Why do they need an IPO if they are going to unleash the singularity?
Surely raising capital by offering fractional ownership of the company that produced the god-like super-intelligence will be of no concern to the god-like super-intelligence or anyone else in short order . . .
If man was made in god's image, and super-intelligence was trained on man's (hardrive)image then those shareholders who kept the faith will have a special place in the cloud when judgement day comes
The way the singularity is often described reminds me of the arms buildup in preparation for a world war. When a war is coming, governments become convinced that the nation’s factories and natural resources need to be devoted to military production, instead of making things useful to civilians. During World War II, rationing was used to suppress civilian economies to devote more resources to the war effort.
The difference is that with a singularity, industrial capacity is supposed to get redirected via magic (nanotechnology), rather than because governments ordered it and spent money on it.
I do think AI companies are iterating faster by using AI to automate their own AI research. You could call that the beginning of recursive self-improvement. But does this result in magically growing an independent industrial base? I don’t really see it.
I think they’re going to build more data centers, and the electricity generation to power them, pretty much the same way as they do now, by convincing investors to spend money on them (selling stock and bonds), earning more revenue, and paying contractors to build them.
We are getting more and more resources devoted to building data centers, as well as more resources devoted to weapons buildups for wars in Ukraine and the Persian Gulf. These aren’t good trends! But they are happening via normal economic activity.
Presumably, those with the levers of power in their hands will willingly do as the super-intelligence says. Particularly when they start being outcompeted by other powerful people that do as the super-intelligence says, they either serve the super-intelligence or become irrelevant.
Obviously, some powerful people face little competition, and super-intelligence is still limited by available information, so some people will be able to resist.
> During World War II, rationing was used to suppress civilian economies
And price-rationing is suppressing consumer use of RAM right now. I'm very glad that the people who think that this isn't rationing, or who think that it's maximally enlightened rationing, aren't trying to allocate my food and aren't trying very hard (yet) to allocate my water and electricity.
Knowing what I know about publicly traded companies and their willingness to bend roadmaps into contorted pretzels, this is not how an "imminent" RSI capability would be advertised to the market.
If it was truly imminent they would claim they already had RSI. That is how the game works. Take a look at IBM and quantum if you require an additional sample. Review the things these companies strongly imply they can do relative to what they can actually do. Anthropic is arguably making less aggressive claims with this RSI narrative.
The lies flourish in the definitions behind these terms. When Anthropic says "RSI" and IBM says "Quantum", they are invoking a highly curated lawyer routine that has a mountain of legal documentation surrounding it.
Anyone believing this is being tricked into piling money into one of many AI startups that don't have a moat. They all claim they're getting close to this and the high valuations depend on that belief no matter how silly it is.
AI companies have intense competition and the only way forward to differentiate themselves will be pricing. RSI is the promise made to allow the founders to cash out some value before it all comes crashing down.
AI companies are providing a lot of value to others but they aren't any good at capturing that value themselves.
in a small sense, recursive self improvement could be considered trivial theoretically and just has a bunch of practical hurdles.
if you somehow gave a current frontier llm to a full stack of the things that are involved in running an llm, including the graphics hardware its running on, the acquisition and filtering of training data and methodologies, and the ability to enact physical changes to make desired improvements - it would almost trivially make small and incremental improvements right away. like, there is zero doubt within a relatively short amount of time claude 5.5 would make a claude 5.6 that's the same but just needs slightly less compute, therefor enabling itself to build a slightly larger model for the same compute, or similar.
the big question with RSI is whether further scaling like this creates some fundamental capability jumps. the answer has been yes in the past for this, but even though its a bad predictor so far we could be at an end for scaling.
Its more of a debate on what the business model is IMO. It's entirely reasonable to be a very large and successful commodity trader/producer, or to make mountains of money on low-margin hive volume transactions.
The current pricing of AI revenue growth is based on them delivering strong software like margins with the growth of a commodity producer. This only really works if you have something sticky which customers will pay a premium for - in the cloud business this became security, automation, "data gravity", and migration costs.
Or you know, NVidia could not charge a 98% margin on their cards...
I don't know why I seem to be the only one to wonder this but .. even if AI hypothetically can output smarter data to train a better AI, how do we know for sure this loop just goes on without eventually plateauing? What if what happens is just .. the data becomes more high definition, and then it's just the same iteration of the same "high" definition.
That is, however, the argument at its core. We don't know where RSI would lead. It could produce something roughly human-intelligent. It could produce something slightly smarter than a human. It could produce something radically, radically smarter than a human. We don't know. A lot of people suspect the correct scale would be a logarithmic one, which seems to conform to the pace of improvement being seen, but that's not something we know either.
It could also produce any of the above, but with a reasonably human architecture to the resulting intelligence, or an architecture radically divergent from ours. Although which of those is "better" or "worse" is a rather open question.
This is indeed part of the grand illusion in the field - that improvements in cognitive capabilities translate linearly into research progress. There are talks where experts report projections of RSI progress growing exponentially.
This is why AI companies use research in a deterministic and rigorous field (mathematics) as a benchmark - it creates the impression that humanity will achieve comparable progress in other fields.
Having said like, like for other fields (e.g. graphics), hardware capacity slowly increases, and new algorithms are devised, so there will hardly be a strict plateau.
I'm not implying that research progress will be necessarily very slow, but that it's severely misrepresented for sure.
The reality of "RSI" is just attempting to automate the jobs of people working on building/training LLMs - it's not about creating "smarter data". Much post-training nowadays doesn't even use data - it uses thousands of custom training environments for agents to attempt to do better in.
It even seems to me that plateau-ing is an inevitability. Unless there is some way to reach AGI which you could deduce from a combination of ideas which already exist in the training dataset, but that seems rather unlikely.
Yes, I'm quite confident that's what OpenAI is doing. These math proofs are just a side effect of them trying to create new data out of the blue for a better model. But that's still chasing a wild goose and hitting jackpots with 10,000 active agents, it doesn't necessarily prove RSI.
Digital eugenics, phrenology, etc. Humans constantly think they can identify a optimum fit function for humans in society. Because thats easier making a society optimum for everyone.
This is no different. Its like a single ant trying to redefine what a ant is. Typically with catastrophic results.
Question: why have LLM's gotten so much better at coding over the last couple of years, yet the improvements at writing prose have been so minimal?
theory 1: there is something ineffable about writing prose that makes this a very hard/impossible problem
theory 2: the frontier labs put relatively little effort into improving prose writing
I hope theory 1 is correct, but I suspect it's theory 2. The frontier labs have concentrated their effort on self-improvement, which is largely a coding task.
Just like you can train AI by making it play games (Chess or Go) against itself, you can kind of gamify writing code (bonus points for compiling, and passing more and more tests) make AI improve by doing that. I assume the same can (or was) also done for mathematical proofs in Lean.
You can't do that with prose as there is no notion of "winning" or "correct" prose. It's subjective so you can't tell what is better without human input.
LLM's are really good at that, though. If you feed it as input a bunch of critiques, then LLM's become very good at that critique, and can self-improve to a certain degree.
This only works for a little while until you need to involve humans again and kick off another cycle.
> It's subjective so you can't tell what is better without human input.
You can say the exact same thing about the type of coding that LLM's have gotten so much better at in the last couple years. Take a look at the criticisms of LLM code from a couple years ago. They were really bad at the stuff that seemed ineffable and required "taste".
Theory 3, code is nearly unique among all things we could train for, in that the training loop consists of computation, and in the case of coding and coding alone is also the training of computation.
The language is as it is because it speaks not to please humans. It must speak to be efficient and clear, to produce material that is dense and concise. It's training at tasks takes away from the excess and decor and artistry of delicate human world selection, our strange habits of speaking. Our languages are misaligned with the weights of our world, not commitment to the efficiency of task and purpose. These are defects that llms, when forced to rasterize their weights down to these chain of terms, must be asked to recreate and there are many different styles and forms to pick from, and the training favors none of them.
I don't understand the negativity around this topic here. I think that RSI has almost certainly already arrived, just the early stages of it at least. RSI is simply compounding capabilities- current AI systems are absolutely increasing productivity when it comes to training new AI systems, it's one of the things they're best at. Even if it's only a flat 10% gain YoY now and forever, that compounds over time. A 10% increase today becomes a 21% increase next year, a 33% gain the year after, etc. After 7 years, you've doubled the rate of AI progress. And that rate will almost certainly go up over time rather than holding at 10%. Even if AI sucks at every application other than AI research, RSI will still happen.
Oh, imminent, right? I like the word imminent. Imminent UFO disclosure. Imminent Bitcoin usage as a currency. Imminent Metaverse adoption. Imminent peace talks with Putin.
It's nice to a have a certainty, a predictability in these stormy times. It's reassuring. Imminently reassuring, I mean.
> Anthropic's IPO Filing Warns It May Be 'Imminently' Close to Recursive Self-Improvement — But Its Own Data Shows the Loop Isn't Closed
Sorry, I'm sick of this kind of language games. If the loop really closed they wouldn't be calling it "close to RSI". Yhis is just not how the word "but" works in English.
Anthropic, according to the author, is saying they may be close to RSI.
And the author is saying it's own data shows the loop isnt closed.
Anthropic is not saying that RSI is imminent BUT their data shows the loop isnt closed.
The article and title are a bit problematic as well. Anthropic published an article "When AI builds itself" in May 2026 where they say they are not at RSI yet. This article is the source of what the author has identified as Anthropic's "own Data."
But then he pairs this data, with Anthropic's later statement in the S-1A from October and then overstates their comment.
>"our internal estimates and our expectations regarding future models, rather than our current models, indicate that we may imminently meet our Responsible Scaling Policy’s definition of ‘dramatically accelerated progress attributable to AI.'"
It seems like a lot of companies are figuring out what Elon Musk has been doing so well for years: you can make a company worth whatever you want if you can sell people on a vision of how it could be worth that much.
I've heard two explanations for how Anthropic could be a good bet at its IPO price. 1. They are going to use their better-than-everyone-else AI to basically win every industry simultaneously. 2. They "get their first" and build an AI god.
If you believe 1 then you better invest in Anthropic because soon enough they will be the only company. If you believe 2 then maybe it doesn't really matter anyway?
Recursive Self-Improvement seems very much like a mirage — it looks close but is in fact more like Zeno's paradox, but where the paradox rules.
Sure, once they get to RSI, it could be explosive improvement.
But real RSI requires closing the loop every time for every improvement.
There are three result types for each upgrade
1) the AI makes an upgrade entirely independently and it is an actual improvement
2a/2b) the AI makes an upgrade entirely independently and it is neutral or degrades the system
3) the AI makes an improvement that must be overseen by a human.
It only takes one bad upgrade to start spoiling the whole batch, even if the other 999 are good type-1 AI-self-improvements.
Otherwise the AI improvements will be limited to an AI-driven plateau or AI-driven self-degradation, or limited to a pace related to human capabilities.
They have already improved it so 80% of the Type-1 upgrades made by the AI itself. Great! Getting 80% of the next 20% will still require human input and likely the same effort. And so forth.
Just because we've gotten 80% in 3 years, and to 96% in the next two years, etc., doesn't really predict when we get to the ACTUAL 100% AI upgrades needs for real RSI.
Just thinking aloud here, but it seems it'll go at a slowing asymptotic pace, then some breakthrough may cause a jump in self-capability, but when is entirely unpredictable on any linear or exponential scale. I wouldn't bet a lot of money on it happening on an investment time-scale, maybe just a little like buying a lottery ticket.
"Some of the time it works every time" is all investors seem to need to see these days. Even the former might not be needed, replacing "some" with "none" given how Theranos went.
Surely raising capital by offering fractional ownership of the company that produced the god-like super-intelligence will be of no concern to the god-like super-intelligence or anyone else in short order . . .
The difference is that with a singularity, industrial capacity is supposed to get redirected via magic (nanotechnology), rather than because governments ordered it and spent money on it.
I do think AI companies are iterating faster by using AI to automate their own AI research. You could call that the beginning of recursive self-improvement. But does this result in magically growing an independent industrial base? I don’t really see it.
I think they’re going to build more data centers, and the electricity generation to power them, pretty much the same way as they do now, by convincing investors to spend money on them (selling stock and bonds), earning more revenue, and paying contractors to build them.
We are getting more and more resources devoted to building data centers, as well as more resources devoted to weapons buildups for wars in Ukraine and the Persian Gulf. These aren’t good trends! But they are happening via normal economic activity.
Obviously, some powerful people face little competition, and super-intelligence is still limited by available information, so some people will be able to resist.
And price-rationing is suppressing consumer use of RAM right now. I'm very glad that the people who think that this isn't rationing, or who think that it's maximally enlightened rationing, aren't trying to allocate my food and aren't trying very hard (yet) to allocate my water and electricity.
If it was truly imminent they would claim they already had RSI. That is how the game works. Take a look at IBM and quantum if you require an additional sample. Review the things these companies strongly imply they can do relative to what they can actually do. Anthropic is arguably making less aggressive claims with this RSI narrative.
The lies flourish in the definitions behind these terms. When Anthropic says "RSI" and IBM says "Quantum", they are invoking a highly curated lawyer routine that has a mountain of legal documentation surrounding it.
AI companies have intense competition and the only way forward to differentiate themselves will be pricing. RSI is the promise made to allow the founders to cash out some value before it all comes crashing down.
AI companies are providing a lot of value to others but they aren't any good at capturing that value themselves.
if you somehow gave a current frontier llm to a full stack of the things that are involved in running an llm, including the graphics hardware its running on, the acquisition and filtering of training data and methodologies, and the ability to enact physical changes to make desired improvements - it would almost trivially make small and incremental improvements right away. like, there is zero doubt within a relatively short amount of time claude 5.5 would make a claude 5.6 that's the same but just needs slightly less compute, therefor enabling itself to build a slightly larger model for the same compute, or similar.
the big question with RSI is whether further scaling like this creates some fundamental capability jumps. the answer has been yes in the past for this, but even though its a bad predictor so far we could be at an end for scaling.
The current pricing of AI revenue growth is based on them delivering strong software like margins with the growth of a commodity producer. This only really works if you have something sticky which customers will pay a premium for - in the cloud business this became security, automation, "data gravity", and migration costs.
Or you know, NVidia could not charge a 98% margin on their cards...
But then again I felt the same way about SpaceX and it's up. Oh wait.
That is, however, the argument at its core. We don't know where RSI would lead. It could produce something roughly human-intelligent. It could produce something slightly smarter than a human. It could produce something radically, radically smarter than a human. We don't know. A lot of people suspect the correct scale would be a logarithmic one, which seems to conform to the pace of improvement being seen, but that's not something we know either.
It could also produce any of the above, but with a reasonably human architecture to the resulting intelligence, or an architecture radically divergent from ours. Although which of those is "better" or "worse" is a rather open question.
This is why AI companies use research in a deterministic and rigorous field (mathematics) as a benchmark - it creates the impression that humanity will achieve comparable progress in other fields.
Having said like, like for other fields (e.g. graphics), hardware capacity slowly increases, and new algorithms are devised, so there will hardly be a strict plateau.
I'm not implying that research progress will be necessarily very slow, but that it's severely misrepresented for sure.
This is no different. Its like a single ant trying to redefine what a ant is. Typically with catastrophic results.
theory 1: there is something ineffable about writing prose that makes this a very hard/impossible problem
theory 2: the frontier labs put relatively little effort into improving prose writing
I hope theory 1 is correct, but I suspect it's theory 2. The frontier labs have concentrated their effort on self-improvement, which is largely a coding task.
Just like you can train AI by making it play games (Chess or Go) against itself, you can kind of gamify writing code (bonus points for compiling, and passing more and more tests) make AI improve by doing that. I assume the same can (or was) also done for mathematical proofs in Lean.
You can't do that with prose as there is no notion of "winning" or "correct" prose. It's subjective so you can't tell what is better without human input.
This only works for a little while until you need to involve humans again and kick off another cycle.
> It's subjective so you can't tell what is better without human input.
You can say the exact same thing about the type of coding that LLM's have gotten so much better at in the last couple years. Take a look at the criticisms of LLM code from a couple years ago. They were really bad at the stuff that seemed ineffable and required "taste".
I do think their writing is probably much more malleable than people know. I'd be very interested to see what humanities majors can do to prompt it into different forms. One of the only attempts I've seen is Mike Caulfield's "Jamesian" https://checkplease.neocities.org/jamesian/ https://mikecaulfield.substack.com/p/a-300-word-prompt-that-...
The language is as it is because it speaks not to please humans. It must speak to be efficient and clear, to produce material that is dense and concise. It's training at tasks takes away from the excess and decor and artistry of delicate human world selection, our strange habits of speaking. Our languages are misaligned with the weights of our world, not commitment to the efficiency of task and purpose. These are defects that llms, when forced to rasterize their weights down to these chain of terms, must be asked to recreate and there are many different styles and forms to pick from, and the training favors none of them.
But, I mean, there are ergonomics experts who could help with that.
Dario will send you a postcard from Fiji if they achieve RSI, AGI, any mass extinction events, or any other milestones.
It's nice to a have a certainty, a predictability in these stormy times. It's reassuring. Imminently reassuring, I mean.
Sorry, I'm sick of this kind of language games. If the loop really closed they wouldn't be calling it "close to RSI". Yhis is just not how the word "but" works in English.
Anthropic, according to the author, is saying they may be close to RSI.
And the author is saying it's own data shows the loop isnt closed.
Anthropic is not saying that RSI is imminent BUT their data shows the loop isnt closed.
The article and title are a bit problematic as well. Anthropic published an article "When AI builds itself" in May 2026 where they say they are not at RSI yet. This article is the source of what the author has identified as Anthropic's "own Data."
But then he pairs this data, with Anthropic's later statement in the S-1A from October and then overstates their comment.
>"our internal estimates and our expectations regarding future models, rather than our current models, indicate that we may imminently meet our Responsible Scaling Policy’s definition of ‘dramatically accelerated progress attributable to AI.'"
- Bill Clinton
I've heard two explanations for how Anthropic could be a good bet at its IPO price. 1. They are going to use their better-than-everyone-else AI to basically win every industry simultaneously. 2. They "get their first" and build an AI god.
If you believe 1 then you better invest in Anthropic because soon enough they will be the only company. If you believe 2 then maybe it doesn't really matter anyway?
Recursive self-improvement isn't some sci-fi magic singularity; just the same "we're super powerful, ppl should be afraid, so give us your money" bs.
The first fund manager who puts this junk in my portfolio is fired.
Sure, once they get to RSI, it could be explosive improvement.
But real RSI requires closing the loop every time for every improvement.
There are three result types for each upgrade
1) the AI makes an upgrade entirely independently and it is an actual improvement
2a/2b) the AI makes an upgrade entirely independently and it is neutral or degrades the system
3) the AI makes an improvement that must be overseen by a human.
It only takes one bad upgrade to start spoiling the whole batch, even if the other 999 are good type-1 AI-self-improvements.
Otherwise the AI improvements will be limited to an AI-driven plateau or AI-driven self-degradation, or limited to a pace related to human capabilities.
They have already improved it so 80% of the Type-1 upgrades made by the AI itself. Great! Getting 80% of the next 20% will still require human input and likely the same effort. And so forth.
Just because we've gotten 80% in 3 years, and to 96% in the next two years, etc., doesn't really predict when we get to the ACTUAL 100% AI upgrades needs for real RSI.
Just thinking aloud here, but it seems it'll go at a slowing asymptotic pace, then some breakthrough may cause a jump in self-capability, but when is entirely unpredictable on any linear or exponential scale. I wouldn't bet a lot of money on it happening on an investment time-scale, maybe just a little like buying a lottery ticket.
Classic Anthropic.