Interesting take! I feel like 2 of them are maybe overstated:

> - extent to which correctness of solution be easily specified and checked

I don't think most software is like solving a math problem or series of math problems. Algorithmic problems are very narrow and might be more like this though, where an oracle that verifies answers as either correct or incorrect exists beforehand.

The correctness function of most software is how much users want to use/pay for it, which is a pretty fuzzy problem. Since the cost of copying software is effectively zero, software systems also tend to be be unique rather than being exactly like something else, and don't converge to be like another software system but rather diverge.

The prior art point is an interesting one. At least for applications as a whole, there isn't really prior art for a material amount of all the problems/tradeoffs a non-trivial software application embodies. For a todo list app or make a social network project, there's plenty of prior art to be sufficient to build something with an LLM system, but probably not most apps.

That's my initial intuition anyway.

Let's assume AI is only good at software and math.

If we can use AI to write lots of good software for cheap, that software can automate away a lot of jobs.

So AI does not have to take the jobs directly, software written by AI can.

Especially if that works for software for robots.

The job of a programmer isn't to write code, but to automate things. Code itself doesn't have any value unless it solves some real problem not related to coding.

So if the work of a programmer can be automated then this means that any work can be automated. So no, it's not about software engineering only.

> Ironically it couldn’t be further from the truth… and likewise the predictions of widespread labor obsolescence

Could you explain what you mean here?

It feels like there is one bucket of verifiable work - programming, math etc that AI will clearly excel at.

There is another large bucket of like law/ accounting/ financial analysis where I don’t have any reason to think AI won’t be super human at, but the work is more on bringing all the domain expertise into harnesses and software.

Is there aspects of knowledge work that you think AI wouldn’t excel at in the long run?

- the extent to which solutions could be implemented as text: not sure about that. AlphaFold is basically a mechanical/geometrical/Chemical problem. There are other scientific transformer based models.

- the extent which solutions exist online - if you have a strong verification tool, you can generate examples, you can generate feedback, i think you could start with small/smaller prior art

- the extent which solutions could be specified and checked - if you have a lot of priort art, maybe llm's can find the good "patterns" and compare against them, and at least get close to a good results - but you'd still need human verification.

Many white collar jobs are verifiable. Make a robot and suddenly real world tasks are verifiable too.

> the very architects of AI are the people whose jobs are most easily automated by AI

Think very hard about what this implies for the future pace of AI R&D

Human: I specialize in tasks in which the correctness of my solution cannot be easily specified or checked. Truuuuussssttttt meeeeee.

I'm just saying, let's not get painted into that corner completely as a species :)

per dwarkesh its also 'grindablity' in training .