Jason Wei 这类一线大模型研究者写的不是教程目录，而是可迁移的判断。把「What's left for humans?」放到智能体和评测系统里，真正要回答的是：它改变了训练、产品还是人的分工。下面用中文把核心论点重写成可执行的工程笔记，去掉博客导航和无关装饰。

问题怎么来的

I recently got a Tesla, and using full self-driving has been a wake up call to just how many advantages AI has over humans. The few times I disengaged it because I thought it was going into the wrong lane, it turned out that the car was right and I was wrong. I realized that there is no hope of me driving better than a neural net that knows every road, sees in every direction at once, and never gets tired or distracted.

Given that AI has certain inherent advantages over human intelligence, what kind of moats will remain for us as humans? It's a big question.

几个能用的判断

One short-term answer is that the world we live in was created for humans, and in some domains, AI has not closed the gap yet. For instance, AI still struggles to use internet user interfaces. While any computer-literate human can navigate a web page with ease, AI is still not great at making accurate clicks and drags because image embeddings are not optimized for such precision. If the internet were designed to be fed into language models instead of rende

Robotics is another area where we humans have a home-field advantage. Most tasks in the physical world are designed around fingers and opposable thumbs, which have been pretty hard to build into robots so far. While it is clear that machines can outperform humans in environments optimized for automation, like large-scale manufacturing lines, for now, most of the world is still built for humans. However, these capability gaps are only temporary. There will

放到产品里会怎样

What are the real moats that humans will have? Anything involving private knowledge that language models do not have access to feels like a solid moat to me. Romantic matchmaking and high-end real estate are two examples where inventory is often not advertised publicly and matches are made through being in the right circles. Venture capital is another example—although some research and decision making can be automated with AI, much of success hinges on und

A second area where humans seem to have a real moat is in entertainment and the arts, which are inherently valued for their human aspects regardless of how well machines can do them. Watching Usain Bolt sprint one-hundred meters is beautiful as an expression of the peak of human ability, even though cars can drive much faster. Watching chess at the amateur or intermediate level is more relatable and satisfying than watching two superhuman AIs play each oth

落地时先钉死的事

More broadly, human presence is a feature that will be, by definition, challenging for AI to automate. For example, a teacher remembering your name or a parent supporting you is valuable even though AI can easily remember your name and probably give better life advice. Someone spending part of a finite life on you counts because their time runs out. As a personal anecdote, I remember the first time I worked with someone who I considered an amazing AI resea

Intelligence has been the defining feature of humans and it will be a big change for AI to automate that over the coming decades. In the near term, certain types of intelligence will become very cheap and automate away old jobs, but the moats I described above will not be the only places where humans can hold value. In the same way that computers took away the jobs of secretaries and manual accountants but created far more jobs via the IT industry, I belie

落到智能体和评测上的 5 件事

1. 先写清任务能不能被自动验证：能验证的，优先做 verifier 和评测，而不是只堆更大生成模型。
2. 评测集要能区分「看起来会」和「稳定会」，避免只刷平均分。
3. 涌现式能力一旦出现，产品接口、配额和失败回退要一起改，不要只改 Prompt。
4. 人的时间放在定义问题、写 verifier、审高风险动作，而不是重复模型已经会的步骤。
5. 研究节奏按实验闭环来：假设、最小实验、记录、复现，而不是等灵感。

和龙虾PRO怎么接

龙虾PRO做 OpenClaw 中国垂直落地时，评测、工具调用和人机协同都吃同一套原则：能验证的交给系统，不能验证的留给人审。数字员工、技能市场和网关不该各自发明一套成功标准。

本文侧重全链路风控方法论。落地时请用自身业务单据做回放验证，不要把示例阈值直接当生产策略。 相关：风控体检 · 方案资源