把这条4月的推文翻出来再说一遍，因为大家对LLM能力的认知差距正在*不断扩大*。现在已经不太像是“两拨人在各说各话”，而更像一个急剧收窄的漏斗。 - 粗略估算，大约有60亿人（约占总人口的75%）几乎完全没接触过LLM。 - 大约10到20亿人（约20%）是免费版ChatGPT类产品的轻度、偶尔用户。这群人看到的是有点蠢的聊天机器人，把它当成一个更好用的谷歌搜索、写作辅助之类的工具。也许偶尔有个agent试着帮你订张机票。我有一些非技术圈的朋友，他们说这东西对自己没什么用（我觉得这挺合理的）。甚至很多数学和编程领域之外的专业人士也在这个层级。比如高管和许多其他职能的人，大量时间都在跟人打交道，所以虽然他们在理智上明白发生了什么，但终究是二手信息，有点抽象。 - 接下来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，过去要花几周甚至几个月的大型复杂项目，现在agent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……这一切都发生得非常快、非常近——不到一年前，我还在手动写代码，一个字符一个字符地把记在脑子里的计算机代码命令敲进代码编辑器，偶尔按一下Tab自动补全一小段代码。 - 最后是大约5000人（约0.00006%），他们能在内部接触到前沿级系统。外部世界已经看到了预览。那看起来像是数千个agent组成的集群，在数周内协作完成软件巨型项目：铸造零日漏洞、以机器速度发起和防御网络攻击、发现新科学、推进数学前沿。这些事放在以前，需要业内顶尖专家花上数年。与此同时，人类的审查和理解能力开始跟不上。比如，人们至今还在对几个月前OpenAI-HF那件事做“考古”。数学家们可能还要花很长时间仔细研读那722篇前沿数学手稿。 这个漏斗是由几个因素共同驱动的： 1. 影响力随野心、问题规模和视野而放大。一个一段话就能回答的问题，几乎不会给系统带来什么压力。你需要储备一批你真正在意的、又大又难的问题。这个因素驱动了漏斗在消费者/专业人士这个维度上的分化。 2. 系统的参差不齐（这一点我另外写过很多）。能力在数字化、可验证且具有经济价值的领域达到峰值。这是因为LLM的能力来自强化学习，基于可验证奖励，在由收入潜力驱动的精选环境组合上训练。这个因素主要驱动了漏斗在领域（比如数学和编程）这个维度上的分化。 3. 访问权限。免费层、付费层、内部层。 所以这就是当下的诡异之处。普通大众大多没跟这些系统打过交道。就算打过，看到的也就是个有点蠢的聊天机器人。大多数专业人士看到的仍然只是温和的提升。而一小撮专业人士正在体验曲线垂直上升带来的眩晕感。而这一切，正在同时发生。
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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