Ethan Mollick argues advanced model use at labs remains concentrated in a few fields

What happened

Ethan Mollick argues that many of the 5,000 people at labs with access to advanced models still haven’t used them to their full extent, with use concentrated in cybersecurity, math, and AI R&D. There are many other fields to advance and problems to solve, he adds, responding to @karpathy’s argument that the gap in shared understanding of LLM capabilities is widening.

The story so far

Andrej Karpathy argued that people’s understanding of LLM capabilities is diverging into a “sharp funnel,” and Ethan Mollick responded that even many people with internal access at AI labs have not used advanced models to their full extent. Their exchange describes differences in both access to powerful systems and the range of problems people use them to tackle. Karpathy estimated that roughly 6 billion people have barely encountered LLMs, 1–2 billion casually use free-tier ChatGPT-like products, about 20 million see frontier-model capabilities in math and code, and roughly 5,000 have internal access to frontier systems. He attributed the divide to the ambition of users’ problems, uneven capabilities across tasks, and access tiers. Mollick challenged the idea that access alone means broad use: he said many of those roughly 5,000 people at labs still have not used advanced models to their full extent, with use concentrated in cybersecurity, math, and AI research and development. He argued that many other fields and problems remain to be addressed.

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