Every time I hear a moderate AI argument (e.g. AI will be an aid for searching literature or writing code), it’s like, “Look, it’s impressive that the AI managed to do this. Sure, it took about three dozen prompts over five hours, made me waste another five hours because it generated some completely incorrect nonsense that I had to verify, produced an answer that was much lower quality than if I had just searched it up myself, and boiled two lakes in the process. You should acknowledge that there is something there, even if it did take a trillion dollars of hardware and power to grind the entire internet and all books and scientific papers into a viscous paste. Your objections are invalid because I’m sure things are gonna improve because Progress.”

I am doubly annoyed when I turn my back and they switch back to spouting nonsense about exponential curves and how AI is gonna be smarter than humans at literally everything.

I did not think anything could make me sympathetic to the authors who put 0.1pt white text in their papers so that any reviewer lazy enough to use an LLM would get prompt injected, but here we are.

More AI bullshit hype in math. I only saw this just now so this is my hot take. So far, I’m trusting this r/math thread the most as there are some opinions from actual mathematicians: https://www.reddit.com/r/math/comments/1o8xz7t/terence_tao_literature_review_is_the_most/

Context: Paul Erdős was a prolific mathematician who had more of a problem-solving style of math (as opposed to a theory-building style). As you would expect, he proposed over a thousand problems for the math community that he couldn’t solve himself, and several hundred of them remain unsolved. With the rise of the internet, someone had the idea to compile and maintain the status of all known Erdős problems in a single website (https://www.erdosproblems.com/). This site is still maintained by this one person, which will be an important fact later.

Terence Tao is a present-day prolific mathematician, and in the past few years, he has really tried to take AI with as much good faith as possible. Recently, some people used AI to search up papers with solutions to some problems listed as unsolved on the Erdős problems website, and Tao points this out as one possible use of AI. (I personally think there should be better algorithms for searching literature. I also think conflating this with general LLM claims and the marketing term of AI is bad-faith argumentation.)

You can see what the reasonable explanation is. Math is such a large field now that no one can keep tabs on all the progress happening at once. The single person maintaining the website missed a few problems that got solved (he didn’t see the solutions, and/or the authors never bothered to inform him). But of course, the AI hype machine got going real quick. GPT5 managed to solve 10 unsolved problems in mathematics! (https://xcancel.com/Yuchenj_UW/status/1979422127905476778#m, original is now deleted due to public embarrassment) Turns out GPT5 just searched the web/training data for solutions that have already been found by humans. The math community gets a discussion about how to make literature more accessible, and the rest of the world gets a scary story about how AI is going to be smarter than all of us.

There are a few promising signs that this is getting shut down quickly (even Demis Hassabis, CEO of DeepMind, thought that this hype was blatantly obvious). I hope this is a bigger sign for the AI bubble in general.

EDIT: Turns out it was not some rando spreading the hype, but an employee of OpenAI. He has taken his original claim back, but not without trying to defend what he can by saying AI is still great at literature review. At this point, I am skeptical that this even proves AI is great at that. After all, the issue was that a website maintained by a single person had not updated the status of 10 problems inside a list of over 1000 problems. Do we have any control experiments showing that a conventional literature review would have been much worse?

Feeding the output of the AI back into itself? Nothing could possibly go wrong with that!

Alesia was the turning point. With the fall of Gaul, Julius Caesar became the most powerful man in the Roman world.

We conveniently forgot about all the civil wars that happened afterwards that resulted in the dismantling of the Roman republic and Caesar taking absolute power. Oh yeah, I see it now.

Most restaurant origin stories involve someone sharing their favorite taco recipe or whatever. These guys start off with a bad pop-history explanation of the battle of Alesia. That’s how you know their food is great.

There’s more where the founder of the company talks about how he really hated working at his family’s restaurant while growing up (good sign). Knowing that his family came from China adds another layer of weirdness, in my opinion. The characters where the company name comes from (改革) can be read in both Chinese (gǎigé) and Japanese (kaikaku) and mean the same thing (reform) in both languages. It just feels so weird that he talks so much fluff about Julius Caesar, mentions his family from China and then, out of the blue, uses a Japanese name for the company. What is with these people fetishizing ancient Rome and Japan so much?

In the 21st century, the Antichrist is a Luddite who wants to stop all science.

As opposed to the current administration that is destroying science by cutting the NSF’s funding. An administration that Peter Thiel supports. He might want to look into that.

Lately I’ve been mildly annoyed when I just want to relax and watch gaming videos on Youtube and I see recommendations for some AI critihype. Out of morbid curiosity, I decided to click on one of them and of course the “original paper” the video is based on is the stupid Anthropic blog post about how the AI blackmailed someone (after it was told to blackmail someone). I was even more annoyed to find out how popular it is, but at least it shows how the general public has such a negative opinion of AI. Some of the comments are thankfully pushing back against the video and focusing on the real harms.

I thought that by now we would have learned from the tobacco companies to never trust “research” done by a company about their own products.

Oh yeah, he wrote an update saying that the LLM is still great, even if the result is already known, because it saves him time. We have come full circle back to the exact same value proposition as the vibe coders.

After seeing this, I reminded myself that I’ve seen this type of thing happen before. Over the past half year, so many programmers enthusiastically embraced vibe coding after seeing one or two impressive results when trying it out for themselves. We all know how that is going right now. Baldur Bjarnason had some great essays (1, 2) about the dangers of relying on self-experimentation when judging something, especially if you’re already predisposed into believing it. It’s like a mark believing in a psychic after he throws out a couple dozen vague statements and the last one happens to match with something meaningful, after the mark interprets it for him.

Edit: Accidentally hit reply too early.

Pigs will be a true method of space exploration if they can fly to Mars in 1 hour.

I don’t know any quantum physics and I’ve only taken one class on quantum computing, but the part about real vs complex numbers is quite funny to me. The very first homework exercise in that class was showing that, in quantum computation, there is no difference in using real or complex amplitudes (you can simulate any pure state with complex amplitudes using real amplitudes and only one extra qubit). The real reason to use complex amplitudes is “Why not, real numbers are complex numbers anyway.” It does help that the quantum Fourier transform is far more convenient with complex amplitudes.

Not sure if analog turing machines provide any new capabilities that digital TMs do, but I leave that question for the smarter people in the subject of theorethical computer science

The general idea among computer scientists is that analog TMs are not more powerful than digital TMs. The supposed advantage of an analog machine is that it can store real numbers that vary continuously while digital machines can only store discrete values, and a real number would require an infinite number of discrete values to simulate. However, each real number “stored” by an analog machine can only be measured up to a certain precision, due to noise, quantum effects, or just the fact that nothing is infinitely precise in real life. So, in any reasonable model of analog machines, a digital machine can simulate an analog value just fine by using enough precision.

There aren’t many formal proofs that digital and analog are equivalent, since any such proof would depend on exactly how you model an analog machine. Here is one example.

Quantum computers are in fact (believed to be) more powerful than classical digital TMs in terms of efficiency, but the reasons for why they are more powerful are not easy to explain without a fair bit of math. This causes techbros to get some interesting ideas on what they think quantum computers are capable of. I’ve seen enough nonsense about quantum machine learning for a lifetime. Also, there is the issue of when practical quantum computers will be built.

From the ChatGPT subreddit: Gemini offers to pay me for a developer to fix its mess

Who exactly pays for it? Google? Or does Google send one of their interns to fix the code? Maybe Gemini does have its own bank account. Wow, I really haven’t been keeping up with these advances in agentic AI.

On one side, we have a trolley problem thought experiment involving hypothetical children tied to hypothetical train tracks and some people sending him rude emails. On the other side, we have actual dead children and actual hospitals and apartments reduced to rubble. I wonder which side is more convincing to me?

It’s the same pattern of thought as rationalists with AI, trying to fit everything they see into their apocalypse narrative while ignoring the real harms. Rationalists talk a good game about evidence, but what I see them do in practice is very different. First, use mental masturbation (excuse me, “first principles”) to arrive at some predetermined edgy narrative, and then cherry pick and misinterpret all evidence to support it. It is very important that the narratives are edgy, otherwise what are we even writing 10,000 word blog posts for?

OpenAI claims that their AI can get a gold medal on the International Mathematical Olympiad. The public models still do poorly even after spending hundreds of dollars in computing costs, but we’ve got a super secret scary internal model! No, you cannot see it, it lives in Canada, but we’re gonna release it in a few months, along with GPT5 and Half-Life 3. The solutions are also written in an atrociously unreadable manner, which just shows how our model is so advanced and experimental, and definitely not to let a generous grader give a high score. (It would be real interesting if OpenAI had a tool that could rewrite something with better grammar, hmmm…) I definitely trust OpenAI’s major announcements here, they haven’t lied about anything involving math before and certainly wouldn’t have every incentive in the world to continue lying!

It does feel a little unfortunate that some critics like Gary Marcus are somewhat taking OpenAI’s claims at face value, when in my opinion, the entire problem is that nobody can independently verify any of their claims. If a tobacco company released a study about the effects of smoking on lung cancer and neglected to provide any experimental methodology, my main concern would not be the results of that study.

Edit: A really funny observation that I just thought of: in the OpenAI guy’s thread, he talks about how former IMO medalists graded the solutions in message #6 (presumably to show that they were graded impartially), but then in message #11 he is proud to have many past IMO participants working at OpenAI. Hope nobody puts two and two together!

Hmm, should I be more worried and outraged about genocides that are happening at this very moment, or some imaginary scifi scenario dreamed up by people who really like drawing charts?

One of the ways the rationalists try to rebut this is through the idiotic dust specks argument. Deep down, they want to smuggle in the argument that their fanciful scenarios are actually far more important than real life issues, because what if their scenarios are just so bad that their weight overcomes the low probability that they occur?

(I don’t know much philosophy, so I am curious about philosophical counterarguments to this. Mathematically, I can say that the more they add scifi nonsense to their scenarios, the more that reduces the probability that they occur.)

There’s really no good way to make any statements about what problems LLMs can solve in terms of complexity theory. To this day, LLMs, even the newfangled “reasoning” models, have not demonstrated that they can reliably solve computational problems in the first place. For example, LLMs cannot reliably make legal moves in chess and cannot reliably solve puzzles even when given the algorithm. LLM hypesters are in no position to make any claims about complexity theory.

Even if we have AIs that can reliably solve computational tasks (or, you know, just use computers properly), it still doesn’t change anything in terms of complexity theory, because complexity theory concerns itself with all possible algorithms, and any AI is just another algorithm in the end. If P != NP, it doesn’t matter how “intelligent” your AI is, it’s not solving NP-hard problems in polynomial time. And if some particularly bold hypester wants to claim that AI can efficiently solve all problems in NP, let’s just say that extraordinary claims require extraordinary evidence.

Koppelman is only saying “complexity theory” because he likes dropping buzzwords that sound good and doesn’t realize that some of them have actual meanings.

I study complexity theory and I’d like to know what circuit lower bound assumption he uses to prove that the AI layoffs make sense. Seriously, it is sad that the people in the VC techbro sphere are thought to have technical competence. At the same time, they do their best to erode scientific institutions.

Username called “The Dao of Bayes”. Bayes’s theorem is when you pull the probabilities out of your posterior.

知者不言，言者不知。 He who knows (the Dao) does not (care to) speak (about it); he who is (ever ready to) speak about it does not know it.