Why AI's Biggest Deals Price Assets Before Revenue

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Why AI's Biggest Deals Price Assets Before Revenue<br>A strange pattern runs through the biggest AI deals: price arrives before ordinary proof. No shipped product. No durable customers. The check still clears at nine or ten figures.

Yumi W. Kimura<br>May 23, 2026

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On a SaaS spreadsheet, that looks irrational. To OpenAI, Google, Microsoft, Amazon, NVIDIA, and frontier investors, the logic is simpler: own scarce inputs before revenue makes them obvious.<br>Three inputs keep showing up: proprietary data, models, and people who turn both into leverage. Buyers value famous people fastest and rights-cleared data slowest. That gap is the trade.<br>Thanks for reading The Agentic Enterprise! Subscribe for free to receive new posts and support my work.

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Billion-Dollar Prices Are Arriving Before Revenue

Safe Superintelligence is the cleanest case: $1 billion raised at a reported $5 billion valuation in September 2024, then another reported $2 billion at $32 billion in April 2025. It was still pre-product, but it had Ilya Sutskever, co-founder of OpenAI.<br>OpenAI’s io Products deal priced product taste before device sales. In May 2025, TechCrunch reported that OpenAI agreed to buy Jony Ive’s company for nearly $6.5 billion before consumer hardware shipped. It bought design judgment, recruiting pull, and the iPhone’s defining designer.<br>AMI Labs priced research authority before product. In March 2026, TechCrunch reported that Yann LeCun’s new company raised a $1.03 billion seed round at a $3.5 billion valuation. LeCun’s credential made diligence legible: Turing Award winner, former Meta chief AI scientist, and central researcher.

Across these ten cases, disclosed capital raised, acquisition consideration, and reported licensing fees exceed $15 billion. Headline valuations push the implied value much higher. Revenue was usually not the anchor.<br>The legal forms differ: funding rounds, acquisitions, licensing deals, and hiring-heavy structures. The common move is simple: buyers and investors paid before old-style revenue proof could do the work.<br>Buyer type changes what gets priced. Strategic buyers pay for people, model rights, product acceleration, and missing capability. Investors pay for recruiting power, compute access, and credible research direction. Data becomes explicit value when it is rare, rights-cleared, strategically missing, and hard for a model builder to reproduce.

Three Assets Keep Reappearing

The transactions resolve to three assets: data, models, and people. Revenue matters later. At deal time, it was often not the valuation anchor.

Every case in the table is a bet on at least one asset. Buyers pay for famous people faster than they inspect the data that makes those people productive.

Famous Chefs Still Get Paid Before Rice

The fame premium has a rational core. Elite founders attract talent, capital, compute access, and buyer attention. But exclusive domain data can be harder to replace.<br>There is a Chinese saying: 巧妇难为无米之炊. Even the cleverest cook cannot make a meal without rice.<br>The metaphor works because talent transforms data but cannot substitute for it. The researcher is the chef. The data is the rice. Investors still pay for the chef before they pay for the rice.<br>The pricing gap is visible in public data deals. Reddit’s AI licensing across Google and OpenAI is roughly $130 million a year. News Corp’s journalism archive was reportedly priced at $250 million over five years. Those are large checks for media companies and small next to a $32 billion pre-product valuation for SSI.<br>Infrastructure deals point to the same bottleneck. Scale AI was valued at $29 billion with roughly $2 billion of ARR, treating labeled data operations as core infrastructure. Salesforce paid about $8 billion for Informatica. IBM paid about $11 billion for Confluent. Buyers are paying to organize, govern, move, and stream data.<br>Enterprise deployment evidence is consistent. The MIT NANDA GenAI Divide reportfound that about 95% of enterprise AI pilots failed to produce measurable impact, with failures concentrated around missing proprietary context, workflow integration, and tools that did not learn from enterprise data. Generic models do not know a company’s exceptions until they see them.<br>The talent bottleneck is real, but the broad talent pool is expanding. Roughly 5,900 machine learning PhDs graduate every year. For many open models, fine-tuning can now run on a single consumer GPU. There are more competent chefs every year. Reddit’s historical conversation archive exists once.<br>Capital still flows through narrow channels. Crunchbase found that startups with Stanford, Harvard, and MIT alumni as founders drew more than 30% of the funding rounds it tracked among U.S. university-affiliated founders. In the AI Power Map, 77 of 420 influential people in core AI are Stanford-affiliated. The talent pipeline is wide. The funding...