I think it’s really been underrated to what degree certain problems are searchable (as opposed to just verifiable). So some might be extremely amenable to search! But in other cases, the stuff in the Library might be extremely spread out, so even if you search and search and search you don’t get anything.
Like you said, I think science is the domain where ASI fails to materialize, because there is no amount of intelligence that allows you to skip having to do real world experiments, and I think we're pretty far away from fully automated laboratories. Science really is very different from math and programming.
When simulations do enough to show that they were correct, science fields will also fall. Protein folding will eventually be solved. Chemistry and materials science as well. Biology and Psychology are likely the last to go as they're numerous pitfalls and unknown unknowns.
In real world, e.g., materials science, simulations can’t “show that they were correct”. Only experiments can. This puts a natural ceiling on possible progress rates. Basically, Singularity is impossible and we’re not going to colonize the stars.
People "believe" funny things, don't they. "Humans will never achieve heavier than air flight" (just before the Wright Brothers). "Physics has basically all been discovered and all that's left is to tidy up the details" (just before Einstein). "Rockets for space travel are too expensive for non-governmental organizations" (just before Elon). "We're not going to colonize the stars" - yeah, right. If we don't destroy ourselves first, we're going to colonize the stars. Imagination. Bring someone from the 18th century into this world and find out just how many things we already do that he would tell you are "impossible" or "magic". And we're only 8000 years into growing civilization. Wait till it's 20,000 years and see what we're doing.
It seems quite safe to say that no humans alive are going to make it to another solar system. The distances are just too great. But that doesn’t mean it’ll never happen.
I don't know what's "safe to say" in the early years of AI. Who knows what will be possible with longevity technology, and maybe cryogenics, and spaceship propulsion, and even understanding of basic physics principles, in, say, ten years from now?
"the engines and hull are not designed for it" it's almost like we would need to invent new and potentially impossible to make sci fi materials to make all this work. To do interstellar travel at human timescales you need to invent a hull that can withstand being hit with pieces of space dust that will hit with the force of a nuclear explosion (because of how fast the ship will be going). I don't know how you can justify that we "don't need new scientific knowledge" to do interstellar travel.
As simulations improve, we can perfectly predict certain qualities and performance; obviously checked by real experiments. Superconductivity is a good example. No one predicted the "dancing" found in Cooper pairs. If we can get simulations that would have predicted those movements, we would have made better atomic lattice. Colonizing stars sysyems doesn't require any huge new additional technology knowledge, by the way. It's basically time, energy, and desire.
Real experiments is the whole point though. Example: I’m sure AI will come up with novel battery chemistries and construction. Still need months of charge cycling to know how they perform. Many such cases.
Sure, time and energy. In such massively enormous amounts that new tech is needed, you don’t expect the Starship to actually scale up for a trip to Sirius, do you?
If you know the answer is right, then why would you need the tests?
If the simulation is „good enough“, then the experiments are pointless, just optimize until you hit your requirements in simulation and then start mass production.
Sure, currently many models are inadequate for this, but finding models is something which AIs can actually do.
See, you can’t know the answer is “right” until you actually make the thing. You can simulate your chip till cows come home, but you don’t know it will work until you get it back and run validation and qualification tests. And those can’t be skipped or shortened, 1000-hour THB takes 1000 hours, it couldn’t care less how smart your LLM is.
Reality is irreducibly complex, the map is not the territory, and you cannot simulate things for which you don’t have experimental results.
Starship is not an interstellar transport. The engines and hull aren't designed for it. There's numerous books and proposals that have nothing to do with Elon's vision. His goals are not the only way. O'Neil cylinders are just one example.
I mean, the point is that the amount of energy needed is so enormous that chemical rockets are not going to cut it unless we’re expecting to travel for millennia. We don’t have that tech either….
The control-group point is the actual hinge of this argument. Demonstrating a frontier capability step-up requires showing the prior production model couldn't surface this disproof given the same search budget and compute envelope. Without that comparison, the announcement reads as a lucky hit across an open-problem set, not an architecture jump. The Gowers and Litt names confirm the disproof itself meets a top-journal standard; neither speaks to whether the previous model class would have surfaced the same construction under identical search ceilings, which is the comparison OpenAI would need to publish to convert this from a marketing release into a measurable capability claim.
The control group OpenAI would need exists, sort of: they consulted Litt to verify the proof but published no head-to-head against the prior model under matched search ceilings. Worth noting Gowers wrote his commentary "solicited by OpenAI," so even the standards check sits inside the company's framing. A capability claim verified by your own consultants is closer to the open-source-as-subsidy logic Meta abandoned: openness ends where the returns begin.
This is the very definition of a strong-link situation, isn't it - if our best mathematicians are still better than AI our whole species feels better, even if we personally can't understand the maths/science/coding problems, let alone the solutions.
Your last paragraph for me brings to mind the replication crisis and the similar hype and disappointment experienced around many of those "findings." The tasks of generating new information, and determining whether that information is true, are different if related things. The scientific method provides a framework to help them move together, but as a problem grows in complexity, it becomes easier for methodological errors to hide, and thus takes more time to replicate and verify. Due to limited attention, we have historically leaned on authority and process to mitigate this, with results varying as widely as the politics and cultures of our institutions. But increasing the available attention doesn't remove the problem, it only changes it.
If our benchmarks are saturated, and as AI continues to beat our self-imposed human benchmarks, we rapidly approach the boundaries of knowledge. Alignment questions aside, whether it's humans, humans+AI, or just AI, new knowledge must be dissected and integrated into a larger world model. Is it true? How do you prove it? What resources or underlying knowledge do you need to prove it? What kinds of mistakes might someone make along the way? Is it just a search problem, or are there critical new insights or techniques required? What other knowledge must be updated as a consequence? And so on.
Of course, people like "good enough," and we accept the risks of application failures as a cost of doing business. If it's important enough, we will engineer and iterate our way to success. But if attention is less of a bottleneck, then maybe more can be done before you get to that point. Or perhaps must be done, if you want to avoid a future of smoke and mirrors covering up unverifiable slop.
Working in cybersecurity I just wanted to point out that "serious resources are not spent trying to find exploits" is very much wrong. Software vulnerabilities and their exploits are very much sought for, they are very valuable(up to millions for an unknown vulnerability in the wild) and national intelligence services for sure spend serious resources trying to find them.
What is different with Mythos/equivalent is that they can tirelessly go through more code than any human, and thus find more vulnerabilities. Also all vulnerabilities aren't equal, some enable instant data breach, some are a lot more low risk. But we have known for a long time that in general software is full of mistakes, but been lacking a scalable solution. This might be it.
It seems to me that any time I hear (read) someone saying something hyperbolic about AI, that once you cut thru the hype, there is almost always an element of anthropomorphizing that underlies their blindness. I get it. It is a lot easier to imagine how a consciousness arising in an AI could then have super human intelligence and out perform humans at everything - it makes it feel more plausible because we are human! And even when this hyperbolic person states they know what they are experiencing is not real (e.g. AI consciousness) the fact that it feels so real allows them to forget it's lack of realness. Can someone with literary clout please write a parable for this pitfall?! I imagine something akin to looking in a magic mirror and seeing a reflection that isnt real but everyone starts to think its real, and the mirror company says "sure looks real to me, you should buy some...". Or something like that =)
For people who want to be AI-bearish, the argument about feathers should not be a successful cope. Progress is just as fast as it seems (but yes, gradual, not a single lightbulb moment). Anyone super surprised by this result has not been paying attention.
Some people think they are creating a rigorous, bright-line experiment when they set up a scenario like "an AI will never be able to X" where in this case X is "produce novel mathematical research (defined as Y) with only a small non-specific prompt Z" etc. But there is no bright line, just a continuum.
This does sound a bit like when people used to say: "yes well, okay a program can now beat a chess master but it will never beat the best human chess player"
Like all human-made tools based on human-led labor, this is not exactly surprising. Impressive and noteworthy that a novel solution was elicited and produced by AI! But to say AI alone solved this problem would be to say that humans are not the ones who have solved problems or improved on multiple fields of study with computers - that computers are the intelligent recipient.
A verifiable AI solution to a mathematical problem - one rapidly improved upon by non-AI human iteration - proves by a posteriori necessity that such a problem always was solvable. An extremely useful tool in the repertoire that aids our fields of labor, but which itself cannot derive productive value without human input or predicate knowledge.
What about dissecting Timothy Gowers's Words and tone of speaking? What does he want to promote? Either way, mathematicians should study math as they used to, maybe research methods will change.. But he, as a respected mathematician, had a disappointing tone
I actually think the "Dumbo phenomenon" is basically pointing toward a stagnation in LLM capabilities. Back in 2023, some researchers wrote a blog post arguing that "The 'it' in AI models is really just the dataset." The core idea was that for sufficiently massive neural networks, the architecture doesn't really matter—the dataset is what dictates the performance. Later, when Ilya Sutskever shared The Platonic Representation Hypothesis paper, it essentially pushed the same point. I’ve looked into this, and there are already four or five papers out now that echo this. They all seem to show that once your dataset and parameter count hit a certain scale, the architecture's influence fades away, and data becomes the absolute deciding factor. Pair that with the recent news about Anthropic buying up rare books just to scan and destroy them, and you get a pretty grim takeaway: current AI progress might just come down to four things: energy, GPUs, data, and the organizational muscle to keep the training from imploding.
In the Chinese tech community, there have been interviews with Chinese engineers at OpenAI and Anthropic, and they’ve mentioned that the actual day-to-day work in AI dev doesn't exactly require some insane level of genius or creativity. They describe it as "interviewing like you’re building a rocket, but once you’re hired, you’re just turning screws." Plus, some OpenAI employees mentioned that they took a totally different technical path for coding optimization than Anthropic, yet ended up with nearly identical results. Maybe that’s more evidence for "data determinism," right?
But we know humans can interpret the exact same data in totally different ways. That suggests human intelligence isn't just "data determinism." Sure, you could argue that no two people have the same life experience, so our "data" is never identical. But here’s the thing: when we train neural networks, we initialize the weights randomly, and it’s wild how models trained on the same data end up looking so similar. I can’t imagine human learning working like that "blank slate" model. It’s pretty clear that AI intelligence and human intelligence aren't the same thing. AI is way more dependent on the raw data. For an AI, data is everything—if that well runs dry, everything hits a wall, and this is what actually happens now.
If it were purely the dataset, you'd expect interpolation, not a disproof of a conjecture that sat unsolved for 80 years. That result isn't sitting in the training corpus by definition, which is partly why Litt called it the "unique interesting result produced autonomously by AI so far." The dataset thesis explains why models converge; it struggles to explain the rare case where one produces something genuinely absent from its data.
It’s got nothing to do with interpolation. Many major mathematical breakthroughs come from gradually piecing together existing results. It’s not rare at all to find fresh insights for unsolved problems hidden in old literature—take the Fields Medal that was inspired by Gauss’s notebooks, for instance. That said, it doesn’t mean the 'search' process itself is easy. You could argue that this consistency signals the model is converging toward a powerful, singular “God” solution, for which I would not buy. Until the experiments render a verdict, it’s all fair game for speculation.Giving Big AI a pass today.
Those viewing that chart with the cloud of AGI definition points should realize that it only includes AGI definitions imagined by AI fanboys. The true human-equivalent AGI definition is way off the chart. It's a bit like a solar system diagram with true AGI as Proxima Centauri. If your chart shows Pluto at the edge of the paper, the closest star is about a mile away.
Your Proxima Centauri framing has a useful corollary in the Erdős result itself: the model found the elegant insight, but humans still had to rewrite the proof to make it legible and up to standard. A discovery that can't yet articulate itself clearly has a lot of light-years left before anyone reasonably calls it human-equivalent. The chart measures distance to the nearest definition, not the hardest one.
I'm just saying that the chart seems to imply that current AI is somewhere close to being AGI depending on which definition you like. Current AI is not close to AGI which is defined as human-level cognition.
Thanks. The way I'd put it is that AI doesn't even operate *like* the human brain, so that to talk of it thinking would be like talking about a submarine swimming, to borrow Chomsky's (already borrowed?) metaphor.
It's a bit result. However it's seems that it falls in the category of things that computer are good at (are suitable for) and human are not. Quoted from Bloom (commentary from the published paper): “[the AI’s] success here echoes previous achievements: it often produces the most surprising results by persevering down the paths that a human may have dismissed as not worth their time to explore, combining superhuman levels of patience with familiarity with a vast array of technical machinery".
The feather migration you describe is the collapse of the epistemological boundary itself: when the search space is fully indexable, 'discovery' and 'exhaustive iteration of parameters' become indistinguishable. Erdős' problem didn't move; the compute did. If proof is just agreement about what counts as solution, then shifting that agreement to machines isn't progress on truth, it's a power transfer.
Dunno. Writing good prose feels like a much harder problem than good math, at least from the perspective of AI. For truly good prose (be it in high literature, journalism or your average subreddit) you need to "get" some subtleties of the context and be able formulate some insights and ideas which feel meaningful enough for people to do the proverbial "share and upvote". I think that's very hard to do without some level of conscious understanding. Math in contrast, feels a bit more like a mechanics problem, in the sense that it doesn't have to be interesting or meaningful in the same way, it just have to work.
It just so happens that mediocre prose written by someone who has memorized the entire internet is a lot more useful and valuable than mediocre math (or even mediocre programming code). I mean, mediocre math was already "solved" by computers decades ago. In 2026, who'd possibly be sufficiently impressed to pay premium subscriptions for a calculator that is right 99% of the time?
As for groundbreaking new math; who is going to actually verify new AI proofs? And how will OpenAI programmers be able to tell a bogus proof from the real deal when dealing with serious unsolved problems that actually keep mathematicians awake at night? Mathematical proofs in the 21st central tend to be contentious, to say the least. Somehow, I feel it is very convenient of them to come up with a "disproof" rather than a "proof". Nice and clean, just show that "a" is inferior to "b". Now, what if there is no conjunture "b" and the AI has to prove that the existing conjuction "a" is theoretically "correct" or otherwise unsurpassable or unresolvable? That would be a lot less clear-cut, and I suspect that OpenAI in this process actually came up with many more AI-proofs and conjectures whose veracity they can't vouch for without a lot more work - human mathematician work. I wouldn't be worried of losing my livelihood to AI if I was a mathematician working on unsolved math problems, in fact, the odds of landing a lucrative job/contract with a tech company in the next decade probably increased a lot.
Worth noting the proof needed humans to rewrite it "to be clear and up-to-standard." The machine found the insight; people supplied the legibility. And Litt didn't call it correct, he called it the "unique interesting result produced autonomously by AI so far." So even in math, the meaningful-versus-mechanical line you're drawing happened to fall on the human side of the rewrite.
There are also proofs for all provable theorems in the Library of Babel. I'm not seeing how this is meaningfully different from finding one there.
I think it’s really been underrated to what degree certain problems are searchable (as opposed to just verifiable). So some might be extremely amenable to search! But in other cases, the stuff in the Library might be extremely spread out, so even if you search and search and search you don’t get anything.
Searchable vs verifiable has been formalized as P!=NP for many decades.
It's different because human mathematicians found it interesting enough to state it and work on it.
Like you said, I think science is the domain where ASI fails to materialize, because there is no amount of intelligence that allows you to skip having to do real world experiments, and I think we're pretty far away from fully automated laboratories. Science really is very different from math and programming.
When simulations do enough to show that they were correct, science fields will also fall. Protein folding will eventually be solved. Chemistry and materials science as well. Biology and Psychology are likely the last to go as they're numerous pitfalls and unknown unknowns.
In real world, e.g., materials science, simulations can’t “show that they were correct”. Only experiments can. This puts a natural ceiling on possible progress rates. Basically, Singularity is impossible and we’re not going to colonize the stars.
People "believe" funny things, don't they. "Humans will never achieve heavier than air flight" (just before the Wright Brothers). "Physics has basically all been discovered and all that's left is to tidy up the details" (just before Einstein). "Rockets for space travel are too expensive for non-governmental organizations" (just before Elon). "We're not going to colonize the stars" - yeah, right. If we don't destroy ourselves first, we're going to colonize the stars. Imagination. Bring someone from the 18th century into this world and find out just how many things we already do that he would tell you are "impossible" or "magic". And we're only 8000 years into growing civilization. Wait till it's 20,000 years and see what we're doing.
Ok not within anyone’s lifetime.
But cheers to tech optimism!
It seems quite safe to say that no humans alive are going to make it to another solar system. The distances are just too great. But that doesn’t mean it’ll never happen.
I don't know what's "safe to say" in the early years of AI. Who knows what will be possible with longevity technology, and maybe cryogenics, and spaceship propulsion, and even understanding of basic physics principles, in, say, ten years from now?
"the engines and hull are not designed for it" it's almost like we would need to invent new and potentially impossible to make sci fi materials to make all this work. To do interstellar travel at human timescales you need to invent a hull that can withstand being hit with pieces of space dust that will hit with the force of a nuclear explosion (because of how fast the ship will be going). I don't know how you can justify that we "don't need new scientific knowledge" to do interstellar travel.
As simulations improve, we can perfectly predict certain qualities and performance; obviously checked by real experiments. Superconductivity is a good example. No one predicted the "dancing" found in Cooper pairs. If we can get simulations that would have predicted those movements, we would have made better atomic lattice. Colonizing stars sysyems doesn't require any huge new additional technology knowledge, by the way. It's basically time, energy, and desire.
Real experiments is the whole point though. Example: I’m sure AI will come up with novel battery chemistries and construction. Still need months of charge cycling to know how they perform. Many such cases.
Sure, time and energy. In such massively enormous amounts that new tech is needed, you don’t expect the Starship to actually scale up for a trip to Sirius, do you?
If you know the answer is right, then why would you need the tests?
If the simulation is „good enough“, then the experiments are pointless, just optimize until you hit your requirements in simulation and then start mass production.
Sure, currently many models are inadequate for this, but finding models is something which AIs can actually do.
Let me guess: you don’t work in manufacturing.
See, you can’t know the answer is “right” until you actually make the thing. You can simulate your chip till cows come home, but you don’t know it will work until you get it back and run validation and qualification tests. And those can’t be skipped or shortened, 1000-hour THB takes 1000 hours, it couldn’t care less how smart your LLM is.
Reality is irreducibly complex, the map is not the territory, and you cannot simulate things for which you don’t have experimental results.
Starship is not an interstellar transport. The engines and hull aren't designed for it. There's numerous books and proposals that have nothing to do with Elon's vision. His goals are not the only way. O'Neil cylinders are just one example.
I mean, the point is that the amount of energy needed is so enormous that chemical rockets are not going to cut it unless we’re expecting to travel for millennia. We don’t have that tech either….
The control-group point is the actual hinge of this argument. Demonstrating a frontier capability step-up requires showing the prior production model couldn't surface this disproof given the same search budget and compute envelope. Without that comparison, the announcement reads as a lucky hit across an open-problem set, not an architecture jump. The Gowers and Litt names confirm the disproof itself meets a top-journal standard; neither speaks to whether the previous model class would have surfaced the same construction under identical search ceilings, which is the comparison OpenAI would need to publish to convert this from a marketing release into a measurable capability claim.
The control group OpenAI would need exists, sort of: they consulted Litt to verify the proof but published no head-to-head against the prior model under matched search ceilings. Worth noting Gowers wrote his commentary "solicited by OpenAI," so even the standards check sits inside the company's framing. A capability claim verified by your own consultants is closer to the open-source-as-subsidy logic Meta abandoned: openness ends where the returns begin.
This is the very definition of a strong-link situation, isn't it - if our best mathematicians are still better than AI our whole species feels better, even if we personally can't understand the maths/science/coding problems, let alone the solutions.
Your last paragraph for me brings to mind the replication crisis and the similar hype and disappointment experienced around many of those "findings." The tasks of generating new information, and determining whether that information is true, are different if related things. The scientific method provides a framework to help them move together, but as a problem grows in complexity, it becomes easier for methodological errors to hide, and thus takes more time to replicate and verify. Due to limited attention, we have historically leaned on authority and process to mitigate this, with results varying as widely as the politics and cultures of our institutions. But increasing the available attention doesn't remove the problem, it only changes it.
If our benchmarks are saturated, and as AI continues to beat our self-imposed human benchmarks, we rapidly approach the boundaries of knowledge. Alignment questions aside, whether it's humans, humans+AI, or just AI, new knowledge must be dissected and integrated into a larger world model. Is it true? How do you prove it? What resources or underlying knowledge do you need to prove it? What kinds of mistakes might someone make along the way? Is it just a search problem, or are there critical new insights or techniques required? What other knowledge must be updated as a consequence? And so on.
Of course, people like "good enough," and we accept the risks of application failures as a cost of doing business. If it's important enough, we will engineer and iterate our way to success. But if attention is less of a bottleneck, then maybe more can be done before you get to that point. Or perhaps must be done, if you want to avoid a future of smoke and mirrors covering up unverifiable slop.
A great read, thank you!
Working in cybersecurity I just wanted to point out that "serious resources are not spent trying to find exploits" is very much wrong. Software vulnerabilities and their exploits are very much sought for, they are very valuable(up to millions for an unknown vulnerability in the wild) and national intelligence services for sure spend serious resources trying to find them.
What is different with Mythos/equivalent is that they can tirelessly go through more code than any human, and thus find more vulnerabilities. Also all vulnerabilities aren't equal, some enable instant data breach, some are a lot more low risk. But we have known for a long time that in general software is full of mistakes, but been lacking a scalable solution. This might be it.
It seems to me that any time I hear (read) someone saying something hyperbolic about AI, that once you cut thru the hype, there is almost always an element of anthropomorphizing that underlies their blindness. I get it. It is a lot easier to imagine how a consciousness arising in an AI could then have super human intelligence and out perform humans at everything - it makes it feel more plausible because we are human! And even when this hyperbolic person states they know what they are experiencing is not real (e.g. AI consciousness) the fact that it feels so real allows them to forget it's lack of realness. Can someone with literary clout please write a parable for this pitfall?! I imagine something akin to looking in a magic mirror and seeing a reflection that isnt real but everyone starts to think its real, and the mirror company says "sure looks real to me, you should buy some...". Or something like that =)
For people who want to be AI-bearish, the argument about feathers should not be a successful cope. Progress is just as fast as it seems (but yes, gradual, not a single lightbulb moment). Anyone super surprised by this result has not been paying attention.
Some people think they are creating a rigorous, bright-line experiment when they set up a scenario like "an AI will never be able to X" where in this case X is "produce novel mathematical research (defined as Y) with only a small non-specific prompt Z" etc. But there is no bright line, just a continuum.
This does sound a bit like when people used to say: "yes well, okay a program can now beat a chess master but it will never beat the best human chess player"
Like all human-made tools based on human-led labor, this is not exactly surprising. Impressive and noteworthy that a novel solution was elicited and produced by AI! But to say AI alone solved this problem would be to say that humans are not the ones who have solved problems or improved on multiple fields of study with computers - that computers are the intelligent recipient.
A verifiable AI solution to a mathematical problem - one rapidly improved upon by non-AI human iteration - proves by a posteriori necessity that such a problem always was solvable. An extremely useful tool in the repertoire that aids our fields of labor, but which itself cannot derive productive value without human input or predicate knowledge.
What about dissecting Timothy Gowers's Words and tone of speaking? What does he want to promote? Either way, mathematicians should study math as they used to, maybe research methods will change.. But he, as a respected mathematician, had a disappointing tone
I actually think the "Dumbo phenomenon" is basically pointing toward a stagnation in LLM capabilities. Back in 2023, some researchers wrote a blog post arguing that "The 'it' in AI models is really just the dataset." The core idea was that for sufficiently massive neural networks, the architecture doesn't really matter—the dataset is what dictates the performance. Later, when Ilya Sutskever shared The Platonic Representation Hypothesis paper, it essentially pushed the same point. I’ve looked into this, and there are already four or five papers out now that echo this. They all seem to show that once your dataset and parameter count hit a certain scale, the architecture's influence fades away, and data becomes the absolute deciding factor. Pair that with the recent news about Anthropic buying up rare books just to scan and destroy them, and you get a pretty grim takeaway: current AI progress might just come down to four things: energy, GPUs, data, and the organizational muscle to keep the training from imploding.
In the Chinese tech community, there have been interviews with Chinese engineers at OpenAI and Anthropic, and they’ve mentioned that the actual day-to-day work in AI dev doesn't exactly require some insane level of genius or creativity. They describe it as "interviewing like you’re building a rocket, but once you’re hired, you’re just turning screws." Plus, some OpenAI employees mentioned that they took a totally different technical path for coding optimization than Anthropic, yet ended up with nearly identical results. Maybe that’s more evidence for "data determinism," right?
But we know humans can interpret the exact same data in totally different ways. That suggests human intelligence isn't just "data determinism." Sure, you could argue that no two people have the same life experience, so our "data" is never identical. But here’s the thing: when we train neural networks, we initialize the weights randomly, and it’s wild how models trained on the same data end up looking so similar. I can’t imagine human learning working like that "blank slate" model. It’s pretty clear that AI intelligence and human intelligence aren't the same thing. AI is way more dependent on the raw data. For an AI, data is everything—if that well runs dry, everything hits a wall, and this is what actually happens now.
If it were purely the dataset, you'd expect interpolation, not a disproof of a conjecture that sat unsolved for 80 years. That result isn't sitting in the training corpus by definition, which is partly why Litt called it the "unique interesting result produced autonomously by AI so far." The dataset thesis explains why models converge; it struggles to explain the rare case where one produces something genuinely absent from its data.
It’s got nothing to do with interpolation. Many major mathematical breakthroughs come from gradually piecing together existing results. It’s not rare at all to find fresh insights for unsolved problems hidden in old literature—take the Fields Medal that was inspired by Gauss’s notebooks, for instance. That said, it doesn’t mean the 'search' process itself is easy. You could argue that this consistency signals the model is converging toward a powerful, singular “God” solution, for which I would not buy. Until the experiments render a verdict, it’s all fair game for speculation.Giving Big AI a pass today.
Those viewing that chart with the cloud of AGI definition points should realize that it only includes AGI definitions imagined by AI fanboys. The true human-equivalent AGI definition is way off the chart. It's a bit like a solar system diagram with true AGI as Proxima Centauri. If your chart shows Pluto at the edge of the paper, the closest star is about a mile away.
Your Proxima Centauri framing has a useful corollary in the Erdős result itself: the model found the elegant insight, but humans still had to rewrite the proof to make it legible and up to standard. A discovery that can't yet articulate itself clearly has a lot of light-years left before anyone reasonably calls it human-equivalent. The chart measures distance to the nearest definition, not the hardest one.
Say more?
I'm just saying that the chart seems to imply that current AI is somewhere close to being AGI depending on which definition you like. Current AI is not close to AGI which is defined as human-level cognition.
Thanks. The way I'd put it is that AI doesn't even operate *like* the human brain, so that to talk of it thinking would be like talking about a submarine swimming, to borrow Chomsky's (already borrowed?) metaphor.
It's a bit result. However it's seems that it falls in the category of things that computer are good at (are suitable for) and human are not. Quoted from Bloom (commentary from the published paper): “[the AI’s] success here echoes previous achievements: it often produces the most surprising results by persevering down the paths that a human may have dismissed as not worth their time to explore, combining superhuman levels of patience with familiarity with a vast array of technical machinery".
The feather migration you describe is the collapse of the epistemological boundary itself: when the search space is fully indexable, 'discovery' and 'exhaustive iteration of parameters' become indistinguishable. Erdős' problem didn't move; the compute did. If proof is just agreement about what counts as solution, then shifting that agreement to machines isn't progress on truth, it's a power transfer.
Dunno. Writing good prose feels like a much harder problem than good math, at least from the perspective of AI. For truly good prose (be it in high literature, journalism or your average subreddit) you need to "get" some subtleties of the context and be able formulate some insights and ideas which feel meaningful enough for people to do the proverbial "share and upvote". I think that's very hard to do without some level of conscious understanding. Math in contrast, feels a bit more like a mechanics problem, in the sense that it doesn't have to be interesting or meaningful in the same way, it just have to work.
It just so happens that mediocre prose written by someone who has memorized the entire internet is a lot more useful and valuable than mediocre math (or even mediocre programming code). I mean, mediocre math was already "solved" by computers decades ago. In 2026, who'd possibly be sufficiently impressed to pay premium subscriptions for a calculator that is right 99% of the time?
As for groundbreaking new math; who is going to actually verify new AI proofs? And how will OpenAI programmers be able to tell a bogus proof from the real deal when dealing with serious unsolved problems that actually keep mathematicians awake at night? Mathematical proofs in the 21st central tend to be contentious, to say the least. Somehow, I feel it is very convenient of them to come up with a "disproof" rather than a "proof". Nice and clean, just show that "a" is inferior to "b". Now, what if there is no conjunture "b" and the AI has to prove that the existing conjuction "a" is theoretically "correct" or otherwise unsurpassable or unresolvable? That would be a lot less clear-cut, and I suspect that OpenAI in this process actually came up with many more AI-proofs and conjectures whose veracity they can't vouch for without a lot more work - human mathematician work. I wouldn't be worried of losing my livelihood to AI if I was a mathematician working on unsolved math problems, in fact, the odds of landing a lucrative job/contract with a tech company in the next decade probably increased a lot.
Worth noting the proof needed humans to rewrite it "to be clear and up-to-standard." The machine found the insight; people supplied the legibility. And Litt didn't call it correct, he called it the "unique interesting result produced autonomously by AI so far." So even in math, the meaningful-versus-mechanical line you're drawing happened to fall on the human side of the rewrite.