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@Andrej Karpathy (@karpathy)
Karpathy：LLM 能力认知差距正在扩大为漏斗
Karpathy 重提 4 月推文，认为对 LLM 能力的共同理解差距正在扩大，形成漏斗：约 60 亿人（约 75% 人口）几乎未接触 LLM；约 10-20 亿人（约 20%）偶尔使用免费 ChatGPT 类产品，视其为更好的搜索或写作辅助；约 2000 万人（0.2%）在数学与代码中专业使用前沿模型，可让 agent 完成过去数周数月的复杂项目；约 5000 人（0.00006%）可内部访问前沿系统，看到数千 agent 协作数周完成软件巨型项目、挖掘零日漏洞、推进数学前沿。漏斗由三因素驱动：影响随问题规模与时间跨度放大；系统能力锯齿性，峰值出现在数字、可验证、经济价值高的领域，源于可验证奖励的强化学习；以及免费、付费、内部访问层级。
AI推荐理由 Karpathy 以分层估算描述 LLM 能力认知差距：约 60 亿人几乎未接触 LLM，10-20 亿为免费产品轻度用户，约 2000 万人在数学与代码中专业使用前沿模型，约 5000 人可内部访问前沿系统。他提出漏斗由任务规模与野心、系统能力锯齿性（数字、可验证、经济价值高的领域峰值）、以及访问层级三因素驱动。这是个人观点与粗略估算，非官方数据，但提供了对 AI 落地不均衡的具体框架，对理解 Agent 与前沿模型应用边界有参考价值。
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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.