings against TTD. But it describes the environment in which they were commissioned.

11

WPP’s Freefall and the Industry’s Financial Pressures

Understanding why the agencies are fighting so hard right now requires understanding just how severe their financial situation has become. WPP’s story is the most dramatic illustration.

Agency Holding Companies vs. The Trade Desk — March 2026 (Sources: WPP 2025 Preliminary Results Feb 2026; StockAnalysis.com; PitchBook; TTD Q4 Earnings Release)

WPP’s market capitalisation of approximately $3.3 billion as of mid-March 2026 is smaller than the value of the accounts it lost to Publicis alone in 2025. The Mars global media account — approximately $1.7 billion — went to Publicis. The Coca-Cola North America media business — approximately $700 million — also went to Publicis. Its top 25 clients fell 4.1% like-for-like in 2025. WPP Media, its trading arm, declined 5.9% for the full year and 10.8% in Q4 alone. The company was removed from the FTSE 100 in December 2025. New CEO Cindy Rose acknowledged publicly that WPP Media had ‘lost its way.’

The structural pressure across all holdcos comes from the same source: AI is systematically compressing the cost of the functions agencies have historically been paid to perform — media planning, data analysis, creative iteration, audience segmentation. A 2025 Bannerflow report found 83% of senior brand marketers already use AI to target digital ads. Basis Technologies found 92% of advertising agencies use AI in some capacity. The margin that complexity used to justify is under pressure from every direction. WPP launched ‘WPP Open Pro’ in October 2025, a self-service AI marketing product for smaller clients — an acknowledgment in product form that its traditional service model cannot economically serve a large portion of its potential market.

In November 2025, Omnicom completed the acquisition of IPG — transforming the ‘Big Six’ holding companies into effectively a ‘Big Two or Three.’ The new Omnicom includes IPG’s Acxiom data platform, creating first-party data infrastructure competitive with platform data from Google and Meta. Publicis has Marcel AI and Epsilon. WPP has WPP Open. All of them are racing to demonstrate that their AI investments justify the fees they charge. The Trade Desk is watching from a position of financial strength but competitive vulnerability in this race.

12

The Clients Are Finally Noticing

The most striking statistic in the entire industry transparency conversation is from a 2025 World Federation of Advertisers report: 18% of marketers surveyed did not know whether principal-based buying had been part of their media activity in the past year. Not 18% who had concerns about it. 18% who did not know whether it had happened. This is the information asymmetry that makes the practices described in the Foster lawsuit possible at scale.

The ANA’s most recent benchmark found 54% of U.S. brands have an in-house media unit, and 51% have partially moved their programmatic buying function in-house. More than half of agency contracts, per the same WFA report, lacked clear penalties or enforcement mechanisms for principal-buying non-compliance.

Tucker Matheson, co-founder and managing partner of Markacy, told Digiday that his agency had moved spend toward direct buys and other platforms for a reason that is more honest than most: ‘TTD hadn’t done anything egregiously wrong — the alternatives had simply grown up.’ This is a useful corrective to narratives that frame every client departure as a verdict on TTD’s practices. Sometimes the market develops and dominant platforms face more competition. That is a legitimate commercial outcome, not a scandal.

The in-housing trend has genuine limits. Building a real in-house programmatic function requires technical expertise, organisational commitment, and ongoing investment that is not economically viable for most brands. The hybrid model — brand owns the DSP seat and the data relationship, agency manages execution — is the most common outcome. In this model, the agency’s margin on the technology layer disappears. The agency must compete on genuine intellectual value: strategy, creativity, analysis. This is, it turns out, what transparency looks like when it reaches the client relationship. It is not comfortable for business models built on the absence of it.

13

The Verdict — And What Comes Next

The Trade Desk built something that programmatic advertising genuinely needed. In an ecosystem structured around opacity, it built a platform on transparency. In a market where everyone was working both sides, it chose one side and stayed there for fifteen years. It delivered on that promise with sufficient consistency that 95% of its customers renewed, year after year.

The agencies that built their business models on the practices the K2 report documented in 2016 — the practices that led to law enforcement raids in China in 2023, to a $100 million whistleblower lawsuit in 2025, to internal documents describing a financial model whose primary beneficiaries, according to those documents, were not the clients paying for it — these agencies did not suddenly discover transparency concerns in 2026. They discovered that TTD’s growth was making their opacity structurally harder to maintain. And they reached for the language of transparency to address it, from a position whose relationship with that concept is, as the factual record demonstrates, complicated.

Jeff Green is right that the agencies auditing his platform are doing so from glass houses. He is right that an industry that routes vast budgets to Meta, Google, and Amazon without auditing their mechanics is applying transparency standards selectively. These are accurate observations.

Where TTD Must Do Better

The allegedly failed Publicis audit is not fully resolved by pointing at WPP’s court filings. TTD’s refusal to supply certain data to FirmDecisions — however contractually justified — is not what ‘passing an audit’ looks like to the clients observing the situation. The company that has spent fifteen years saying ‘look at how transparent we are’ cannot, at the first genuine audit stress-test of that claim, respond primarily by critiquing the auditor’s clients. The specific allegations — fee stacking, auto-opt-ins without authorisation, refusal to validate costs — deserve a specific, published, detailed response, not just a blanket denial.

Two CFOs in twelve months was a self-inflicted governance failure. The Kokai rollout execution was a genuine operational lapse for which Green publicly apologised. The account team instability reported by Digiday is correctable. These are real issues in a company that holds itself to a higher standard. Holding itself to that standard means addressing them.

The Structural Outlook

TTD’s financial position is stronger than its current narrative would suggest. Full-year 2025 revenue of $2.9 billion, up 18%. Net income $443 million. Adjusted EBITDA margins approximately 41%. $1.5 billion in cash. No long-term debt. Customer retention above 95% for eleven consecutive years. Jeff Green personally investing $148 million in his own company’s stock between March 2 and 4, 2026. These are not the characteristics of a company in existential crisis.

The agency holding companies face a harder structural question. The practices that generated margin in the past are under legal scrutiny through the Foster lawsuit, under client scrutiny through WFA transparency reporting, and under competitive scrutiny because the AI tools compressing their business are also making opacity harder to maintain at scale. Publicis and Omnicom, investing heavily in AI and data infrastructure, may navigate this transition. WPP, at $3.3 billion in market capitalisation with a CEO who has publicly acknowledged the company lost its way, faces an uphill climb.

The clients who fund the entire system have the most to gain from demanding genuine accountability from all sides. The 18% who do not know whether they participated in principal buying last year are subsidising a system that profits from their not knowing. That is an information problem, and the only people who can solve it are the ones writing the cheques.

In programmatic advertising in 2026, everyone is standing in a glass house. The Trade Desk built its out of glass on purpose, because it understood that transparency is not just a principle — it is a competitive advantage, if you are actually committed to it. The question the next twelve months will answer is whether it remains committed to it under pressure. The question the industry needs to answer is whether it will hold all parties — not just the convenient one — to the same standard.

Somebody, at last, is watching. The question is who blinks first.

Read the second part of this article here :

SOURCES — VERIFIED PUBLIC RECORDS ONLY

All facts in this article are drawn from the following verified sources. Every claim traceable to a named, published document, court filing, or analyst note. All allegations from Foster v. WPP are attributed to the complaint. WPP disputes the material characterisation of all practices. No court has ruled. The case is ongoing.

• ANA / K2 Intelligence — Media Transparency Initiative Full Report, June 2016 (public document)

• AdWeek — Exclusive: Dentsu and WPP Quietly Exited The Trade Desk’s OpenPath, February 2026

• AdWeek — Publicis advises clients to stop using The Trade Desk following allegedly failed audit, March 2026

• AdWeek — Publicis-TTD Epsilon Core ID exclusive DSP partnership announcement, 2021

• AdWeek — Publicis Epsilon SSP conflict-of-interest reporting, September 2025

• Ian Colley, CMO, The Trade Desk — LinkedIn post on OpenPath allegations, February 2026 (verified verbatim)

• Jeff Green, CEO, The Trade Desk — LinkedIn post on agency transparency, March 2026 (verified verbatim)

• Jeff Green, CEO, The Trade Desk — AdExchanger interview on agency partnerships, 2016 (verified verbatim)

• Jeff Green, CEO, The Trade Desk — Q4 2025 earnings call quote on competitive complexity, February 2026

• Digiday — In Fighting a Whistleblower Suit, WPP Gave Away the Game, February/March 2026 (WPP motion to dismiss details, Project Claridges, executive descriptions)

• Digiday — The Numbers Behind the WPP Whistleblower Case, March 2026 (97.4%, $13.4B, Google 0.51% stats)

• Digiday — Agency Shopping Around on The Trade Desk, February 2026 (account team friction, rate increase incident)

• Digiday — In-housing programmatic analysis, February 2026 (Tucker Matheson quote)

• B&T Australia — Former WPP Exec Sues Holdco (detailed Foster complaint, executive roles), November 2025

• Campaign Asia-Pacific — GroupM China rebate practices investigation and raid coverage, October 2023

• Campaign US — Former CEO within WPP Media sues WPP (case filing details, verified roles), November 2025

• Storyboard18 — Jeff Green LinkedIn post verbatim analysis, March 2026

• Brewer Attorneys & Counselors — Official press release on Foster v. WPP filing, November 12, 2025

• WPP plc — 2025 Preliminary Results and Strategy Update, February 2026 (revenue figures, account losses)

• WPP plc — Q3 2025 Trading Update, October 2025

• The Trade Desk — Q4 and Full Year 2025 Earnings Release, February 25, 2026 ($2.9B revenue, $847M Q4, net income $443M)

• The Trade Desk — Q1 2026 Guidance ($678M minimum), February 2026

• The Trade Desk — Ventura Ecosystem launch, February 24, 2026

• The Trade Desk — OpenAds launch, October 2025 (first wave of publishing partners)

• The Trade Desk — Official IPO press release, September 20, 2016 (priced at $18.00/share)

• Nasdaq.com — TTD IPO first-day close $30.10, up 67%; September 21, 2016

• MacroTrends — TTD all-time high closing price $139.51 on December 4, 2024; 52-week high $91.45

• TradingView — TTD all-time high $141.53 on December 4, 2024; ATL $2.20 November 2016

• SEC Form 4 filing — Jeff Green insider purchase, 6 million shares, $148M, March 2-4, 2026

• Motley Fool — Jeff Green $148M purchase analysis, March 5, 2026 (confirms $23.49-$25.08 weighted avg)

• Stifel Research — Analyst note: Publicis as >10% of TTD gross billings; likely negotiating tactic, March 2026

• Bank of America — Analyst note citing Kokai execution as factor in Q4 2024 earnings miss

• StockAnalysis.com — WPP market cap $3.28B as of March 18, 2026; TTD stock data

• PitchBook / market data — Omnicom ~$26B market cap; Publicis ~$21B market cap, March 2026

• ANA — In-house media benchmark report, 2023 (54% have in-house unit; 51% partially in-housed programmatic)

• WFA (World Federation of Advertisers) — 2025 report: principal media, audit rights; 18% marketers unaware stat

• W Media Research — Amazon and Roku CTV authenticated partnership, Cannes Lions 2025

• Bannerflow / Basis Technologies — AI adoption statistics in advertising, 2025

• The Times (UK) — Ivan Fernandes quote on WPP court filing commercial significance (reported in Digiday)

• Court filing — Foster v. WPP plc and GroupM Worldwide LLC d/b/a WPP Media, filed November 2025 / December 2025, Supreme Court of New York, New York County / US District Court Southern District of New York

Disclaimer: It’s a super long article with some real insights and suggestions in second part. The best time to read it is when you are lying in the bed at night and doomscrolling your social media feeds. Also, in this article, I focus on Salespeople first and will follow through on my own kind (Product Managers and Engineers) in the next version when I complete my research and reading about it.

The Man Who Scored Every Job in America (Then Deleted It)

Andrej Karpathy does not post hot takes. He was a founding member of OpenAI, Tesla’s head of AI, and now runs Eureka Labs, an AI education company. When he ships something, you stop and pay attention.

On March 15, 2026, Karpathy published a project on GitHub called, simply: jobs. Within hours, it was gone from his website. The original page at karpathy.ai/jobs was deleted the same day. But not before it was archived. You can still see it at the Wayback Machine.

The project scraped all 342 occupations from the US Bureau of Labor Statistics (BLS, the government agency that tracks employment and job growth across every sector of the American economy) Occupational Outlook Handbook. It used a large language model to score each occupation’s AI exposure on a scale of 0 to 10. Area of each block in the interactive treemap is proportional to employment. Color runs from green (safe) to red (exposed). Below is a recreation of the map focused on sales-adjacent roles.

Recreation of Karpathy’s AI Exposure Treemap (karpathy.ai/jobs, published and deleted March 15, 2026, archived at web.archive.org). Gold borders indicate sales roles. Score 6-7/10.

The scoring logic is honest and specific. The key signal: if a job can be done entirely from a home office on a computer, AI exposure is inherently high. If a job requires physical presence, manual skill, or judgment that cannot be replicated on a screen, there is a natural barrier.

Here is the full scorecard. Read it carefully.

Roofers: 1 out of 10. The squirrels on your roof have better job security right now than the average sales development rep.

Plumbers and electricians: 2 out of 10. No amount of compute will snake your drain.

Registered nurses: 4 to 5. Physical presence. Real-time life-or-death judgment. A natural barrier.

Sales representatives (B2B and SaaS): 6 to 7 out of 10. High exposure. The same bracket as managers, accountants, and engineers.

Software developers: 8 to 9. The people building the tools are also being consumed by the tools. Poetic.

Medical transcriptionists: 10 out of 10. Already gone. The job title still appears in some org charts the way fax machine does in some offices.

The average AI exposure score across all 342 occupations is 5.3 out of 10. Sales sits comfortably above average. The reason sales scores 6 to 7 is not because an AI is about to walk into a boardroom and shake hands. It is because most of what salespeople spend most of their time doing is deeply digital. Cold emails. CRM updates. Proposals. Sequences. Research. Lead qualification. Forecasting. Every one of those tasks fails the screen test.

Karpathy’s scoring also factors in both direct automation, meaning AI does the work outright, and indirect displacement, meaning AI makes a smaller number of humans so productive that fewer total headcount is needed. Sales is being hit by both. Simultaneously. From opposite directions. We will get to the data shortly.

“The most valuable skill won’t be coding. It will be communicating with AI.” -- Andrej Karpathy

The implication for salespeople is precise. The ones who survive this wave are not the ones who use AI the most. They are the ones who communicate with it best, feed it the right context, catch it when it is wrong, and bring genuine judgment to everything the model cannot handle. Most salespeople today are doing none of that. And there is a specific psychological reason why.

Anthropic Just Published a Warning. About Its Own Product.

On March 5, 2026, Anthropic’s own economists, Maxim Massenkoff and Peter McCrory, published a research paper titled Labor Market Impacts of AI: A New Measure and Early Evidence. The full paper is also available as a PDF download.

A company publishing research about the economic disruption caused by its own product. That is worth pausing on. It would be like a cigarette company funding the first serious lung cancer study. Except in this case the company is not hiding the results. It is building an early warning system.

What the paper actually measures

Previous AI-and-jobs research only measured theoretical capability: which tasks could an LLM theoretically perform? Massenkoff and McCrory went further. They built a new metric called observed exposure, which combines theoretical capability with actual real-world usage data from Claude. This is the first measure of its kind: not what AI could automate, but what is already being automated in professional settings right now.

The gap between those two numbers is enormous. And it is the gap that matters most.

Recreation of Figure 2 from Anthropic’s March 2026 paper: Theoretical AI capability (blue) vs. observed real-world usage (red) by occupational category. Source: anthropic.com/research/labor-market-impacts

Look at the gap between blue and red in every single category. Business and finance: 94% theoretical capability, but actual observed AI coverage today is a fraction of that. Office and admin: 90% theoretical. Management: 91%. Computer and math: 94% theoretical, only 33% actual.

That gap is not a sign that things are fine. It is a description of what is coming as capabilities improve and adoption deepens. The red area will grow toward the blue. It is not a question of whether. It is a question of when.

What the BLS projections confirm

The BLS publishes employment projections covering predicted changes in every occupation from 2024 to 2034. The Anthropic paper cross-referenced those projections against the observed exposure scores. The finding is direct: for every 10 percentage point increase in AI task coverage, the BLS projected job growth drops by 0.6 percentage points. Two completely independent methodologies pointing at exactly the same thing.

The paper also identifies who is most exposed. Workers in the top quartile of AI exposure are more likely to be female, more educated, more highly paid, and almost four times more likely to hold a graduate degree. This is not replacing the warehouse floor. It is replacing the office floor. Quietly. In ways that are hard to see in aggregate unemployment data until the effect becomes undeniable.

The paper notes that unemployment in exposed occupations has not yet risen sharply. Massenkoff and McCrory are clear about what this means: the effect has not yet shown up in the most lagging indicator available. But job postings for highly exposed roles have already slowed, and hiring of workers aged 22 to 25 in exposed occupations has already declined by 6 to 16 percent, driven primarily by companies simply not backfilling roles when people leave.

“By laying this groundwork now, before meaningful effects have emerged, we hope future findings will more reliably identify economic disruption than post-hoc analyses.” -- Massenkoff and McCrory, Anthropic, March 2026

That is academic language for: we are setting up the instruments before the earthquake hits, not after. If the paper does not make you at least slightly uncomfortable about your current skill set, read it again.

Meet Sanjay

Sanjay is a B2B SaaS Account Executive (AE) -- a sales executive responsible for managing and closing deals with business clients -- at a mid-market DevOps platform. Five years in. He hit quota in 2022. Missed it in 2023. In 2024, he described himself as finding his groove, which is the professional equivalent of saying your flight is delayed but the lounge has decent samosas.

It is Q1 2026. Sanjay walks into his QBR with the energy of a man who has just discovered a life hack. He opens his MacBook. He says: I have been really leaning into AI in my workflow.

Priya, his VP of Sales, looks up. She is cautiously optimistic. She has seen fourteen QBRs in three years and has developed the particular expression of someone who has heard the words lean into used professionally approximately four hundred times. Tell me more, she says.

I use Claude and ChatGPT to research prospects before calls, Sanjay says. I pull their LinkedIn, their company website, recent press releases, drop it all into a chat, and get a brief. Takes me fifteen minutes instead of four hours.

Priya nods slowly. And what do you do with the brief?

I use it in the call.

How?

A pause. The kind that has its own weather.

I read through it before the call.

Priya sets down her coffee. Sanjay, your deal conversion dropped fourteen percent last quarter. Your discovery calls average twenty-two minutes. Your champion map on your top three open deals has one name on it and that person left the company in January. So what exactly is the AI helping you with?

Sanjay opens his mouth. Closes it. Opens it again. The Slack notification that dings in the background goes unchecked. It senses this is not the moment.

Here is the precise thing Priya understands that Sanjay does not. Sanjay is not lazy. He is not dumb. He thinks he is using AI. He is actually outsourcing his brain to a machine that makes things up and does not feel bad about it.

To understand what went wrong, you need to go back twenty years. In 2005, being good at Google was a genuine skill. You learned which results to trust. You developed a feel for credible sources. You clicked through twenty links over four hours, read them, synthesized the conflicting information yourself, and applied your own judgment to figure out what was true and what was noise. The process had friction. And that friction forced thinking.

Then comes 2026. Sanjay drops a company name into Claude and gets a polished, confident brief in ninety seconds. It has headers. It has bullet points. It reads like analysis. And some of it is wrong. The funding round it mentions may be eighteen months old. The product feature it highlights may have been deprecated last quarter. The competitive concern it flags may apply to a different company in the same sector with a similar name. Sanjay cannot tell which parts are wrong, because he did not build the understanding himself. He received a finished product and called it preparation.

COGNITIVE OFFLOADING (noun): The practice of delegating memory and reasoning to an external tool or system to reduce mental effort. Humans have always done this to a degree -- writing notes, using calculators, trusting GPS. The psychological danger arrives when the external system is confidently wrong and the person has lost the habit of verifying. Studies show that AI tools create a particularly acute form of this problem: the output looks authoritative, reads fluently, and provides no visible signal of the errors embedded within it. The result is not just ignorance. It is confident ignorance, which is considerably more dangerous.

---

Researchers at Aalto University published a study in the journal Computers in Human Behavior in late 2025 that made this very specific. When people use AI tools to solve problems, everyone overestimates their own performance afterward. But the researchers found something that should keep every AI-enthusiast salesperson up at night: the more AI-literate the person, the more overconfident they become. The classic Dunning-Kruger effect, where incompetent people overestimate their abilities, does not just vanish when AI is involved. It actually reverses.

“When it comes to AI, the Dunning-Kruger effect vanishes. In fact, what’s really surprising is that higher AI literacy brings more overconfidence.” -- Professor Robin Welsch, Aalto University, Computers in Human Behavior, 2025

Sanjay is not the exception. Sanjay is the research subject. He has gotten good at using the tools. He has gotten good at producing plausible-looking outputs. He has not gotten good at knowing when the plausible-looking outputs are wrong. And in sales, where a single inaccuracy about a prospect’s business can end a call in four minutes, that gap is expensive.

The Numbers Are Not Being Subtle

36% of B2B SaaS companies cut their SDR or BDR headcount in the past twelve months, the highest reduction of any sales function, per Emergence Capital’s survey of 560+ B2B software companies, April 2025.

Email-based SDRs who run cold outbound sequences and qualify inbound leads will be 90% displaced by AI within the next twelve months. That is not a prediction from a newsletter. That is Jason Lemkin, who founded SaaStr and has deployed over 200 million dollars into B2B SaaS companies.

SaaStr replaced 10 humans with 1.2 humans and 20 AI agents. The agents sent 70,000 hyper-personalized emails for SaaStr London. Their human team had sent 7,000. Ten times the volume, slightly better quality, and the agents generated 15% of the event’s revenue. The entire experiment is documented in Lemkin’s own post.

AI adoption in sales jumped from 39% to 81% between 2023 and 2025. It took email a decade to achieve similar penetration in sales workflows. AI did it in two years.

By 2027, Gartner projects that 60% of B2B sales interactions will be AI-mediated, either initiated, assisted, or completed by tools. Your company’s holiday party will happen twice before this is no longer deniable.

The Anthropic paper found that higher observed AI exposure correlates directly with lower BLS-projected job growth through 2034. Two independent methodologies. Same direction.

“Our AI agents are better than a mid-pack AE or SDR. Not better than the best. But better than the 50th percentile person I’ve worked with over my career. And that changes everything. If you’re a mid-pack GTM professional who doesn’t want to work harder and smarter than a year ago, these jobs are in terminal decline.” -- Jason Lemkin, SaaStr, December 2025

The median sales executive is now in direct competition with software that costs 500 to 5,000 dollars a month. That software does not sleep, does not cherry-pick leads, does not add items to its follow-up list and then quietly not follow up, and does not quit without notice the week before your biggest event of the year. If your value proposition as a salesperson is primarily that you show up and send emails, that is not actually a value proposition. That is a scheduled task.

Four Things That Must Be Buried, Permanently

1. Cold email as a strategy (not a tactic)

The honest diagnosis: cold email is a lottery, not a strategy. It occasionally works. It cannot be the foundation of your pipeline. If it is, you will be replaced and you will have no one to blame but the person who thought sending 300 emails a week counted as building a sales motion.

Here is the specific problem with how most salespeople use cold email today. They treat it as a volume game. Send enough emails, get enough replies, book enough meetings, close enough deals. The logic feels sound. The results are catastrophic. Average cold email reply rates in B2B SaaS are between 1% and 3%, and have been falling for years as inboxes fill up and buyers grow immune.

AI has made this dramatically worse, not better. AI SDR tools can now send thousands of emails per day, all of them “personalized” with the first line referencing the prospect’s latest LinkedIn post. Every single vendor in your market has access to the same tools. Every single buyer in your target accounts has received six emails this week that start with “I noticed your recent post about digital transformation.” The personalization has become the new template. Buyers are not reading it. They are deleting it before the third word.

Here is what actually works: a cold email sent to someone you have a genuine, specific, and timely reason to contact. Not a sequence. One email. With something in it that demonstrates you spent more than fifteen minutes thinking about their actual situation. Something that shows you understand what their current priorities probably are, why this particular moment matters for them, and what you can specifically offer that they cannot easily get elsewhere.

That kind of email takes research, judgment, and domain expertise to write well. An AI can help you write it faster once you have done the thinking. It cannot do the thinking for you. And if you are depending on volume cold email as your primary prospecting method, you are not building a pipeline. You are playing a lottery. And the house keeps changing the odds.

2. Discovery as a checklist

The honest diagnosis: most salespeople do not actually do discovery. They run a checklist of qualification questions while waiting for permission to start the demo. That is not discovery. That is lead scoring with a human face.

Real discovery is understanding a buyer’s world deeply enough that you know things about their problems they have not articulated yet. It requires genuine curiosity about their business, their team structure, their internal politics, their previous failed attempts to solve this problem, and the specific pressures their decision-makers are under right now. None of that information is on their website. None of it is in any AI brief. You can only get it by asking, listening, and being comfortable sitting in silence when the answer does not come immediately.

Here is where it gets specific and uncomfortable. Many salespeople, when they reach a technical question they cannot answer on a discovery call, say something like: great question, let me bring in someone from our engineering or product team to walk you through that. That sentence sounds responsible. It is not. It is a confession.

The second half of the discovery failure is equally specific. Most salespeople, when they wrap up a discovery call, write down a list of features the prospect asked about. They build the next demo around those features. They call it following up on your priorities.

But a prospect’s stated feature requests are not their real problem. They are the prospect’s current hypothesis about what might solve their real problem. A prospect who says we need better reporting is telling you their team is making decisions without good data. The real question is why. Is it because the data exists but is inaccessible? Because the data is unreliable? Because the people looking at reports do not understand what they are seeing? Because the executives who need the reports are not seeing them in time? Each of those is a different problem with a different solution.

Reading between the lines of what a buyer says, versus what they mean, versus what they actually need, is the irreplaceable skill in discovery. An AI brief can tell you what their company does. It cannot tell you what keeps their VP of Engineering awake at three in the morning. Only listening can do that. And most salespeople are too busy getting to the demo to listen long enough to find out.

3. Volume as the only variable

The honest diagnosis: volume-based selling is the professional equivalent of trying to win at roulette by betting every number simultaneously. You will have some wins. The overall math will not save you.

The SaaS sales model of 2018 to 2022 ran on cheap money, high buyer demand, and a market where digital transformation was a budget priority almost everywhere. You could fill a pipeline by touching enough accounts. That era ended. Budget scrutiny has returned. Sales cycles have lengthened by an average of 32% since 2022, per Gong’s 2025 Win Rate Benchmarks. Buying committees have expanded. The VP who used to say yes in six weeks now says let us revisit in the next budget cycle, which is the corporate way of saying no while keeping the relationship alive.

In this environment, going wide is a losing strategy. The sales executive who has spent four months building a genuine relationship with three high-fit accounts, understands those businesses deeply, knows the internal champion by name and the economic buyer by reputation, and has earned enough trust to get a phone call on a bad Tuesday, that person is infinitely more valuable than the one who has 200 accounts in their CRM and has had a fifteen-minute discovery call with most of them.

Here is the replacement math that Lemkin spells out clearly. An AI SDR costs 500 to 5,000 dollars per month. A human SDR costs 60,000 to 70,000 dollars per year in salary alone, plus benefits, management overhead, and tools. The AI does not cherry-pick leads. It follows up on every single one. It responds instantly at 11pm on a Saturday. The human SDR who adds a lead to their list and gets to it when they can is not competing with the top 10% of human sellers. They are competing with a subscription service. And losing.

4. Performing AI fluency without having it

The honest diagnosis: there is a new game being played in every SaaS sales team. It is called demonstrating AI engagement without actually changing how you work. The tells are consistent and easy to spot.

You attend every AI training the company runs. You use the word prompting in at least two meetings per week. You have a Notion page of prompts that you built four months ago and have not updated since. You share LinkedIn posts about AI tools roughly three times a week. You have mentioned ChatGPT in a customer call at least once this month, which you felt good about afterward.

Meanwhile, your actual sales workflow is almost identical to what it was in 2023. You still spend the first fifteen minutes of every discovery call establishing context that your research should have covered. You still send follow-up emails that summarize the previous meeting rather than advance the next one. You still forecast deals as likely based on gut feel. You have not built a single domain-specific prompt that reflects months of accumulated knowledge about your buyer’s world. Not one.

The Aalto University study found that AI tools function not as a supplement to thinking but as a replacement for it. The productive struggle, meaning the cognitive effort of actually working through a problem and building understanding, was being bypassed entirely. In sales, that productive struggle is what builds domain expertise. It is sitting with a difficult prospect and figuring out their real constraint. It is losing a deal and understanding precisely why, rather than marking it as lost in the CRM and moving to the next one. Bypass enough of those struggles and you get a salesperson who has been in the industry for five years and has learned almost nothing in the last two because the tool kept handing them finished outputs.

“AI curiosity is now a firing offense to lack. By the end of this quarter, team members who aren’t genuinely AI-curious should be let go. This isn’t about being an AI expert. It’s about demonstrating active engagement with AI tools and genuine interest in how they can transform sales outcomes.” -- Kyle Norton, CRO at Owner.com, SaaStr AI Summit 2025 (

Full session at SaaStr)

What the Sales Executives Still Standing in 2028 Are Building Now

AI fluency means catching it when it’s wrong

There is a real difference between using AI and being good at it. Being good at AI in a sales context means you have built prompts that are specific to your ICP, your competitive landscape, and your buyer’s language. Not generic prompts you copied from a YouTube video. Prompts that encode six months of accumulated knowledge about your vertical so that the model produces outputs that reflect genuine expertise rather than a confident-sounding average of everything it has ever read about your market.

More importantly, it means you read every brief the model produces with active skepticism. You verify the facts that matter before you walk into a room. You use the tool to accelerate your thinking, not substitute for it. Forrester Research found that only 16% of workers had high AI readiness in 2025. Only 23% of organizations offered any structured prompt training. That gap is the competitive opportunity available to anyone willing to close it while everyone else is still using the same prompt they downloaded from Reddit.

Domain depth that a brief cannot replace

The generalist sales executive who covers all of mid-market across several verticals is in structural trouble. AI has completely leveled the playing field for surface-level preparation. Any rep with a prompt and thirty minutes can now produce a passable pitch for any company in any industry. What cannot be leveled is depth.

A sales executive who has worked exclusively in fintech for two years, who has sat through eight compliance conversations, who understands what a SOC 2 Type II audit actually costs a buyer in internal time and anxiety, who can speak to a CISO in language that demonstrates real operational fluency, that person cannot be replaced by a brief. A brief can only summarize what is already public. It cannot replicate lived context.

Pick one vertical. Go deep enough that buyers in that sector start calling you. Know the regulatory landscape. Know the typical organizational structure at different stages of growth in that sector. Know the political dynamics between engineering and procurement in your specific buyer segment. Become the person buyers want to talk to because you understand their problems before they finish describing them.

Business cases that CFOs actually respect

Most salespeople cannot build a real business case. Not a value prop slide. Not a calculator where you plug in the number the prospect suggests and the result is always wonderful. An actual defensible case that starts with the cost of the problem, establishes the value of solving it, accounts for implementation and adoption costs, and arrives at a payback period that a finance team can push back on and still find credible after scrutiny.

This is not an intellectually high bar. But it eliminates a significant proportion of the current sales workforce who have been pitching features and trusting that the buyer handles the economics on their own. In a market where every purchase requires a written justification and AI agents are competing for the same accounts on cost, the sales executive who can build the case and defend it in the room is the one who gets the signature.

Relationships that predate the opportunity

Your best leads for the next three years are not in your CRM. They are in companies where you did good work in the past. The champion who trusted you is now at a new company with a new budget. The VP whose difficult procurement process you helped navigate remembers you. Most sales executives treat past champions as closed accounts. The ones who will still be winning in 2028 treat those relationships as the most valuable assets they have, because they are the one thing an AI agent absolutely cannot generate from scratch.

“AI is replacing the jobs people don’t want to do today and it is displacing the mid-pack and the mediocre.” -- Jason Lemkin, SaaStr, January 2026

Back to Sanjay

The QBR did not end well. Initially.

After the room cleared, Priya asked Sanjay to stay. She did not threaten. She did not issue a performance improvement plan with the warmth of someone reading terms and conditions. She did what good sales leaders do when they have decided to be honest instead of comfortable.

She turned her laptop to face him and pulled up the Wayback Machine archive of karpathy.ai/jobs. The treemap filled the screen. Every job in America, color-coded by how much trouble it is in. She found sales representatives in the yellow-orange cluster. 6 to 7 out of 10.

Then she opened the Anthropic paper. She showed him the chart of theoretical versus observed exposure by occupational category. Business and finance at 94% theoretical coverage, observed usage a fraction of that today, and the gap closing as every new model release cuts through another layer of tasks.

She said: the reason your conversion dropped is not that you are using AI badly. It is that you are using it as a search engine upgrade. You are outsourcing the thinking that makes you valuable in this room. You are walking into calls feeling informed when you are operating on information you have not verified, cannot contextualize, and did not earn.

He said: so what do I actually do?

She said: you go get specifically good at something. Pick one vertical. Go deep on eight accounts instead of shallow on eighty. Learn to catch the model when it is wrong before you walk into a call. Figure out how to build a business case a CFO can actually defend. And stop bringing in someone from product every time a buyer asks a technical question, because that is not value-add. That is a scheduling service.

Three months later, Sanjay was doing something genuinely different. He picked fintech. He went deep on eight accounts. He verified every fact in every brief before using it. He started asking the question after the question that buyers actually answered, and staying in the silence until the real answer came. His champion map expanded from one name to six.

His conversion rate went up. His calls got shorter because he already knew things that used to require the first twenty minutes to establish. His cold email reply rate went up not because the emails were fancier but because each one was specific enough, and accurate enough, and clearly written by someone who had actually thought about the recipient’s world, that people were occasionally surprised to receive it.

Which is probably the most depressing sentence in this article. A salesperson who has genuinely done their homework is remarkable. That is the bar. That is the entire competitive opportunity available to every sales executive right now.

References

Primary Research

1. Massenkoff, M. and McCrory, P. (2026). Labor Market Impacts of AI: A New Measure and Early Evidence. Anthropic Economic Research. March 5, 2026. https://www.anthropic.com/research/labor-market-impacts

2. Karpathy, A. (2026). AI Exposure of the US Job Market. Published and deleted March 15, 2026. Original: karpathy.ai/jobs. Archived copy: https://web.archive.org/web/20260315050821/https://karpathy.ai/jobs/

3. Karpathy, A. (2026). karpathy/jobs GitHub repository (methodology, scoring rubric, source data). https://github.com/karpathy/jobs

4. Welsch, R. et al. (2025). AI makes you smarter but none the wiser: The disconnect between performance and metacognition when using AI assistance. Computers in Human Behavior. Aalto University. https://neurosciencenews.com/ai-dunning-kruger-trap-29869/

5. Brynjolfsson, E. et al. (2025). Employment effects of AI among young workers in AI-exposed occupations. Referenced in Massenkoff and McCrory (2026). Found 6 to 16 percent fall in employment among workers aged 22 to 25 in AI-exposed roles, primarily via hiring slowdown, not increased layoffs.

6. Eloundou, T. et al. (2023). GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. Task-level theoretical AI exposure scoring framework. Referenced throughout Anthropic 2026 paper.

Industry Data and Reports

7. Emergence Capital (April 2025). B2B SaaS Sales Function Survey, 560+ companies. 36% of companies reduced SDR/BDR headcount.

8. Gartner (2024). 60% of B2B sales interactions will be AI-mediated by 2027.

9. Forrester Research (2025). Future of Work AI Readiness Report (AIQ Index). 16% of workers had high AIQ in 2025. 23% of organizations offered prompt engineering training.

10. Gong.io (2025). Win Rate Benchmarks Report. Average B2B mid-market sales cycle has lengthened by 32% since 2022.

11. Fortune (March 2026). Anthropic just mapped out which jobs AI could potentially replace. A Great Recession for white-collar workers is absolutely possible. https://fortune.com/2026/03/06/ai-job-losses-report-anthropic-research-great-recession-for-white-collar-workers/

12. Axios (March 2026). Anthropic launches AI job destruction detector. https://www.axios.com/2026/03/05/anthropic-ai-jobs-claude

13. Yale Budget Lab (2026). Evaluating the Impact of AI on the Labor Market. https://budgetlab.yale.edu/research/evaluating-impact-ai-labor-market-novemberdecember-cps-update

Practitioner Sources

14. Lemkin, J. (January 2026). Jason + Lenny: The Real Future of AI in Sales. SaaStr. Source for 90% SDR displacement claim and 70K vs 7K email comparison. https://www.saastr.com/jason-lenny-are-back-the-real-future-of-ai-in-sales/

15. Lemkin, J. (December 2025). We Deployed 20+ AI Agents and Replaced Our Entire Human SDR Team. SaaStr. https://www.saastr.com/we-deployed-20-ai-agents-and-replaced-our-entire-sdr-team-heres-what-actually-works-video-pod/

16. Lemkin, J. (November 2025). 6 Months of AI SDRs: What’s Worked, How They Brought In $1M+ in 90 Days. SaaStr. https://www.saastr.com/6-months-of-ai-sdrs-whats-worked-how-they-brought-in-1m-in-90-days-and-the-real-data-everyones-asking-for/

17. Lemkin, J. and Norton, K. (June 2025). AI, Sales + GTM in 2025/2026: This Changes Everything. SaaStr AI Summit. Source for Kyle Norton quote on AI curiosity. https://www.saastr.com/ai-sales-gtm-in-2025-2026-this-changes-everything-with-jason-lemkin-and-owner-cro-kyle-norto/

18. Lemkin, J. (January 2026). Replacing Your Sales Team with AI Agents. Revenue Leadership Podcast. Full transcript with AI agent vs median AE comparison.

The Revenue Leadership Podcast

Replacing Your Sales Team with AI Agents (Jason Lemkin, Founder & CEO @ SaaStr)

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5 months ago · 15 likes · 5 comments · Kyle Norton

19. Lenny’s Newsletter (January 2026). We replaced our sales team with 20 AI agents. Full Lenny Rachitsky conversation with Jason Lemkin.

Lenny's Newsletter

We replaced our sales team with 20 AI agents—here’s what happened | Jason Lemkin (SaaStr)

Jason Lemkin is the founder of SaaStr, the world’s largest community for software founders, and a veteran SaaS investor who has deployed over $200 million into B2B startups. After his last salesperson quit, Jason made a radical decision: replace his entire go-to-market team with AI agents. What started as an experiment has transformed into a new operating model, where 20 AI agents managed by just 1.2 humans now do the work previously handled by a team of 10 SDRs and AEs. In this conversation, Jason shares his hands-on experience implementing AI to run his sales org, including what works, what doesn’t, and how the GTM landscape is quickly being transformed…

Listen now

4 months ago · 109 likes · 1 comment · Lenny Rachitsky

CareerPlot is an AI-powered career planning platform. Visit careerplot.com.

Yes. Unlearning is a science. If someone says that unlearning is an art, tell them to unlearn the “unlearning is an art” first. Unlearning is linked to human behaviour and how our brain works. Whether it is about getting dopamine hits by proving your supremacy over others, or your amygdala’s response as “fight or flight” in a high stakes situation, or your pre-frontal cortex just retrieving your ill formed “quick 2 cents” from shallow memory and not activating neocortex to dive deep into deep archival memory consolidated by your hippocampus, unlearning and learning is deep rooted in the complex functioning of the brain as a result of your actions since birth.

Alright! Enough of showing off my superficial and theoretical understanding of the human brain after reading the book The Brain by well known neuro-scientist David Eagleman. Let’s move to the main topic.

I was recently hunkered down with the final two chapters of The Psychology of Money by Morgan Housel, trying to absorb some financial Zen, when my phone buzzed with a LinkedIn notification. I generally turn off notifications when doing just deep focus activity like reading a physical book but I forgot this time. Okay! No more digressions. The notification was about an article by my friend Pranav Bhasin: “10 Mental Models You Must Dump Before AI Takes Over”.

Between Pranav’s warning about clinging to dying frameworks and Housel’s take on the history of risk, I felt like I’d been double-slapped by reality. Housel quotes B. H. Liddell Hart in the book:

“History cannot be interpreted without the aid of imagination and intuition. The sheer quantity of evidence is so overwhelming that selection is inevitable. Where there is selection, there is art. Those who read history tend to look for what proves them right and confirms their personal opinions. They defend loyalties. They read with a purpose to affirm or to attack. They resist inconvenient truth since everyone wants to be on the side of the angels. Just as we start wars to end all wars.”

Reading that coupled with Pranav’s nudge to dump my mental baggage, it felt like a high-speed collision with my own ego. It reminded me of the countless times in my 24+ year career where I stuck to a failing strategy, not because I was right, but because I was too arrogant to admit I was wrong. I called it “strategic consistency.” In hindsight, I just look like a guy trying to operate a smartphone with a rotary dial.

But here is the brutal truth: hindsight is a liar. We love to look back and think, “I’ve learned my lesson; I’ll do better next time.” No, you won’t. Hindsight gives you the illusion of learning, but every situation, every market condition, and every moment is unique. You never get a second chance to fix that specific mistake because that specific moment is gone forever. Thinking your “experience” guarantees a better result next time is just another ego-driven hallucination. If you haven’t unlearned the garbage that caused the first failure, you’re just waiting for a brand-new way to mess up.

This is exactly why most people are terrible at learning. We don’t actually want to learn; we want to be right about what we already know. But here is the brutal truth: if you want to actually grow, you have to get comfortable with the idea that half the stuff in your head is probably garbage. Before you can learn anything new, you have to unlearn the old, dusty ideas holding you back.

Most people defend their outdated opinions to the death just to maintain a sense of supremacy. They claim to have an “open mind” while actively sabotaging their own growth. It is hypocritical, it is exhausting, and it is the ultimate speed breaker on your career path.

Unlearning is much harder than learning, but without it, your new knowledge is just a coat of paint on a rotting fence.

The “I Don’t Know” Growth Hack: Why the Clueless Win

Let’s look at how the world actually changes. Every time there is a massive paradigm shift in an industry, the people who get crushed are the ones who were “experts” in the old way. The ones who win are the ones who were willing to say three simple words: “I don’t know.”

Think about the shift from physical retail to e-commerce. The experts at massive department stores knew everything about supply chains and retail footprints. They had decades of experience. And that experience was exactly what killed them. They couldn’t unlearn the idea that people need to touch a product before they buy it.

Take someone like Bill Gates or Jeff Bezos in their early days. They weren’t winning because they had thirty years of industry experience. They were winning because they didn’t have the baggage of “how things are done.”

When the world changes, your 20 years of experience often becomes a backpack full of bricks. You’re trying to run a marathon while clinging to trophies from a sport that isn’t being played anymore. Those who win are the ones who can drop the bag.

There is a legendary story about Andy Grove at Intel. In the mid-80s, Intel was getting slaughtered in the memory chip business by Japanese competitors. Memory was their identity; it was everything they knew. Grove sat down with Gordon Moore and asked, “If we got kicked out and the board brought in a new CEO, what do you think he would do?” Moore said, “He would get us out of memory.” Grove’s response? “Why shouldn’t we walk out the door, come back in, and do it ourselves?”

They had to “unlearn” their own history to survive. Most people cannot do that. They would rather go down with the ship than admit they are holding a map of a world that no longer exists. This is the blood sport of ruthless execution, where the strategy of saying no to your own history is the only way to move forward.

“In a world of change, the learners inherit the earth, while the learned find themselves beautifully equipped to deal with a world that no longer exists.” — Eric Hoffer

The Three Horsemen of Intellectual Stagnation

Why is unlearning so much harder than learning? Because your brain has built-in defense mechanisms that keep you stuck. Daniel Kahneman, the Nobel laureate who proved we are all basically irrational toddlers in expensive suits, identified the Illusion of Control. We have a desperate, almost pathetic need to believe the world is predictable.

One of his most stinging observations is about the Illusion of Control. Kahneman noted that we have an almost desperate need to believe the world is predictable and that we are the ones steering the ship. When we are confronted with something we don’t understand—like a new technology, a shifting market, or a younger colleague’s superior method—we don’t just say “I don’t get it.” Instead, we refuse to accept our lack of understanding because to admit we don’t understand is to admit we have lost control.

Losing control feels like losing power. For an “expert,” power comes from knowing. If you don’t know, you aren’t powerful. So, you cling to your old mental models like a life raft in a storm, even as that raft is sinking.

Here are the three core principles of why people cannot unlearn:

1. “I Want to Sound Smart” : Confirmation Bias

Kahneman’s work on Confirmation Bias shows we only see what proves us right. We don’t want to solve problems; we want to be admired. We think letting go of old info makes us look weak. We value sounding “knowledgeable” in meetings over actually being effective.

In a B2B setting, this looks like a Product Manager who spent six months building a feature nobody wants. When the data shows zero adoption, instead of unlearning the “killer feature” hypothesis, they double down. They say the marketing was wrong, or the users “need to be educated.” They are cherry-picking the one positive feedback from a cousin to ignore the 10,000 users who closed the tab. It’s a massive blind spot that keeps leaders from seeing the iceberg right in front of them.

2. The IKEA Effect: Loving Your Own Trash

We overvalue things we’ve built ourselves. If you spent five years designing a specific workflow, you will defend it even if it’s clearly broken. This is the IKEA Effect—you love the wobbly table because you put the screws in the wrong holes. You try to retrofit this old, clunky knowledge into new problems, wasting effort and ignoring the fact that the world has moved on.

In corporate life, this is the “Legacy System” champion. They wrote the original COBOL or the spaghetti-code Java monolith in 2008, and they treat any suggestion of a modern microservices architecture as a personal insult. They aren’t defending the tech; they are defending their 15 years of sunk-cost effort. They are emotionally attached to their own inefficiency.

3. The Dunning-Kruger Trap: The Confidence of the Ignorant

The less you know about a new field, the more you think your “general management wisdom” covers it. To learn, you have to be the “dumbest” person in the room for a while. For a “high-pedigree” executive, this is a fate worse than death. So, they close their minds and start lecturing about “fundamentals” while the competition is busy building the future.

The Dunning-Kruger effect creates a “Peak of Mount Stupid” where your confidence is at its highest just before you realize you have no idea what you’re talking about. I’ve seen CEOs lecture AI researchers on “model optimization” because they once read a Wikipedia page on linear regression. They live on that peak forever because the view is nice and they don’t have to admit they can’t see the summit from there.

“Part of the reason I have been a little more successful than most people is I am good at destroying my own best loved ideas.” — Charlie Munger

The Unlearning Protocol: How to Not Be a Dinosaur

1. Admit You’re Clueless (In Public)

Stop “private learning” where you hide in your office googling terms so you can look smart later. Say “I don’t know” in the meeting.

When a junior engineer asks a hard question about why we are using a specific vector database or how the RAG (Retrieval-Augmented Generation) pipeline handles hallucinations, don’t sideline them. Don’t say, “Let’s take that offline” (the universal corporate code for “I have no clue and you’re embarrassing me”).

Practice saying, “You’re right, I’m wrong,” or “I don’t actually know the answer to that, tell me more.” It doesn’t matter if you have 25 years of experience or an Ivy League degree. If you can’t admit you don’t know in front of your team, you aren’t a leader; you’re a fraud holding your own career hostage to satisfy your ego. Develop a habit of listening. Start believing that a junior with a fresh perspective can teach you something better than your dusty “best practices” manual.

2. Get Back to First Principles (Avoid The Trap)

Everything works from first principles—breaking a problem down to its core truths. But most people prefer “cosmetic unlearning.”

Look at how companies implement Agile. Companies implement the buzzwords: small teams, DRIs, “Two-Pizza” squads, PODs. They have the stand-ups and the fancy Jira boards. But the decision-making, tracking, and success metrics are still pure, old-school Waterfall.

They track productivity by hours worked, number of meetings, or lines of code. This is an illusion of velocity. People easily fake these metrics. They “sprint” in circles, producing documents and “alignment” while accomplishing zero actual outcomes. They haven’t unlearned the need for control, so they just renamed their bureaucracy “Scrum.” This is why organizations fail—they measure the wrong things because they’re afraid to stop controlling the wrong things.

“I think it’s important to reason from first principles rather than by analogy... We do this because it’s like something else that was done, or it’s like what other people are doing.” — Elon Musk

3. The Habit of Doing (The “Shut Up” Rule)

Everyone has an opinion. Everyone wants to be a “consultant” or an “advisor.” Everyone can tell you how to do it better. If you are telling everyone how to solve a problem but you have never solved it yourself, sit quiet. Let the people who are actually doing the work solve it.

Maybe you solved a similar problem in 2010. Guess what? The world changed. Your 2010 solution might actually be dangerous today. Your old solution might be detrimental now. If you have never coded, do not advise engineers on algorithms. If you have never done sales, do not tell the sales team how clients react. If you have never traveled, do not give advice on how to handle a flight delay.

Your past solutions are often detrimental because they are built for a world that doesn’t exist anymore. If you haven’t done it yourself, you don’t know it. You just have a memory. Real unlearning happens when you become part of the team and actually get your hands dirty.

Example: If you’ve never traveled, don’t give advice on traveling. If you’ve never built a business model, don’t critique a startup’s unit economics based on a textbook from 1994.

“If you cannot successfully do something, do not think you can tell others how it should be done.” — Ray Dalio

The Tale of the “AI Transformation” Mandate

Forget the jar of old tea; that’s too poetic. Let’s talk about a real-world B2B horror story. I recently watched a “Digital Transformation” leader at a massive enterprise drive an “AI First” initiative. This guy had 30 years of experience in business process outsourcing. To him, “AI” was just a fancy word for the rule engines he used in the 90s.

He issued a mandate: every employee must use AI for 20% of their tasks. He set up a dashboard to track “AI Usage.” How did he define usage? If you clicked “Summarize” in an app, you got a point.

He treated the LLM like it was a giant spreadsheet of if-then-else statements. He couldn’t unlearn the idea of deterministic software. When the engineers tried to explain that LLMs are probabilistic—that they don’t “calculate” but “predict” based on weights—he ignored them. He asked why he couldn’t just “hard-code the correct answer into the AI.”

The engineers were crying in the breakroom. The boss was celebrating “100% AI Adoption” on LinkedIn. Meanwhile, the actual output was 100% AI slop—hallucinated legal contracts and sales emails that addressed the customer as [INSERT_NAME_HERE] variable showing up in the email.

He was driving a Ferrari like it was a tractor because he refused to unlearn how a tractor works. He had an illusion of control over a system he didn’t fundamentally understand. He wasn’t building the future; he was just automating the past with more expensive tools.

Learning is Doing: The Coach vs. The Player

True learning only happens through doing, not through theory. If you cannot build it yourself, you cannot teach others. A coach has always been a player. It does not matter if they were a huge success or a failure, but they had to have been on the field. They had to feel the pressure and make the mistakes.

True learning isn’t reading a PDF; it’s getting your hands dirty. Theory is where egos go to hide. Practice is where they go to die.

Once you’ve cleared the junk out of your head, you can finally learn. But don’t learn from a textbook. Learn by doing. If you want to understand a business model, try to build a small version of it. If you want to understand a technology, try to use it to fix a small problem in your life. Theory is a comfortable lie we tell ourselves so we don’t have to face the messiness of reality.

I am working on this project called CareerPlot. I had 25 years of ideas about how it should work. I had a grand vision. But when I actually started building it, I realized half of my ideas were based on a world that existed ten years ago. I had to throw away my best loved ideas and start from what the users actually needed. I had to unlearn my own expertise to build something that actually worked.

Start by unlearning your own importance. Admit you’re a beginner. Build something, break it, and then try again. That’s the only learning that actually counts. Everything else is just “interpretations” and “loyalty to history”.

Unlearning is harder because it requires you to admit you were wrong. It requires you to lose power and control. But without it, you are just an expert in a world that is already gone.

Alright! First of all, the term Coffee Budget is not a standard term. I coined this term in context of Entrepreneurship and Product Management. In my entrepreneurial journey of 5 years or so, I have met hundreds of people that include other fellow entrepreneurs at similar level as mine, the beginners, founders of successful startups, Angel investors, VCs of almost all shapes, sizes and stages, advisors, mentors, corporate big wigs or senior management officials, govt. officers and most important of all, existing customers and potential customers.

The only common factor in my all of these meetings was one thing : COFFEE. Sometimes, it was cappuccino, or a latte or filter coffee.. Now, a lot of times, it was tea (or a chai tea latte if it was Starbucks or Costa). If I calculate the amount of money spent in these meetings in the last 5 years, it can easily become an angel investment for an early stage startup ( talking about the investment when a startup raises a $100,000 from 15 people :-) ) .

So, you get the drift of the number of meetings I would have had. and I am sure you did the calculation of $100,000 / 15 = $6666.67 as the money spent on Coffee… and if you are from India, you have already converted it into Indian currency of 6666.67 * 65 = INR 4,33,334 . Holy Shit. That’s what you would have said. That’s a lot of money and If you are an entrepreneur, you are still calculating how much money you have spent in your meetings till now. and if this money was saved, you would have actually paid an annual salary of a junior developer in India.

Okay. Let’s get to the point.

Why I had these meetings ?

Meetings are an integral part of your work. But when I met all these people outside office for a cup of coffee, It was a neutral ground for both of us to have an open discussion. As a product manager or an entrepreneur, I wanted to achieve the following

• Validation of my ideas or product concepts

• Research what is the market size and needs

• Figure out whether my customers or potential customers feel the need of the product / feature I am thinking of.

• Whether the potential customer defined by me is really a potential customer or I got the customer segmentation wrong.

• What are the ideas other people have in the same domain ?

• Who all are my competitors ? and how much differentiation I am going to make in the market

• Whether my product idea is a need, want or a wish ?

• Figure out how much people will be willing to spend for my product

• Most important of all, Is there really a problem in the area/domain I am exploring opportunities in ? Or is it just my idea trying to find a problem for the solution that I have thought of.

and there were many more questions that needed an answer. I am sure a lot of you go through this a few times every year in your professional life. Whether you are an entrepreneur, product manager, software engineer, or an engineering manager or into sales or operations, it does not matter because this meeting phenomena does affect you a lot and everyone of you are looking for answers to your questions.

So, What exactly is this coffee Budget ? Plain and Simple. You need to meet various kind of people. You’ll need to meet existing customers for feedback, potential customers for opportunity exploration and to discover consumer behaviour, investors for validation, understand market trends, conditions and other related stuff, other founders for advise, technology people to figure out all the tech requirements and then, a loads of other people in the value chain to reach out to the right people to meet.

and very often, these meeting will be done in some cafe. and every time, you’ll make a trip to coffee shop, you’ll need to spend on a coffee. and each coffee spend is going to cost you significantly.

It’s an investment that hits the pocket very heavily and still goes un-noticed. So much so, on the day of the meeting, you check your wallet twice to make sure that you have your credit card and enough cash in case your credit card does not work. Now, think that you have to repeat this action every alternate day. That means, you need to plan a separate budget for coffee…

In plain and simple language, Coffee Budget is a money planned in advance to be spent on meetings with people who are important for your product and business.

Why do you need this coffee budget ?

The idea of having a coffee with someone is always to have a conversation that is intense and is focused in a particular direction. And because you are having a coffee outside office premises, it becomes a neutral ground where biases get reduced to a significant level. Also, when a person makes an effort to meet you, he has shown some concern and seriousness towards this meeting. It also shows how eager and serious you are towards the meeting. This also helps you in planning the use of the other person’s time very effectively. Usually, a coffee meeting lasts for an hour. So, that means you’ll be focused to make the maximum utilization of the time and get the best out of other person.

Coffee is just a backdrop for having great conversations. During these conversations, you have to be a great listener. You are there to collect information and learn from others. You’ll do less of talking and more of listening. That’ll help you in understanding the purpose and the thought process other people bring to table. Also, if you are meeting the same person for second or third time, you can also show the progress or action taken from the outcome of last meeting. This gets people more engaged with you. No one is going to meet you for the fourth time if their advice is not taken care of.

These meetings also helps you in changing your behaviour if you feel awkward in meeting people for first time. It helps you in prepare in practicing your idea pitch. When you pitch or talk about your own idea many times repeatedly to different people, you’ll notice that you have significant improvements in your product and its pitch. Over many conversations, the idea has evolved and may be you have pivoted from the original thought process for a better opportunity.

#CoffeeBudget is the best way to drive your market research.

As per MBA curriculums, market research is based on primary and secondary research, surveys, competitive analysis and other techniques of collecting and validating data points. But having 10 coffee meetings in one month with different stakeholders/experts to get their opinions can beat any market research any day. For example : the people who make sensational statements in public forums can give you insights on why they said that or do they even believe in what they said. Many a times, statements are made to give directions to their own business for PR and media purposes but in reality, they have a different opinion. If you go by public statements then your market research will say something else but having a coffee with the same person can get you the real data or the stuff which is important for your product.

How can you plan your coffee budget effectively?

This is a difficult part. and it may sound ridiculous to some people. It depends on the stage of your product. But let’s assume that you are just at the beginning where the idea is just seeded in your brain. First of all, outline all the kinds of people you’ll need to meet. Most of the time, the first thing people consider to figure out investors for meetings but take my word, meeting investors at the idea stage is useless. Most of the investors will not be able to help you in any way. Having said that, still meet some of the people who may be investors but still are very good advisors first who are helping you because they genuinely wish to help someone grow. Other than that, meeting potential customers should always be 60% of the whole meeting time in any month.

For a thumb rule, Let’s say you should meet at least 15 different people every month for 3 months. That comes down to 1 meeting every alternate day. Now, consider one fact that who spends on the coffee in the meeting.

• If you are meeting any existing or potential customer, make sure you pay for everything during the meeting.

• If you are meeting fellow entrepreneurs, pay in whole or share

• If you are meeting senior/successful entrepreneurs, do offer to pay. Generally, you’ll find that these guys take care of the bill as they try to help you save money for the venture. Every penny counts and they have also lived the same struggle.

• If you are meeting advisors or investors, again the same as above given point. People who are genuinely helping you always offer to pay the bill because the last thing they wish for you is to get stressed out on the cost of coffee and it’s a gesture to prove that they got your back.

• Any other people you meet, you need to pay for the bills.

• I would always like to mention one thing. Whenever I have come across people who come to help you as a senior/advisor and do not even put a hand in his/her pocket, you’ll notice that the person is actually not very serious about helping. He/She is there to build his/her own brand. Avoid these kind of people in any kind of meetings. Now, this is not a rule but an observation. Whether you agree or not, mention your thoughts and experiences about this in the comments section below.

So, let’s come back to calculations:

If you are meeting 15 people on an average in a month with 60% of them being customers in the first 3 months. You’ll get enough information to build product vision and direction. then, as the product is building, you’ll meet potential customers more and that means, you’ll meet 12 out of 15 people who are existing or potential customers for next 6 months. and then, it’ll be time to go back to drawing board to meet investors , advisors, competitors, PR and media professionals etc.. and this cycle repeats every 8-9 months for 3 years or so. After 3 years, you can definitely drive a conclusion of having a successful business or having failed at it.

Now, if you want to make a yearly budget.

Average Cost of one meeting : INR 500/- ( assuming both people have coffee and in some meetings, you ordered a muffin or a sandwich). Also, considering the place to be Costa or Starbucks.

Average number of meetings in a year : 180

Total Budget Required : 500 * 180 = INR 90,000

Now, I generally round it upwards to INR 1,00,000 /- annually because there will be some meetings which happen in 5 star hotels or I have ended up travelling to another city just to meet that one person.

In USD, this amount is equivalent to $1300 approximately

This amount might look very high to you but believe me you’ll actually have these expenses anyway.

The idea of #CoffeeBudget is not calculate money and expenses but to get the focus on the product direction

The good part of this #CoffeeBudget concept is that it helps you focus on outcomes of the meetings. The idea of coffee budget is to drive the point that meeting people from various walks of life is essential and is the best form of market research. When you sit in a cafe and have discussions, you get to hear the viewpoints which are not said on stage by the same people. Because these meetings are off the record, people open up and share the views they hold in reality and not because of peer pressure or media presence and that’s the key to get your product vision and mission in the right direction. Coffee is just an excuse to get to meet these people and learn from them.

You’ll meet people anyway for a cup of coffee. Then, Why not understand the purpose and focus to achieving the maximum output from that single hot cup of coffee…

Think about this article whenever you write an email or message to someone “Let’s meet up for a cup of coffee”

Need to validate your idea ? Just meet people over a cup of coffee …

In 2001, I was working on developing a software licensing system that managed distributed license keys for any software using server based floating licensing mechanism. Yes, Remember the old days of client server architecture and buying software was still not so common as it was expensive. Nostalgia aside, I was a software engineer coding in Java who believed that the system was working fine and my piece of code was working as expected. So, I sent the code for review to my manager ( whom I “later on” started respecting a lot for his programming and analytical skills) who just took a brief look at the code and rejected the whole component within 30 minutes. It was my 20 days of effort that he didn’t care about. Of these 20 days, I had spent last two days continuously working in office with almost no sleep and surviving on pizza, coke and coffee.

I asked him the reasons and he explained that my basic assumption on one of the encryption mechanism was wrong and that I needed to rewrite the whole component. I went back and started working again. Just that, this time I tried to fix it by repurposing the code with a supposedly correct assumption. After a few days, my manager again rejected the code and explained me another issue I wasn’t able to see through. By the third time, I had lost my patience and I argued with my manager thinking that he doesn’t know what he is talking about.

The IKEA Effect

Have you ever faced a situation when you worked very hard on a product or a feature spending weeks and months and then, someone on your team started asking the basis of your fundamental assumptions ?

Most probably, yes. That happens with most of us. But the fun part is when the questions continue and you fail to understand why the team member is asking so many questions. It seems to you that the team member is either being stupid or doesn’t like you or the product you are working on. This is where the “IKEA effect” is taking place.

Prof. Dan Ariely described the “IKEA effect” in his 2011 paper that he published along with Prof. Michael Norton and Prof. Daniel Mochon as “labor alone can be sufficient to induce greater liking for the fruits of one’s labor: even constructing a standardized bureau, an arduous, solitary task, can lead people to overvalue their (often poorly constructed) creations.”

As a product manager, we spend so much effort in building the product requirements and work on achieving the product market fit trying to reduce failure as much as possible. But as we do that, we keep on moving on an iterative path where our effort ( measured as sunk cost) is getting bigger as the days go by. With the sunk cost getting higher, we get into a defensive justification mode whenever some one asks us a seemingly harmless or basic question. This is for a simple reason that we start over valuing the effort we have put in such that we dread to change the path ( if required) as it may lead to delayed timelines or pseudo loss of reputation or supposedly , may get us bad performance rating ( if that ever happens).

The Endowment Effect, Loss Aversion and Status Quo

Now, as we invest more time and effort, our sunk cost is getting higher making us attached emotionally to the product we are working on. That leads us to another problem called “endowment effect”.

Prof. Daniel Kahnemann, along with Prof. Jack Knetsch and Prof. Richard Thaler explained the endowment effect in his 1991 paper describing it as people tend to value the items they own more highly than they would if those items did not belong to them. If you have invested your time and effort heavily on the product that you are working on, as a product manager, it is natural human tendency to develop an affinity towards the product. Sometimes, it becomes so difficult to separate your own identity from the product. Identify the scenarios when some product managers start calling a product as their baby because they conceptualised the idea and have worked on it day and night.

Both “IKEA Effect” and “Endowment Effect” stem from a basic cognitive bias called “Loss Aversion”. Loss Aversion is described as the fear of loss is greater than the joy of gain.

Assume that the product you are working on is moving very smoothly and is on target for release. While, everything is going good, a colleague on another team ask you some questions about the way feature works and she suggests a change which will result in a change of direction for your product but at the same time, may result in 50% gain in estimated revenue. but the impact of making that change is delay in project release by 6 months.

While we all believe that we think rationally as we are rational people, most of the product managers will still fear the loss and hence, try to avoid the loss by continuing on the path as the fear of losing 6 months can be considered greater than 50% gain in estimated revenue. if the company work culture allows for taking a short term hit in favour of long term benefit, the product manager needs to change the direction but that may result in heartburns in the various teams and significant stakeholder management. So, a risk averse product manager may rationalise the decision of sticking to the course and not change direction, citing Sunk Cost, indirectly justifying the decision and maintaining the status quo.

When this status quo is maintained and the questions get postponed so as to find the answers at later stage, this results in inability of finding the product market fit for many products and then, products pivot at a late stage leading to losses mounting to millions of dollars in so many cases.

Whether it is “IKEA Effect”, “Endowment Effect”, “Loss Aversion” or “Status Quo Bias”, it doesn’t matter if you don’t remember these terms. What matters most is the way these unconscious biases can be avoided by training your subconscious mind to look out for the symptoms. The way you train your mind is by following a simple common sense technique called “First Principle Thinking”.

First Principle Thinking

“First Principle Thinking” helps you to ask the very fundamentals questions that form the premise of the problem you are trying to solve. Whether it is “Socratic Questioning” method or “5 Whys” of the famous author Simon Sinek, or a child like inquisitive method of asking questions, just go down to base of the problem and why you are really interested in solving the problem. Identify the win-win formula for everyone and do not stop asking the “Why” until you are convinced that there is no further doubt or question that can arise. if a question arises somewhere along the path even after you started, go back to the drawing board for course correction and cut your losses early. This is where when someone said “Fail Fast & Fail Often”.

Long story short: watch out for the feedback signals you receive, even a faintest signal can help you fix the problems early on and recognise the gaps in your plan.

By the way, I did go back to my manager who rejected my code multiple times after a few weeks to understand his explanation once again and that’s when, before explaining the software design, the first thing he taught me was the concept of “loss aversion” and a varied form of “first principle thinking”. That helped me see beyond the personal biases and also, learnt for the first time about something called “cognitive biases”.

References

1. The “IKEA Effect” : When labor leads to love

2. Anomalies : The Endowment Effect, Loss Aversion and Status Quo Bias

3. First Principles : The building blocks of true knowledge

4. The Feynman Learning Technique

5. Thinking Fast and Slow

In 2009, My team and I were working on a prototype assuming that one day TVs will be connected to the internet and when that happens, advertising will become programmatic similar to what was online display advertising on the internet in those days. but more than that, people will also have second screen devices and the content they’ll be watching on the TV will be influenced with their second screen devices activity. AWS cloud was catching up steam and getting popular and we were working on how to get STB software on the cloud and also, build a developer platform for TV apps just like android play store or apple app store.

During the project discussions, one of my team members started a pitch to management with an underlying assumption that it’ll not be TVs that’ll be connected but Set Top boxes will be connected to internet. While his experience if STB domain was limited, he had a rich experience of working with one of the largest TV OEMs in the world. He was quite confident that TV manufacturers will always depend on STB or similar devices for any content consumption. Also, this was due to the fact that the company we worked for was one of the largest STB software / Pay TV company in the world. He was very confident about his idea as it would have resonated well with the management too given that everyone wanted STB market to grow leaps and bounds. at that time, Connected TVs was an idea that could have killed the STB industry in itself. Netflix was a small player and was contained to desktops/mobile devices.

To build the pitch, he gathered all data points from his previous experience and research and along with STB industry reports, he started working on the case. During a few discussions over a month, couple of us asked him to think about STB market and the risks associated with it. Technically, enabling an STB with internet seemed a lot more challenging that enabling a TV with the internet. And applying the Porter’s five forces, if TV manufacturers were ready to invest in HD and 4K televisions, adding internet capability will be a peanut cost to a television compared to an STB and a threat of substitute was imminent too. As a manager, I still wanted to make sure that we had all the options open on the table. but my team member was adamant on his proposal.

A few weeks back, during recent discussion online with a group of product managers about blind spots that a product manager can face, I got reminded of this past episode. and that made me talk about Dunning-Kruger Effect to those product managers.

Dunning-Kruger Effect

For those who do not know what Dunning Kruger effect is :

The Dunning–Kruger effect is a cognitive bias whereby people with low ability, expertise, or experience regarding a certain type of a task or area of knowledge tend to overestimate their ability or knowledge.

In their research paper published in 1999, David Dunning and Justin Kruger implied that people who have limited experience are more confident about their knowledge compared to the people who are vastly experienced. This high confidence with limited knowledge stems from the ignorance they have about their own knowledge levels or capabilities resulting in them thinking that they know a lot more than others and hence, falsely makes them believe that they know it all. In reality, people with real experience and knowledge tend to have a room for self-doubt on their knowledge levels thinking that others in the same domain will also have similar knowledge levels and hence, there may be things to learn and possibly, there are more unknowns to discover.

In context of product management, this is highly relevant. Product managers deal will high level of ambiguity on a regular basis and are responsible for influencing decisions that can affect the future of organizations. Being a high visible and high stakes role of product manager, it is all the more required to be careful to evaluate all aspects of the discussion, being open to adverse feedback, listen to completely tangential viewpoints and then, try to present the probabilities of the various options rather than presenting one’s own line of thought as a fact.

It is well know saying “A players hire A players, B players hire C Players” . What it means is people who really understand their own capabilities want to work with similar high calibre people but people with mediocre capabilities want to hire less capable folks so that they can be confident and manage superiority over others. In most of the cases, B players are always have high confidence levels with quite less intellectual humility and it is visible even during interviews when they are asking questions to prove that they knew more than the candidate. This is nothing but a representation of Dunning-Kruger effect where low calibre people overestimate their ability and end up rejecting high calibre people because such smart people had different opinions or ideas that did not match the expectations.

Intellectual Humility

To achieve this, intellectual humility is highly important for a product manager. But people who face Dunning-Kruger Effect also lack in intellectual humility. Intellectual humility can be defined as the importance of knowing that one can be wrong. As a product manager, we learn management methodologies like SWOT ( Strength, Weakness, Opportunities, Threats), Porter’s five forces and many other theories either in business schools or by experience. but to actually apply these theories or methodologies, intellectual humility is a lot more important. Being humble helps us listen to the opportunities and challenges that others might know about. but if a person is facing Dunning-Kruger effect, it also makes him arrogant at the same time. it makes the person thinks that he knows everything but then the world is not listening to him and makes him go into a downward spiral where his or her team player abilities get affected without him realising about it.

Many of you might have heard of Amazon’s 14 leadership principles and how they apply these principles in their interviewing process. One of the amazon’s 14 leadership principle is “Are right .. a lot”. It is defined as “Leaders are right a lot. They have strong judgement and good instincts. They seek diverse perspectives and work to disconfirm their beliefs.”

This is a great principle for a product manager. To execute any ambigious open ended project, product managers need to be right, a lot. But that come with a caveat of intellectual humility. That’s what it means in the second part of that definition when it asks to seek diverse perspectives and work to disconfirm their beliefs. But for a person with Dunning-Kruger effect, that’s where confirmation bias steps in.

People with Dunning-Kruger Effect seek out diverse opinions or perspectives to depict themselves as a team player but in reality, it is to prove their point so that they can claim they are right, a lot. Confirmation bias drives people to identify only those opportunities or data points that help them to confirm their beliefs. To persevere with one’s beliefs, the person can even start interpreting the data that goes against the belief into a favourable condition or completely ignore that point. Confirmation bias makes people selective in their choice of data points. Sometimes, it makes them selective to an extent that they choose to work with people (or invite only those people in meetings) who will tend to agree with them or have similar opinion and ignore the rest who may have a different opinion.

Such situations are a hotbed for conflicts. This is a perfect situation for a PM behavioural interview question “tell me an example when you didn’t agree with one of your team mates and how you arrived at a decision”. The answer to this question will reveal certain traits of a person on dealing with conflicting situations but more than that, it may also reveal about how a person thinks about himself, whether he is highly confident about his own views but still considers others opinions seriously or not. Even if people practice to answer this question several times, the way they’ll answer will still give away a lot.

How to avoid such conflicts?

The idea of any discussion is to focus on the problem and not on the people. There are always exceptions to this rule where some discussions are done only to escalate a conflict (pun-intended towards the politicians across the world)

For a product manager, admitting mistakes or acknowledging the knowledge gaps help in reducing the potential errors and sets us up for success. Also, it helps in building trust about our capabilities in the teams we work with. As a PM, we should consider all the estimations and data published as probabilities and not facts. When some data or statement is considered a probability, our brain makes us think about its chances of failure, in turn, making us anxious. Anxiety forces us to think a lot more on edge case scenarios or “pit-falls” that can be avoided. This anxiety also makes us consult not only people who are subject matter experts but also others who can guide us out of this ambiguous problem statement helping us opening up our mind to broader set of possibilities. Thus, we become more upfront in outlining challenges and threats in decision making. That makes us a calculated risk taker while being proactive and optimist at the same time.

Intellectual humility is the path that every product manager should follow to be aware of one’s own knowledge gaps. Once we are aware of our own knowledge gaps, it makes us honest in our conversations and helps saying “I don’t know” a lot more than ever. Every time a product manager says “I don’t know”, it helps to go back, study, learn a lot more about the topic to gain knowledge and to become a real expert. Any expert we meet today will always add an element of doubt in their statements even if they are almost 100% sure about them being right. That element of doubt stems from their awareness and knowledge of probability of almost everything in the world can never be 100%. There is always a chance of failure and that chance makes them intellectually honest and humble.

Epilogue

The incident I mentioned in the beginning happened in 2009. As with all the jobs, people move on. I moved on in 2012 to become an entrepreneur. My team member moved on to join another company. That incident just became one of those vague work memories that don’t make any sense in hindsight. In 2013, Netflix launched the series “House of Cards” changing the world of content production and TV viewing. We are in 2022 now. While Set Top Boxes still exist, Connected TVs have taken over the world. Even the biggest of satellite TV providers moved to online streaming. The advertising models have changed due to CTV ( acronym for connected TV) being a dominant force. Not only the TVs became connected, the data became the new oil that feeds into advertising. What we called second screen devices then is just a context with respect to TV but essentially, personal devices are the ones that drove the change.

Seed references to know more

1. Wikipedia : Dunning-Kruger Effect

2. Paper : Dunning-Kruger Effect

3. Wikipedia : Confirmation Bias

4. Understanding Michael Porter

LEGAL DISCLAIMER AND SURVIVAL GUIDE: The following article is a work of high-octane, hallucinogenic fiction. If you read a sentence and think, “Wait, that sounds exactly like my Monday morning stand-up,” that is just your suppressed corporate trauma speaking. Any resemblance to actual persons (living, dead, or currently wearing a Patagonia vest in a coworking space), actual companies, or actual “game-changing” cloud telephony startups is entirely coincidental—in the same way that a power cut in Bangalore hitting exactly when you have a critical deployment is a “coincidence.” The events described have been exaggerated to a degree that would make a Bollywood director blush, specifically to drive the point home without needing a 400-slide deck. In absolute reality, the CEO of the actual company was a legitimate gem of a human being, a strategic wizard who built a top-tier team and successfully navigated us to a glorious acquisition while the rest of us were still trying to figure out how to work the office printer. No CEOs, engineers, or coconuts were harmed in the making of this narrative. Mostly.

The conference room in our MG Road office in Bangalore felt like a pressurized cabin in the middle of a monsoon. It was second half of 2015, and our cloud telephony startup was supposedly “disrupting” how India talked. Outside, the MG Road traffic was a gridlocked symphony of frustrated horns, neon signs, and the smell of wet asphalt. Inside, our CEO—a man who once spent twenty minutes explaining how a coconut was a metaphor for a scalable backend—stood at the head of a mahogany table. He didn’t look at the catastrophic churn numbers on the screen. He didn’t look at the lead engineer, who was vibrating with a rage that only a week of failed SIP trunk deployments can produce.

Instead, he pointed to a slide that featured a single, lonely circle in the middle of a vast white space. “This,” he whispered, “is the void. And our five-year plan is the light that will fill it. We aren’t just routing calls for e-commerce delivery boys anymore. We are architecting the collective consciousness of the mobile-first consumer.”

I looked at my notepad. My only note from the last hour was a doodle of a tombstone with our company logo on it. We were a telephony platform. Our “light” was currently flickering because our primary API was held together by duct tape and prayers. But in that room, the “Void” was more important than the fact that our biggest taxi-aggregator client was currently on the phone with our lawyers because their drivers couldn’t reach their customers.

This is the central pathology of tech. We are so in love with the Light that we forget how to walk in the dark. We treat strategy like a religious text, planning like a prophecy, and execution like a chore for the help.

1. Strategy is the Art of Brutal Subtraction

Strategy is the most abused word in the corporate dictionary. Most people think strategy is a thirty-page slide deck filled with words like “synergy” and “leveraging.” It isn’t. Strategy is simply the art of deciding what you are not going to do.

The Case of the Chinese Wall: The Trade Desk vs. Google

In the late 2000s, the adtech world was a wild west of “do-it-all” giants. Google was building a “full-stack” solution. They wanted the supply side (the publishers), the demand side (the advertisers), and the data marketplace in the middle. They wanted to be the auctioneer, the buyer, the seller, and the guy who owned the building where the auction happened.

Jeff Green, the founder of The Trade Desk, saw this and made a choice that looked like suicide at the time. He focused only on the demand side (the buyers). For the first ten years—long after becoming a public and NASDAQ 100 company—he refused to touch the supply side.

Why? Because it created a strategic moat of trust. Meanwhile, Google was playing a dangerous game. By owning both sides, they created a massive conflict of interest. This lack of a “Chinese Wall” eventually led to the infamous Project Bernanke. While it only came to light years later through legal filings, the groundwork was laid in the late 2000s. Google allegedly used its “God’s eye view” of the market to manipulate auctions, taking data from other advertisers to give their own bids a secret advantage. They weren’t just the house; they were playing at the table with marked cards.

Google’s strategy was “total dominance,” which is great until the market realizes you’re playing a rigged game. The Trade Desk’s strategy was total alignment. They chose not to take the easy money from publishers so they could be the undisputed champion for advertisers. That is a strategy. If your strategy doesn’t hurt a little bit, it isn’t a strategy; it’s just a hobby.

“I’m as proud of many of the things we haven’t done as the things we have done. Innovation is saying no to a thousand things.” — Steve Jobs

2. The North Star: Navigation vs. Decoration

Every startup in Bangalore in 2015 had a “North Star Metric.” For some, it was “Total Registered Users.” For others, it was “Number of Cities Launched.” Ours was “Minutes of Usage.”

The North Star is supposed to be the one high-level metric that best captures the core value that your product delivers. If that number goes up, the company is winning. If it goes down, you are dying.

The Vanity Metric Trap

Most companies pick a North Star that looks good on a pitch deck but means nothing for the bottom line. If your North Star is “Registered Users,” but only 2% of them ever make a second phone call, your North Star is actually a black hole.

A real North Star for a cloud telephony company should have been “Successful Call Completions.” That is the only thing the customer actually pays for. But that was a hard number to move because it required fixing the messy reality of Indian telecom infrastructure. It was much easier to track “App Downloads.”

• Airbnb: Their North Star isn’t “App Installs.” It is “Nights Booked.” That aligns the host, the guest, and the company.

• Practicality Check: If your North Star hits a million but your bank account is empty, did you actually succeed? If the answer is no, throw that metric in the bin.

3. Goals: The Three-Year Fairytale

In tech, we love to pretend we are architects, but we are actually more like gardeners. An architect draws a blueprint and the building doesn’t move. A gardener plants a seed, and then the weather, the bugs, and the soil decide what actually happens.

Theoretical planning tells you to build a three-year roadmap with quarterly milestones. Practicality tells you that a three-year roadmap in tech is just fan fiction for your board of directors. You need to break your goals into three distinct horizons:

• Short-Term (The Dirt): This is the next two weeks. This is where you track every line of code. If you don’t know exactly what is happening on Monday at 9:00 AM, you aren’t planning; you are dreaming.

• Mid-Term (The Horizon): This is the next three to six months. These are bets, not promises. You might say: “We want to reduce latency by 20%.” You don’t know exactly how yet, but that is the target.

• Long-Term (The Star): This is the next year and beyond. This is just a direction. It should be broad enough to allow for a total pivot if the market explodes.

Jeff Bezos once said, “We are stubborn on vision. We are flexible on details.” Most tech managers do the opposite. They change their vision every time a competitor releases a feature, but they are stubborn about the stupid details of a project plan made six months ago.

4. KPIs: The Math of Reality

If the North Star is the destination and Goals are the milestones, KPIs (Key Performance Indicators) are the gauges on your dashboard. They tell you if you have enough fuel or if the engine is overheating.

The Trinity of Metrics

“In business, the idea of measuring what you are doing, picking the measurements that count like customer satisfaction and performance, you thrive on that.” — Bill Gates

To stay honest, you need three specific types of data:

1. Leading Indicators: These tell you what is going to happen. For us, it was “New API Keys Generated.” More keys today meant more call volume in three months.

2. Lagging Indicators: These tell you what already happened. Revenue and Churn are lagging. By the time they look bad, the damage was done months ago.

3. Health Metrics: These are the constraints. For us, it was “Uptime.” You can hit revenue goals, but if your uptime is 90%, you are a dead man walking.

5. Tactical vs. Strategic: The Build-Buy Suicide Note

“Value is discovered by what goes into the basket, not by what is promised on the label.” — Thomas Edison

This is where the bodies are buried. A tactical decision is a shortcut to hit a deadline. A strategic decision is an investment in your core identity.

In our Bangalore office, we needed a billing engine. We could build it in-house (9 months) or “buy” it via a third-party API (2 weeks). Tactically, buying was the “smart” move. We hit our targets, and the CEO got to tweet about “velocity.”

But two years later, that vendor was bought by a direct competitor. Suddenly, our “strategic engine” was owned by the enemy. They saw our traffic patterns and raised our prices until our margins vanished.

Case Study: Apple vs. Google Maps

Early iPhones shipped with Google Maps. Tactical win? Yes. Strategic disaster? Absolutely. They gave their primary rival (Google) mountains of location data. Apple eventually spent billions to build Apple Maps from scratch to regain their sovereignty.

6. The Competition: The Rear-View Mirror Trap

Competitive research is like looking in the rear-view mirror while driving a rickshaw at 100 km/h. It’s good to know where the other cars are, but if you stare too long, you’re going to hit a cow.

In 2015, we were obsessed with Twilio. Every meeting was: “What is Twilio doing?” We were so busy trying to build a Twilio-killer that we ignored our own customers who were screaming for better reliability in rural India. We were building APIs for developers in San Francisco while our users in Mysore couldn’t get a dial tone.

When to Ignore, When to Attack

The giants in adtech were afraid to be transparent because their margins relied on a “Black Box.” The winners weren’t those who copied features; they were the ones who out-trusted the competition.

If you spend all your time looking at the other guy, you end up making a “me-too” product. Use the competition only to see where they are failing their customers. Use their mistakes as your roadmap. Don’t copy their features; copy their solutions to problems they haven’t solved yet.

“The competitor to be feared is one who never bothers about you at all, but goes on making his own business better all the time.” — Henry Ford

7. Execution: The Religion of the “Friday Pulse”

“Ideas are easy, Execution is everything.” — John Doer

Execution is the sound of a thousand small gears turning in the same direction. To drive it, you must kill the “Status Update” meeting—the one where everyone says “on track” while the Jira board looks like a crime scene.

High-Performance Discipline

• The Weekly Detail: If a task takes longer than a week, it’s a project. Break it down. “Working on the database” is a lie. “Migrated user table to new schema” is execution.

• The Friday Pulse: Every Friday, ask: “Did the SIP trunk failover test pass? Yes or no?” No excuses.

• Tolerance for Mistakes: Celebrate the failures that teach (e.g., a new architecture that didn’t scale). Fire the people who make the same mistake three times because they didn’t check the logs.

• Alignment: Driving alignment across silos means telling the sales team “No” and the engineers “Not now.” You build alignment by making everyone’s bonus dependent on the same goal.

8. The Trinity: Strategy, Planning, and Execution

To win, you must treat these as a circular ecosystem, not a linear line.

The Bridge Process

1. Strategy (The Why): The choice of where to play and where to ignore. This stays stable for years.

2. Planning (The How): The bridge between vision and work. Updated quarterly. Sets the KPIs.

3. Execution (The Do): The daily grind. The Friday Pulse. This is the only part that is real.

PhasePurposeProcessStrategyDefine the MoatThe “Strategic Filter”: Does this help us own our core value?PlanningMap the RouteThe “OKR Reset”: Check every 2 weeks. Kill irrelevant goals.ExecutionDrive the EngineThe “Alignment Audit”: Does engineering work support sales goals?

9. The Friday Blood Sync: The Cross-Functional War Room Checklist

I. The Binary Commitment Wall (No “90% Done” Allowed)

Every department lead has 60 seconds to report on their one major “Brick” from last week.

• Engineering: “Did the latency-reduction patch for the Singapore gateway go live?” [Yes/No]

• Product: “Is the final PRD for the ‘Call-Masking’ feature locked and approved by Legal?” [Yes/No]

• Sales: “Did we close the high-volume pilot with the Delhi logistics giant?” [Yes/No]

• Customer Success (CS): “Have all Tier-1 tickets regarding the ‘Dropped Call’ bug from Tuesday been resolved?” [Yes/No]

• Infrastructure/DevOps: “Was the database migration completed without a packet loss event?” [Yes/No]

The Rule of Shame: If the answer is “No,” the lead must explain the Blocker, not the Excuse. Was it a dependency on another team? A tactical mistake? Or did we just overestimate our own brilliance?

II. The Cross-Functional Friction Audit

This is where we find out who is accidentally sabotaging whom.

• The Sales vs. Engineering Trap: Did Sales promise a “custom integration” to a client this week that isn’t on the roadmap? If so, kill the promise or kill the roadmap. You cannot have both.

• The Product vs. CS Gap: CS reports the top 3 complaints from the week. If Product isn’t working on a fix for at least one of them, justify why we are ignoring our customers.

• The Marketing vs. Reality Check: Is Marketing promoting “99.99% Uptime” while Infra is currently holding the servers together with prayers and caffeine? Alignment on the “Truth” is mandatory.

III. The Dashboard of Hard Truths (Hard Metrics)

No “vibe-based” metrics. If it isn’t a hard number, it’s a hallucination.

IV. The Strategic Veto: The Art of Saying “No”

To maintain ruthless prioritization, we must identify what we are dropping to stay fast.

4. The “Shiny Object” Cull: What was the most distracting idea suggested this week? (e.g., “Let’s add AI to the dialer!”) Action: Officially veto it for the next 90 days.

5. Technical Debt Payment: What feature did we decide not to build this week so that Engineering could fix a core stability issue?

V. The Monday Morning “Zero-Hour” Plan

Execution isn’t finished until the pivot for next week is set.

• The Carry-Over Tax: Any “No” from Section I is automatically the Top Priority for Monday 9:00 AM.

• The Clean Slate: If a team is consistently failing their binary commitment, do we need to reduce their scope or change the lead? (Be brutal, be honest).

• The “Delhi” Sign-off: Before the CEO catches that flight back to Delhi, does he agree that this week’s “Bricks” actually support the “Bridge”?

The Closing Question: “Is there any silent disaster currently brewing that no one has mentioned yet?”

(Silence for 10 seconds. If someone speaks up, stay another hour. If not, go home.)

10. Prep To Prevent The Friday Blood Sync: Get The Strategy and Plan Right

To ensure the “Friday Blood Sync” doesn’t turn into a finger-pointing exercise, you need the underlying scaffolding to be just as ruthless. If the Execution is the engine, the Strategic and Planning frameworks are the chassis and the GPS.

Here is the three-section breakdown for the Strategic Framework, the Planning Framework, and the final Measurement Scorecard.

I. The Strategic Framework: The “Moat & Anchor” Model

This framework is used only once a quarter (or whenever a massive market shift occurs). Its goal is to define the boundaries of your universe.

1. The Strategic Filter (The “Moat”)

Every potential initiative must pass through the Moat Test. In our cloud telephony world, our moat was “Unrivaled Latency in Tier-2 Indian Cities.”

• The Question: Does this project deepen the moat?

• The Outcome: If it doesn’t improve call quality or reliability, it is a “Secondary Priority.” No matter how “cool” the feature is.

2. The Subtraction List (The “Anchor”)

Strategy is defined by what you drop.

• The List: Formally document the three high-revenue opportunities you are ignoring this quarter to stay focused.

• Example: “We are ignoring the Enterprise Video market to own the SMB Voice market.”

3. The Chinese Wall Protocol (Integrity)

Referencing our Trade Desk vs. Google lesson:

6. The Rule: Define where your interests stop to maintain trust.

7. Action: Document the “No-Go” zones. (e.g., “We route the calls; we never compete with our customers by launching our own call center.”)

II. The Planning Framework: The “Rolling Horizon” (70-20-10)

Planning is not a fixed document; it is a living breathing organism. We use the 70-20-10 rule to ensure we aren’t hallucinating about 2029 while the 2026 servers are smoking.

1. The 70% (The Dirt - 2-Week Sprints)

• Granularity: High. Individual tasks, owners, and hourly estimates.

• Flexibility: Zero. Once a sprint starts, the only way to change it is to declare a “State of Emergency.”

2. The 20% (The Horizon - 3-Month OKRs)

4. Granularity: Medium. These are “Key Results” (e.g., “Migrate 40% of traffic to the new SIP gateway”).

5. Flexibility: Moderate. If the “Dirt” reveals a major technical blocker, we pivot the Horizon.

3. The 10% (The Star - 1-Year Vision)

• Granularity: Low. Themes and North Stars.

• Flexibility: High. This is our “Fan Fiction.” It exists to keep the CEO’s “Delhi” dreams aligned with the Bangalore reality.

III. The Measurement Scorecard: The “Hard Truth” Ledger

This is the final scorecard that combines the Strategy, Planning, and Execution into one brutal document. It is the only thing that matters during the Friday Blood Sync.

IV. The “Perfect Execution” Audit

To ensure this isn’t just more “theoretical practice,” the leadership must answer three questions every Sunday night before the week begins:

1. The Sovereignty Check: Are we “Building” the core and “Buying” the fluff? (Did we accidentally outsource our soul to a vendor this week?)

2. The Competition Blindfold: Are we building this feature because a competitor released it yesterday or does it really help us differentiate in the market to add value to our customers’ business ?

3. The Alignment Sanity: If I ask a junior developer and the Sales Lead what our #1 goal is, will they give me the same answer?

11. The Closing: Back To The Story

The boardroom in MG Road finally emptied around 4:00 AM. The “Void” slide was still glowing, but the CEO had already caught the red-eye flight back to Delhi. I stayed behind with the lead engineer. We didn’t talk about “sentimental resonance engines.”

We sat in the dark, listening to the rain, and talked about rewriting three lines of code so delivery drivers could receive calls at breakfast. We looked at the real KPIs—the ones showing our server was about to melt—and ignored the “North Star” of App Downloads.

That company did get acquired after a few years. Everyone did make some money. But they were so busy being “strategic” that they forgot to be functional. They had the map and the route and they could have made it much bigger than Twilio, but they let the engine catch fire because they were too busy arguing about the destination.

Hindsight is a great teacher.

Strategy is your map. Planning is your route. Execution is your engine. If you spend all your time looking at the map while the engine is on fire, you are never going to get where you are going.

“The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.” — Bill Gates

I recently had coffee with two founders in Singapore, and the contrast explained everything you need to know about the next decade of tech.

Founder A was beaming. He just closed his Series A. “We’re scaling up,” he told me, puffing out his chest. “We’re moving into a new office in the CBD. I just hired a VP of People, a VP of Strategy, and we’re opening a new engineering hub in Bangalore. We’re going to hit 80 employees by Q4.”

Founder B looked confused. She runs a company with similar revenue ($3M ARR), but she looked rested. “We just hired our third employee,” she said. “He’s a full-stack architect who manages our swarm of AI agents. I think we might need one more person for legal compliance, but otherwise, we’re good.”

Founder A is building an Empire. Founder B is building a Fortress.

In the old world, Founder A wins. In the AI world, Founder A is dead. He just built a massive, high-friction organization that will be outmaneuvered by Founder B’s three people and 50,000 GPUs.

There is a comforting lie floating around LinkedIn right now. It goes: “AI won’t replace you; a person using AI will replace you.”

It’s a nice sentiment. It suggests that everyone gets to keep their job, just with better tools. I2t implies that the junior developer who struggles to center a div will suddenly become a 10x engineer just because they have Copilot.

This is mathematically impossible. If you multiply Zero by 100, you still get Zero.

AI is not a magic wand that bestows competence on the incompetent. It is a Force Multiplier. It amplifies the intelligence of the user. If you are a brilliant architect, AI makes you a god. If you are a mediocre “ticket taker” who just patches bugs, AI makes you obsolete.

We are witnessing the end of the “Code Monkey” era, the collapse of the Global Labor Arbitrage model, and the rise of a new corporate metric: Profit-Per-Employee.

1. From “Specialist” to “God-Tier Generalist”

For the last decade, we fetishized hyper-specialization. We broke engineers into tiny fragments: “React Frontend Developers,” “Postgres DB Admins,” “DevOps Engineers.”

This fragmentation created a massive Inefficiency Tax. A simple feature required a meeting between the Frontend guy, the Backend guy, and the DB guy. It required a Project Manager to coordinate them. It required a Jira board to track them.

But in the Vibecoding era, the Specialist isn’t just dying—they are evolving.

The AI removes the barrier of Syntax. The Backend Engineer no longer needs to memorize CSS flexbox rules; the AI handles that. This allows a high-competence engineer to escape their silo. They are no longer just a violinist; they are the conductor and the entire orchestra.

“Specialization is for insects.” — Robert Heinlein

If you are a developer who defines your value by “knowing React” rather than “solving business problems,” you are in trouble. The syntax is free. The value has shifted entirely to System Architecture, Data Modeling, and Taste.

2. The Death of Human Middleware (Baumol’s Cost Disease)

The casualties won’t just be engineers. The “Non-Technical Middle Manager” is walking dead.

This is an economic phenomenon known as Baumol’s Cost Disease. Usually, this theory explains why services (like education) get more expensive while goods (like TVs) get cheaper. In Tech, the “Good” (Code) is dropping to zero cost. The “Service” (Management) remains expensive. The market will correct this imbalance by eliminating the management layer entirely.

The Non-Technical Product Manager

We all know the type. The PM who is a “Domain Expert” but doesn’t understand how the sausage is made. They write vague user stories like “As a user, I want the dashboard to load faster,” and then they wait for Engineering to tell them if it’s possible.
In an AI world, this PM is useless. Why? Because you cannot prompt an AI if you don’t understand the architecture. If you ask an AI agent to “build a dashboard,” it will build garbage. You need to tell it: “Build a React dashboard using Recharts, pulling from the Snowflake API, caching via Redis to reduce latency.”

The End of the SDR and Onboarding Manager

• Sales Development Reps (SDRs): The job of sending 100 generic emails a day is dead. An AI agent can send 10,000 hyper-personalized emails, read the responses, and book the meeting. The “Volume Game” belongs to bots now.

• Customer Success: Why are we paying humans to teach other humans how to use software? AI agents will guide users through the product in real-time. The “Onboarding Manager” reading from a script is redundant.

3. The Economics of Labor Arbitrage (Ronald Coase & The Transaction Cost)

This brings us to the most uncomfortable reality: The collapse of the Outsourcing Model.
For 20 years, the software industry relied on Labor Arbitrage. The math was simple:

• The US Reality: An engineer in San Francisco costs $200,000.

• The Offshore Reality: An engineer in Bangalore costs $30,000.

• The Arbitrage: You hire the offshore team. Even if they are slower, the price difference is so massive you tolerate the inefficiency.

AI breaks the arbitrage math.

We can explain this using Ronald Coase’s Theory of the Firm. Coase argued that firms exist to reduce Transaction Costs (search, coordination, contracting). Outsourcing increases transaction costs (communication latency, cultural context, quality control), but low wages offset it.

AI is a massive deflationary force that eliminates the transaction costs.

• Old Math: 1 Hired Engineer ($200k) vs. 5 Offshore Engineers ($200k).

• New Math: 1 Hired Engineer + AI ($300k) = 10x Output.

Suddenly, the single US engineer is more productive than the team of 5, but without the Transaction Costs—no timezone delays, no language barriers, no “Project Manager” layer.

“The most expensive thing in software is communication. If you can eliminate the need to communicate by having one person do the work of ten, you win.” — Naval Ravikant

The “Managed Services” model (Infosys, Wipro) is the next Blockbuster. They sell “seats” and “man-hours” for maintenance and testing. But AI does maintenance and testing instantly for free. If your business model depends on billing hours for work that requires little creativity, you are technically a “human API,” and you are about to be deprecated.

4. The New Scoreboard: Profit-Per-Employee (PPE)

Because of this, the metric for success is flipping. For decades, founders like Founder A measured their worth by the size of their “Empire” (Headcount). “We just crossed 100 employees!” was a badge of honor.

In the AI era, Headcount is a liability. The smart money is watching Revenue-Per-Employee (RPE).

• Legacy SaaS Co (Founder A): $3M ARR / 80 Employees = **$37,500 RPE**.

  • Status: Burning cash. Heavy management tax. Slow velocity.

• AI-Native Co (Founder B): $3M ARR / 3 Employees = **$1,000,000 RPE**.

  • Status: Massive cash flow. Zero middle management. High velocity.

The AI-Native company can undercut the Legacy company on price by 50% and still be more profitable. They can spend 10x more on customer acquisition.

The “Throwing Bodies at the Problem” Fallacy (Brooks’s Law)

Legacy companies are addicted to Linear Scaling. When a problem arises (e.g., “Support tickets are piling up”), their reflex is to treat it like a manufacturing problem: Throw more bodies at it.

Step 1: Hire 20 more support agents.
Step 2: Realize 20 people create chaos.
Step 3: Hire 2 Managers to manage them.
Step 4: Hire a Director to manage the Managers.
You just built a pyramid of human routers.

This triggers Brooks’s Law: “Adding manpower to a late software project makes it later.” In business terms, this is Diseconomies of Scale. Each new human adds “Communication Overhead.” As you add people, the complexity of the network increases exponentially (N(N−1)/2), slowing down decision-making.

This is Organizational Debt. Just like technical debt, you are borrowing against future speed to solve a problem today with the wrong tool (humans).

In the AI era, this is suicidal. When that same problem arises in an AI-native company, they don’t open a job requisition. The Full-Stack Engineer fine-tunes an LLM on the last 10,000 tickets.

• Cost: $50 in compute.

• Time: 2 hours.

• Headcount Added: Zero.

• Result: The AI-native company scales exponentially (Software economics), while the Legacy company scales linearly (Service economics). The Legacy company will eventually be crushed by its own weight.

5. The Pivot: If Building is Free, What Do We Sell? (Liability-as-a-Service)

This leads us to the existential crisis facing the software industry. We are rapidly approaching a zero-marginal-cost reality for code generation. If a small team—or even a single “non-technical” founder—can prompt an AI agent to “vibecode” a functional Salesforce clone in a weekend, the fundamental value proposition of SaaS is shattered.

Why would an enterprise customer pay Salesforce $300 per seat/month for software that a college student just replicated on their laptop for the cost of an API token?

The answer lies in a shift from Capability to Accountability. The answer is Trust.
We are leaving the era of Software-as-a-Service (SaaS) and entering the era of Liability-as-a-Service (LaaS).

The Code is a Commodity; The Shield is the Product

In this new paradigm, the ability to write code is table stakes. It is abundant, cheap, and accessible. You can direct an AI to build a payroll application in 48 hours. It will have a sleek UI, it will connect to bank APIs, and it will run perfectly… until it doesn’t.

The “product” is no longer the software functioning correctly when things go right; the product is what happens when things go wrong.

• The Payroll Scenario: Imagine you use a custom, AI-generated payroll script. It works for six months. Then, the AI “hallucinates” during a routine update—perhaps misinterpreting a new state tax code—and fails to withhold taxes for 500 employees. The Result? You, the employer, are liable. The IRS does not care that your AI hallucinated. You face audit penalties, lawsuits from employees for back-taxes, and potentially jail time for gross negligence.

• The Workday Scenario: Conversely, when an enterprise pays Workday or ADP millions of dollars, they are not paying for the SQL database that stores employee names. They are buying an insurance policy. If Workday glitches and messes up tax withholdings, Workday pays the fine. Workday deploys an army of lawyers and accountants to fix it.

The “Throat to Choke”

For the Fortune 500, software purchasing decisions are driven by risk mitigation, not just feature acquisition. This brings us to the concept of the “Throat to Choke.”

In corporate governance, there is a massive premium placed on shifting blame. If a CIO buys a custom AI solution from a nimble startup and it causes a data breach, the CIO is fired for recklessness. If the CIO buys Microsoft or Oracle and the same breach happens, it is considered an “unfortunate vendor incident.” The CIO keeps their job because they made the “safe” choice.

Real-World Example: The CrowdStrike Outage

Consider the massive CrowdStrike outage of 2024. When a faulty update crashed millions of computers globally, it cost Delta Airlines alone over $500 million.

• Scenario A (The Startup): If that update had come from a cheap, AI-generated endpoint protection tool, Delta would have had no recourse. The startup would have simply declared bankruptcy, leaving Delta with the bill.

• Scenario B (The Incumbent): Because it was CrowdStrike, a massive public entity, Delta had a target for litigation. The “product” CrowdStrike sells is not just antivirus; it is the capital reserves and insurance policies required to absorb the shock of failure.

The Moat of the Legacy Dinosaur

The “Moat” for legacy giants—the Salesforces, SAPs, and Epics of the world—is no longer their source code. Their code is often legacy spaghetti that is worse than what an AI could write today.

“Trust is the coin of the realm.” — George Shultz

Their moat is their Indemnity Shield.

1. SOC2 & ISO Compliance: These are grueling, expensive, human-centric auditing processes that prove a company handles data safely. An AI agent cannot “vibecode” a SOC2 Type II audit report.

2. Regulatory Navigation: In healthcare (HIPAA) or finance (PCI-DSS/GDPR), the software must adhere to laws that change frequently. Legacy giants have armies of humans monitoring regulatory changes to update the compliance layer of the software.

3. Financial Backstop: Enterprises buy software to offload risk. They pay a premium for the legal guarantee that if something breaks, it’s not the CTO’s fault.

The New Bifurcation

This creates a clear split in the market:

The Micro-Enterprise (Speedboats)The Legacy Giant (Tankers)Sells: Innovation, Speed, CustomizationSells: Stability, Compliance, IndemnityUser: Startups, Creators, SMBsUser: Governments, Banks, HospitalsValue Prop: “We solve the problem fast.”Value Prop: “We are the Throat to Choke.”

Conclusion: Don’t Build an Empire, Build a Fortress

To the founders reading this: Stop trying to hire your way to success. Stop measuring your self-worth by the size of your All-Hands meeting.

In the previous era, we built Empires. We hired armies of humans because human labor was the only way to scale output. An Empire is impressive to look at, but it is cognitively fragmented. Intelligence is fragmented across hundreds of nodes (people), slowing down the collective brainpower to the speed of a calendar invite.

In the AI era, you must build a Fortress.

A Fortress isn’t just about automation; it’s about concentrated intelligence. It is not defined by how many hands are typing, but by the clarity of the mind directing them.

The difference is physics.

• The Empire operates on Distributed Intelligence. It relies on the consensus of the many, moving at the speed of biology (meetings, emails, persuasion).

• The Fortress operates on Amplified Intelligence. It relies on the judgment of the few, amplified by silicon, moving at the speed of thought.

The Tale of Two Futures

Let’s go back to those two founders one last time.

Founder A (The Empire) is currently in a 4-hour “strategy offsite” in Singapore. He is mediating a dispute between his VP of Product and his VP of Engineering about “roadmap alignment,” while his offshore team in Bangalore waits 12 hours for instructions. He is burning $400k a month to generate $3M in value. He feels important, but he is actually just a high-paid babysitter for a bloated organization.

Founder B (The Fortress) is at the beach. She isn’t checking Slack because there is no Slack. Her agents are executing the strategy she designed last week. She isn’t working in the machine; she is designing the machine. She is burning $40k a month to generate the same $3M in value.

The Reality Check

The market is about to run a ruthlessly efficient garbage collection algorithm on the tech sector.

For the last ten years, we confused “Headcount” with “Brainpower.” We thought that if we hired 100 mediocre engineers, we would get the output of 10 geniuses. We were wrong. We just got 100 times the complexity.

The next Amazon or Google will not have 100,000 employees. It might not even have 1,000. It will be a small, terrifyingly efficient Fortress of “God-Tier” Generalists using AI not just to write code, but to simulate futures, predict risks, and execute decisions instantly.

In the future, there are only two roles left:

1. The Architect who provides the judgment.

2. The Artifact that executes the work.

The asteroid is already here. Stop building for the dinosaurs.

Lock your doors, hide your high-yield coffee beans, and delete your search history for “how to explain a 4% raise during 8% inflation.” It’s that magical time of year again: The Annual Appraisal Cycle.

Forget The Last of Us or Squid Game; those are rom-coms compared to the psychological warfare of the Corporate Finance Bell Curve. This is the time when your manager—who hasn’t seen a spreadsheet cell since the Obama administration—suddenly becomes a mathematical savant, hell-bent on proving that despite your 80-hour work weeks and “10x impact,” you are, statistically speaking, just “aggressively average.”

Welcome to the Corporate Hunger Games, where the Tribute from Accounting must fight the Tribute from Compliance over a single “Exceeds Expectations” slot that was actually promised to the CEO’s nephew three months ago.

Imagine you’ve just spent the last twelve months absolutely crushing it. You shipped the core API three weeks early, you mentored four juniors until they stopped crying in the breakroom, and you saved the company’s flagship product from a catastrophic security breach while the rest of the leadership team was at a “mindfulness retreat” in Bali.

You walk into your annual review feeling like a gladiator returning from a successful conquest. You’re ready for that “Exceeds Expectations” rating, a bonus that makes your mortgage look small, and a promotion that actually reflects your impact.

But as the door closes, the atmosphere shifts. Your manager, Dave—who has the spinal column of a chocolate eclair—won’t look you in the eye. He starts talking about “organizational health,” “budgetary constraints,” and “the broader talent distribution.”

Translation: The HR department just ran your career through a 200-year-old math equation, and you lost.

“Look,” Dave stammers, “You did an incredible job. Truly. But we already gave the ‘Exceeds’ rating to Sarah because she’s been here longer, and the system says only 10% of the team can be ‘Great.’ So, you’re officially ‘Solidly Meets Expectations.’ Here’s a 2% raise and a branded hoodie. Don’t spend it all at once.”

This is the Bell Curve. It is a mathematical mugging. It is the professional equivalent of telling a marathon runner they finished in “last place” because the race director decided that only three people are allowed to be fast today. It is the single most effective way to turn your best employees into your competitors’ best employees by next Monday.

The Gaussian Ghost: A Brief History of Scientific Laziness

To understand why we are still stuck in this nightmare, we have to look at where it came from. The bell curve, or Normal Distribution, was popularized in the early 19th century by Carl Friedrich Gauss. He didn’t design it to measure software engineers or product leads; he used it to track errors in astronomical observations and the physical heights of French conscripts.

It was a way to measure accidents, errors, and static physical traits. It was never meant to measure human potential, creativity, or the non-linear impact of a knowledge worker.

Then came the 1980s. Jack Welch, the then-CEO of General Electric, decided that the best way to run a company was to treat it like a gladiatorial pit. He popularized “Rank and Yank” (also known as the “Vitality Curve”). You put 20% of your people in the top tier, 70% in the middle, and 10% at the bottom. Then, you fired the bottom 10%.

It worked for GE back then because they were an industrial conglomerate making lightbulbs and jet engines. In a factory setting, the difference between a “good” worker and a “bad” worker is marginal. If a guy on the assembly line is 10% faster, he’s a star.

But in the 21st century, we don’t manufacture lightbulbs; we manufacture ideas. And ideas do not follow a bell curve. By forcing a normal distribution on a high-performance team, you are effectively telling your overachievers that their hard work is statistically inconvenient.

The Myth and the Monster: The 10x Engineer

Whenever you criticize the bell curve, an HR “Thought Leader” (usually someone who has never written a line of code or closed a million-dollar deal) will pipe up: “But the 10x engineer is a myth! It’s all about the team!”

Let’s be brutally honest: People who say 10x engineers don’t exist are usually 1x engineers (or 0.5x “B-players”) who are terrified of being measured against them.

What is a 10x Engineer?

A 10x engineer isn’t someone who types ten times faster. They aren’t the ones staying until 2 AM every night to show how “busy” they are. In fact, a 10x engineer often looks like they’re doing less work.

The 1x engineer solves a problem by writing 2,000 lines of complex, brittle code. The 10x engineer spends three hours staring at a whiteboard, realizes the problem shouldn’t exist in the first place, deletes 500 lines of old code, and replaces the whole mess with a 10-line elegant script.

How to Identify Them:

• The Simplifiers: While everyone else is adding features, the 10x-er is removing friction.

• The Force Multipliers: They don’t just do their work; they build tools and systems that make everyone else on the team 2x better.

• The Outcome Obsessed: They don’t care about “Jira tickets closed.” They care about whether the user’s problem was actually solved.

In a bell curve system, the 10x engineer is an anomaly that HR tries to “average out.” But in reality, performance in the tech world follows a Power Law (Pareto Distribution). A tiny fraction of the people produces the vast majority of the value. Trying to put a 10x engineer on a bell curve is like trying to fit a skyscraper into a shoebox.

The Anatomy of the “Brilliant Jerk” (Netflix’s Warning)

The term “Brilliant Jerk” was Netflix’s gift to the world. A Brilliant Jerk is that person who is technically untouchable—the architect who can recite the entire kernel source code from memory—but who treats everyone else like cognitive peasants. They use their “brilliance” as a shield to bully colleagues, ignore processes, and create a “fear-based” environment.

Why the Bell Curve is Their Natural Habitat:
In a forced-ranking system, the Brilliant Jerk is safe. Why? Because the system only measures “Output.” The Jerk produces high output, so they get the “Exceeds” rating. Meanwhile, the five people they bullied into silence or sabotaged are placed in the “Average” bucket because their “productivity dropped.”
Netflix realized that the “Brilliance” of one jerk is never worth the collective productivity loss of the ten people they’ve demotivated. If you have a Brilliant Jerk, you don’t have a high-performer; you have a human dumpster fire with a high IQ. They should be fired immediately, regardless of how “critical” they claim to be.

The Hero Syndrome: A Saboteur with a Cape

We’ve all met the “Hero.” This is the person who swoops in at 3 AM to fix a crashing server or the “Firefighter” who saves a failing account at the last second. The company gives them a plaque. The CEO mentions them in the All-Hands.

Here is the brutal truth: If that person has saved the company more than twice, they aren’t a hero—they are a liability.

Hero Syndrome is often a Brilliant Jerk in disguise. These individuals thrive on crisis. They build “black box” systems that only they understand. They don’t document their work. They subconsciously (or consciously) ensure that the company is constantly on fire so they can be the only one with a hose.

The Test: If your “Hero” goes on vacation and the company falls apart, they haven’t saved you; they’ve sabotaged you. A true 10x performer builds systems that don’t break and mentors others so they are never the single point of failure. If you have a “Hero” who has saved the day three times in a row, don’t give them a bonus. Fire them. They are preventing your company from becoming a professional organization.

The High Cost of the “Average” 80%

HR heads love the Bell Curve because it makes the budget predictable. “If we keep 80% of people in the ‘Average’ bucket, our salary growth remains flat. We’re saving money!”

Congratulations, you just spent $10 million to save $10,000.

A larger team of mediocre people is exponentially more expensive than a small team of superstars.

1. Management Bloat: Mediocre people need managers. Managers need directors. Directors need VPs. Soon, you have six layers of people whose only job is to “coordinate” the lack of initiative.

2. The Drag Effect: Mediocre people build mediocre products. They don’t innovate; they maintain.

3. The Recruitment Death Spiral: Superstars want to work with other superstars. The moment a 10x engineer realizes they are being “curved” alongside a C-player, they leave. You are left with a company full of “average” people who are just happy to have a job.

The Hall of Fame: The Curve Killers

These are the companies that realized that humans aren’t data points and decided to treat talent like a scarce resource.

• Microsoft (The Satya Pivot): Under Steve Ballmer, Microsoft was the poster child for “Stack Ranking.” It turned the company into a circular firing squad. Engineers were more focused on sabotaging their deskmates than beating Apple. When Satya Nadella took over, he killed the curve. He replaced “competition” with “Growth Mindset.” The result? Microsoft’s stock price entered orbit.

• Stripe: They ignore the curve entirely. They evaluate based on Operating Principles. If you don’t meet the absolute bar of being a “Stripe-level” thinker, you don’t stay. They don’t care about buckets; they care about excellence.

• Netflix: The “Keeper Test.” No formal reviews. Just one question: “If this person wanted to leave, would I fight to keep them?” If not, they get a massive severance package. It’s the ultimate removal of mediocrity.

• Google & Meta: They use Absolute Evaluation. You are measured against the Impact you had on the business. If an entire team of 50 people has a “Redefining” year, Google pays all 50 of them like gods. They understand that talent is a Power Law, not a bell.

The Hall of Shame: The Jurassic Relics

• The “Big Four” Consulting Firms (Deloitte, KPMG, PwC, EY): They love the curve because they sell “hours.” They need a massive, replaceable middle of junior associates to bill clients. It’s not about performance; it’s about survival of the most caffeinated.

• Amazon (The Ghost of the Curve): Despite their PR, the culture of “Unregretted Attrition” is legendary. Managers are often pressured to identify a bottom percentage to “move on” every year. It’s The Hunger Games with Prime shipping.

• Legacy Manufacturing (Ford, GE): Still trying to manage creative white-collar workers using the same metrics they use for stamping out truck bumpers.

The Solution: Absolute Evaluation

If you want to kill the bell curve, you must move to Absolute Evaluation Against an Infinite Ceiling.

1. Set an Absolute Bar: Define exactly what “Excellent” looks like for every role. Not “better than Steve,” but “The code is clean, the documentation is perfect, and the stakeholders are happy.”

2. The No-Quota Rule: If every single person on the team hits that bar, every single person gets the top rating and the top bonus. Yes, the budget will be higher. But your Revenue will be 10x higher because your people aren’t spending 40% of their time wondering how to make Steve look bad.

3. Real-Time Feedback: If someone is failing, tell them on Tuesday. Don’t wait for a “Performance Review” in six months to drop a bomb on their career.

4. The Jerk Tax: Add a “Cultural Impact” metric. If a “Brilliant Jerk” has high output but low cultural scores, they get a “Needs Improvement” rating.

5. Remove the Floor: Instead of firing the bottom 10%, fire the people who don’t meet the Absolute Bar. Some years that might be 2%. Some years it might be 20%. Let the work decide, not the graph.

The Final Word

The Bell Curve is a crutch for weak leaders. It is a confession that you don’t know how to measure value, so you’ve decided to measure “relative position” instead.

Back in that review room with Dave, the air is thick with the scent of corporate cowardice. Dave finishes his spiel about “averaging out” and “normalization.” He looks at you, waiting for a nod of acceptance.

But you don’t nod. You stand up.

“Dave,” you say, your voice calm and terrifyingly clear. “You aren’t managing a team. You’re managing a graph. And a graph doesn’t build software. I do. Sarah does. And the fact that you’re willing to sacrifice my performance on the altar of a 19th-century math error tells me everything I need to know about your leadership.”

You walk out. You don’t take the Starbucks gift card. You don’t take the branded hoodie.

By the time you reach the parking lot, you’ve already sent a text to a founder who knows that a 10x engineer is worth more than a thousand “average” drones. You’ve sent a message to a company that uses the Keeper Test, not the Bell Curve.

The era of managing humans like lightbulbs is over. It’s time to stop managing by math and start leading by merit. Throw the curve in the trash. Burn the spreadsheets. Your best people—the outliers, the superstars, the legends—are already halfway out the door.

Are you going to keep the curve? Or are you going to keep your talent?

Part I: The Funeral of a Job Title

Let’s start with a scene you probably know too well. Let’s talk about Jason.

Jason is a Product Lead at a mid-sized tech company. For six months, Jason has been feeling uneasy. But he pushes it down with the help of too much coffee, a false sense of security, and the comforting glow of his dual monitors.

Last Tuesday, at 9:00 AM sharp, Jason joined a mandatory all-hands video call. The CEO was calling in from what looked suspiciously like a ski lodge in Aspen, wearing a turtleneck that cost more than Jason’s car. He spoke somberly about “macroeconomic headwinds” and “right-sizing the ship to optimize operational synergy.” Jason zoned out because he was too busy messaging his work bestie, Sarah, making a joke about how the CEO looked like a Bond villain who just bought a Peloton.

By 9:04 AM, Jason’s screen went black. His Slack access was gone. His email was locked. His job title evaporated into thin air. He was part of the 12% workforce reduction.

By 9:30 AM, Jason was panicking. By 10:00 AM, he was in the Denial Phase. And by 10:15 AM, he was doing what every desperate professional does when their career implodes. He opened a Word document.

For the last three weekends, Jason has treated his resume like it is a sacred text. He has polished it like a diamond. He agonized over whether “spearheaded” sounds more authoritative than “led.” He sprinkled in corporate words like “cross-functional collaboration” and “dynamic innovator” as if they were magic spells meant to exorcise the demon of unemployment.

He wrote a stunning piece of corporate fan fiction starring himself as the hero. He hit the Easy Apply button on two hundred jobs on LinkedIn. Then he sat back, staring at his inbox, waiting for the gods of recruiting to descend from the clouds and crown him the Chosen One.

And what did he get?

Deafening silence. Broken only by the occasional automated rejection email from a robot named “No-Reply” telling him they decided to move forward with other candidates.

Here is the cold, brutal truth that nobody wants to admit. Your resume is just a blood test.

It is a boring, clinical readout of data points. It confirms you were, in fact, present at a desk from 2021 to 2023. It proves you were alive. It does not explain that you spent half that time staring into the abyss of your lukewarm coffee questioning every life choice that led you to a Tuesday morning meeting about TPS reports.

As Warren Buffett famously said, “Only when the tide goes out do you discover who’s been swimming naked.”

Well, Jason. The tide is out. And you are naked.

Part II: The Market Has No Morality (And It Hates Your Rent)

We need to be honest about the dumpster fire that is the current job market. For the last ten years, we lived in a fantasy land.

Money was free. Interest rates were zero. Tech companies hired people just so their competitors couldn’t hire them. You could get a job as a “Happiness Manager” for a hundred and forty grand a year just by vibing correctly in the interview.

That party is over. The lights are on. The music stopped. And the police are here.

According to Layoffs.fyi, over 260,000 tech workers lost their jobs in 2023, and another 140,000+ followed in 2024. This isn’t a “blip.” It is a correction.

Companies are no longer optimizing for growth. They are optimizing for efficiency. When ten thousand highly qualified engineers from Google and Meta flood the market on the same Tuesday, your proficiency in Microsoft Excel suddenly looks a lot less impressive.

You are no longer competing with the lazy guy who sleeps under his desk. You are competing with the guy who built the desk, the software running on the desk, and the AI that is currently rewriting your cover letter.

As Jack Welch, the former CEO of GE, ruthlessly put it: “Face reality as it is, not as it was or as you wish it to be.”

The reality is that “spray and pray” applications are dead. Data from recruiting platforms shows that applicants on LinkedIn have a response rate of just 3% to 13%. Compare that to employee referrals, which are 4x more likely to result in a hire.

Applying to fifty jobs a day without a strategy isn’t grit. It’s spam. You are just buying lottery tickets with your time.

Part III: AI Is Not a Skill, It’s Oxygen

Here is the part where Jason thinks he is safe because he knows how to use ChatGPT to write a limerick.

Jason lists “Generative AI” as a skill on his resume, right next to “Microsoft PowerPoint.” He thinks this makes him special.

Jason is wrong.

AI is going to complicate the job scenario in ways that most people are not ready for. A recent Goldman Sachs report estimates that AI could displace 300 million full-time jobs globally and that 25% of all work tasks in the US could be automated.

Here is the kicker: Junior roles are disappearing. A study on the impact of ChatGPT showed that job postings for junior software developers dropped by 20% compared to senior roles. Why? Because AI can do the junior work. It can write the basic code. It can draft the basic email. It can summarize the meeting.

If your job is “taking information from Pile A and moving it to Pile B,” you are in trouble.

Being able to “use” AI is no longer a competitive advantage. It is table stakes. It is like knowing how to type. You don’t get a medal for knowing how to use a keyboard, and you won’t get a job just because you can write a prompt.

The bar has been raised. You need to be the person who directs the AI, not the person who just chats with it. You need high-level strategic thinking because the low-level execution is now free.

As Jensen Huang, the CEO of Nvidia, said: “It is not AI that will take your job, but the person who uses AI that will take your job.”

If you are not using these tools to become 10x faster, you are already behind the curve.

Part IV: The Frog in Boiling Water (Ignoring the Signals)

The most painful part of this story isn’t the layoff itself. It is the shock. It’s the sheer, unadulterated surprise on Jason’s face when his access card stops working.

Most people navigate their careers with the situational awareness of a toddler wandering through a construction site. They believe that if they just “do a good job,” they are safe. This is adorable. It is also dead wrong.

We are currently living through a corporate “Vibe Shift” of catastrophic proportions, and you—like Jason—are probably ignoring the flashing red lights because they are inconvenient.

The “Quiet Firing” Playbook

Let’s look at the data. According to recent workforce reports, nearly 70% of companies admit to using “Quiet Firing” tactics—subtle changes designed to make you miserable enough to quit so they don’t have to pay you severance.

Jason missed the signals because he thought they were just “policy changes.” They weren’t. They were smoke signals.

Signal #1: The Snack Downgrade Index

It started three months ago. The office kitchen used to be stocked with Kind Bars, coconut water, and artisanal beef jerky. Then, one Monday, Jason walked in to find… pretzels. Just a massive, sad tub of pretzels and a coffee machine that had been downgraded to “generic sludge.”

• The Translation: When the perks vanish, the cash flow has vanished. If they won’t pay for the good granola, they definitely don’t want to pay for you.

Signal #2: The “Return to Office” Mandate (With a Twist)

Suddenly, the CEO who spent two years preaching about “remote-first flexibility” sent a memo titled “Re-igniting Our Culture.” It demanded everyone be in the office 4 days a week.

• The Reality: This wasn’t about culture. This was a headcount reduction strategy. Data shows that 40% of tech workers say they would quit if forced back to the office full-time. The company knows this. They are counting on it. Every person who rage-quits over the commute is one less person they have to pay unemployment to. Jason didn’t quit; he bought a train pass. He fell for the trap.

Signal #3: The “Ghost” Calendar

Remember when Jason’s boss, “Chad,” used to micromanage him? Chad was annoying. But then, Chad stopped. He cancelled their weekly 1:1 three weeks in a row. He stopped asking for status updates.

• The Translation: Jason thought, “Awesome, he trusts me!” No, Jason. He doesn’t trust you. He is avoiding eye contact with a dead man walking. When a manager stops managing you, it’s because you are no longer a resource; you are a line item waiting to be deleted.

Signal #4: The Consultant Invasion

Two months ago, a group of people in sharp suits from “Strategy Corp” started wandering the halls. They didn’t talk to Jason. They just asked for “access to the data room” and “org chart visualization.”

• The Translation: These are the Bobs. They are not here to “optimize workflow.” They are here to calculate how much money the company saves if they fire your entire department and replace you with a ChatGPT subscription and a freelancer in a different time zone.

We prefer the comfort of routine to the discomfort of reality. We tell ourselves lies like, “They can’t fire me, I’m the only one who knows how to run the legacy reporting system.”

Newsflash: They don’t care about the legacy reporting system. They will replace it (and you) with a Python script and a twenty-two-year-old intern named Kyle who runs on Red Bull and desperation. And they will do it in a weekend.

Jason was comfortable. He thought his seniority was a shield. He forgot the golden rule of business survival, perfectly summarized by Bill Gates:

“Success is a lousy teacher. It seduces smart people into thinking they can’t lose.”

Jason thought he couldn’t lose because he hadn’t lost yet. He sat in the pot, feeling the water get warmer, telling himself it was just a nice jacuzzi, right up until the moment he was boiled alive.

Part V: Maybe You Are Climbing the Wrong Tree

Here is a question that hurts even more than the layoff. What if you shouldn’t even be applying for these jobs?

We get so obsessed with getting a job that we forget to ask if we want this job.

According to Gallup’s State of the Global Workplace report, only 23% of employees are actually engaged at work. That means nearly 80% of people are just showing up, doing the bare minimum, and collecting a check.

I see so many people desperate to get back into roles they hated. They were miserable as Product Managers. They hated the politics. They hated the stakeholders. But the moment they lose the job, they are desperate to get it back. Why? Because of the sunk cost fallacy.

Maybe you aren’t a bad employee. Maybe you are just a fish trying to climb a tree.

You might have excellent skills in analysis, but you are forcing yourself into sales because that is where the commission is. You are setting yourself up to be mediocre and miserable.

The market is brutal right now. If you are only 70% committed to a role, you will be crushed by the person who is 100% committed. You cannot fake enthusiasm when you are competing against fanatics.

Steve Jobs said it clearly: “Your work is going to fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work. And the only way to do great work is to love what you do.”

If you are just looking for a paycheck, the person who loves the work will beat you every single time.

Part VI: The “Open To Work” Desperation Banner

Now, let’s talk about the green ring of death on LinkedIn. The #OpenToWork banner.

I know, I know. It is supposed to be a community signal. “Help me!” But let’s be brutally honest about human psychology and negotiation leverage.

When you slap that green frame on your photo, you are essentially walking into a bar, standing on a table, and screaming, “I am currently single and very, very lonely! Please, someone buy me a drink!”

Does that make you attractive? No. It makes you look desperate.

Recruiters are like cats. If you chase them, they run away. If you ignore them, they jump in your lap.

When a recruiter sees “Open to Work,” they subconsciously register a few things:

1. This person has no current leverage.

2. I can lowball their salary offer.

3. Why hasn’t anyone else snapped them up yet? Is there something wrong with the merchandise?

It is a harsh, unfair psychological bias. But it is real. The best candidates—the ones who get headhunted—are the ones who look like they are too busy being successful to care about a LinkedIn banner. They project value, not availability.

As Charlie Munger, the late partner of Warren Buffett, wisely said: “It is remarkable how much long-term advantage people like us have gotten by trying to be consistently not stupid, instead of trying to be very intelligent.”

Broadcasting your desperation to the world is not smart. It destroys your leverage before you even enter the room.

Part VII: Structure is the Antidote to Chaos

So if resumes are a distraction, the job market is rigged, AI is coming for your lunch, and your instincts are wrong… what’s left?

Structure.

I have been a Product Manager for a long time. In product, we don’t just build things and hope they work. We have a roadmap. We have data. We have a plan.

You need to treat your career like a product.

Peter Drucker, the father of management thinking, said: “What gets measured gets managed.”

Most people do not measure their job hunt. They operate on vibes and panic. They wake up and ask, “What should I do today?”

That is the wrong question. You should already know what you are doing today because you have a system.

This is where CareerPlot comes in. It isn’t a magic wand. It is a set of guardrails for the chaos.

We need to move away from the “guru” model where a billionaire tells you to “crush it,” and move toward a structural model where you actually have a plan.

1. The Reality Check (AI Resume Analyzer)

First, you need the truth. CareerPlot uses an AI that acts like a ruthless hiring manager. It doesn’t just check your grammar. It runs a SWOT analysis (Strengths, Weaknesses, Opportunities, Threats). It might tell you that your industry is shrinking by 5% annually, or that your skills gap is the reason you aren’t getting callbacks. It hurts, but it’s the data you need.

2. The Whole Life Audit (AI Coach)

Most career advice fails because it ignores your life. CareerPlot’s AI Coach runs an 18-Dimension Life Assessment. If you have high financial obligations and low risk tolerance, it won’t suggest you join a volatile crypto startup. It aligns your career path with your actual reality.

3. The Roadmap (Career Tracker)

The Career Tracker kills the “spray and pray” method. It gives you a weekly action plan. “Learn this specific AI tool.” “Message these three alumni.” “Fix this portfolio item.” It gamifies the process so you stop doom-scrolling and start executing.

Part VIII: Closing the Tab

Let’s go back to Jason.

It’s now 4:00 PM. The sun is setting on his first day of unemployment. He has twelve versions of his resume saved on his desktop. Jason_Resume_FINAL, Jason_Resume_FINAL_v2, Jason_Resume_REAL_FINAL.

He realizes, finally, that version 13 isn’t going to change anything.

He closes the Word document. He closes the 47 tabs of “Easy Apply” jobs he didn’t actually read. He takes down the “Open to Work” banner because he realizes his value isn’t defined by his availability.

He opens CareerPlot. He stops treating his career like a lottery ticket and starts treating it like a project.

He runs the analysis and realizes his lack of AI-tooling experience is a major threat. He sets up the tracker and assigns himself a task: “Learn Prompt Engineering Basics - Due Friday.” He stops panicking about the “market” and starts building his own market value.

For the first time all day, the panic subsides. The chest tightness loosens. He has a plan. He isn’t just hoping for a job anymore. He is engineering his next move.

Your career isn’t collapsing from a lack of ambition or a shortage of dreams. It’s collapsing because you are drowning in useless advice, starving for a simple plan, and there’s nobody around to call you on your own excuses.

Stop polishing the blood test. Start working on the patient.

Ready to stop guessing? Build your structure at CareerPlot.com

The Story That Started It All

Picture Arjun — 29 years old, four years into a product management role at a mid-size tech company, good at his job, respected by his team. On paper, things look fine. But inside, Arjun is quietly anxious.

He has no idea where he’s going.

Every few months he fires up LinkedIn. He refreshes job boards. He updates his resume, tweaks it for a role he half-wants, sends it into the void, and hears nothing back. Or worse — he hears back, goes through four rounds of interviews, and realises halfway through that this wasn’t actually what he wanted either. He’s not lazy. He’s not unambitious. He’s just never had a map.

He has goals — vague ones. He has skills — unorganised ones. He has ambitions — unvalidated ones. What he doesn’t have is a structured, intelligent, personalised career plan.

Arjun’s story isn’t rare. It’s universal. Millions of professionals around the world — from fresh graduates to 15-year veterans — are navigating their careers the same way: reactively, opportunistically, and almost entirely alone.

We built CareerPlot for Arjun. And for everyone like him.

Why Career Planning Has Been Broken

The way most people manage their careers looks something like this: wait for something to go wrong (a bad manager, a missed promotion, a layoff), panic-apply to whatever looks relevant, land somewhere, repeat.

We treat careers like a series of point-in-time decisions rather than what they actually are — a long-form journey that can be intentionally designed.

The tools that exist today don’t help much. Resume builders optimise the document but not the direction. Job boards show you what’s available, not what’s right for you. Career coaches are expensive and hard to access. And no single platform brings your goals, your skills, your resume, your mentors, and your progress together in one intelligent place.

Until now.

Introducing CareerPlot — Your Career Planning Platform

CareerPlot is live. And it’s built on a single, powerful idea: your career deserves the same level of strategic planning you’d give a product roadmap, a business plan, or a long-term investment.

Where most tools help you get a job, CareerPlot helps you build a career.

We bring together AI and data with human intelligence — the wisdom of mentors, the insight of evaluators, the precision of machine learning — into one cohesive platform that meets you where you are and helps you get to where you want to be.

Here’s what’s inside.

The Four Core Features

🔍 1. AI Resume Analysis

Your resume is usually the first thing standing between you and an opportunity. But most people have no real idea how their resume reads to an ATS system, a recruiter, or a hiring manager — let alone whether it actually reflects their strongest self.

CareerPlot’s AI Resume Analysis engine reads your resume the way an experienced recruiter would, then goes further. It identifies gaps in positioning, flags missing keywords for your target roles, evaluates the clarity and impact of your language, and gives you specific, actionable feedback — not vague suggestions. It also maps your current experience against real-world role requirements so you understand not just what to fix, but why.

The result: a resume that doesn’t just look good — it actually lands.

🗺️ 2. Career Roadmaps

This is the heart of CareerPlot. Not a list of job openings. Not a personality quiz. A real, structured, milestone-driven career roadmap built around your goals, your current position, and the gap between the two.

You tell CareerPlot where you are and where you want to go. The platform — powered by AI trained on real career trajectories — maps the path: the roles in between, the skills to acquire, the timelines to expect, and the actions to take at each stage. Think of it as a GPS for your professional life. You’re not guessing your next turn. You have directions.

And crucially, the roadmap isn’t static. It evolves with you — as you complete milestones, gain new skills, or shift your goals, your roadmap updates. Your career is a living thing. Your plan should be too.

🤝 3. Mentor Connect

Knowledge is most powerful when it comes from someone who’s walked the road before you.

Mentor Connect bridges the gap between ambitious professionals and experienced mentors who’ve been exactly where you want to go. CareerPlot intelligently matches you with mentors based on your career goals, your industry, and the specific challenges you’re facing — not just broad fields or titles.

Whether you need guidance on transitioning into a new sector, navigating a leadership leap, or simply making sense of your next step, Mentor Connect puts real human intelligence in your corner. Because AI can chart the path. But sometimes, you need someone to walk it with you.

📊 4. AI Evaluations & Guidance

Knowing what to do is only half the equation. Understanding how you’re progressing — and getting real-time course corrections — is what separates people who reach their goals from those who just have them.

AI Evaluations & Guidance gives you ongoing, intelligent assessments of where you stand relative to your roadmap milestones. It evaluates your skills against industry benchmarks, tracks your progress over time, and surfaces precise guidance on what to focus on next. It’s like having a career coach in your pocket — one who knows your entire professional history, never forgets a detail, and is available at 11pm when you’re thinking about your future.

This is Just the Beginning

CareerPlot v1.0 is our first release — and we mean that with both humility and excitement. The platform is live, the features are real, and the results are already meaningful. But we’re building this with you, not just for you. Every feature will deepen. Every insight will sharpen. The roadmap for CareerPlot is as ambitious as the roadmaps we help you build.

Start Plotting Your Career Today

If you’ve ever felt like you were drifting in your career rather than driving it — this is for you.

If you’ve refreshed a job board at midnight wondering if there’s more — this is for you.

If you have ambitions that deserve more than guesswork — this is absolutely for you.

Sign up free at

https://careerplot.com

and build your first career roadmap today. It takes minutes. The clarity it brings might last a lifetime.

Back to Arjun — he signed up last week. He’s already mapped a 3-year roadmap to a VP of Product role, identified two skill gaps he can close in six months, and had his first mentor session with someone who made that exact leap four years ago. He doesn’t look anxious anymore.

That’s what a map does. It doesn’t remove the journey. It makes you ready for it.

CareerPlot — Where AI and Data meet Human Intelligence.

https://careerplot.com

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