The constant shuffling of key figures between OpenAI, Anthropic, and Google highlights that the most valuable asset in the AI race is a small group of elite researchers. These individuals can easily switch allegiances for better pay or projects, creating immense instability for even the most well-funded companies.

Anthropic's team of idealistic researchers represented a high-variance bet for investors. The same qualities that could have caused failure—a non-traditional, research-first approach—are precisely what enabled breakout innovations like Claude Code, which a conventional product team would never have conceived.

Legora intentionally hires people with high learning velocity ("high Y slopes") over deep experience ("high Y intercepts"). In a rapidly evolving AI landscape, this ensures the team can scale their capabilities as exponentially as the company grows.

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.

The firm's strategy isn't to back every foundation model. It centers on identifying singular talents whose past work demonstrates a unique ability to achieve foundational breakthroughs. The belief is that in the current AI landscape, a few specific individuals can move the entire field forward.

Mark Zuckerberg's AI strategy is not about hiring the most researchers, but about maximizing "talent density." He's building a small, elite team and giving them access to significantly more computational resources per person than any competitor. The goal is to empower a tight-knit group to solve complex problems more effectively.

In a group of 100 experts training an AI, the top 10% will often drive the majority of the model's improvement. This creates a power law dynamic where the ability to source and identify this elite talent becomes a key competitive moat for AI labs and data providers.

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.

While speed is a key business strategy, it's insufficient in a market where the technological foundation shifts weekly. The priority for AI startups should be building high talent density. This enables the company to change direction correctly and quickly, avoiding the trap of moving fast towards an obsolete goal.