Contrary to the post-COVID trend of tech decentralization, the intense talent and capital requirements of AI have caused a rapid re-centralization. Silicon Valley has 'snapped back' into a hyper-concentrated hub, with nearly all significant Western AI companies originating within a small geographic radius.

Early AI training involved simple preference tasks. Now, training frontier models requires PhDs and top professionals to perform complex, hours-long tasks like building entire websites or explaining nuanced cancer topics. The demand is for deep, specialized expertise, not just generalist labor.

The 30-40% pay premium for AI PMs isn't just because "AI is hot." It's rooted in the scarcity of their specialized skillset, similar to how analytics PMs with statistics backgrounds are paid more. Companies are paying for demonstrated experience with AI tooling and technical fluency, which is rare.

Fei-Fei Li expresses concern that the influx of commercial capital into AI isn't just creating pressure, but an "imbalanced resourcing" of academia. This starves universities of the compute and talent needed to pursue open, foundational science, potentially stifling the next wave of innovation that commercial labs build upon.

With industry dominating large-scale model training, academic labs can no longer compete on compute. Their new strategic advantage lies in pursuing unconventional, high-risk ideas, new algorithms, and theoretical underpinnings that large commercial labs might overlook.

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.

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.

The mantra 'ideas are cheap' fails in the current AI paradigm. With 'scaling' as the dominant execution strategy, the industry has more companies than novel ideas. This makes truly new concepts, not just execution, the scarcest resource and the primary bottleneck for breakthrough progress.

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.