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oswald nitski

T3 · host / generalist

Oswald Nitski is CPO at Mercor, a hypergrowth company providing human evaluation and training data for frontier AI models; he previously worked at other hypergrowth startups and moved from Canada to San Francisco to join the AI ecosystem.

1 call·1 name·100% bull·last heard 2 months ago·20VC
track record

no scored calls yet — needs a stated position or a categorical verdict, with a matured window vs SPY

top calls

highest conviction · one per company
1sthigh conviction
$MERCORMercorposition

Mercor CPO reveals hypergrowth economics: cash flow insane, can't spend money fast enough

Mercor operates a tech-enabled services model providing human evaluation and training data that directly drives model performance gains for frontier labs and enterprises; the business is hypergrowing with 10x headcount/revenue growth, exceptional cash flow, and expanding into enterprise self-serve and robotics data.

20VC2026-07episode →

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  • $MERCOR

recurring themes

  • Enterprise AI Adoption1
  • AI Economics & Business Models1
  • AI Infrastructure1
  • Cybersecurity1
  • Robotics & Physical AI1
1 total
$MERCOR
Mercor
HIGHoswald nitski·20VC·2 months ago·Why Large Enterprise is Scared to Partner with Frontier Labs | Mercor Head of Product· position
Mercor CPO reveals hypergrowth economics: cash flow insane, can't spend money fast enough
Mercor operates a tech-enabled services model providing human evaluation and training data that directly drives model performance gains for frontier labs and enterprises; the business is hypergrowing with 10x headcount/revenue growth, exceptional cash flow, and expanding into enterprise self-serve and robotics data.
"we end every week with like millions more in the bank right so it's it's it's funny how you can have i've been at other companies where i've seen you know interesting financial en…"
37:33
8
Enterprise AI Adoptionheadwind
Enterprises fear sharing core IP with frontier labs, creating data sensitivity barrier
Large enterprises are highly skeptical of sharing proprietary workflow data with frontier model providers; sensitivity is highest for core differentiated work (legal advice, medical decisions) while commoditized workflows (HR, procurement) are less sensitive, creating a structural barrier for closed-model adoption in high-value use cases.
8
AI Economics & Business Modelstailwind
Token spend as % of salaries rising toward 100% at hypergrowth AI companies
At hypergrowth AI companies, token spend on coding agents and model inference already exceeds 100% of engineering salaries and is viewed as rational growth investment; macro trend suggests enterprise AI spend as percentage of payroll will increase well beyond current 3-4% levels as unit economics improve and ROI tolerance remains high during exploration phase.
8
AI Infrastructuretailwind
RL environments and simulation data emerge as new frontier for model training bottlenecks
Reinforcement learning environments — high-fidelity simulations of apps (Salesforce, laptop OS) with complex start states — are the fastest-growing data type, replacing preference ranking as the frontier bottleneck; solving environment setup complexity will unlock enterprise adoption just as SFT and RLHF tooling matured previously.
8
Cybersecuritytailwind
Cybersecurity data demand growing rapidly due to adversarial, uncapped reward dynamics
Unlike sufficiency-based workflows (CRM updates), cybersecurity is inherently adversarial with constantly moving goalposts between offense and defense, creating uncapped demand for evolving data types; AI-generated code vulnerabilities amplify this trend, making cyber a structural growth category for human data providers.
7
Robotics & Physical AItailwind
Robotics data market nascent but poised for significant growth over next 3 years
The data market for robotics (RL environments, simulation, real-world physical data) is currently small relative to GenAI and autonomous vehicles but will grow substantially as labs solve simulation-to-real transfer; Mercor is positioning early to capture this demand which requires high-fidelity environments mimicking deployment settings.