if your job is legible enough that people can make a dataset clearly pointing out what is right and what is wrong, you are at the highest risk for an AI model being “superhuman” at your job. It is even more risky if it is possible to articulate your thought process in a way that is verifiable.

Looking at this perspective, it makes more sense that competitive programming and mathematics have been attacked first by these models. Not only do they provide a clear source of ground truth, you can also hire people to give intermediate steps and there are tools like proof assistants to tell you if the proof is correct or not.

The best placed people are those for whom it will be unprofitable or extremely difficult to create or procure datasets that give a clear signal. These people, those who are “illegible” to the outside world, are those who are least likely to have their job, or parts of their job be automated by models like the ones discussed above.

A good example of this is the economist and public intellectual Tyler Cowen. You might think that public intellectuals would be the first to be automated because all they offer are rambling arguments with no substance. But Tyler’s work on Marginal Revolution, his food blog, podcast and grant program, (confession: I’m a grantee but did not get paid to write this), are exactly the sort of things that one would find hard to put in a dataset for a model. IWhile you could easily collect his outputs - the blog posts, food reviews, grant selections - into a dataset, there’s no way to capture or verify the thought process that generates these insights. Unlike mathematics or coding where we can break down and verify each step of reasoning, there’s no way to create a “verifier model” that could check if someone is thinking like Tyler does, or generate synthetic examples of his style of analysis. You can’t systematically label his cognitive steps as “right” or “wrong” the way you can with a mathematical proof or a programming solution. There is no clearly labelled dataset like there is for mathematics or coding, and it is not clear how it is possible to generate a dataset about someone whose thought process is as illegible as Tyler’s.

Another example is that of my friend who is a website designer. Let us call him K to protect his privacy. K’s clients include several people who work in technology, and want their personal websites designed. What they do is that they give him a vibe, and he works through what they actually want from their websites. K’s value isn’t the technical prowess of the website design: most of it is basic HTML, CSS and JavaScript. His value actually comes from understanding what his clients want, the general aesthetic they are going towards, and then converting this to a working website with excruciating detail. It is hard and expensive to find people to label this as ‘right’ or ‘wrong’ - the success of each design decision depends on contextual factors and the specific client’s unstated preferences. Unlike mathematics problems where there’s a clear correct answer, it is not clear at all how to build a dataset or a verifier model of his thought process to guide LLMs to the right answer.

Both Tyler Cowen and K share the same trait: their value comes from navigating ambiguous human preferences and synthesizing disparate information in ways that can’t be reduced to clear right or wrong answers. In other words, they have taste . Their work resists dataset creation because success is contextual, subjective, and often relies on tacit knowledge that can’t be easily formalized.

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