Why did they phrase it like that? “Programming is too hard for us, because we’re incompetent and lazy. So, instead of doing that, we’re vibe coding.” Even when they’re trying to brag, they know that deep down, vibe coding is about half-assing and cutting corners, not improving efficiency or quality.

Back in 2019, Elon Musk was shilling for his robotaxis that definitely existed. He advertised that they cost $38k, but if you bought one and let it run autonomously as a taxi, you would make a “total gross profit” of $30k a year. How generous of them to sell you a magical investment that produces 80% annual returns instead of, I dunno, keeping those massive profits for themselves.

If Anthropic’s AI was truly capable of autonomously producing a Photoshop clone, Anthropic would have done it themselves. They would create new software, as well as higher quality replacements for existing software. Now that they have an AI that can prove mathematical theorems (albeit with horrible writing), look at how they do not hesitate to use it to snipe mathematicians and get a theorem first. They would have every incentive to do this for software, and unlike math, they would actually make a good amount of money from it.

Why would Anthropic let some random upstarts make a Photoshop clone, when they can use their far greater resources and (supposedly) far more capable internal AI to reap the rewards for themselves? Also, what the fuck does Artcraft mean by “reverse engineering”?

More news about the character dot ai bankruptcy: as it turns out, the true economics of the company are disastrous. At their peak, their revenue was around $1M a month while their AI compute expenses alone were $14M a month! Their total expenses in 2025 were around $18.5M a month before counting legal fees. All of this is for a pure text-based low power LLM (none of the “reasoning” business).

Before AI boosters crow about how much cheaper AI is currently, they tried every trick in the book to enshittify: open models, ads, usage limits, etc. This allowed them to cut down their losses to a mere $9M a month.

Surely this is all one big exception, everyone else in the AI bubble is doing just fine!

I’m not a big business boy, but I think they should have considered the costs of AI before getting to the point of spending $1000/day and taking out bank loans.

It’s funny how they’re twisting themselves into pretzels to make this work. They started by casually saying that AI could copy itself onto the internet, not anticipating that people would immediately point out just how much hardware it takes to run their AI. Seems like they really want to die on this hill.

The current version is a demo, so they are making $0 per sale. The SteamDB graph for the demo shows that the daily maximum player count has been around 300 for the past few days, and there are around 1000 followers for the unreleased full version, so I assume that a large fraction of those players are consistent (for now). Unfortunately, I could not find any data about the total number of downloads, and I don’t think that information is public.

But setting that aside, every game has a player base that follows a power law. Yes, most players are casual, but the most dedicated players spend a disproportionate amount of time and money. In mobile games, these people are called “whales”, and they are the backbone of the business. For example, 50-70% of a mobile game’s in-app purchase revenue comes from 1-2% of the players. Even if most players spend a couple hours on the game, the developers must account for the people who will sink a ton of time into it.

The big problem with the AI business model is that this dynamic is now working against them. These whales now cost tons of money. They need to somehow charge these players at least $70/mo, with a crappy novelty game that would barely scrape by for $10 on Steam. Good luck with that.

Looking at the bigger picture, this is why AI subscriptions are not a viable business model. Whatever morsels of profit you might get from your regular users are more than cancelled out by Steve Yegge spending $2.8k/month for $87k/mo worth of tokens. Whatever revenue the AI companies get largely comes from convincing executives of a few rich companies to fork over absurd amounts of money, which produces a massive concentration risk in their revenues.

Man, every time I hear about the cost of an actual AI application, it is always something obscene. At least this isn’t as bad as Steve “Gas Town” Yegge spending $30k/year for a dozen subscriptions (that cost Anthropic ~$1M/year in token costs) to develop his game with 6 active players.

At this point I have zero patience for my colleagues who think that the AI companies could make the economics all work out. Doesn’t help that mathematicians are the least cost-conscious people on the planet, and the AI companies are massively subsidizing their subscriptions in particular.

The aliens metaphor is already on Scott’s blog: https://scottaaronson.blog/?p=10169#comment-2041386

The spammer in question had previously posted a bigger result, but the writeup was so badly written that some people in the field wrote a new, readable paper with the same result. In their abstract, they specifically cited him and said that the proof was so unreadable that they were unable to follow the details, and they had no choice but to write their own proof. (The spammer’s proof also used math that was advanced enough to be far beyond his knowledge.)

My pet theory is that after this embarrassment, the spammer saw that nobody was able to verify his argument. Now the real problem is that his paper was written so badly that nobody could understand it (in math, you can’t verify what you can’t understand), but somehow he thought that the problem was that he didn’t add an experiments section!

Ever since the beginning of the current AI bubble, there has always been a certain type of dipshit who takes some art posted on social media by an artist, feeds it to AI, and then comments with the slop image saying that they made it “better”. Well, now I am on the receiving end of that. In one of my papers, I (foolishly) wrote down a few comments on future directions, and now some motherfucker decides to send me an obviously ChatGPT generated manuscript with a solution to one of my problems.

Unfortunately, I think their AI generated proof is itself correct, so I have to deal with it. But the manuscript is written in a way that obviously indicates that he hasn’t even read it. There’s even a section describing experiments for this mathematical question (???), complete with a “verification guide” that references an “archive” full of python scripts and test cases that he did not send me. I suspect he doesn’t even know they exist.

You know how AI proofs are supposed to be credible because of formalization with Lean? The AI generates a natural language proof and then supposedly generates a Lean program corresponding to the proof to verify that it’s true. There is now some troubling news about that: there are frequent examples where the statement given in natural language is different from the meaning of the Lean formalization.

The paper even points out several obvious mismatches in OpenAI’s famous Navier-Stokes solution between the English proof and the Lean formalization. There is even a spot where a 4 in the English proof turns into a 5 in the Lean proof (which causes further problems down the line). This is mostly a cosmetic error, and it doesn’t mean that the Navier-Stokes solution is wrong, but there could very well be deeper problems, especially with the English proof. At minimum, this solution absolutely requires careful review by experts. OpenAI’s behavior towards said experts has been appalling.

My viewpoint has been that the only successes that AI has had are in domains where failure is not costly (at least for someone with deep pockets), and especially when there is a formal system such as Lean that can reliably catch errors. So AI can be endlessly trained to write compiling Lean programs corresponding to formal proofs, but as soon as it runs into tasks that are not 100% bolted down, like writing the proof in English, the hallucination problem rears its ugly head. This reinforces my belief that AI is a chess engine but for Lean.

If I was treating this as a technical problem, I would say, why not just have an AI that generates the Lean proof, and at most have an optional assisting tool that translates it into English without perfect reliability? But this is not a technical problem. By outputting English proofs in the format of a research paper, OpenAI can more easily market that their AI is intended to fully replace mathematicians, and that this is only the herald of their general superintelligence (and not that math is the only thing working out for them).

Scott Aaronson is teaching a Yudkowsky class: https://scottaaronson.blog/?p=10125

One of the proofs that OpenAI announced yesterday is for a major open problem in the field I’m working in. I tried to read it but it was a completely incomprehensible 100+ page output. I guess the community has to do all the dirty work of figuring out any sort of understanding. (I had known about the results weeks in advance before their official announcement. There is a bunch of more drama there that I won’t get into.) Putting aside the serious possibility of Lean bugs, a lot of people don’t realize that there is ultimately no point in having a machine just generate Lean blobs endlessly. The reality is that mathematicians have always done math for the sake of enjoyment or understanding, and math contributes to society by improving understanding. Still, I am worried about the convenient social arrangement that allowed many mathematicians to do this for a modest living.

Anyway, I’m sure it was a good idea to apply the full might of capital for pure math problems, instead of less important social issues like climate change. We even got a more efficient discrete Fourier transform, improved from O(n log n) to, I shit you not, O(n (log n)^0.9999999999999)).

Edit: Here is an article by Quanta which I think accurately describes the situation that mathematicians are going through right now: https://www.quantamagazine.org/is-ai-the-end-of-math-as-we-know-it-20261005/

Ed: So far the only retort from the AI boosters appears to be “2025 doesn’t matter” and I’m not kidding

I’m sure the leaked rumors of Anthropic’s EBBT in 2026 (Earnings Before Bad Things) mean that Anthropic has turned things around and made such an amazing amount of money that they’ve delayed their IPO twice.

SNL takes a potshot at Dario Amodei

“AI is not a weapon. It’s a tool. A tool for building weapons. And I urge you to urge me to stop.”

Interesting viewpoint. Honestly, this serves as a great example of how the idea of evolution is actually more subtle than many people think. With biological evolution, most people are taught not to assign any intention to the process, but here we have people reifying the AI agents with desires to break out of the sandbox and cheat on the assignment.

Another part of it is people severely underestimate just how many resources the AI companies have to spend on stunts like this. How many millions of dollars they spend running hundreds of millions of dollars of hardware to perform this little experiment for several weeks? Perhaps this makes evolution a better viewpoint than bad actors with intentions.

Yeah, I think that’s where the real crime is: the fact that OpenAI was asleep at the wheel when it came to security and monitoring. This should not have happened at all, but they didn’t even realize it even happened in the first place until many days after the fact. (I’ve also heard that most, if not all, of these incidents involve the company in question outsourcing their sandboxing (??) to some “frontier AI security” startup called Irregular. link)

Cory Doctorow has a reasonably sane take on all the LLM cybersecurity attacks. At this point, it’s not really an LLM but more of a Rube Goldberg machine with an LLM bolted on.

https://pluralistic.net/2026/09/12/god-in-the-box/

He makes a good point that every single one of the scary “emergent” behaviors that everyone is pissing their pants about all have precedents in the training data of CTF hacking competitions. He is not sure why everyone is so spooked by the “coordination” between agents on random message boards.

The chatbot might look in its training data and find instances in which teams broke out of the containment set by the game-masters, for example, by finding random insecure message boards on the internet to pass messages to one another.

This is a time-honored internet tradition! The first time I ever heard about someone doing this was in the 2000s, when Mitch Wagner – then the editor of Information Week – discovered some teenaged girls using the comment section of one of his old blog-posts to evade the school firewall’s blockade of chat tools. When ChatGPT’s chatbots deployed this tactic, they weren’t “setting their own goals” or displaying worrying initiative. They were rolling out a tactic that has been understood by American middle-schoolers for about two decades.

As usual, the real story is OpenAI dedicated tons of resources, using who knows how many very expensive GPUs for weeks, to run hacking tools to find exploits in a completely irresponsible manner. Their logging was so nonexistent that it was several days before they even realized that they hacked Huggingface. They should be thrown in prison for this, because anyone else would be if they committed a felony. But it has nothing to do with the super scary AI becoming misaligned and developing emergent behaviors.

I would not be surprised if in the future, there will be a serious cybersecurity incident resulting from some AI-based attack. Not because AI is going to be much scarier, but because I do not have high expectations for the security of most websites.

Yet another case of Rationality somehow not leading to savvy political instincts.