AI safety organizations struggle to hire despite funding because their bar is exceptionally high. They need candidates who can quickly become research leads or managers, not just possess technical skills. This creates a bottleneck where many interested applicants with moderate experience can't make the cut.

The intense talent war in AI is hyper-concentrated. All major labs are competing for the same cohort of roughly 150-200 globally-known, elite researchers who are seen as capable of making fundamental breakthroughs, creating an extremely competitive and visible talent market.

Companies like DeepMind, Meta, and SSI are using increasingly futuristic job titles like "Post-AGI Research" and "Safe Superintelligence Researcher." This isn't just semantics; it's a branding strategy to attract elite talent by framing their work as being on the absolute cutting edge, creating distinct sub-genres within the AI research community.

Traditional hourly billing for engineers is obsolete when AI creates 10x productivity. 10X compensates engineers based on output (story points), aligning incentives with speed and efficiency. This model allows top engineers to potentially earn over a million dollars in cash compensation annually.

Multi-million dollar salaries for top AI researchers seem absurd, but they may be underpaid. These individuals aren't just employees; they are capital allocators. A single architectural decision can tie up or waste months of capacity on billion-dollar AI clusters, making their judgment incredibly valuable.

Anthropic's resource allocation is guided by one principle: expecting rapid, transformative AI progress. This leads them to concentrate bets on areas with the highest leverage in such a future: software engineering to accelerate their own development, and AI safety, which becomes paramount as models become more powerful and autonomous.

Anthropic's commitment to AI safety, exemplified by its Societal Impacts team, isn't just about ethics. It's a calculated business move to attract high-value enterprise, government, and academic clients who prioritize responsibility and predictability over potentially reckless technology.

After reportedly turning down a $1.5B offer from Meta to stay at his startup Thinking Machines, Andrew Tulloch was allegedly lured back with a $3.5B package. This demonstrates the hyper-inflated and rapidly escalating cost of acquiring top-tier AI talent, where even principled "missionaries" have a mercenary price.

Paying a single AI researcher millions is rational when they're running experiments on compute clusters worth tens of billions. A researcher with the right intuition can prevent wasting billions on failed training runs, making their high salary a rounding error compared to the capital they leverage.