I admire how persistent the AI folks are at failing to do the same thing over and over again, but each time coming up with an even more stupid name. Vibe coding? Gas Town? Clawdbot, I mean Moltbook, I mean OpenClaw? It’s probably gonna be something different tomorrow, isn’t it?

It’s a big club and you ain’t in it!

Holy shit, I didn’t even read that part while skimming the later parts of that post. I am going to need formal mathematical definitions for “entangled limit”, “all possible computations”, “everything machine”, “maximally nondeterministic”, and “eye wash” because I really need to wash out my eyes. Coming up with technical jargon that isn’t even properly defined is a major sign of math crankery. It’s one thing to have high abstractions, but it is something else to say fancy words for the sake of making your prose sound more profound.

I study complexity theory so this is precisely my wheelhouse. I confess I did not read most of it in detail, because it does spend a ton of space working through tedious examples. This is a huge red flag for math (theoretical computer science is basically a branch of math), because if you truly have a result or idea, you need a precise statement and a mathematical proof. If you’re muddling through examples, that generally means you either don’t know what your precise statement is or you don’t have a proof. I’d say not having a precise statement is much worse, and that is what is happening here.

Wolfram here believes that he can make big progress on stuff like P vs NP by literally just going through all the Turing machines and seeing what they do. It’s the equivalent of someone saying, “Hey, I have some ideas about the Collatz conjecture! I worked out all the numbers from 1 to 30 and they all worked.” This analogy is still too generous; integers are much easier to work with than Turing machines. After all, not all Turing machines halt, and there is literally no way to decide which ones do. Even the ones that halt can take an absurd amount of time to halt (and again, how much time is literally impossible to decide). Wolfram does reference the halting problem on occasion, but quickly waves it away by saying, “in lots of particular cases … it may be easy enough to tell what’s going to happen.” That is not reassuring.

I am also doubtful that he fully understands what P and NP really are. Complexity classes like P and NP are ultimately about problems, like “find me a solution to this set of linear equations” or “figure out how to pack these boxes in a bin.” (The second one is much harder.) Only then do you consider which problems can be solved efficiently by Turing machines. Wolfram focuses on the complexity of Turing machines, but P vs NP is about the complexity of problems. We don’t care about the “arbitrary Turing machines ‘in the wild’” that have absurd runtimes, because, again, we only care about the machines that solve the problems we want to solve.

Also, for a machine to solve problems, it needs to take input. After all, a linear equation solving machine should work no matter what linear equations I give it. To have some understanding on what problems a Turing machine can solve, Wolfram would need to analyze the behavior of the machine on all (infinitely many) inputs. He doesn’t even seem to grasp the concept that a machine needs to take input; none of his examples even consider that.

Finally, here are some quibbles about some of the strange terminology he uses. He talks about “ruliology” as some kind of field of science or math, and it seems to mean the study of how systems evolve under simple rules or something. Any field of study can be summarized in this kind of way, but in the end, a field of study needs to have theories in the scientific sense or theorems in the mathematical sense, not just observations. He also talks about “computational irreducibility”, which is apparently the concept of thinking about what is the smallest Turing machine that computes a function. This doesn’t really help him with his project, but not only that, there is a legitimate subfield of complexity theory called meta-complexity that is productively investigating this idea!

If I considered this in the context of solving P vs NP, I would not disagree if someone called this crank work. I think Wolfram greatly overestimates the effectiveness of just working through a bunch of examples in comparison to having a deeper understanding of the theory. (I could make a joke about LLMs here, but I digress.)

Surely this is a suitable reference for a math article!

I’d say Aumann’s theorem is even narrower than that, after reading the Wikipedia article. The theorem doesn’t even reference “reasoning”, unless you count observing that a certain event happened as reasoning.

I’d say even the part where the article tries to formally state the theorem is not written well. Even then, it’s very clear how narrow the formal statement is. You can say that two agents agree on any statement that is common knowledge, but you have to be careful on exactly how you’re defining “agent”, “statement”, and “common knowledge”. If I actually wanted to prove a point with Aumann’s agreement theorem, I’d have to make sure my scenario fits in the mathematical framework. What is my state space? What are the events partitioning the state space that form an agent? Etc.

The rats never seem to do the legwork that’s necessary to apply a mathematical theorem. I doubt most of them even understand the formal statement of Aumann’s theorem. Yud is all about “shut up and multiply,” but has anyone ever see him apply Bayes’s theorem and multiply two actual probabilities? All they seem to do is pull numbers out of their ass and fit superexponential curves to 6 data points because the superintelligent AI is definitely coming in 2027.

The sad thing is I have some idea of what it’s trying to say. One of the many weird habits of the Rationalists is that they fixate on a few obscure mathematical theorems and then come up with their own ideas of what these theorems really mean. Their interpretations may be only loosely inspired by the actual statements of the theorems, but it does feel real good when your ideas feel as solid as math.

One of these theorems is Aumann’s agreement theorem. I don’t know what the actual theorem says, but the LW interpretation is that any two “rational” people must eventually agree on every issue after enough discussion, whatever rational means. So if you disagree with any LW principles, you just haven’t read enough 20k word blog posts. Unfortunately, most people with “bounded levels of compute” ain’t got the time, so they can’t necessarily converge on the meta level of, never mind, screw this, I’m not explaining this shit. I don’t want to figure this out anymore.

Randomly stumbled upon one of the great ideas of our esteemed Silicon Valley startup founders, one that is apparently worth at least 8.7 million dollars: https://xcancel.com/ndrewpignanelli/status/1998082328715841925#m

Excited to announce we’ve raised $8.7 Million in seed funding led by @usv with participation from [list a bunch of VC firms here]

@intelligenceco is building the infrastructure for the one-person billion-dollar company. You still can’t use AI to actually run a business. Current approaches involve lots of custom code, narrow job functions, and old fashioned deterministic workflows. We’re going to change that.

We’re turning Cofounder from an assistant into the first full-stack agent company platform. Teams will be able to run departments - product/engineering, sales/GTM, customer support, and ops - entirely with agents.

Then, in 2026 we’ll be the first ones to demonstrate a software company entirely run by agents.

$8.7 million is quite impressive, yes, but I have an even better strategy for funding them. They can use their own product and become billionaires, and now they can easily come up with $8.7 million considering that is only 0.87% of their wealth. Are these guys hiring? I also have a great deal on the Brooklyn Bridge that I need to tell them about!

Our branding - with the sunflowers, lush greenery, and people spending time with their friends - reflects our vision for the world. That’s the world we want to build. A world where people actually work less and can spend time doing the things they love.

We’re going to make it easy for anyone to start a company and build that life for themselves. The life they want to build, and spend every day dreaming about.

This just makes me angry at how disconnected from reality these people are. All this talk about giving people better lives (and lots of sunflowers), and yet it is an unquestionable axiom that the only way to live a good life is to become a billionaire startup founder. These people do not have any understanding or perspective other than their narrow culture that is currently enabling the rich and powerful to plunder this country.

When capitalism did contribute to innovation and technological advancement, it was through stuff like Bell Labs, which was funded by a corporation but functioned in practice like its own research institute. I think that the idea of Bell Labs is a little offensive to present day venture capitalists, though. What do you mean, innovation comes from scientists and engineers? We all know that innovation comes from plucky, young, hotshot founders with big ideas who go against conventional wisdom!

These worries are real. But in many cases, they’re about changes that haven’t come yet.

Of all the statements that he could have made, this is one of the least self-aware. It is always the pro-AI shills who constantly talk about how AI is going to be amazing and have all these wonderful benefits next year (curve go up). I will also count the doomers who are useful idiots for the AI companies.

The critics are the ones who look at what AI is actually doing. The informed critics look at the unreliability of AI for any useful purpose, the psychological harm it has caused to many people, the absurd amount of resources being dumped into it, the flimsy financial house of cards supporting it, and at the root of it all, the delusions of the people who desperately want it to all work out so they can be even richer. But even people who aren’t especially informed can see all the slop being shoved down their throats while not seeing any of the supposed magical benefits. Why wouldn’t they fear and loathe AI?

So many CRITICAL and MANDATORY steps in the release instruction file. As it always is with AI, if it doesn’t work, just use more forceful language and capital letters. One more CRITICAL bullet point bro, that’ll fix everything.

Sadly, I am not too surprised by the developers of Lean turning towards AI. The AI people have been quite interested in Lean for a while now since for them, it is a useful tool to have AIs do math (and math = smart, you know).

There are some comments speculating that some pro-AI people try to infiltrate anti-AI subreddits by applying for moderator positions and then shutting those subreddits down. I think this is the most reasonable explanation for why the mods of “cogsuckers” of all places are sealions for pro-AI arguments. (In the more recent posts in that subreddit, I recognized many usernames who were prominent mods in pro-AI subreddits.)

I don’t understand what they gain from shutting down subreddits of all things. Do they really think that using these scummy tactics will somehow result in more positive opinions towards AI? Or are they trying the fascist gambit hoping that they will have so much power that public opinion won’t matter anymore? They aren’t exactly billionaires buying out media networks.

Don’t forget the other comment saying that if you hate AI, you’re just “vice-signalling” and “telegraphing your incuruosity (sic) far and wide”. AI is just like computer graphics in the 1960s, apparently. We’re still in early days guys, we’ve only invested trillions of dollars into this and stolen the collective works of everyone on the internet, and we don’t have any better ideas than throwing more money compute at the problem! The scaling is still working guys, look at these benchmarks that we totally didn’t pay for. Look at these models doing mathematical reasoning. Actually don’t look at those, you can’t see them because they’re proprietary and live in Canada.

In other news, I drew a chart the other day, and I can confidently predict that my newborn baby is on track to weigh 10 trillion pounds by age 10.

EDIT: Rich Hickey has now disabled comments. Fair enough, arguing with promptfondlers is a waste of time and sanity.

Yeah, it’s not like reviewers can just write “This paper is utter trash. Score: 2” unless ML is somehow an even worse field than I previously thought.

They referenced someone who had a paper get rejected from conferences six times, which to me is an indication that their idea just isn’t that good. I don’t mean this as a personal attack; everyone has bad ideas. It’s just that at some point, you just have to cut your losses with a bad idea and instead use your time to develop better ideas.

So I am suspicious that when they say “constructive feedback”, they don’t mean “how do I make this idea good” but instead “what are the magic words that will get my paper accepted into a conference”. ML has become a cutthroat publish-or-perish field, after all. It certainly won’t help that LLMs are effectively trained to glaze the user at all times.

AI researchers are rapidly embracing AI reviews, with the new Stanford Agentic Reviewer. Surely nothing could possibly go wrong!

Here’s the “tech overview” for their website.

Our agentic reviewer provides rapid feedback to researchers on their work to help them to rapidly iterate and improve their research.

The inspiration for this project was a conversation that one of us had with a student (not from Stanford) that had their research paper rejected 6 times over 3 years. They got a round of feedback roughly every 6 months from the peer review process, and this commentary formed the basis for their next round of revisions. The 6 month iteration cycle was painfully slow, and the noisy reviews — which were more focused on judging a paper’s worth than providing constructive feedback — gave only a weak signal for where to go next.

How is it, when people try to argue about the magical benefits of AI on a task, it always comes down to arguing “well actually, humans suck at the task too! Look, humans make mistakes!” That seems to be the only way they can justify the fact that AI sucks. At least it spews garbage fast!

(Also, this is a little mean, but if someone’s paper got rejected 6 times in a row, perhaps it’s time to throw in the towel, accept that the project was never that good in the first place, and try better ideas. Not every idea works out, especially in research.)

When modified to output a 1-10 score by training to mimic ICLR 2025 reviews (which are public), we found that the Spearman correlation (higher is better) between one human reviewer and another is 0.41, whereas the correlation between AI and one human reviewer is 0.42. This suggests the agentic reviewer is approaching human-level performance.

Actually, now all my concerns are now completely gone. They found that one number is bigger than another number, so I take back all of my counterarguments. I now have full faith that this is going to work out.

Reviews are AI generated, and may contain errors.

We had built this for researchers seeking feedback on their work. If you are a reviewer for a conference, we discourage using this in any way that violates the policies of that conference.

Of course, we need the mandatory disclaimers that will definitely be enforced. No reviewer will ever be a lazy bum and use this AI for their actual conference reviews.

I’m a nerd and even I want to shove this guy in a locker.

Referencing the telephone game does not prove anything here. The telephone game is shows that humans are not good at copying something exactly without changes, which computers are better at. But the question here is if AI can achieve deeper understanding of a work, which is needed to produce a good summary. This is something humans are far better at. The AI screws up the summary here in ways that no reasonable person who has watched the TV series (or played the games) would ever screw up.

A game/sneer where you are a venture capitalist with billions invested in generative AI: https://woe-industries.itch.io/you-have-billions-invested-in-generative-ai

Although I never use LLMs for any serious purpose, I do sometimes give LLMs test questions in order to get firsthand experience on what their responses are like. This guide tracks quite well with what I see. The language is flowery and full of unnecessary metaphors, and the formatting has excessive bullet points, boldface, and emoji. (Seeing emoji in what is supposed to be a serious text really pisses me off for some reason.) When I read the text carefully, I can almost always find mistakes or severe omissions, even when the mistake could easily be remedied by searching the internet.

This is perfectly in line with the fact that LLMs do not have deep understanding, or the understanding is only in the mind of the user, such as with rubber duck debugging. I agree with the “Barnum-effect” comment (see this essay for what that refers to).