Andrej Karpathy 在引用推文中提出,人们对 LLM 能力的认知正分裂成四个层级:约 60 亿人(75%)几乎未接触 LLM,约 10–20 亿人(20%)偶尔使用免费版 ChatGPT,约 2000 万人(0.2%)在数学与代码领域专业使用前沿模型,仅约 5000 人(0.00006%)能在内部接触最前沿系统。
Andrej Karpathy 认为:人们对 AI 能力的认知,正在分裂成四个层级;越往里的人,看到的越不可思议
第一层:~60 亿人(约 75%),几乎没有接触过 LLM
第二层:~10–20 亿人(约 20%),偶尔用免费版 ChatGPT,视之为“更好的谷歌搜索”或写作助手「许多非技术背景的高管也停在这一层」
第三层:~2000 万人(0.2%),在数学与代码领域专业使用前沿模型,亲眼见到原本需数周甚至数月的大项目,现在一次提示就能由 agent 完成
第四层:~5000 人(约 0.00006%),能在内部接触最前沿系统:数千个 agent 协作数周,执行软件巨型项目、零日漏洞挖掘、网络攻防、发现新科学、推进数学前沿「人类的审阅速度已经跟不上产出」
漏斗为何形成?三个结构性原因
1. 影响力随问题规模扩展:你让 AI 做的事情越大、越有野心,感受到的能力差距就越大。用 AI 查菜谱和用 AI 建造复杂系统,看到的是两个完全不同的东西。
2. 能力的“锯齿状”:LLM 的能力分布极不均匀:在数字化、可验证、有明确经济价值的领域(代码、数学)最强,在模糊、主观、缺乏反馈信号的领域则弱得多。处于不同工作场景的人,摸到的是这把锯子的不同锯齿。
3. 访问权限分层:免费版 / 付费版 / 内部前沿系统,物理上就是不同的产品。绝大多数人终其接触到的,只是能力光谱的最底端。
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.在 X 查看被引用的帖子
来源:meng shao · x.com