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According to Karpathy, tasks that could previously have required some of the industry's most experienced professionals years to complete can now be completed in weeks with the help of advanced AI. The development, he suggested, illustrates how significantly AI capabilities have progressed in highly technical fields.
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
— Andrej Karpathy (@karpathy) October 11, 2026
Widening AI access gap
In the post, Karpathy grouped people based on how they interact with AI to explain why people have very different opinions about its capabilities. He estimated that around 6 billion people, or roughly 75% of the global population, have barely interacted with large language models (LLMs). He further added that over 1 billion to 2 billion people use free AI tools for tasks such as basic web research, writing, or other chatbot-related tasks.
AI adoption was noticed more in the programming and mathematics fields, as around 20 million people use frontier-grade LLMs professionally in such areas. However, he acknowledged that a large group of people does not have internal access to most advanced systems.
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AI is advancing faster than humans can review it
Amid the advancement, Karpathy raised concerns over the difficulty of keeping up with the output of increasingly capable AI systems. He stated that when thousands of agents work together on complex projects, the volume of output can make human review and comprehension more challenging.
He also stated an example, saying, “People are still involved in the "archeology" of the OpenAI-HF (Hugging Face) incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.”
Therefore, the broader challenge is not whether AI can perform tasks faster. It is whether humans can timely understand, verify, and analyse what these systems produce as their capabilities expand.
“So this is the weirdness of the moment,” Karpathy said. “The general public has mostly not interacted with these systems. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical.”