安德烈·卡帕西: “约 60 亿人（约占人口 75%）几乎没接触过大语言模型，而能在内部接触前沿级系统的只有约 5000 人”

由 AI 翻译，经编辑部审校

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.”

— 安德烈·卡帕西, X, 11.10.2026 ↗

译文

“把四月的这条推文翻出来，是因为人们对大语言模型能力的共同认知差距正在*扩大*。现在与其说是“两群人各说各话”，不如说是一个陡峭的漏斗。 - 粗略估算，大约 60 亿人（约占人口的 75%）几乎没接触过大语言模型。 - 大约 10 亿到 20 亿人（约 20%）是免费版 ChatGPT 类产品的偶尔、低频用户。这群人看到的是有点傻的聊天机器人，把它们当作更好用的谷歌搜索、写作助手之类。也许会有个智能体试着帮你订机票。我有些不做技术的朋友说（我觉得说得有道理）这东西对他们没多大用。甚至很多数学和编程以外的专业人士也处在这一层。比如高管和许多其他岗位的人大量时间都在和人打交道，所以虽然他们在理智上明白正在发生什么，但那仍是二手的、有点抽象的。 - 再往下是在数学和编程中专业使用前沿级大语言模型的人。大约 2000 万人（0.2%）亲眼看到，过去要花他们几周甚至几个月的大型复杂项目，现在智能体凭一个提示就能完成。开发应用、复制应用、翻译应用、从二进制文件反编译应用……这一切发生得非常快、非常近——不到一年前，我还在手写代码，在编辑器里一个字符一个字符地敲出背下来的命令，偶尔按 Tab 自动补全一小段代码。 - 最后是约 5000 人（约 0.00006%），他们能在内部接触到前沿级系统。外界看到的只是预览。那看起来像是由成千上万个智能体组成的集群，连续数周协作推进软件巨型项目：批量挖掘零日漏洞、以机器速度进行网络攻防、发现新的科学、推进数学前沿。这些事原本要行业顶尖专业人士花上数年。与此同时，人类的审查和理解开始跟不上。比如，人们至今还在对几个月前的 OpenAI-HF 事件做“考古”。数学家们可能还要花很长时间研读那 722 篇前沿数学手稿。 这个漏斗由几个因素共同造成： 1. 影响随雄心、问题规模和时间跨度而增长。一个用一段话就能回答的问题几乎不会给系统带来压力。你需要储备一批你真正在乎的大而难的问题。这一点决定了漏斗的“消费者 / 专业人士”维度。 2. 系统的参差不齐（我另外写过很多）。能力在数字化、可验证且有经济价值的领域达到峰值。这是因为大语言模型的能力来自在按营收潜力精选的环境组合上、基于可验证奖励的强化学习。这一点主要决定了漏斗的“领域”维度（例如数学和编程）。 3. 访问权限。免费版、付费版、内部版。 这就是当下的怪异之处。普通大众大多没有用过这些系统。即便用过，看到的也像是个有点傻的聊天机器人。大多数专业人士只看到适度的提升。而一小部分专业人士正经历着曲线陡然直立带来的眩晕。而这一切都在同时发生。”