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howie.serious· @howie_serious · X·· 2 小时前AI 评分60
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作者引用 Karpathy 的分析,认为全球约 80 亿人因对大模型能力的接触程度不同被分成四层。约 60 亿人几乎没接触过大模型,10 到 20 亿人偶尔用免费版产品,约 2000 万人重度使用前沿模型和 Agent,约 5000 人能接触 AI 实验室内部系统,且这一认知差距仍在扩大。作者另补充中文用户还有能否用 Claude Code 的第 3.5 层。

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80 亿人被某种程度上分成“三六九等”:AI 版认知鸿沟,比互联网带来的认知鸿沟要严重得多

  • level 1: 约 60 亿人几乎没接触过大模型;
  • level 2: 10 亿到 20 亿人偶尔用免费版,把它当更好的搜索或写作工具;
  • level 3: 约 2000 万人日常使用、重度使用前沿模型,用agent 深入改造自己的工作学习和生活;
  • levle 4: 还有约 5000 人能接触ai lab 的内部模型和内部系统。

当然了,对中文用户还有一个 level 3.5:能不能用 Claude code 😭

引用Andrej Karpathy@karpathy
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.
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