从四月份开始删除这个微博，因为对于LLM能力的共识差距正在扩大。 现在少了"两组人互相说话"，更是尖锐的漏斗。
- 纳普金数学大约6B人(占人口的75%) 几乎没有接触LLMs。
- 大约1-2B(~20%)是自由级ChatGPT类产品的临时和少见用户。 这个组看到derpy聊天员，并把它们稍微像更好的Google搜索，写作辅助等等。 也许agent想给你订个航班我有些非科技朋友(合理的，莫)说，甚至许多数学和密码以外的专业人员也处于这一层次。 例如，执行者和其他许多功能都花了很多时间与其他人交谈，所以虽然他们从智力上理解正在发生的事情，但是它仍然是二手的，有点抽象。
- 现在，我们得到专业使用前沿级LLMs 在数学和代码。 大约20万人(0.2%)的某个地方，亲眼看到过去需要几周/月的大型复杂项目，构建应用，复制应用，翻译应用，从二进制解析应用。.. 这一切都发生得非常迅速，最近 - 不到一年前，我手动写代码，将记忆中的计算机代码命令按字符输入代码编辑器字符，有时按 Tab 自动完成一小块代码。
最后我们到达5 000人(~0.006%) 进入国内前沿系统。 外部世界已经看到了预览。 它看起来像成群的agents 在数周内合作软件巨型项目：薄荷零天，运行网络攻击和防御机器速度，发现新的科学，推进数学前沿。 行业年头最需要专业人才的东西与此同时，人的审查和理解开始落后。 例如，人们仍然参与许多月前的OpenAI-HF事件的"考古学". 数学家们可能暂时对关于边疆数学的722本手稿进行热议。
漏斗是由多种因素驱动的：
1. 具有雄心、问题大小和视野的影响尺度。 带有段落的问题几乎不强调系统。 你需要一大堆你真正关心的难题这个方面驱动着漏斗的消费者/专业方面。
2. 该系统的杂乱无章(我已单独写了很多)。 在数字化、可核查和具有经济价值的领域，能力达到顶峰。 这是因为LLM能力来自对收入潜力驱动的可核查环境混合物的奖励的强化学习。 这个方面主要驱动漏斗的区域(如数学和编码)维度。
3. 准入。 自由层次付费层次内部
这就是现在的怪异之处一般公众大多没有与这些系统互动。 当他们有，它看起来像一个烂聊天机。 大多数专业人员仍然只看到适度的提升。 一小撮专业人士正在经历垂直曲线的晕眩。 这一切都同时发生。
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根据我的判断，对AI能力的理解差距越来越大。
我认为，第一个问题在于合理性和使用水平。 我想很多人去年在某个地方尝试了ChatGPT的自由等级，显示更多
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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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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 Show more
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