OpenAI financial data: its stock-based pay averaged ~$1.5M per employee in 2025, ~7x Google pre-IPO and ~34x the average pay of other pre-IPO peers, per Equilar
The company's stock-based compensation in 2025 reached an average of $1.5 million per employee
Context & Ripple Effects
OpenAI had already told investors that stock compensation rose to $4.4B, exceeding revenue for the period, even as it projected a much lower share by 2030. The per-employee figure gives that earlier surge in equity compensation a concrete talent-cost dimension.
The company’s ability to make equity valuable to employees has also been supported by a $6.6B staff secondary sale at a $500B valuation. That liquidity backdrop makes stock awards a more immediate retention tool than equity at a typical private startup.
First-order effects
- OpenAI’s workforce receives an unusually large equity-based retention incentive relative to the cited pre-IPO benchmarks, while the company carries a correspondingly high compensation and potential dilution burden.
- Investors and employees gain a clearer benchmark for evaluating whether OpenAI’s equity outlay is consistent with its stated goal of reducing stock compensation as a share of revenue.
Second-order effects
- Rival AI companies competing for the same technical talent may face pressure to improve equity packages or create more credible employee-liquidity paths, especially where cash compensation cannot match OpenAI’s offer.
- The contrast between large awards and OpenAI’s growing revenue makes compensation efficiency a more central question for private-market investors, alongside model and infrastructure spending.
Third-order effects
- If comparable packages persist, frontier-AI talent competition could become increasingly financed through private-company valuation and secondary-market liquidity rather than salary alone.
- The pattern points to a tougher eventual trade-off: firms must convert talent-led growth into revenue quickly enough to sustain equity incentives without making dilution and stock-based costs a lasting constraint.
The trend: Frontier AI companies are using high-valuation equity and employee liquidity to finance competition for scarce technical talent, putting AI unit economics under closer scrutiny.