❯ Karpathy says the gap between how the public and heavy users see AI is widening, with most people having only met a “derpy chatbot”

A funnel that keeps narrowingAI researcher Andrej Karpathy posted on X on October 11 that the gap in shared understanding of what large language models can do is widening, in the shape of a sharp funnel. By his rough estimate, about 6 billion people have barely come into contact with LLMs, and 1 to 2 billion use free-tier chat products occasionally, treating them as a better search engine or writing aid. The post has passed 1.55 million views.

20 million people and 5,000 peopleFurther in are about 20 million professionals using frontier models for math and code, who see first-hand that large projects that once took weeks or months can now be completed by agents from a prompt. At the innermost layer are about 5,000 people with internal access to the most advanced systems. He describes swarms of thousands of agents collaborating over weeks on large software projects, cyber attack and defense, and frontier mathematics, with human review and comprehension starting to fall behind.

Three reasonsKarpathy attributes the divide to three factors. First, impact scales with the size of the problem; a question answered in a paragraph barely uses the system’s capability. Second, capability is uneven: models are strongest in domains that are digital, verifiable and economically valuable, because their ability comes from reinforcement learning on verifiable rewards. Third, access: free, paid and internal tiers see different things.

Three experiences at the same momentHe summed up that the general public sees a derpy chatbot, most professionals feel only a modest uplift, and a small sliver are experiencing the vertigo of a curve going vertical, all at once. That explains why discussions about AI so often talk past each other: the two sides may be using products several tiers apart. Managers who judge only by their own experience with a free tier may underestimate what is changing.

▮ SIGNALAI capability spreads unevenly; to judge where the technology stands, first ask which tier of product the speaker uses and how big a problem they point it at.