Karpathy：对 LLM 能力的认知差已成一道陡峭漏斗，从 60 亿人收窄到 5000 人

中文译文

• 粗略估算，大约 60 亿人（约占全球人口的 75%）几乎没有接触过 LLM。
• 大约 10 亿到 20 亿人（约 20%）是免费版类 ChatGPT 产品的轻度、低频用户。

• 再往下，是数学和编程领域对前沿级 LLM 的专业使用。大约 2000 万人（0.2%）亲眼看到：过去要花几周、几个月的大型复杂项目，现在给 agent 一句提示就能做完。

• 最后是大约 5000 人（约 0.00006%），他们能接触到内部的前沿级系统。外部世界看到的是预览。

1. 影响的大小随野心、问题规模和周期一起放大。一个用一段话就能答完的问题，几乎给不了系统什么压力。你手里得有一池子你真正在乎的大而难的问题。这一条决定的是漏斗上「消费者／专业人士」这一维。
2. 系统的能力是锯齿状的（这一点我另外写过不少），它在数字化、可验证、有经济价值的领域出现尖峰。这是因为 LLM 的能力来自在可验证奖励上的强化学习，而训练环境的配比由收入潜力推动。这一条主要决定漏斗上「领域」（比如数学和编程）这一维。
3. 访问权限。免费层、付费层、内部。

英文原文

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.