Quote · All-In with Chamath, Jason, Sacks & Friedberg
Nikesh Arora: Mythos is Real, Analytical SaaS is Dead, and Google can be a $10T company
Where this was said
If models become a utility, where will the money be made?
At 20:00 · chapter starts 14:06
The conversation turns to model economics and where value will ultimately accrue in the AI stack. Nikesh offers a vision of AI models becoming pure utilities — you buy 120 IQ for routine tasks, 250 IQ for complex ones, paying fractions of cents versus dollars. The implication: model providers won't own the profit pools. That's why OpenAI is racing to build Codex and Anthropic is building Claude Code — they're attacking the application layer, which is where enterprise software spending already lives. [1] — Nikesh Arora "AI models will commoditize into a utility layer where you buy intelligence on demand at different price-performance tiers. The profit pools…" 14:06 But here's Nikesh's key observation: the AI-native application companies that will replace legacy SaaS are not yet fully formed. Fifty thousand enterprises need the same HR system, the same sales platform — it's wildly inefficient for each to build their own on top of raw model APIs. The opportunity waiting to be captured is building that next generation of enterprise application companies, complete with harnesses, memory, and data pipelines.
AI models will commoditize into a utility layer where you buy intelligence on demand at different price-performance tiers. The profit pools are in the application layer — which is why OpenAI is pushing Codex and Anthropic is pushing Claude Code. They know that's where the money will be.
A major AI model CEO told Nikesh Arora that the full weights of their newest frontier model fit on a single USB stick and can be distilled in under 48 hours. That fact alone makes a 6-month export control regime essentially meaningless — the IP walks out the door.
A CEO of a leading AI model company told Nikesh Arora that the entire weights of their newest frontier model fit on a single USB stick, distillable in under 48 hours.
Mythos had a 30% false positive rate — meaning nearly 1 in 3 vulnerabilities it found didn't exist. For offense that's fine; for defense it's catastrophic. The real challenge of deploying AI in enterprise isn't getting the newest model, it's driving false positives from 20% down to 0.01%.
Claude's Mythos model had a 30% false positive rate when used for vulnerability detection — great for offense, but problematic for defense.
