The constant movement of researchers between top AI labs prevents any single company from maintaining a decisive, long-term advantage. Key insights are carried by people, ensuring new ideas spread quickly throughout the ecosystem, even without open-sourcing code.
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Arthur Mensch argues that the core knowledge for training advanced AI models is limited and circulates quickly among top labs. This diffusion of knowledge prevents any single company from creating a sustainable IP-based lead, which is accelerating performance convergence and commoditization across the industry.
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 creation of talent agency CAA in 1975 by agents who defected from a larger firm mirrors the current AI landscape, where top researchers leave established labs like OpenAI to found competitors like Anthropic. This suggests that talent-driven industries consistently see cycles of unbundling led by key players.
The drama at Thinking Machines, where co-founders were fired and immediately rejoined OpenAI, shows the extreme volatility of AI startups. Top talent holds immense leverage, and personal disputes can quickly unravel a company as key players have guaranteed soft landings back at established labs, making retention incredibly difficult.
Top AI labs face a difficult talent problem: if they restrict employee equity liquidity, top talent leaves for higher salaries. If they provide too much liquidity, newly-wealthy researchers leave to found their own competing startups, creating a constant churn that seeds the ecosystem with new rivals.
The current trend toward closed, proprietary AI systems is a misguided and ultimately ineffective strategy. Ideas and talent circulate regardless of corporate walls. True, defensible innovation is fostered by openness and the rapid exchange of research, not by secrecy.
Marc Andreessen observes that once a company demonstrates a new AI capability is possible, competitors can catch up rapidly. This suggests that first-mover advantage in AI might be less durable than in previous tech waves, as seen with companies like XAI matching state-of-the-art models in under a year.
The frenzied competition for the few thousand elite AI scientists has created a culture of constant job-hopping for higher pay, akin to a sports transfer season. This instability is slowing down major scientific progress, as significant breakthroughs require dedicated teams working together for extended periods, a rarity in the current environment.
Contrary to the belief that distribution is the new moat, the crucial differentiator in AI is talent. Building a truly exceptional AI product is incredibly nuanced and complex, requiring a rare skill set. The scarcity of people who can build off models in an intelligent, tasteful way is the real technological moat, not just access to data or customers.
The idea that one company will achieve AGI and dominate is challenged by current trends. The proliferation of powerful, specialized open-source models from global players suggests a future where AI technology is diverse and dispersed, not hoarded by a single entity.
