State of AI, Summer 2026 – #116

**Steve Hsu** (0:03)
This message is for some friends at Mechanize, a startup that builds environments for training and evaluating frontier LLMs. Its customers include the top AI labs, and it has contributed to the breakthrough encoding capabilities of frontier models. Mechanize is hiring. See the links in the show notes. Compensation is extremely competitive for technical roles, $300,000 to $500,000 per year in salary, plus additional benefits and equity compensation. They are also seeking smart generalists. For example, a research engineer position focused on alignment. This person will build evals that test for misaligned model behavior, salary $500,000 a year. Puzzle maker. This person will design interesting and original puzzles that LLMs cannot yet solve. The salary component of compensation is $300,000. Mechanize understands that my readership and my listenership on this podcast is highly selected. There is a very good chance you will be interviewed if you apply via the link in the show notes.
I already know of departments that have discussed, not that they've implemented, but they've discussed, reducing the number of PhD students that they admit, because the professors can get a lot of productivity out of using AI, whereas bringing a student up to speed, taking a student who just completed their undergraduate degree, and bringing them up to the frontier where they can actually do things that are useful, to a professor, that takes years, and it's super labor intensive, and a lot of professors would just rather use the models to do their research than go through that process with the students. Now, in the long run, this is a problem because we need to train the next generation of physicists or mathematicians or scientists. But it's possible we won't need as many, given the productivity multiplier that we get from AI.
Welcome to Manifold. We're here at the Mandarin Oriental Hotel in Taipei, Taiwan.
I am recording this episode all by myself. The title of this episode is going to be AI Summer 2026 The whole episode will be about things related to AI, and also a little bit about my summer travels. So in the last few months, I've been in San Francisco and Berkeley, Bangkok, Singapore, Hong Kong, Beijing, and Taiwan.
I attended these crazy conferences at Lighthaven in Berkeley called Less Online, and the Manifest Conference. I also worked on a documentary film called Machine God, which I'll talk about in this podcast. What you're seeing on the screen is, if you're watching the YouTube version of the podcast, what you're seeing on the screen is images taken from my travel. So these are all photographs that I myself took, and I've just got them playing in the background here. For those of you that are just listening to the audio version of this podcast, you of course won't see these photographs, but you can see them if you go to the YouTube channel.
So let me start with topic one, which is AI and Math and Physics.
If you've been following this, you may remember that about eight or nine months ago, I wrote a paper, a physics paper which was published in Physics Letters B. It was about non-linear modifications to the Schrodinger equation. The main ideas for that paper actually came from GPT.
So what I did was I submitted the paper under my own name, and it was accepted after review, and only after it was accepted did I post a companion paper, which describes how that paper was written, and revealing that the main ideas for the paper had actually come from suggestions from GPT.
I got a lot of pushback on that paper. I think a lot of people, because it was an AI-generated paper, really didn't like it. I don't think there's anything technically wrong with the paper. I think it's actually all correct, and I think, at least to me, quite interesting. But I think a lot of people just had a negative reaction to the fact that AI was involved.
Now, fast forward just six to nine months, and the situation has changed drastically, so that now there have been many important results published, which either had a significant amount of AI participation in the generation of those results, or even the results were generated almost entirely by AI. And these are papers in areas like math and theoretical physics, and probably also some other areas, although I don't track these other areas quite as much. And so I think most researchers now in fields like math and theoretical physics, especially the younger ones, realize that the situation is changing, and these models are extremely useful for researchers. Most of the researchers that I know are using the models on a daily basis, and using them to check calculations, review what's in the literature.