Brendan Foody

Mercor CEO confronts the hack rumors head-on: there was an incident — the attacker used “a swarm of coding agents” to gain access — but the claim that revenue flatlined is false. Mercor added $300 million in net new ARR in the last 60 days, engaged Mandiant immediately, and added security as a seventh company value. The Twitter narrative, he says, included “one person that’s very prominent who’s invested in multiple competitors” tweeting an untrue claim that Chinese actors accessed the data.

The revenue is real revenue, not GMV: customers buy tasks (e.g. $1,000 per task delivering model improvement) at a 30-40% gross margin, with Mercor running the full stack — expert sourcing, platform, AI project management, quality checks. The business is “very profitable,” has over $500M in cash, more cash than it has ever raised, and has “almost 4x’d” since the $10B round at ~$400M run rate in fall 2025.

The core investment call: infrastructure upstream of OpenAI/Anthropic beats application layer downstream over the next 12 months, because “the model is the product” and app-layer defensibility is increasingly difficult. 2025 was the year a model makes a PR; “2026 is the year of how do you get the model to clone Slack end-to-end” — those capabilities land in models within 12 months. The litmus test for surviving SaaS: network effects (Salesforce integrations, Slack Connect, Carta) — companies without them face severe difficulty.

Token spend will exceed headcount spend at the average enterprise within 5 years — Mercor is already there: “right now, we’re spending more on tokens for our internal agents than we are on employee headcount.” Benioff’s $300M Anthropic spend is only ~3.8% of Salesforce developer salaries, showing how early this shift is. Every Fortune 500 will need a per-workflow eval “system of record” — which commoditizes the API layer (zero switching costs, new frontier model every 2 months) while stickiness lives in workflows.

The guest would put at least one of OpenAI/Anthropic above $10 trillion in 5 years — a change of mind: he used to doubt labs could hold pricing power, but “the sheer revenue ramp of these businesses” convinced him they’ll be the most valuable companies in the world. Yet he simultaneously expects the majority of inference in 5 years to run on open-source, fine-tuned or distilled models, not frontier ones; per-workflow evals are often “a 10x lever on price performance.” Nvidia may lose its monopoly to a multi-chip future but even at 30-40% share of “the largest market in the world by far” remains the most valuable company.

Training agents is “the fastest job category ever created in history”: Mercor pays out $3 million a day to its 5M+ talent network, estimated to roughly triple, perhaps quadruple, in 12 months. All knowledge work converges on training agents because it’s “structurally more efficient to do something once” — and on Mercor’s Apex benchmark the frontier model scores ~40% versus o1 at 1% just 12 months ago.

Pricing elasticity: Harry cited Nebius raising prices 30% with zero demand impact, while Mercor “has the demand to double overnight” but lacks capacity — yet the guest says pricing must balance near-term optimization against competition because “high margins invite competition.”

The talent market is dislocated: one candidate held a $20M/year liquid offer from Meta’s superintelligence group, top AI researchers cost “tens of millions of stock per year,” and demand outstrips supply 10:1. Europe, he says, has lost the model race to talent network effects and should accept it — labs will simply “hire 10,000 people in France to teach the models French law,” gutting the sovereignty argument.

🔗 Original source & video: Mercor CEO: Why Application Layer Companies Have No Moat & The Cost of Hiring AI Researchers

Mercor says it grew from $1 million to a $500 million revenue run rate in 17 months—one month faster than Cursor—with growth still accelerating at the endpoint. It had already reached nine figures before Scale AI was acquired and has quadrupled since; capacity is now the constraint because Mercor turns down projects daily and “could double overnight if we can meet capacity.”

Foody argues the moat is identifying the 10–20% of experts who drive most model improvement, not merely supplying more labor. Mercor’s marketplace pays an average $95 an hour versus roughly $30 at Scale and Surge, and uses referral networks reaching Goldman, McKinsey, FAANG, medical and legal talent. Labs may initially spread work across vendors, but Foody says performance eventually forces concentration around partners finding those “10x contributors.”

Synthetic data does not eliminate human-data demand while people can still perform tasks that models cannot. Foody expects humans to remain necessary in 10 years and calls superintelligence within three years “totally wrong”: models can win Olympiad gold medals yet still fail to draft his email, schedule a meeting or complete a multi-tool workflow.

RL environments are a major opportunity because they convert real human workflows into learnable, verifiable tasks. Foody estimates Mercor has 50–60% of this emerging market and says lab executives believe it could “subsume the entire economy”—humans define how work should be done, then models learn to perform the repetitive execution.

Foody considers academic benchmarks poor proxies for the capabilities enterprises actually buy. The fix is closing the “real-to-sim gap” with evals modeled on financial analysis, consulting research, software development and other real workflows: “If the model is the product, then the eval is the PRD.”

For AI investors, retention and margins matter more than spectacular first-contract revenue, while switching costs determine whether subsidies create durable value. A company whose pilots fail 95% of the time is weak regardless of growth, while temporarily poor margins can work if distillation makes inference an order of magnitude more efficient within 12 months and sticky customers produce high LTV. Subsidizing low-switching-cost products is far more dangerous because users can leave as soon as the subsidy ends.

Mercor’s capital strategy remains deliberately conservative despite its growth and likely near-term financing. Foody says the profitable company does not need cash and another few hundred million would not materially alter investment, but a low-dilution round could signal category leadership; he also sees the benefits of a “fortress balance sheet.” His unresolved question is whether capital efficiency is prudence—or whether Mercor should spend $100 million subsidizing supply and demand to press its advantage.

🔗 Original source & video: Mercor CEO & Co-Founder, Brendan Foody: How They Grew from $1M to $500M in 17 Months

Mercor has raised $100 million and surpassed a $100 million revenue run rate while recruiting thousands of people for top AI labs. Founded in 2023, it began as a general talent-matching business, but human data is transitioning from crowdsourcing low- and medium-skilled workers toward vetting highly capable people who can work directly with researchers at the capability frontier.

Agent evaluations are the bottleneck between impressive benchmarks and economically useful automation. Acing SWE-bench is far removed from replacing a software engineer who coordinates with product teams, chooses tools, and exercises taste. Foody expects a years-long, industry-specific build-out because “the evals are upstream” of capabilities that labs and application companies want.

Mercor’s two compounding loops are talent supply and customer performance feedback. Its free career tools address labor marketplaces’ typical 50-to-1 supply-demand imbalance, while outcome data improves predictions about who will excel. Foody believes the less-obvious data flywheel may ultimately matter more than the marketplace network effect.

Foody expects knowledge-work displacement to arrive quickly, painfully, and politically. Customer support and recruiting are already areas where displacement is being reported, though much has not happened yet; physical work and roles valued for human interaction should automate more slowly. His survival heuristic is versatility, because verifiable skills such as math and soon code “will get solved very quickly,” while taste and founder judgment have sparse feedback.

Reinforcement fine-tuning could unlock enterprise agents with only hundreds to thousands of examples. Unlike supervised fine-tuning, RFT specifies the desired outcome and rewards the model for discovering how to produce it. Foody argues that base models already have the necessary reasoning capabilities; what they need is company-specific knowledge of tool use and “what good looks like in that role.”

Creating evaluations could become the world’s most common knowledge-work job—even though workers are helping automate themselves. The economic logic shifts labor from repeatedly performing a task, a variable cost, toward defining an evaluation once, a fixed cost. That fixed-cost opportunity lasts as long as there is a frontier for human evaluation; if models become superhuman, humans may become unnecessary in many other parts of the economy as well.

🔗 Original source & video: From Job Displacement to AI Trainers, Brendan Foody on Work in the AI Age