Slovak-Canadian AI researcher and co-founder of OpenAI, Andrej Karpathy opined that there is a huge gap between how a large section of the human populace views artificial intelligence compared to that of the top tier 5000 people with access to frontier AI systems.
In a post on X, Karpathy said that the gap in shared understanding of LLMs and its use has widened in the past few months, taking the shape of a sharp funnel.
As per Karpathy, rough maths puts 75% of the human population i.e. 6 billion people in the category of no contact with LLMs, followed by 1-2 billion people casual/infrequent users of free tier AI chatbots who use them as a better alternative to a simplistic google search or to draft emails.
Karpathy now puts a sharper layer in the funnel of 20 million people (0.2%) who use frontier grade LLMs in mathematics and code related complex projects.
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
The sharpest layer of the funnel belongs to the 5000 people with access to frontier AI grade systems internally. Karpathy puts it as the “experiencing the vertigo of the curve going vertical.”
“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,” said Karpathy.
Karpathy, who served as co-founder of OpenAI and director of AI at Tesla has recently joined Anthropic to work on pre-training under team lead Nicholas Joseph.
The AI researcher said that the widening gap in shared LLM understanding is largely driven by the impact scales with ambitions, problem size and horizon. For example, a single user might have questions that deserve barely a paragraph while AI research institutions deal with complex topics, red-teaming and other aspects.
“Another reason is that 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,” added Karpathy.
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