Some systems like SynthID (for Google’s AI) get around this problem. In fact you don’t need to know the LLM’s internal state, and defeating it would likely involve breaking up most blocks of 3 words. The oversimplified explanation is that it introduces a function g that gives a score to each word, with the score being (pseudo)randomly determined by your secret key. For each next word the LLM generates, the LLM produces a small list of candidate next words, and the one with the highest score according to g is selected. You should expect that the LLM will generally pick words with a high score, but the score itself is independent of the LLM. To detect a watermark, you need to know g and the secret key, and you check if the average score is much higher than expected from normal text.

Now, one question is, will this bias to the LLM to favor certain words? The solution is that for each next word, you append the last 3 words (nothing special about 3, just a small number) to the secret key for g, and this repeatedly scrambles which words have a high score. To defeat the watermark, you would need to break up most blocks of 3 words. I’m sure there are deeper issues with this, but I have not studied the topic that much.

From a technical perspective, I don’t really know how to let it track specific users. The goal is that if some AI vendor provides their key, it is easy for them to prove that their AI produced the text. So I guess if a user wanted to show that they generated the text, they could prove it. But usually AI users don’t want people knowing that they used the slop cannon. In any case, Anthropic’s own announcement states that their watermark does not contain identifying information for users.

In any case, some proud AI users are now frightened that there might be a way for people to identify that they use such a wonderful tool. How will I ever deliver low quality slop for my contracts now?

He said he uses AI for code reviews and translations, and worried an AI label on code shipped to clients could raise questions about authorship or trigger contract penalties.

Having code attributed to Claude “definitely would be not desirable,” he said, as he would not want professionally shipped code to carry an invisible marker that could raise questions about authorship, compliance, or client policies.

My pet theory is that their reading comprehension has degraded, so this is all they can handle now.

Anthropic is now watermarking the outputs of its AI. For once this is some AI news that doesn’t completely piss me off, and it’s amusing to see all the uninformed boosters get in a tizzy about this.

I actually understand at a reasonable level how this watermarking works. A year ago, I watched Scott Aaronson give a talk about it, and from what I know he was somewhat involved in developing the theory behind it while working for OpenAI. But at the time my thought was, “He is naive if he thinks these companies would ever implement this out of the goodness of their hearts.” And I was right; Anthropic is only doing watermarking now thanks to the EU AI Act, even though the theory has long been developed.

Watermarking doesn’t mean adding an extra watermark that can be easily removed. It instead directly affects the output of the chatbot itself. Fundamentally, an LLM is still a most-likely-next-word-predictor. More precisely, an LLM produces a probability distribution of what the next word can be. For example, “my pet is a …” could give a distribution of 60% dog, 30% cat, and 10% axolotl. Normally, an LLM would randomly choose the next word based on this distribution, and this is one reason why LLMs are nondeterministic (there’s another parameter called “temperature” that affects this, but no need to get into that).

With watermarking, instead of a truly random choice, the randomness instead comes from a cryptographic pseudorandom generator seeded with a secret key from the AI company. If you don’t know the secret key, then you can’t really tell that watermarking was used. But if you do know the secret key, then the idea is you can tell when the text was generated by the LLM because you know exactly what word should be next. It would be a freak coincidence if some non-AI text just happened to choose the correct next word every time. Thus, you can provide a service to tell if some text was generated by the LLM. (This technically makes the LLM “deterministic”, in a completely useless sense.)

Now, I think this is a step in the right direction, but it has its limits. The biggest problem is that you don’t want people to just move to a different LLM without watermarking, and that’s exhibit #832593 why government regulation is important. Another issue is that sometimes there is very little randomness in what the next word should be (“The first president of the USA is George …”). Finally, watermarking can be defeated by editing the output, although you would have to break up most of the blocks of consecutive words. I have a feeling most AI users are not the type to put in extra effort after copy-pasting the output directly from the chat window.

I suppose it will discourage some of the “use cases” of LLMs, such as drowning the world with spam Slopstack essays. Ah, who am I kidding? Everyone could already tell it’s AI generated, they don’t care!

Mathematicians are not entirely happy about OpenAI’s ten mathematical results. As it turns out, the proofs did not cite many of the existing techniques in the literature. The plagiarism bot is still a plagiarism bot. https://www.scientificamerican.com/article/openais-latest-math-breakthroughs-commit-research-misconduct-experts-say/

“They are running roughshod over the work of others who came before them in a deliberate way,” says Steven Miller, a mathematician at Yeshiva University, who argues that OpenAI has effectively plagiarized his own research. “It seems completely systematic to me, and it points to research misconduct.”

The LLM-generated proof hinges on a particular mathematical argument that it presented as its own but that actually first appeared in a 2016 paper by Miller and a collaborator.

The discovery stunned Francesco Fournier-Facio, a mathematician at the University of Cambridge, who studies group theory—at least until he “engaged with this breakthrough as I would if a human had written it,” he says. The result, he and some of his colleagues found, wasn’t as novel as it first appeared. Like a number of recent AI breakthroughs, it pasted together ideas from the mathematical literature to build a new theorem. Once again, the LLM’s trick is its superhuman patience for assembling puzzle pieces, not the ability to make some profound intellectual leap.

In particular, Astra’s key mathematical step combined ideas first found in two papers from 2016 and 2019. Andreas Thom, a mathematician at the Dresden University of Technology, who co-authored the 2019 paper, summarized the result on MathOverflow.com, calling it “creative and at the same time elementary.”

He is spending the equivalent of $1 million a year on tokens just to develop this game with 6 concurrent players on a good day. Of course, he isn’t actually paying that much. Instead, he just has a dozen different $200/mo Max subscriptions to get the same amount of tokens for $33k a year.

AI economics, baby!

Clammy Sammy acknowledges Ed Zitron for the first time! https://xcancel.com/sama/status/2084663673570971990

His response is the Live Laugh Love of 2015 Silicon Valley platitudes:

i would rather be an optimist and work hard than a pessimist posting about why things won’t work.

it’s much more difficult and the most likely path is failure, but society fails if people don’t try.

no amount of “it will never work” essays will drive society forward.

Of course, building data centers that use untold amounts of rare earth metals, electricity, and water while spewing out tons of pollution and scraping every last bit of human content in order to remove people from their jobs by doing them badly is what drives society forward. Profits fail if oligarchs don’t try.

The OpenAI hacking incident has put everyone on edge. Anthropic, not to be outdone, decides to publicly announce that they have committed not one, not two, but three crimes! Come on! I was told this wasn’t a marketing campaign!

Before the days of AI 2027, he became famous in 2024 for posting one of the original pieces of writing in the line-go-up genre: Situational Awareness. It seems that like any good grifter, he used this opportunity to make money (in this case by starting a hedge fund).

There is something about this picture, a warehouse full of books to be destroyed by Anthropic for training data, that just makes me sick. Knowing that they are destroying books in the abstract is bad enough, but seeing the sheer scale of it makes me viscerally angry.

Karen Hao called her book Empire of AI for a very good reason. The empire has all these noble narratives of bringing civilization and progress to the colonies, but in reality the empire only sees resources to be extracted, repackaged, and sold back to their subjects at a fee. Kudos to those who saw this immediately and fought against it. Shame on those who believed in the narrative of progress, to the point where many of them are collaborators with the empire.

Whenever a smarmy fellow on Linkedin posts about how AI is making so much progress in coding or math, I want them to see this picture. I want them to see the videos of families in tears after a data center takes away their home with eminent domain, and other families who have to deal with the resulting pollution. Go beyond the bloodless math of tokens and instead see for yourself what this all truly costs. I hope it was worth it.

It gets worse. I found another article about the same podcast: Sam Altman says AI won’t shorten the workweek because humans secretly enjoy being busy.

“Technology, for a long time, has been promising people that they’re going to work less and they’re going to have all this leisure,” Altman said. “But somehow we never get the promise of the four-hour workweek at mass scale in society,” he added. “And I don’t expect AI to change that.”

“It’s like a relative game. People are very focused on how they’re doing relative to other people,” the CEO said.

“I think we’re all going to be much busier than we thought we were supposed to be in a post-superintelligence world. We’re still going to complain about it, but secretly we’re going to be happy,” Altman concluded.

The chuds are all at r/accelerate now. “We’re the unapologetically Pro-AI alternative to r/singularity, r/futurology, r/artificial & r/technology which are becoming filled with Luddites, Techno-Decelerationists, Techno-Reactionaries, & Anti-AIs.”

Clamuel did another podcast. I don’t want to waste my time watching it, but I want to sneer at some choice quotes from news articles breathlessly reporting this stuff as gospel 1, 2

“We are now, like, in the singularity”

Is the singularity in the room with us right now? Even the people on X the Everything (Including CSAM) App aren’t so convinced.

Altman said on the podcast that just a decade ago, the so-called singularity still felt like a distant and improbable dream — something he and his colleagues would discuss casually over lunch.

“Now we’re actually in the moment that we used to talk about at the lunch table in a very not-serious way,” he said. “I’ve been waiting for this my whole life, and I think it’s going to be incredible, hugely positive, awesome for the world.”

Do you feel it? Do you feel incredible? Do you feel hugely positive? Do you feel awesome, now that the singularity is here? Oh wait, he’s speaking in the future tense again, never mind. Force of habit.

It’s also funny how he talks about how positive things are gonna be immediately after the super scary hack done on Hugging Face (really due to incompetence on both sides). Oh no, the AI is gonna kill us all! It’s going to be incredible, hugely positive, awesome for the world.

Later in the podcast, Altman criticizes AI leaders who have repeatedly warned that AI is dangerous. He didn’t name Anthropic, his primary competition, but its CEO, Dario Amodei, is well-known for making dire predictions about the future in his calls for greater attention to safety.

“I also think some of the alternative visions painted by other companies are quite terrifying,” Altman said. “I’m going to make sure that gets pushed against and is not what happens.”

At least Clammy realizes that all the doom trolling about how AI will replace and destroy all humans turns out, surprisingly, to lead to overwhelming negative public opinion. But this statement truly reassures me that Scammy has good intentions about AI safety. He will make sure this is pushed against and is not what happens. He wouldn’t want another super scary, dangerous hack that just so conveniently turns out to be excellent marketing.

OpenAI CEO Sam Altman says one of his biggest concerns about AI isn’t whether it becomes too powerful in and of itself, but that a single company could control that power.

Yeah, it would be really bad for Anthropic to control all the power of AI. It would be a disaster to hand everything over to Anthropic.

“Every time that humanity has traded off its liberty for safety, it’s been a long-term net loss,” Altman said. “We are going to put this in the hands of people. We’re going to empower them. We are going to let society express its ideas and use this technology in the way they want.”

Society is expressing its ideas about AI, all right. But I feel like they aren’t what he has in mind.

Altman, meanwhile, said preventing such power from becoming overly concentrated has long been central to his vision for AI, even as his company continues to keep its own models closed.

“For so long now, I have felt focused on this singular goal of abundant intelligence and a belief that incredible human prosperity will come from that, as long as we don’t have a weird power concentration and kind of a new authoritarianism,” he said.

Well, we seem to have a weird power concentration and “kind of” a new authoritarianism, but I don’t see any abundant intelligence or incredible human prosperity.

Lots more sneers on the Better Offline subreddit.

It is surprising how many exceptionally strong mathematicians have started working for OpenAI and Anthropic. These people would have easily become professors at top universities if they stayed in academia. I think many mathematicians, especially the competitive ones at the top, have a “progress at any cost” attitude (and I’m sure the paychecks helped). As for the results, you still need good mathematicians to sift through all the output to identify that the proofs are valid.

I would honestly be positive about universities developing their own specialized math AI (in an ethical manner) to help mathematicians get these kinds of results, but right now, AI is inseparable from these evil companies. Thankfully, I believe this is a likely outcome in the future because the AI companies will one day implode.

From what I’ve seen, most prompts are in plain English. I suppose the part where the AI parses the statement correctly is much easier than the part where it boils a couple lakes in the process of bashing its head against the wall trying millions of different combinations of random shit from the literature to slap together a proof. For one of the big results (cycle double cover), the prompt specified that the AI could use 64 subagents and was required to not give up for at least 8 hours. The tokenmaxxers would be proud, we didn’t need that forest anyway. Thank god math doesn’t have a CTO to look at the expense reports.

Among the three big results I’ve looked at (unit distance problem, cycle double cover, Jacobian), two were counterexamples and one of them had a short 3 page proof using ideas from the 1970s. The Jacobian conjecture is an extreme case because a single counterexample is enough (for unit distance, you technically need a family of counterexamples), and it is easy to check with very basic computations. It is telling that all of these announcements came from OpenAI or Anthropic employees, who presumably have unlimited access to their AI. Nobody really knows how many resources they spent on this, or what else they tried. Nobody really seems to care about this question, either.

I think there is a phenomenon where supposedly hard questions are much easier than expected, because by chance nobody found the right approach for a while, and eventually it becomes famous as a “hard problem” which makes nobody want to attempt it.

What I’m more worried about is many people starting to use AI to try and prove small lemmas for them in their projects. Of course, a $200/mo subscription is absolutely necessary to them. This honestly feels like a repeat of Claude Code back in February. The software engineers eventually realized that AI is absurdly expensive after the AI companies realized that spending $14000/mo to service a $200/mo subscription is a bad idea. If the AI vendors couldn’t squeeze money out of rich software companies, what exactly are they gonna get out of poor mathematicians and universities? Also, there is the cognitive decline caused by overuse of LLMs that has yet to set in.

I think a serious possibility is that AI generated papers flood the zone with uninteresting incremental results that are eventually meaningless and full of mistakes. Right now, math is full of smart, dedicated people, so at least major results are reviewed carefully. But as AI alarmism drives away many honest people from the field, the remaining mathematicians will be burdened with far more work to review, and their cognitive faculties will be eroded by LLM use. Despite 4 years of development, $3 trillion of debt, mountains of stolen data, all the agents and harnesses and loops and other expensive tricks, as well as the advantages of Lean in math research, LLMs still hallucinate.

I believe this is happening with software, but at least there are objective consequences for screwing up there (guy gets his home directory deleted, email is sent on a guy’s behalf without permission, small business gets every customer subscription cancelled). But nothing bad happens if there is a mathematical mistake in a paper and nobody catches it. One could say to just provide a Lean proof, but there is still the issue of making sure the Lean code actually matches the content of the paper. Exactly what force will correct things?

Still, I don’t think this is the most likely possibility. The AI companies are extremely unsustainable financially, and it’s not like they’re very popular. Once they collapse, I believe there will be a re-evaluation of how LLMs should be used in research. If they are used (let alone trained), someone is going to have to pay the bills.

In the end, we have to ask ourselves the question of why one does math. To me, math is not really a field where you memorize trivia. The real value comes from being able to think abstractly and rigorously from first principles, and from understanding why something is true rather than just knowing it is true. It is another aspect of your ability to reason as a free human. A few dedicated people go into math research, but your skills can easily go to many places. If you’re starting undergrad, you have plenty of time to see how this all pans out before making a decision.

Here’s A Taxonomy of Omnicidal Futures Involving Artificial Intelligence by Critch and Tsimerman, which approvingly cites our favorite AI 2027. Nobel disease is still a thing (I think the rationalist beliefs are quite pervasive among mathematicians).

I find it really funny how after he gets booed he says, “If you don’t care about science, that’s okay, because AI is going to touch everything else as well. Whatever path you choose, AI will become part of how work is done.” Yeah, if you’re worried that AI is only going to fuck up science, don’t worry, it’s going to fuck up everything else as well. Was he trying to stick to a (terrible) script, or is he genuinely this incapable of reading a room?

“When someone offers you a seat on the rocket ship, you do not ask which seat. You just get on.” No, my mom taught me about stranger danger. I know what to do when a sketchy old man named Eric Schmidt pulls up with a rocket ship that says FREE ICE CREAM.

“The rocket ship is here. Let me give you some advice. First, find a way to say yes. Listen.” Thanks for revealing how AI adoption is really about coercion. It doesn’t matter what you think, AI is inevitable and you ignorant Luddites are gonna have to find a way to like it.

Truly a masterclass in public speaking by Eric Schmidt. When the audience reacts negatively to what you said, just double down and shove it down their throats. You’re a billionaire, so you know better than them.

How many people, if they were given $1.3 million just once in their lifetime, would figure out far better uses for that money than this guy?