Anthropic vs Scale AI: employee equity compared
Secondary market prices, valuation trajectory, equity structure, and liquidity outlook for employees choosing between Anthropic and Scale AI.
Anthropic
AI safety lab and maker of Claude.
IPO highly anticipated, likely 2026–2027 given massive scale. One of the most closely watched pre-IPO names in tech.
High-growth AI play; RSU (no exercise cost); fast-moving valuations reward timing
Scale AI
Data-labeling and AI infrastructure platform powering training pipelines for OpenAI, Meta, Microsoft, and the US DoD.
IPO plausible 2027–2029 if growth trajectory holds. Liquidity may come via tender offer or strategic acquisition before listing.
High-growth AI play; ISO/NSO options; fast-moving valuations reward timing
Key differences for employees
Equity structure
Anthropic grants RSU — no exercise cost. Your equity vests and converts to cash or shares automatically at a liquidity event. Scale AI grants ISO/NSO with strike prices from $35–$50.
Secondary market premium
The secondary market is pricing Anthropic at a +-10% premium over its last primary round ($380B → $342B). Scale AI trades at +4% over its last round ($13.8B → $14.4B). A higher secondary premium signals stronger investor demand and potentially better near-term liquidity for employees looking to sell.
Revenue and growth
Anthropic runs at $30B ARR, growing +400% YoY (hypergrowth). Scale AI runs at $1B ARR, growing +70% YoY (fast). Revenue growth rate matters for equity because it drives the peer-multiple valuation — the method most correlated with exit multiples.
Liquidity timeline
Anthropic: IPO highly anticipated, likely 2026–2027 given massive scale. One of the most closely watched pre-IPO names in tech.
Scale AI: IPO plausible 2027–2029 if growth trajectory holds. Liquidity may come via tender offer or strategic acquisition before listing.
Calculate your specific grant
Enter your actual shares, equity type, and strike price. PrivatePulse calculates your personal equity value at both companies using 4 independent methods.