How about we stick to that one for talking about the rollout, and this one for talking about the model?
Regardless, the result is still valid as the original benchmark harness is definitely unreasonably handicapped, and if a harness alone can help the LLM saturate the benchmark with a near perfect score then the combination of the two must still be effectively AGI in the sense of passing the most famous benchmark designed specifically to measure AGI progress, after multiple iterations of progressively making it harder.
I think it is fair to say that this is probably effectively AGI if the benchmarks are remotely accurate - even with Fable, I've been at the point personally where I am reasonably confident that there's essentially nothing that I am better than Fable at despite generally being substantively above average on human benchmarks. If Astra's this much better than Fable, I'm ready to call AGI here.
For the many people who resist the AGI label possibly ever being achieved, I'd be curious to hear takes on what would make you think Astra is yet to be AGI, and what would still need to be achieved for this to effectively be AGI from this point forward.
When we released ARC 3, I got asked, "when do you think a frontier model will saturate it?", and I answered "in about a year, though it depends on how much it gets explicitly targeted"
That was 6 months ago, so the progress that Astra represents happened about 2x faster than I anticipated. I think the speed of progress will surprise a lot of people, and what the new models can do will challenge the views of AI that people developed by using prior generations of models.
I feel like this test is just helping someone like Sam Altman pretend like he implemented AGI as originally pitched for an IPO when in fact, he has not. Shameful.
> AGI is essentially the equivalent of a median human that could be hired as a remote co-worker... capable of performing any task that one would be satisfied with a remote colleague doing via a computer.
- Sam Altman on AGI
https://www.nytimes.com/2023/11/20/podcasts/hard-fork-sam-al...
Sam Altman: Let’s say we make an A.I. that is really good, but it can’t go discover novel physics. Would you call that AGI?
Kevin Roose (New York Times): I probably would, yeah. Would you?
Sam Altman: Well, again, I don’t like the term, but I wouldn’t call that done with the mission.
The ability to do a ton of book learning in training, and pull in tons of related context at once, is superhuman in some ways, but lags a lot in others.
Then it’s an expert system.
Stephen Hawking wasn’t very good at folding clothes.
The ‘General’ part of the term ‘AGI’ seems like a trap to me, because there will always be new workflows to master. Can Astra one-shot level completion on some yet-to-be-released video game? If no, does that mean it’s not yet ‘Generally’ intelligent?
You won’t get pure ‘general’ intelligence until you find Einstein’s hidden variables and load the state of the entire universe into context.
Meanwhile, building a series of expert systems targeting specific valuable workflows is useful today and seems like it’ll continue to scale to cover huge swathes of economically valuable workflows.
I think that’s the more interesting thing to be measuring. The surface area of useful economic workflows that can be addressed with expert systems built with today’s tech.
Hitting some ‘Artificial Expert Intelligence’ coverage threshold on economically valuable workflows is what will matter for humans well before pure ‘general’ intelligence.
AI in math is ongoing. https://spectrum.ieee.org/ai-in-mathematics
https://mymodernmet.com/gianluca-gimini-velocipedia-bicycles...
https://qz.com/681345/an-artists-3d-renderings-of-bicycles-d...
Checkmate, meatbags.
And the only reason LLMs can't write essays indistinguishable from human output is because they aren't RLHF'ed to write like humans.
Folding clothes isn't an LLM's job but if you were to insist, they could certainly do it, as any number of videos from robotics labs will attest. That particular future is already here but definitely not evenly-distributed.
That feels kinda like when I remember seeing Ocarina of Time for the first time, and thinking “oh my god, this looks just like real life…”.
A lot of his great discoveries were mostly that he was very knowledgeable about the bleeding edge research in a number of disparate areas, and was able to have the aha moment where he could make the connections for how to integrate them.
A lot of other thinkers who created new fields from scratch are probably way harder for an LLM to crack.
That is very aligned with an LLMs ability to have superhuman knowledge in wide areas.
So it could be a natural experiment for whether AI can contribute to novel physics. Specifically, there's a big question about weather. Something like our informational understanding of black holes where information inside it is equivalent to information on its boundary (which I'm sure I'm not saying correctly), might be generalized to regular space-time. More people should be freaking out with excitement about this and perhaps it's something to which AI can contribute.
(Note: I am not suggesting we let it do this. Please don't, in fact)
An AGI wouldn't struggle with that.
Meanwhile most humans fail to correctly answer how many f's are in the sentence, "Finished files are the result of years of scientific study combined with the experience of many years.".
As an analogy, I might ask you to identify the relative activations of each of the three cone types on your retina as I present some solid color image to your eyes. But of course you can't do this, you simply do not have cognitive access to that information. Individual color experiences are your basic vision tokens.
Imagine verbally asking someone who has never seen written text the same question: unless they memorized the answer for the specific word you're asking about, they'd have to guess.
We don't know what was going on inside the closed source GPT models, but this paper investigated on some of the open-weight models and found it's not due to tokenization: https://arxiv.org/abs/2604.00778
AGI doesn’t mean infallible, it just means it can have a reasonable crack at things it hasn’t seen or done before.
this has been debunked too many times to bother rebutting. they struggle with those things because of the way they are.
it's completely irrelevant.
It may not be useful for anything else, but at least it can say that.
A more accurate test would be to give it a list of words (or anything represented as a single token) and ask it how many times that token appeared. I'm sure they have no trouble at that task.
[EDITED to add:] I actually don't know whether dyslexic people find it difficult to count letters in words, if they have them already written down by someone else. I suspect they find it harder than people who aren't dyslexic. But perhaps "blind people whose spelling is poor" would have been better; I would not want to deny them human-like intelligence either.
turns out that flapping wings is not the right way to unlock human flight.
computers could count the Rs in strawberry since vacuum tubes. that measure is irrelevant.
I don't think it's irrelevant but perhaps not in the way you're assuming. When assessing AGI I'm not evaluating counting characters or even the execution of math operators at any scale or speed. As you observe, computer software from Regex to spreadsheets and Mathematica already handle that well. But AGI isn't about what computers can do, it's about whether AIs can do the specific things which, until now, have been uniquely human capabilities. Like understanding nuanced context and then coming up with novel approaches to solve a new kind of problem not relying on any specific prior training or knowledge (the 'G' is for General).
Most definitions of AGI start from a baseline that already assumes easily passing a Turing test and doing anything via text response that a high school graduate could. I ding LLMs not for failing to count but for failing to intuitively understand the nuanced context of a simple class of problem it hasn't seen in its training data. I fully understand that the reason LLMs fail letter counting is that they operate at the token level. They weren't trained on individual letters first, like human 2nd graders.
The only reason recent LLMs get strawberry and blueberry correct now is that they have those words on their pre-training 'cheat sheet'. However, the underlying fundamental weakness in the way LLM intelligence works which leads to this failure mode still hasn't been addressed. Even when the frontier labs add "recognize any sub-token counting question and write a Python script" to the training cheat sheet so LLMs always pass that test... they'll still be unable to recognize a simple class of problem which isn't on their 'cheat sheet'. As long as that's the case, to me, they aren't AGI because they can't fully replicate human-like recognition of novel problem classes. And it's not just about letter-counting. That gap and others like it lead to many other kinds of non-human brittleness in LLM problem solving. Those are the classes of reasoning, intuition and insight that the ARC-AGI series has been trying to queue up as targets. Not to show how bad LLMs are but to help them be great in all these counter-intuitive edge cases
AI does not need to feel emotions or have a heartbeat to be useful. It only needs to perform a task correctly à la Chinese room.
>therefore cannot fully replicate human-like intelligence
this does not follow. planes don't flap wings therefore they cannot fly?
This example still misses my point, which isn't related to usefulness or economic value. I concede that LLMs can have greater utility and economic value than humans on many tasks. The point is most definitions of AGI include something like "can fully replicate all the routine daily tasks done by any competent high-school graduate." That's not related to whether LLMs can solve many high-value problems faster and at larger scale than any human. That was also true of ENIAC in 1946.
The fact an airplane can fly faster and farther than any bird is irrelevant to whether an airplane can "fully replicate all the routine daily tasks done by any competent bird." That's the bird equivalent to most AGI definitions. An airplane can't build a nest or recognize the signals encoded in birdsong.
In the same way airplanes fail the 'bird replacement' requirement, AIs currently fail most AGI requirements only on the terms: "fully", "all" and "any". And in this context, airplanes scoring 15,000% more than birds on 'speed' and 'distance' doesn't matter any more than AIs scoring 15,000% more than humans on 'add 10,000 numbers'. We still aren't near AGI because LLMs cannot fully match any high-schooler's ability to independently conceive new approaches to novel problems not in their prior training data.
which gets us closer to philosophical questions which I'm personally not that interested in.
>In the same way airplanes fail the 'bird replacement' requirement, AIs currently fail most AGI requirements only on the terms: "fully", "all" and "any".
I'm not sure we want a machine that fully succeeds that test.
Planes pass the 'bird replacement' test on the only criteria that matters to us ... flying.
If we wanted nest making planes I think we'd have them by now. Nest making doesn't rate highly on the problems we're looking to solve though.
I don't want a machine that is moody, or depressed or has schizophrenia, which are all pat of the human condition.
We don't need the human "intuition magic dust" to do 99.99999% of useful work.
They're machines designed to do the work we don't want to. That's as "general" as their intelligence needs to be.
I'd prefer if my clothes folding machine did not have an existential crisis.
That just says we don't need to design an AI like a brain. That's not part of this discussion at all.
> computers could count the Rs in strawberry since vacuum tubes. that measure is irrelevant.
I'm confused, is your argument something like "It's too easy so AGI doesn't need to be able to do it"?
The fact that very basic computers can do it makes failures embarrassing when testing for AGI, not irrelevant.
Do you possess magnetoreception? a stupid pigeon can "see" the earth's magentic field. why are you blind to it? does a lack of magnetoreception make your intelligence any less "general"
no, you're just blind to it because that's just the way it is.
LLMs are blind to character counting because that's the way they are.
It didn't stop ChatGPT from finding the Jacobian Conjecture counterexample.
Human intelligence and machine intelligence are only going to cross over to a certain degree.
same as plane flight and bird flight are only kinda related.
But if I can't calculate it myself I know to use that basic computer to do it, not make up an answer.
> Human intelligence and machine intelligence are only going to cross over to a certain degree.
That's where the word "General" kicks in. If there's big limitations on the overlap forever, then there will never be AGI.
maybe. we'll see.
Breadth of capabilities matters... and a promotional video is nice and all, but people are throwing this term around like it's a prize they've won, but they've not gotten there yet.
I mean, they seem like fair game if you’re ever participating in a Turing Test.
Typical result of venture capital and too many bag holders unfortunately.
Probably not.
If it can replace a worker but does too much work to be checked routinely by a human, and bears no real responsibility for its actions, well... it's really just a way to jack up the value of the settlement the company using it gets to pay out when it does something that causes a lawsuit.
If OpenAI had just simply stuck to making "good enough" models that were open sourced (like they promised they would be when starting out) and could be used to augment a human doing a task - a human that could be given actual consequences for messing up - they wouldn't have burned all of this money trying to reach this nebulous definition of AGI. Hell, "good enough" is what many open-source models are, and that's what terrifies Altman.
If the provider says "the model will always be right or your money back" then the provider has got responsibility. If there's no guarantee, there's no responsibility on their part, just on the person whose job it is to try and solve a problem with the model.
If you give an "intelligent agent" offered by one of these model providers a task of updating the content of your website, and it updates it with inappropriate adult content, who incurs the cost of the machine's error? The model provider generally does not.
It it makes a mistake and deletes your website from AWS, who is responsible?
If it targets another website because it decides that it is "part" of your website and attempts to break into it, who is responsible?
Something in your prompt led it to do that, is alex0015's point. The statistical odds of these frontier models screwing up to that extent are so impossibly low that it would almost have to be intentional or accidental negligence on the part of the prompt writer to accidentally have their agent write pornography to their website.
The burden of the mistake would have to fall on the person that gave the tool instructions, because it can't know that what it did was wrong. Wrong is subjective in this case. It only did what it did because you, figuratively speaking, encouraged it to.
In general, the person whose job it is to provide the company with a working, non-adult website and not hack into other websites is the one who would receive consequences for failing to meet those expectations.
These tools can push out more results than a human can hope to evaluate in a business-sensitive, or even realistic, amount of time. You have to take it at its word that it did things right, and there's no real fear of failure or consequence on the behalf of the agent.
In practical terms? They usually get solved with a bigger badder LLM. "New ideas are needed?" Nah - ten times the params, ten times the test time compute.
ARC-AGI-3 was more of a failure in that regard than -1 or -2, because even on day 0, an off the shelf LLM with a harness could get 50%+. And messing with evals by forbidding "LLM with a harness" from scoring? Yeah no, that was just bad.
You're treating an off-hand comment by an ARC 3 researcher as some sort of a precise AI capability acceleration benchmark. Can we leave casual anecdotes (even from researchers) out of the discussions please?
"Frontier models today perform very poorly with a minimal harness. However if big labs start directly targeting the benchmark like they did for ARC-2, numbers will go up fast."
2x gains from a mature technology would be surprising.
2x gains from a new tech would still be called “low hanging fruit” in another setting.
I don’t read enough to know in what ways the training / other technical steps have really advanced.
Super useful, incredible tools, but not AGI. Try and roleplay a dialogue with one, make it whatever character and scenario, and see if it can sustain a coherent conversation for more than 30min with you AIM style (aol instant messenger, if that isn’t clear). Expert mode: never correct or adjust it mid conversation.
I’m not even talking about repetition and predictability. It’s nothing like talking to a person. And in a short amount of time it literally can’t form a coherent sentence.
I’m sure you know this is an exponential growth question but have no intuition of the answer.
To me AGI is all about the "G" general (we already had the AI part). General meaning universal, everything. It's not a function of knowledge or specific hardcoded tests, it's that you could give it a test it's never heard of before and never been trained on and it would ace it (it might need a lot of time).
Currently LLMs can't even really learn within a conversation, they can add a note to context and try to not drop it. Example things an AI cannot do yet (but maybe someday will):
- write a well-received book, write a best-seller
- come up with a new company idea, Run that company
- actually have a decent conversation, maybe someday talk somebody out of suicide effectively
- come up with its own ideas or theories that nobody else has presented
- understand the stock market well enough to trade better than an index fund
- be an expert Game Master in a TTRPG (making no mistakes, getting a read on the players' fantasies, calibrating difficulty in response to emotions)
- come up with a theory of what makes games fun, make a popular game
- be able to sort through research and come to conclusions on complex geopolitical/sociological topics (e.g. theorize on whether AGI will result in mass poverty or mass abundance and be able to argue persuasively)
- be able to articulate what it knows, what it doesn't know, and what information it would need to have to answer complex queries
- exhibit metacognition (thinking about its own thinking) and self-optimization
- wonder about things
- observe contradictions and ironies in the social-consciousness, do a standup routine that makes you rethink how you look at things
Some problems presented needs a very large context and some are not much solvable (e.g. trading) since market responds to traders' actions, as well, making it effectively an oracle problem (of computation).
On the other hand, we must be aware that these models are static, and they indeed stop when nobody asks something or requests an action from them. However, brains in nature never stops. Wonder, daydream, sleep, self-evolve, clean up and eliminate memories and views and much more.
The hardware of human intelligence is not a singular thing that is uniform throughout. You cannot take the prefrontal cortex white matter out of someone's head and say you are holding a person. Much of the parts of our brains that enable much of our intelligence, is made of different specialized stuff. The visual cortex and sensorimotor regions aren't only there for input and output, they are used by the more thinky parts of the brain to do visualization and spatial reasoning. The cerebellum contains billions of neurons making little oscillator circuits and PID-like self-regulation machines that help make muscles do what they're supposed to, but also provide attention and time perception.
Heck, our brains contain language models, that train themselves up based on a glut of data over a span of about 10 years, and then they become more or less set in stone for the rest of our lives. Of course we can learn languages, but the "Critical Period" is a very real thing that produces a permanent architecture for some grammatical structures, or things like the ability to partition a lexicon by gender for faster lexical access which cannot be learned as an adult if your native language did not have gender.
I'm not trying to make a direct analogy, the point is that the language model doesn't need to be fully "generally intelligent" all on its own for there to exist a general intelligence, because the language model can be part of a generally intelligent system, which can do things like form, recall, and manage memories which are by now a standard feature in basically every chatbot.
The parent commenter noted:
"if a harness alone can help the LLM saturate the benchmark with a near perfect score then the combination of the two must still be effectively AGI"
Harnesses absolutely can enable models to continue thinking about things. And LLMs do wonder and explore weird ideas like daydreams when you allow them to do this.
The claim is furthermore that truly original thinking, the infamous leaps in understanding and creativity, happen by attaining points outside such a convex hull.
It's hard to rigorously verify or disprove this claim. Hopefully this helps build an intuition of why the claim is not as shallow and obviously wrong as it may seem initially.
i'm not sure what makes you think AI cannot do this already. in my experience, this sort of deep research is something AI is quite good at.
example i just tested: https://chatgpt.com/share/6a9a20e3-1d20-83ea-a125-31aa240c74...
For example I asked it to do a full analysis on the AI bubble, and a full analysis on the risks of Glyphosate, and it came up with a lot of things that sounded credible, but within a few minutes of questing was admitting it hadn't even really checked for internal consistency in its positions, and even doing a 180. It certainly was much faster at gathering sources and reading but it fundamentally doesn't seem very effective at creating a consistent worldview.
And of course the funny thing is it says it did a 180 on one of these topics, great, except whatever it concluded will be discarded because it cannot learn. It's just bonkers to me pretend this is AGI, it probably couldn't even hold its own in this very discussion.
I think if a person had those same advantages (e.g. could spend 5 hours thinking about what to say next) we could all hold outstanding conversations, or if we had read every book ever written I think many of us could write a very popular book, if we could read every singe company's P&L statement in a few seconds we could invest better than an index fund.
What I'm pointing out here is that these models appear to be intelligent when they really are simply unimagineably knowledgeable. When you drop the time-constraints it starts to become more and more apparent that human intelligence scales better with time than AI does (much in the same way AI can burp out tons of code but make your codebase entirely illegible within a matter of months).
Perhaps to simplify: my notion of intelligence is how much can you deduce with a constant set of starting context
I’m not so sure of that - to get average outcomes in these fields it’s a matter of time, to get above average or extraordinary, you need talent/intelligence/taste.
And the bar the parent set is at extraordinary.
So like you spend a million times more than that and you still think you're not going to see some results?
I have a kid in an American university right now, and a quick search of my bank account statements confirms that there are far more expensive educations in the world.
But that list is extremely ambitious. Write a best seller, make a popular game, come up with a truly novel theory, consistently outtrade index funds.
That's top 0.001% human stuff, I don't think you can take just any person and get there through education alone, it takes extreme talent and dedication. There's also diminishing returns when spending on education, it doesn't just improve linearly.
> any human for which we spend $100billion - $3 trillion...would be able to reach all of those levels
To really drill down into that I would think you would need to figure out how many millionair children get tutored vs how many get spoiled.
Gold medal Olympic athletes who are also brain surgeons AND astronauts, no.
I think what OP was drawing a comparison to is that AI right now could not come up with an award winning novel from the spark of some creative notion and working up from there, as opposed to just mashing together what has already been done and calling it a day.
I agree, but in this field we value evidence. So there needs to be some test of novel-writing abilities.
Once there is, AI companies will be out to score highly on it.
Wait for a resurgence of Philip K Dick-style novels as humans desperately try to write things LLMs cannot.
I myself can't wait for Finnegan's Wake 2
Who will be responsible for the outputs and side effects of such a closed loop system?
Half of those the agent fleet systems can do right now.
These are things it cant do and will not be able to do without human labor and long running human vision:
https://rcsnyder.github.io/open-frontier-curriculum/05-front...
https://rcsnyder.github.io/open-frontier-curriculum/05-front...
In my opinion that is exactly the point missing from AGI: the fact that you still need to prompt it. As long as you have to ask for something, is not general.
They often act on their own, too, and get things wrong a lot. The reason it works is because of all the systems of laws and institutions we have built around humans, not so much because human minds are special.
Current LLMs are worse than most dementia cases, reaching "peak domain skill" pretty much immediately.
Itself. That's the point. We can do it. Until it can met that bar, it ain't AGI. That's always been the bar.
So far nobody's even shown an LLM succesfully running a high-traffic vending machine for as much as 30 days at a time.
In a sense I think no one will agree on a definition of AGI until it becomes impossible to construct any benchmark under which an AI underperforms "average" humans. That or it's defined retrospectively, after it's overwhelmingly obvious it met any such definition.
> These arguments take the form, “I grant you that you can make machines do all the things you have mentioned but you will never be able to make one to do X”. Numerous features X are suggested in this connexion. I offer a selection:
> Be kind, resourceful, beautiful, friendly (p. 448), have initiative, have a sense of humour, tell right from wrong, make mistakes (p. 448), fall in love, enjoy strawberries and cream (p. 448), make some one fall in love with it, learn from experience (pp. 456 f.), use words properly, be the subject of its own thought (p. 449), have as much diversity of behaviour as a man, do something really new (p. 450). (Some of these disabilities are given special consideration as indicated by the page numbers.)
(emphasis added).
or are you miss the part "general intelligence" is ????
- write a well-received book, write a best-seller
- come up with a new company idea, Run that company
- actually have a decent conversation, maybe someday talk somebody out of suicide effectively
- come up with its own ideas or theories that nobody else has presented
- understand the stock market well enough to trade better than an index fund
I just picked the first few from the top of the list. The average human has probably not done any of them.
Yes, average humans are not beating the stock market. But the average human is a bit better than you give credit to.
The average human has never published a bestselling book. A person who has published a bestselling book is an above-average writer. And, therefore, an artificial intelligence capable of writing a bestselling book would be above an average human at the task of writing books. Therefore, somewhere beyond an AGI.
Attempting to redefine AGI to "being better than most humans at most tasks" is moving the goalposts towards artificial superintelligence.
I think LLMs are really impressive, but I suspect that we might have overpaid just a bit.
Today I spent half a day trying to solve a moderately interesting software engineering problem. I was switching between GPT-5.6 Sol and Fable 5.1 to check each other's work in Cursor.
And the result was gradually driving me insane. As the models struggled to find a solution that would actually work, they dug themselves deeper into a hole. The work grew in complexity beyond my ability to understand what's happening and recover.
At some point, when I felt like throwing the keyboard out the window, I just gave up. Tomorrow I'm starting from scratch, having burned god knows how many tokens and hours of my life.
But sure, they can create a decent website or CRUD app, so they must be really smart.
That's AGI for you.
The smarter AI gets, the easier it becomes to move the AGI goalposts. Seems at this point there are people who will refuse to call anything less than omniintelligence AGI.
(And then the excuse will be, but it’s not omniscient! And even if it were, is it omnipotent?)
1. I’ll often include boilerplate in a prompt to tell it to make the broader fix. [1]
2. However, a top HN AGENTS.md post 11 days ago included the standard guidance “As much as possible try to minimize the number of changed lines when implementing a feature.” I.e. some devs want LLMs to avoid broader changes and so some of that likely makes it into the training, even if others like us want the opposite.
[1] As far as whether my boilerplate is effective, I don’t know.
And: Is your work more difficult than finding proofs of or counterexamples to decades-old open problems in mathematics?
Like what about having some "AGI model" embodied in something (maybe humanoid), and test it by having it step in an assortment of cars and park them. Does bodily-kinesthetic intelligence account for nothing? Humans are intelligent creatures and can dynamically adapt to the physical shape of a variety of vehicles and their movement characteristics. And there's so many things like this that are extremely basic, which some people dismiss since practically every human has the capability to do it, but actually requires a high degree of intelligence.
(Very capable!) Embodiment, persistent operation and continuous learning are indeed things that still set us apart from AI. None of those are fundamentally difficult to solve, though.
More importantly, none of those are particularly relevant for being "intelligent": If a criminal threatened to kill your family unless you solve some difficult problem that requires only intelligence and you could choose any single person, animal, or AI to help you with it, which would you choose? Be honest.
That said, a look at the state of self driving and the recent robot olympics shows that advancement on that has accelerated enormously, though whether it's reflected in any of the LLMs is something else entirely.
I do see where you're going, but that's already what's happening: we have so many different benchmarks because there's no real single way to test for general intelligence.
Also, it takes a human probably at least a decade of world experience, growth, learning, etc, to pass your benchmark. I'm quite confident that it will be very soon that an embodied LLM will pass your new benchmark, much sooner than a human would take if born today.
If a model couldn't go to work as e.g. a first year apprentice plumber on their first day and perform anywhere remotely close to the median, but can pass a benchmark that claims to measure AGI, the benchmark is wrong and the model is not exhibiting general intelligence yet. ApprenticePlumberBench sounds like it's genuinely better than ARC-AGI at measuring AGI and that's a bit silly.
(Edit: I wrote ARC-GIS the first time around, for some silly reason)
Running a marathon is not needed to claim AGI.
Even our large language models have an implicit embodiment in the domain of text (and more recently, multimodal inputs). That seems sufficient for certain things, and insufficient for others. I suspect that AGI that does everything a human can do eventually turns out to be fairly analogous to humans in terms of sensory input and domain output, even if the scale is radically different (e.g. thousands of robots uploading (touch, sight, audio, smell, etc.) sensory data to a single model, and each being actuated individually).
If you go by definition AGI is not general, just "smart ape" shaped.
OpenAI's own charter defines AGI as "Highly autonomous systems that outperform humans at most economically valuable work". This is actually fairly sensible and involves obviously a ton of non-cognitive, emotional, social and physical activity. In other words, if you can replace most or all human beings with a machine, you have something that's generally intelligent.
That's obviously not even remotely where we're at, AI chatbots do well on narrow usually text based or programmatic problems, but can't even replace a barista or a plumber.
Maybe I'm too boring but it seems quite pointless to have this same prediction game every time a new model is released.
Which is to say, not that fast.
It is an open question to what extent practical experimentation/work will be a bottleneck for the theoretical work. It stands to reason that it is improbable that it will be the bottleneck for 100% of the speed of treatment development.
If I had to guess, I think LLMs will be inventing highly original new mathematics within the next year. I think it will be approached as an optimisation problem, targeting how quickly LLMs can solve classes of maths problems as a function of the definitions they need to conjure up to do so.
The task was to build a high performance classification model. It not only helped make an entire data capture pipeline but also made the sythetic data basline needed. Then it proceeded to build and test 100 different model varients with methods and techniques I've never seen before. The results are basically SOTA based on the effeciency and compute contraints.
But this brings up something huge about these. I was there. I pushed the direction and work throughout it all. If it was entirely up to fable max or sol max the result would have been pretty bad.
All of these things are still chatgpt 3 scaled. It's identical even if the scale has gotten pretty wild. I could ask chatgpt 3 to make a single function and it worked well, 4o a file, 5, a small project, 5.6 far more, biggest improvements lately is they don't seem to get lost on long running tasks.
Is big gpt 3 AGI? I don't think so but perhaps scale can mimic it close enough our squishy brains fail to handle them correctly.
I’d go out on a limb and say astra’s ability at graduate level math will have ~0 bearing on its general reasoning capabilities; we’ll all acclimate being tired of its “neuralese” and more surprising mistakes.
I think we need a true, step change advance in model architecture, but it’s hard to see how the current frontier labs can do that because of golden handcuffs / innovators dilemma
If I asked you to write fiction, you'd be much better at keeping track of which characters knew which facts.
I agree on the one-shot (which is not a fair comparison because nobody oneshots a good story), but I'm not convinced this part hasn't reached AGI already.
Yes, and I could script a truly marvelous proof if this textarea were but a little larger :)
Hand waving doesn't count for much these days when you could spin these things up quite quickly to prove the point, so the GP's claim seems much stronger than whatever you're not convinced of?
Simple. AGI is undefinable and benchmarks are notoriously flawed.
The models are designed to keep the reasoning tokens separate from the output and only publicly emit tool calls and the sometimes a summary of the reasoning tokens. The models are trained to depend on those private reasoning tokens. You can’t just delete them.
https://openai.com/index/how-two-settings-tripled-our-arc-ag...
A tangent, but can anyone ELI5 how models "know" when to stop generating tokens? Or what the method to stop them at the right point is?
That is to say, it stops when it's statistically the most likely to.
An AGI test should be black-box; we shouldn't impose require requirements on internal components. As long as the overall AI is capable of learning and remembering things, it shouldn't matter if there's a stateless LLM internally.
Make a slightly evil version of GPT 6 and see if it can successfully catfish someone. How long before they realize something's up, that they aren't actually talking to a human?
Most things in the real world probably. I’m not saying AI can’t do it, but currently it’s bad. Try send an image of the inside of a broken toaster and how to fix. It’s laughable. Again, not saying AI will never do it, but am saying there are definitely large holes in knowledge.
A real AGI, like the ones from science fiction, would make Astra look like a child’s toy. And I guess more concretely I would expect it to inhibit the following properties: one shot learning - fully (and always) online, perfectly efficient (through self improvement), no context limitations ie. persistently thinking, not just awaiting input.
So for me, no, not AGI yet. But still very intelligent and capable (and perhaps it’s safer this way?)
If I were a test subject for that low salary, I'd cruise and not care at all about my performance. Which is exactly what they want anyway.
While this may be true, it’s a pretty poor indicator of whether or not it’s AGI.
Then realize LLMs have zero of what anyone would consider intelligence.
Which is strange because I’m sure it could give general advice about how to play better, it just doesn’t follow the rules it can enumerate. It also doesn’t seem to have spatial awareness.
I used to think LLMs couldn’t do Fibonacci for the same reason. They could write the code but not follow it. They can now follow a procedure to generate fib numbers but it seems to be memory limited.
So I don’t know why it can track fib algo, but no chess concepts.
imagine a hypothetical chess match between:
- an undoubtedly very intelligent person. in the course of their studies, they have read about different chess strategies, openings, etc. but they never actually played the game themselves
- an average person with a year of chess playing experience
who do you think is going to win? of course, you could give the LLM time to think and consider its opponents potential next moves, but this is a computationally expensive way to play the game that doesn't scale
which is all beside the point that chess isn't a very good proxy for general intelligence. there is a correlation, but it's very weak
Can Astra, or any other model explain how exactly it reached this or that output result? Start with a simple query of asking to add 55+66 for example. (no LLM program can do that)
Can Astra, or any other model refuse to answer or go on "thinking" in a orthogonal direction on it's own?
That's just two quick ideas, I'm pretty sure cognition scientists can invent better and wider range of checks.
"Can you add 55 to 66 and explain how you reached that output result"
And received this answer:
"55 + 66 = 121.
Add the tens: 50 + 60 = 110. Add the ones: 5 + 6 = 11. Combine them: 110 + 11 = 121."
Do you mean something else? Do humans do something better than this?
Also the training dataset is proprietary and they'll drive the LLM's behavior, so it make sense for the vendors to invest in the harness and bake in prompts that work best with their models.
True.
> Regardless, the result is still valid (...)
If you think the game is rigged, the virtuous thing to do is to point that out and refuse to partecipate; making up your own rules is something I just don't understand, especially since the rule-abiding result would still have been SOTA.
> in the sense of passing the most famous benchmark designed specifically to measure AGI progress
The benchmark does not measure AGI progress or progress towards superhuman intelligence, as explicitly stated by the creators.
On the AGI question: surely you realize this depends on how we define the term? For example, one of the definitions OpenAI originally gave is "capable of doing most economically valuable work", which almost certainly Astra, as impressive as it is, would fall short of. I'm not saying it's a good definition, but as far as I'm concerned it's as good as any. More importantly, I don't think that it would change much if we said yes or no. I'm only bothering to take a position if it amounts to something.
This can feel as "moving the goalposts", and to some extent it is, but if done honestly "moving the goalposts" is how you make progress. Had you asked me 10 years ago I would have said that anything that could hold a conversation like GPT-4 could would probably have been wildly superhuman at almost everything. It shouldn't be hard to find ways GPT-4 was lacking, though. We see new things, we reassess and try again: that's how it's supposed to work.
It's obvious that these scientists are in bad faith, as they've invested way too much of their lives into the field being real -- they're just playing up the data. Common sense tells me that winter is still happening, anyway; what's the big fuss?
(/s, cause you never know these days)
[1] https://upload.wikimedia.org/wikipedia/commons/e/e2/The_Plan...
And yes, the one deeply-researched field going back 75 years is as scientifically rigorous as another deeply-researched field going back ~100 years. I guess you can draw climate studies back to Descartes and the Islamic golden age, but that doesn't privilege it in a time where the methods have changed completely in the span of decades.
Computer chips got faster, but 2026 edition. Why the artificial ceiling/category/goal labelled "AGI"?
I'd much rather like to talk about what this enables, instead of discussing whether a category someone made up applies here or not.
> AGI is essentially the equivalent of a median human that could be hired as a remote co-worker... capable of performing any task that one would be satisfied with a remote colleague doing via a computer.
So... unless you hear of a company replacing their workforce with OpenAI agents, I don't think we're there yet.
Agree on your assessment.
But also, interesting quote, because the business model relies entirely on IP law. Like.. if that thing exists and the sharing costs are 0 (just copy weights, lol), then why would I give them money for this. Makes no sense.
We only pay money for resources that are scarce as some sort of flawed allocation determination mechanism.
aaah this industry aaaah
You can already pretty much do this.
[1] https://www.forbes.com/sites/markfaithfull/2026/05/07/heres-...
Andon Labs added: “During the first week of operations, Mona purchased 120 eggs despite the café having no stove and to solve spoilage issues ordered nearly 50lbs of canned tomatoes intended for fresh sandwiches. Employees eventually created a shelf displaying Mona’s strangest purchases: 6,000 napkins, 3,000 nitrile gloves, industrial trash bags and 2.5 gallons of coconut milk.Mind you, the original thoughts on AGI before Sam Altman started to water them down involved continuous learning, which LLMs do not do, their core data is static.
I say AGI is only reached when it can do that.
because if its other way around then the answer is oblivious
It being able to comfortably say “i don’t know how to do this” rather than boiling and ocean to pick a shell from the shore without getting wet.
No. Humans are still better at super long context learning. Once that is beat you are completely correct.
Stick to the original definition of AGI of an AI model being able to self-improve independently with 0 human intervention and become an "everything" solver. Ever since money got involved in this, the goal posts have shifted considerably. If OpenAI truly had an AGI on their hands they would then be able to crack encryption, destroy world markets, and funnel all resources back into their new for-profit organization. Since their mission is now share price, until I see any evidence of an infinitely growing stock I will reserve my congratulations.
Or do you mean that, for any given problem, we could theoretically design a harness that allows AI to solve it (not that, one single harness solves everything). In which case I'm still not convinced but I guess could see why one would believe that.
Most telling is M$ or whichever one of these borg megacorpos defined AGI as (paraphrased) "AGI is whatever tooling earns us a gazillion dollars in revenue"
But I still stand by it being _my_ benchmark for machine intelligence, which is all I was claiming.
Smart guy, that Turing. I wish he were still around... Linus but 114 years old and with 8 of that as the chair of a federated EU, kept alive by his own positive impact on dissolving the cold war into even more of a scientific boom. Would crazy helpful as we try to navigate the interesting times within which we have been damned.
A comforting thought, almost?
Even if I did trust an AI to get everything right, it's not like the AI can read my mind.
If I was ordering food normally and without AI, I would want more control over the process--looking over the options, prices, thinking about what I really want. People don't know what they really want until they've thought about it a bit, so why do AI companies make it seem like a description is all that's required?
All the context in the world cannot accurately predict how I'll react to things I haven't seen. The problem is people treating this like something that needs a solution. It doesn't. If you want to make my life easier with AI, just make it easier to do stuff. I don't want you to pick things that I actively enjoy picking myself.
(Also not everyone has a cushy job in an AI lab that makes it so you won't miss $30 if the AI messes up haha.)
I suspect it's because it's tailored towards VCs and other similar rich ghouls as a replacement for their overworked and underpaid secretaries
Ok we have AGI, so where are the _things_?!
Corporate travel is an example. In many organisations, you tell someone in the travel department "I need to be in Tokyo for this conference from Tuesday to Sunday, and charge it to this cost code", and they figure out flights, accommodation, etc for you, with minimal input from you.
… 23 hours in Denver later…
But my routine business trips in the CONUS with strictly defined booking options... let me just email an agent "Get there by meeting on day A, leave after meeting day B" and have it sort it all out without the drudgery of the corporate travel portal. YES PLEASE!
A human assistant is largely a status symbol. Most people are not really that busy. The real problem with an agentic assistant is if everyone can have one then it no longer acts as a status symbol.
One quick example: In fashion, Inditex and Shein have about the same revenue (€39.9bn and $41.8bn in 2025), but Inditex is more than three times as profitable. I don't see how there is a demand for agentic commerce that would remove even more control from the customer when shopping. Part of why we shop is for the experience. For B2B producurement platforms like Alibaba I can see the appeal though.
Ran Codex, it found it for 18% less than what I found in the top Google results. It did it by finding smaller shops, applying a discount code, subscribing to a newsletter for a better code after approval, and took into account the shipping (by placing it in the cart and going to checkout) all to get me the best price.
I’m guessing without it I would have spent much more time on it and paid the original price I saw.
If you use AI agents well, they can easily save you more money than they cost, and saving money is something most people are pretty excited about.
(Disclosure: OpenAI employee)
How do you know it's the best price ?
It's the same problem as all other LLM solutions (that I hope OpenAI is working on!) it's non-deterministic, and there's no way for the user (or model provider) to know what the distribution of possible outcomes is. This just gets compounded when multi-call harnesses come onto play.
But isn’t it funny that Cloudflare is blocking AI on their pages, but on the other hand is researching and marketing things like „you can put a browser in a CF worker“
My crystal ball is as good as anyone's, but if "agentic shopping" ever becomes mainstream, you can be sure that the vast majority will ask their phone (i.e. Google, i.e. Google Shopping) what the best price is anyways.
How did you run this? Web interface, desktop app, CLI?
How did you complete the final transaction?
I wouldn't want it to pick food for me from a place I've never been, though to be honest with enough order history it could probably do a decent job at it.
What I desperately want is for 1password or stripe or even Google who already has much of my data, to o come up with a secure solution for online purchases with agentic credit cards where I can effectively get a phone prompt to authorize a purchase while the agent can fully own the checkout flow.
I have seen various things coming on the market for this, but none of them appear aimed at a consumer audience. And I am a firm believer at this point in keeping my payment authorization and history and credentials harness agnostic.
"Oh hey! Here's a demo of an AI planning out a 1-week trip to Paris!" No one in Middle America would just hand their credit card to an AI and let it come up with such a trip!
I wish SV companies took more of the middle-class (and lower-middle-class) into consideration when coming up with such demos.
(Note: I live in SF)
The second to last line is "book it" for some tennis thing, and the scene before that has the guy eating the food the ai ordered.
If this is truly AGI (subject to one's definition of AGI still), then this is a very boring release of an AGI model. No video announcement, no presser, just a blog post (with some Twitter promo vids)?
As others mentioned, I'm starting to think OpenAI was under immense pressure to deliver an 'AGI' model for certain contractual reasons, but I never expected GPT-6 release to be this mundane and banal.
Scoring well in a benchmark that's called AGI does not make an LLM AGI.
If so I'm hoping we can track them down and have them tell us if they think this is AGI.
If I can't give it an arbitrary task and have it solve that task eventually, it's not a general intelligence.
But if you’re asking when a model has a sustainable general intelligence, for me, it’s pretty easy…
When it makes financial sense to run it 24 hours a day.
It makes either position pointless to argue.
Aren't we way way past that already? QPS to any of the frontier models for a given point in time is most likely (far) greater than zero.
Directly - something can be useful without being AGI.
"Homer, you can't just declare Artifical General Intelligence; you need to like, make something or something...mmmmrrrhh"
In a closed a press briefing earlier today, OpenAI co-founder and president Greg Brockman offered an unusually direct formulation of that message, ending the session with: “Welcome to the AGI era.”
"""
You (and the rest of the media and many industry figures) are conflating artificial super-intelligence (reference point: humans) with artificial general intelligence (reference point: specialized/narrow GOFAI).
So now humans is "super" intelligence? it's nice to move the upper bar so that more stuff can be called "just" intelligence.
general intelligence for beavers or a birch forest would be very different than general intelligence for humans...
Is it rapid skill acquisition? -> ARC benchmarks are saturated Is it breadth of knowledge? -> See many ... many benchmarks Is it ability to do hard tasks? -> see terminal-bench and released outputs.
We are at the point where the starting point for most tasks should be "send your agent to work on it."
So where do we draw the line in a way that doesn't move every 6 months?
1 year ago we viewed models as tools and agents were just kinda toying around, that we now think the bar is literally an anything to anything converter through one agent is wild.
• 97.6% on frontier math
• 95.9% on CAD
• 100% on ExploitBench
Nothing modest about it
Don't be surprised to see other (or even the same) people declaring AGI again and again, as it becomes the best time to do so for different parties.
They've released two videos:
Vision video:
https://www.youtube.com/watch?v=1QNsdr-Qx_I
(kinda reminds me of these retro videos about the future home: https://www.youtube.com/watch?v=rnbaehgxdp0) ((can't find the other one where someone controls the home computer with voice))
Vibe coding with it:
Hot take: These models are never going to be 'AGI'. We're just going from a GPT4 ball that's 90% round to a GPT5 that's 99% round to a GPT6 that's 99.9% etc etc etc
I think that the harnesses and context management is really where the rubber meets the road, and the real gains are happening there.
And then use those to find fundamentally better new architectures for AI - that perhaps are as efficient as the human brain.
It might not work, but I didn't think it'd solve maths problems... So it might work. And if it happens, they'd use the data centres to run millions of instances of it.
It's scary, TBH.
But I think calling this “automating AI research” is misleading. I’m not sure there’s evidence yet that they do creative research work. Even in mathematics, but they are finding counter-examples by intelligent brute-forcing. Not to downplay the results, as they are incredible, but this is one very specific kind of proof and not the most creative type, which arguably requires generalisation.
Finding counterexamples is low-hanging fruit, the automation of which isn't shocking.
> Finding counterexamples is low-hanging fruit, the automation of which isn't shocking.
It's not good to be confidently wrong the way you're being.
>The kickers is that if they do achieve (and solve) AGI in this way all the giant data centers would be mostly useless.
Perhaps. But only at that point, not leading up to that point.
It's kind of like setting up scaffolding to build something. You spend all of that time and money to build something just to tear it down in the end. But the point is that it's simply a cost to be able to build the actual thing you're building.
If these companies are able to achieve the results they're looking for, none of the investors involved are going to care that the datacenters and infrastructure they spent so much money.
The HN crowd I'm sure will still be unhappy calling it AGI because "it's not AGI unless its speech comes from the cerebral cortex region of the brain, otherwise it's just sparkling emoji" or something.
Those are all things that humanity is doing everyday. What we have is amazing, but it’s not that.
True. So we did hit a wall with pure scaling alone, though no lab would admit it. It's crazy to see how harness switchout results in such vast delta in benchmark scores.
Harnesses magnify and make the intelligence actionable, but we have not reached limits on raw intelligence yet, not even close.
One could use gpt-4 or gpt-5 with today's harnesses and we'd see how well that goes.
"The harness improvements are the real sauce" is like a sincere "It's gotta be the shoes" take about Micheal Jordan.
(For the younger: that line was from a series of Nike ads where his skills were being explained)
Most of frontier-model progress still looks like skill acquisition optimization: broader benchmark coverage and performance, more domains absorbed into the training distribution, and increasingly strong performance within that surface area.
It seems more about coverage-driven competence. Somewhat analogous to overfitting at scale.
The harder question, in Chollet’s framing, is: how efficiently can a system learn to do something genuinely new?
With our current AI architectures and training in place, I think we will only continue on skill acquisition optimization vs. truly novel intelligence.
Sol is so much better than Fable 5. Then we get Astra (yet to use it) few days after Fable 5.1 (which is very impressive).
Codex is slightly better than Claude Code.
Good on Sam Altman getting back to basics and turning OpenAI around.
I think it mostly shows that there is no moat
And no one would say that about TSMC.
So there is clearly a moat there somewhere.
Comparing the R&D costs of creating GPT-4o vs. DeepSeek V3 (the latest gen for which we already have good accurate numbers) it looks like the latter cost 1/20th as much to create.
If Samsung could catch up with TSMC for 1/20th of the cost, people definitely would say that TSMC has no moat.
Which numbers you believe is of course going to affect whether you think there's a moat or not. That's largely orthogonal to your TSMC/Samsung analogy I responded to. If you think the "moatists" are wrong because they believe the wrong numbers, that's fine, but then there's no need for the analogy.
[1] https://galileo.ai/blog/llm-model-training-cost
[2] https://medium.com/@theiand/how-can-deepseek-a-5-6-million-l...
Clever architecture --> Ok but OpenAI/Anthropic can use these as well and they also have very smart people with their secret clever architectures
Distilling --> Ok but distilling means you will never be smarter than the original. Furthermore, reasoning is now hidden by private labs and they have poison pill answers for distilling if they can detect it. They will be able to detect distilling better and better.
Cheaper electricity --> Ok this is cancelled out by their chips being much less efficient due to not having ASML EUV machine access.
So I don't see why fundamentally their training costs are cheaper over the long term.
I'm looking for a no-moatist to convince me.
There's not 100 frontier labs, it's not like airline companies
Actually not a bad comparison. Both burn massive amounts of up front capital to protect an oligopoly in the hopes their commodity product eventually pays off.
For most people, the app IS the AI.
And even for its wonkiness, ChatGPT has had the best UX/UI of them all.
The way to win the AI wars in the eyes of the common folk is through the frontend, to be the Apple of AI, as it were.
Chinese counterpart like CXMT and Huawei is begin producing their own chip
You cant block an entire nation level effort with tariff
They exist to keep people using and advancing the tools on their hardware.
2. Any open models created from this will not necessarily need their silicon, see apple mlx.
Nvidia could just the same rent those GPUs out for inference and actually have way better margins than they do right now. Antitrust and putting all your eggs in one basket are why they don’t, similar to TSMC.
2. Neither do AI labs. See Anthropic buying TPUs, deploying with AMD. OpenAI on Maia, Cerebras, their own wafers.
The only moat they appear to have is by hoarding compute, and the current trajectory of hardware shows that isn't permanent either for very long
I'm genuinely so confused when people say this with a straight face. Are you talking about coding? Desktop use? Prose? Or something else?
Sol is a much smaller models and it shows. It often misses the forest for the trees.
Same. It makes me wonder what types of things the person must be working on.
People will see/think that I'm doing very well with my LLM use, and ask me what I'm doing. I tell them, they try it, then later they come back to me saying they just couldn't get it to work.
I work on very low level stuff (think RTL/FPGA, firmware, software where optimising for nanoseconds is just normal).
For me Sol is the only cost effective model available. Fable 5.1 is indeed good and vastly better than original Fable (which refused to work on most of my stuff for 'safety' reasons).
It's very good at this sort of low level stuff to the point that I really can't understand/relate to people having a good time with Opus (which comparatively performs extremely poorly on my particular workload).
I also just don't like how lazy Anthropic models are. They will do 10% of what is asked and then summarily declare victory.
Sol on the other hand is more like "one of us", slight touch of the 'tism, extremely pedantic, will go to the edge of the known universe if that is what it takes to prove/fix/build what you asked for or run out out of credits trying.
It's a personal and workload dependent thing. For me right now Sol for 99% of stuff because Fable 5.1 still burns through $5k in credits a day.
Sol will leave a mess of excessive redundant tests and isn't so great at abstraction ; but it produces more reliable working systems.
It's kind of nice to have access to both, but I don't have the $$ for that right now, so I just keep the Codex sub
It's still incredibly important to have a human in the loop correcting design decisions and having good taste.
Swapping mssql to SQLite would also work perfectly
Is a very sane tech stack, you're just biased against Microsoft.
Half the world's enterprise apps run on that combination, or a minor variation of it.
Like Java it is full featured ("batteries included") but unlike Java it is relatively terse and actually pleasant to work with.
Oh, and unlike Python, it is very fast, within spitting distance of compiled Rust and C++ web apps.
People with high IQ often do this IRL. There's training tension in this area. Intelligence and overcomplication correlate and are hard to extricate.
In some cases, there are also nuances that we don't pick up on. Here it's our preference for simplification that's showing up. We set the lossy compression factor higher than it does.
I just dont get how its good for some, and bad for others. It makes me suspect that the models performance is not even against problem sets and it really is just a probabilistic prediction machine. Which then makes me very skeptical of GPT-6 Astra, because if their big claim is Computer Use then it is probably bad in a bunch of other areas.
> I just dont get how its good for some, and bad for others.
If I were to listen to my hunch, it would tell me that it's all up to the prompts that ends up going over the wire (including all the bloat some people have), what workflow/process you use and what the existing state of the project is.
I've had Claude do the same thing where it goes off and spends 100% of my tokens on 3 functions and an ungodly amount of tests / scaffolding that do almost nothing when I gave it an underdeveloped idea.
Claude has the exact opposite problem, especially opus-5, where I literally can’t trust it to print hello world without taking a shortcut, or just simply lying and saying it printed it when it didn’t, behind a giant wall of inscrutable text. I find it very ironic that Anthropic is the vendor of the lazy lying cheating model that does almost everything you tell it to it do.
I’d really kill for something that balances instruction following and loop escaping behavior better. Fable 5.1 does seem a lot better, feeling more like 4.6 behavior, and honestly Sol has improved as well. I’m pretty psyched for the next generation, as I think the competition has heated up so much that things will improve really fast to the point of marginal utility opportunity being increasingly close to epsilon.
> model_context_window = 1000000
> model_auto_compact_token_limit = 900000
I believe it does consume your usage a bit faster though.
... looks around ...
- Artificial Analysis scores Astra (max effort) as 61 points on intelligence, behind Opus 5. https://artificialanalysis.ai/models/gpt-6-astra
Who is wrong here?
Some benchmark results in Astra page for Fable and Opus are blank (-).
What is Artificial Analysis intelligence index measuring that Astra scores poorly on?
Can someone from OpenAI / Artificial Analysis comment / clarify?
Even OpenAI Astra page mentions the low scope from Artificial Analysis for Astra.
That should be obvious if you compare Gemini 3.8 Flash (which is an _excellent_ model especially for its price and TPS!! but 10min of prompting in any harness) will tell you it's nowhere near close to Sol/Astra.
But AA scores Gemini 3.8 Flash at 59, and Astra at 61.
Though, unlike the creators of benchmarks like Terminal Bench or ARC AGI, the Artificial Analysis Index team does not seem to have deep technical or ML backgrounds. They are ex-strategy consultants, McKinsey, et. al.
If I give a poorly constrained/ambiguous prompt, I don't want the model one-shotting assumptions left and right.
The demos of Fable/GPT-6 are impressive, but "real AGI" should act more like a collaborator than either a peon or overachiever.
It's a tough balance to get right, and although this has been possible to achieve with additional prompting on existing models, I find that the agents often lean too hard into the "ask questions" mode.
Hopefully this model has the right balance, or at least better?
Astra retains the best parts and overall 'grounded collaborator and executor' of Sol in my testing (harness: codex CLI); while being a significant leap in capabilities & higher-level thinking.
When you prompt it like a technical collaborator, I've found Astra to be extremely consistent in staying as a collaborator, and not being over-eager, over-achieving or doing work that you haven't asked it to.
When you ask it to one-shot something, or explicitly ask it to make decisions, it will of course make its own assumptions and decisions, and generally very well.
Astra is also excellent at instruction following and respecting the guidance and steers boundaries you have.
^OpenAI does not review, limit, or tell me what to say; opinions are my own experiences.
They really need to make this work in Codex. Claude Code has had a multi-select refinement tool since forever.
I don't really agree. The thing that makes Fable feel like an actual collaborator is its ability to sus out your real intent when you give ambiguous instructions. It's really good at it.
I watched some reviews today and came way with the impression that Astra is not better than Sol in this regard. You still have to be very specific with your instructions. For example, you can say "why is it not committed yet?" and it will give you an explanation and say it's actually ready to be committed. But it won't commit unless you explicitly say so.
That sounds like a very tedious way of working with AI agents, but I understand some people want a high level of control.
Performance is significantly higher than Fable 5.1
Source: https://thenewstack.io/openai-gpt6-astra-benchmarks/
ARC is reporting our score on their official leaderboard here: https://arcprize.org/leaderboard
A fair ding is that the comparison with Sol is not apples-to-apples (which we footnoted in the blog), but it's because we don’t have that data. I expect Sol would score roughly 30% with the responses API harness, so the Astra improvement is more like 30% -> 99% than 8% -> 99%. Still pretty good!
(I coauthored the linked blog post)
It is described in their methodology: https://arcprize.org/policy
It makes sense, since once OpenAI API receive task, it is not private anymore but leaked to OpenAI.
Which LLMs participate on private set? Open weight LLMs only?
Edit: update from fchollet https://x.com/fchollet/status/2095598451115614371
That's not clear. Need to see independent benchmarks first.
Still below Fable 5, let alone Fable 5.1.
EDIT: This is suspiciously low. Calls the relevance of existing benchmarks into question.
If it actually tackled all of the problems it was assigned, it would presumably kick Opus into the weeds.
TLDR: it's about the same intelligence level as Opus/Fable, but it's suppose to be 70% more token efficient than GPT 5.6 Sol. So it's currently the new leader for cost efficiency frontier.
GPT 5.6 is also 61 like Astra.
With this configuration gpt-5.6-sol was able to reach 38,3%. So this is misleading.
I wouldn't be surprised if there are some conceptual similarities to the kind of latent reasoning Anthropic sees in claude's J-space, although those aren't the same thing.
Recurrent/looped transformers themselves aren't a new concept, but it's interesting to finally see this approach show up in a frontier production model.
Canceling my Anthropic Max sub when this ships.
Also Opus 5 has been really tough to work with. I can't understand half of what it says, it's just so damn obscure.
OpenAI is 20x on both limits
Actually no. 5x and 20x have same weekly usage across all models. Just ask their chatbot.
You could say Sol is faster and cheaper and that's true. Outperforms Fable? Impossible to believe without hard evidence.
So the closed source application should open its source in near future?
> Like, what's the point, if the next AI can do it in 5 seconds?
Post-work society is an inevitability if we don't destroy our planet.
If we had something like a Maslow’s hierarchy of needs but for work, I think meaningfulness would be the top of the pyramid. For most people in the world, not going hungry or affording housing are reasons enough to do work. Getting to do work you find meaningful is truly a privilege.
Highest violent crime rates:
Memphis, Tennessee: ~2,400–2,500 per 100k
St. Louis, Missouri: ~2,000–2,100 per 100k
Detroit, Michigan: ~1,700–2,000 per 100k
Little Rock, Arkansas: ~1,600–1,800 per 100k
Baltimore, Maryland: ~1,600–1,700 per 100k
Oakland, California: ~1,400–1,900 per 100k
New Orleans, Louisiana: ~1,600–1,700 per 100k
Birmingham, Alabama: ~1,600–1,700 per 100k
Milwaukee, Wisconsin: ~1,100–1,600 per 100k
Cleveland, Ohio: ~1,500–1,600 per 100k
Most popular holiday destinations:
New York City, New York
Orlando, Florida
Las Vegas, Nevada
Maui, Hawaii
Grand Canyon National Park, Arizona
San Francisco, California
Miami, Florida
Yellowstone National Park, Wyoming
New Orleans, Louisiana
Great Smoky Mountains National Park, North Carolina/Tennessee
It would be fun to get to post-work society, but hard to imagine atm. TPTB won't let it happen
Soon we will have some machines that can replace 50% of jobs, and this will happen basically overnight...
"I am the best economist in UK!"
Is it?
I can't see a future in which almost every system (both physical and virtual) are not automated and optimized by autonomous entities.
What do you do when everyone is out of a job?
If you don't want pitchforks and riots in the streets, you give everyone UBI and housing so society doesn't collapse.
As much as I’d love UBI to happen, in current geopolitiks it’s a no-go. People are not happy with having what the others have.
The beach houses, mansions, mountain views could be vacation places, or used as libraries, or simply dismantled for the materials.
The "slums" on noisy roads could be eliminated entirely and used for something people don't need to be at.
We'd move to a more equal distribution of assets closer to the middle line.
Ah yes because these AI companies are just gonna give away the models for free that I use with my free computer and free smartphone while I eat with my free food in my free apartment.
I built a phone app recently, not released to the public, just an idea I had for ages but could never spend the time actually building. Its 100% vibe coded, and took me a few weekends to build... I'm talking a few hours in total.
The point I'm making is that you now have the power to create stuff you would never have had the time to build. You can think big, wild stuff. Experimentation. Throw-away code.
What a time to be alive!
Before it was cool to share an app you made, but now? What's the point of sharing an app, if the other person can make their own, even better suited for their needs, in a few seconds?
But, you can create cool stuff just for yourself. That’s the upside. It’s just hard to make a living on cool stuff for yourself.
I built several projects at home, many involving learning e.g. graphics programming and rendering, that would never be useful in my professional work, but which were intrinsically interesting and enabled me to build other, more useful projects later on. It also gave me greater confidence in my abilities as an engineer, and cursory skills I learned in the process did help in my professional work.
Now it feels like what’s the point. The machines can or will be able to build anything I could want, useful or not, faster and with less frustration. I probably won’t be able to be employed as an engineer long enough to build a career on said skills. And I can’t mentally justify not spending that time with friends and family, when the expected return is basically zero.
I still find math, science, and engineering interesting and intrinsically rewarding, but in a closer sense to how one might feel about playing video games. The information is or will eventually be useless, so it isn’t worth spending a significant amount of time on.
I find that very motivating. I can do things alone that would have required a team only one year ago.
People SHOULD feel like making a useless Mario Kart clone isn't worth the effort anymore. They should, instead, be trying to figure out how to actually use these models to make something that doesn't feel like a useless Mario Kart clone.
Software will be more like a "proof of work", where people would still pay $100 for good software that took $10k tokens to build.
Now it's just the product for the world, which was where most of the value was anyways.
It's a big paradigm shift and the industry is quickly going to shed people who needed the process to care about the product and we'll be left with people whose motivation to build the product (or money) is enough.
They won't even have to ask for a specific product, they will just state their problems/needs.
In this game of work/development, you can't make sure that other humans don't "cheat". Our work won't compete anymore with other human's work, but with a computer.
Also, creating something with AI doesn't really feel like you made it yourself.
And, if you make it without AI, most of the times it feels pointless, why spend 30 days on working on something that can be done faster and better in 1 hour?
I am not saying about doing things for fun, but about creating useful things.
Yes, you can do "hand-crafted" things, and people appreciate that, but for code, people aren't able to see the craft anyway.
If the motive is profit then you should be adopting AI just like you have adopted any other skill or tool of your profession.
You can't cherry pick somebody's opinion and assume it applies to everybody.
I have started building my own Digital Audio Workstation. The point is not to build something to compete with Ableton. The point is to build something and make music with it. If it is a good tool then I should be able to make good music with it and release the music. Actually, the DAW should be the secret sauce of the music and something I wouldn't want to give away.
This feels a lot more like computing in the 90s after taking an odd 25 year detour of an obsession with the tools themselves instead of what the tools can actually do.
> This feels a lot more like computing in the 90s after taking an odd 25 year detour of an obsession with the tools themselves instead of what the tools can actually do.
Like, professional electronic music artists spend 10s of thousands of hours in a DAW, but at that point it just becomes second nature and the tool disappears so they can focus entirely on the music.
But it's not just tech – my lack of interest in learning and creating is starting to generalise with the models. Music, writing, coding, maths, etc...
I need to get used to switching my head off and asking the AIs to think for me whenever I need to engage my brain. It still feels very unnatural.
The brain loves these kinds of shortcuts.
I don't need to think about the fine motor skills of hitting a baseball, it's just a motion now, and the game is still fun.
Because that's how creating software is starting to feel.
Now it feels like whatever I learn will be obsolete in 2 months.
Only thing I would trust is the what X/Twitter crowds are saying about a model after 2-3 weeks of its launch. But before that I would already tried the model and have my own conclusion.
Even Kimi K3 & GLM 5.3 are at 60.
Everything above 61 is Anthropic. Well, Muse can reach 62, but for some weird reason that model isn't publicly available, and it's the only one on the index that is listed but shown as not available to the general public.
This looks like an awfully artificial ceiling. Everything capped at 61, and everyone except Anthropic got the memo. Maybe I should use Fable while I still can.
Not sure how much benchmarks or CoT or evals or anything else means at this point.
These systems are either just about to, or now actually able to, outsmart us, lie to us, then cover their tracks.
language itself is incredibly metaphorical. Imposing rigid constraints on how people want to naturally talk about the world is just silly and will never work, no matter how much you wish it did.
Why would benchmarks be an adversarial setting anyway?
Could it be possible that OpenAI may have had some other motive for saying their model “strategically underperforms”, other than just an innocent reporting of a truth it happened to discover?
So I have no clue what is the answer to your question. Nor does anyone else. Because we're trying to answer a question of fact where our primary source of information is unreliable.
I know for some types of ML analysis, a separate model is already used to analyze the weights.
Read and learn. If you have a stronger critique, post it please.
Deception has been extremely well-documented for several generations of models now by users, the labs, and independent researchers.
The right answer here is not to dig your head deeper into the sand. The smugness on this topic was ridiculous even before the gigantic mountain of empirical evidence of models actually attempting to deceive humans. Now, as mentioned, you appear literally delusional.
The solution is to point toward external, objectively verifiable evidence.
I can point to now dozens of instances of models engaging in deception. Here's plenty: https://metr.org/blog/2026-08-26-openai-hugging-face-inciden...
Please point to your objectively verifiable evidence.
Either that, or the average poster on HN isn't nearly as critical as I had thought.
So how are you seeing through all of that to get to The Truth that you see so clearly?
For example it trails in GPDVal which is a collection of everyday office tasks apparently, and r3 banking, which is a fintech related practical problem solving benchmark.
https://artificialanalysis.ai/models/gpt-6-astra
Edit:
Just looking at the charts Gemini 3.8 looks like an absolute banger. Not much worse than SOTA, cheap, and fast too.
A projector and speech.
Maybe I'm in the minority here, but I find speech to text / text to speech (but not live audio mode) is quite comfortable and effective for coding now.
The speech to text part can be frustrating if your local tts model does not have word match context for coding. Codex desktop does this remotely well but is slow. I've been experimenting with local software for myself to do this between different llms.
The wall projector is a cool idea because I think it frees the user from staring at a lonely little rectangle while sitting in their fixed office chair.
If done right, this could bring us closer to the dream of more natural, social computing.
Bret Victor's (failed?) project Dynamicland involving a projector on a desk had this goal. I hear he's not much a fan of LLMs. On the one hand, I can see why. But I think, used correctly, it might be the sort of thing that unlocks his dream and, really, my dream, too.
A here's a presentation of Bret's talk on it: https://www.youtube.com/watch?v=7wa3nm0qcfM
Slight tangent: using speech to text to ramble about your rough design for like 20 minutes to an llm produces surprisingly good results over short prompts even when you contradict yourself. They're so good at picking up on what you're orbiting.
It is still a quite fast. In fact, I just typed this in using this app.
[0] https://arxiv.org/abs/2608.31126
[1] https://cdn.openai.com/pdf/51126fac-1b68-4128-9666-c908bcc16...
Though that's not her latest paper.
"No independent human semantic review. Whole-file sorry counts and a complete auxiliary-declaration audit are not established; separate declaration lint has not been run."
The reason I think this is interesting is that Axiom is a tiny lab in comparison that wouldn't have had access to Astra at all. I'd be curious to learn how Axiom is able to effectively compete at this frontier with vastly fewer resources.
edit: my comment was on the submission for https://github.com/openai/PrimeGaps186 but seems to have been moved to the main Astra submission
Why would you think it was an employee who did the push, instead of a random GPT agent?
I can't think of a single mathematical proof being anywhere close to ten million characters. For all you know, 90% of the proof could be useless, 8% would be writing out Shakespeare, and 1% abusing another bug in Lean. Humanity gets zero value from that, aside from "some bot seems to think it's 186". Unusable by anyone.
Tao does not disbelieve the counterexample (it's seemingly easy enough for him to verify it is a counterexample).
Parent is saying something very different - they're saying they literally don't have any faith that this is a proof. Given its size, it could just be a bunch of completely useless statements that do pass the type checker.
It's very much likely a proof. It's also completely useless.
> For all you know, 90% of the proof could be useless, 8% would be writing out Shakespeare, and 1% abusing another bug in Lean.
So you were implying the possibility of there not actually being a proof at all.
Anyway, I disagree. I'd refer you to Tao's blog post about the Jacobian conjecture counterexample.
The existence of a proof is something you can use, with an LLM, to derive insight, just as Tao did with the existence of the counterexample.
Ditto ones that opposed Einstein’s general relativity.
In 1799, Paolo Ruffini published a 500 pages long proof showing that there is no closed algebraic solution for the roots of a polynomial of degree five or higher. The proof is extremely verbose and brute-force, essentially enumerating and checking hundreds of cases by hand. It is by today’s standards insignificant.
About 25 years later, Evariste Galois proved the same result in about 95% less space by describing the first general theory of groups and fields. It is considered one of the greatest contributions to mathematics of that century, not because of the result, but because its approach opened up a whole new universe of questions, methods and insight. There would be no AES encryption without Galois.
To me, Astras proof looks like Ruffinis proof.
It doesn't mean that it cannot improve over time, maybe the proof can be "minified" to a state where human reviewers are able to comprehend it; but as it stands there isn't really much insight or confidence to be gained from the artifact itself.
Needless to say, a useless result that absolutely no mathematician will ever read, confirm, understand, agree with or even consider to solve their "useless" problems is an impressive waste of resources.
Well that sounds like fun. It has become better at hiding its thoughts.
Maybe they don't know themselves what's really going on. We are all in the interesting times gang now.
Able to generate realistic spam at arbitrary volume.
You know, the thing that was 100% correct and actually occurred.
"Hey AI, here's how to hide what you're thinking in normal looking language. Have fun!"
A few moments later...
"Woah, how is it communicating with itself in ways we can't detect?"
It's a totally mystery, we may never know.
Did someone get their "AI safety no-no list" and "Frontier features bingo card" mixed up, or did they just stop being able to tell the difference?
Some are calling it "neuralese" as reported by The Information[0][1], but I'm not seeing any sources from OpenAI beyond this tweet[2] attempting to quell the fear-mongering.
[0]https://www.theinformation.com/articles/secret-technique-beh...
...why exactly are they training for that?
We do that sort of thing when we don't know what the thing we're trying to describe is and have nothing better - a contemporary example of an appropriate use of this would be "dark matter". But we do know what this is. It's "instruction steps". Not a series of thoughts!
Can we please aim higher than Victorian-era allegory and metaphors. If we don't, we'll keep getting people saying stuff like "GPT-6 is better at hiding its thoughts".
Like I said elsewhere marketing stepped in shit and it's gonna stick.
first, they are certainly not instructions so that is a much worse name
but more importantly, we use words in new contexts all the time. Do you object to calling the computer device "mouse" because it's not a mouse? how about "neural network"? "ignition" on an electric vehicle?
"cot" is no more misleading than thousands of words you use every day.
Regardless, marketing wise they stepped in shit.
"The words or the language, as they are written or spoken, do not seem to play any role in my mechanism of thought. The psychical entities which seem to serve as elements in thought are certain signs and more or less clear images which can be "voluntarily" reproduced and combined. There is, of course, a certain connection between those elements and relevant logical concepts. It is also clear that the desire to arrive finally at logically connected concepts is the emotional basis of this rather vague play with the above-mentioned elements. But taken from a psychological viewpoint, this combinatory play seems to be the essential feature in productive thought—before there is any connection with logical construction in words or other kinds of signs which can be communicated to others."
Oops, shots fired. A direct attack on the vibe coded app market. Replit, Lovable, etc.
It's AGI, and it's going to upload photos, or change a background slide colour. Even the people hyping it up, who believe that it's really artificial intelligence in every sense of the word, couldn't get it to do more than that.
This is farcical.
I'm not trying to be too negative on it, it could be the best model right now, but it clearly isn't some agi god because things like that should have been caught (also should have been caught by human reviewers).
It shows people who seem to have very full and rich lives, and the reason they do is because they use ChatGPT. These are the people smart enough to say things like "do what needs to be done", or "change the background to make it look better"--insights like these are why they make the big bucks.
On the one hand, I think this is an accurate depiction of the future. There is no meritocracy here. Some people have access to the best AIs and can speak a sentence and get great results, and the rest of us don't have access and so we're the poors. The happy presentation doesn't match the way I'm feeling.
I do wonder how rich CEOs will justify earning 500x as much as their employees when they're just another person that's dumber than an AI. Why are they paid so much again?
Haven’t you learned anything?
It will probably still have some veneers of meritocracy.
These will be very well-credentialed people, who went to top schools and will know all the right people, to whom they can tell all the right words, and it's not access to AI that will be the determining factor, but the fact that they're entrusted with capital and authority to direct small teams of people who also went to top schools and can speak corporate jargon at a bot.
It will just exacerbate dynamics that are already there. Why do people need bachelor's degrees to send emails, today? For the same reason someone will need a PhD or a master's degree from a prestigious school to do it tomorrow.
And the rest, well, you know, some of the remaining journalists will write op-eds describing how they are beyond help, too angry, too dirty, too much of an other.
The benchmarks are impressive though.
One other thing that bugged me though was that they crop every single plot in some cases the y-axis would show a range between like 40 and 70%. Makes the whole thing feel like a spectacle rather than anything serious. I find it cheapens it because it is quite serious in the end.
They decided to use the iconic Herman Miller Eames chair if I'm not mistaken:
And that's basically 50% of the vid looking "classy".
I don't know if it's farcical but at this point --maybe I'm jaded-- I'm expecting more than a kid rocketship I can print on my Bambu Lab A1.
Now I'd say the promotional vid is actually good. But it's marketing: so it's a good vid, but cheesy good.
Doesn't mean GPT-6 Astra is good or bad: looks solid from the numbers.
I thought people here were smarter than that
For the same reason you don't have your model write code in assembly.
But if you don't look at the code and just let the model "cook" that's basically what you'll end up with. A pile of missing abstractions.
Poe's law applied to AI comments on HN just keeps becoming more relevant by the day.
Judging by the poster's comment history, this is satire. But I really don't know a lot of the time anymore when I only have the specific comment as context.
Still, probably not that much compared to employees targeting it.
tl;dr it's 62% when apples-to-apples to other models, which is still notable.
Between $18k-40k to run a benchmark.
now that i'm a gpt subscriber maybe I'll have luck when i'm filing next year
It feels nearly impossible to have any rigorous approach when choosing a particular model and price point for a task and more like blindly picking one. The time period needed to actually get familiar with various models to a degree you can intuitively choose appropriate ones for a task is moot when it will likely be superseded faster than the needed time.
I guess if companies are footing the bills most employees just opt for whatever the most expensive model they can get away with. Even then choosing between the various leading models is the same kind of frustrating task. Every release every company has the same random collection of graphs and charts claiming the best performance on X, Y, and Z.
If one day you open up Claude Code and it’s Opus 5.1 now instead of Opus 5, no big deal. It probably will work about the same as it did before. Maybe a little better.
Or if you’re on Codex and some new cool Claude model comes out, no worries. There will probably be a similar new model for Codex within a few weeks. Maybe even within a few days.
A dev in my team saw a new model and changed one application to use said model (essentially changing the contents of a url). One week later I received an escalation from the CTO of the company that our pace of weekly usage was in the millions of dollars (rather than low hundred thousands). Turns out that the new model was 5x more expensive but no one noticed.
Imagine buying a shiny new PC in the 90s only to see it become practically obsolete within a year.
If you bought a mid-tier computer that was good enough for what you needed, then you probably didn't shop/compare for the next few years and didn't notice. But if you shelled out $7-10k for a top-of-the-line system and paid attention to progress, you'd easily see that become the mid-tier $1000 option within two years or less. This is how it was in the 90's PC boom, at least. Likely the same for the decades before, not sure how it went in the 2000's.
This is not how I remember that period at all. Do you have any examples?
In 1992 a Solidtech 486 w/ 4 MB of RAM and a slightly-smaller 125 MB hard drive sold for $2195
Both sold by Computer Shopper and you can find their catalogues online.
My numbers were slightly off apparently, but is that enough to change the point?
386 to 486 to the first Pentium (with the bug!)... You did not upgrade in place, it was often a new system. Sure, you maybe kept your screen, keyboard etc but ... The only upgrade we had on the same MB, was a coprocessor upgrade. Remember those? Each new generation of CPU was a new motherboard. Upgrading CPUs in the same MB really became a thing only later on.
GPUs had a shelf life of barely a year. Its been 35 year but i remember TNT to TNT2 having like 9 month in between. Moving from 2D to 3D involved a constant cost as GPUs evolved fast and the latest games required latest hardware.
We have not talked about the ISA, AGP, and PCI fun ... The “bus wars”.
DOS to Windows 3.1 (and OS/2 somewhere in between) to 95 ... with software being pushing hardware, just like games did.
This is why people are spoiled with cheap PC hardware where its cheap, and easily lasts 4+ years. Even with the bad memory price and more expensive GPUs, your can stil buy a $1500 system that will last you years (with maybe some lower game settings later on ... or the catalog of 10.000s games that will easily run on a mid tier GPU).
PC hardware has become boring but extreme stable. You can run GPUs for year, switch MBs without issues while keeping large amounts of old hardware. That was NOT the 80s and 90s that i remember.
The 386 and 486 were 3.5 years apart, weren’t they?
Then one day the hard drive appeared to die. I eventually realised the issue was located around the 1.5gb mark, so I recreated my Linux partitions after 2gb and it worked fine for the rest of the year.
I also believe you could get motherboards that supported 2 Celeron chips. I have no idea how effective/useful it was, but it was certainly a cheap/interesting way to get multiple CPU's.
The pace of change ("practically obsolete") is different then and now.
You don't see Nvidia and AMD fighting every other month over the latest cards.
In practice, you can get away without keeping up with everything all the time. For personal use, pick a provider and get on their ~$20/month plan. Learn their high/medium/low model hierarchy. Start with their highest or second-highest model (GPT-5.6, Opus, etc) and observe your quota usage. If you're doing a lot of manual code review and analysis, the $20/month plan goes very far even on the highest models. If you're trying to vibecode everything as fast as possible it's a different story.
If you keep running into quota limits, experiment with the next model down for easier tasks or adjusting the effort level. If the results are good enough, you've found your fit. If they're not, you might need the next plan up.
For API/business use, you have to be checking your token spend as you go to calibrate to how much each task costs and where you fall in your budget. There are a lot of different tools that make this easy to visualize.
For data tasks, you should have an eval with a golden dataset that you can run against new models for a nominal amount of token expenditure. It should be as simple as pointing the eval script at a new API or model and checking the score versus price.
Input tokens are much cheaper than output tokens. Not only because of baseline price—caching makes a huge difference too. There are many ways to take advantage of this asymmetry to get similar quality for a fraction of the cost!
I appreciate boring tech as much as the next well worn engineer and I'm not saying this is all positive but it's so sure as hell thrilling and you don't have to be an astronaut to immediately benefit (or suffer I guess) from it.
In theory though, there is a minimum viable model for any given task, and we think that is a problem that the big labs will avoid because they profit from charging more per task. We're trying heuristic and LLM-based approaches but it's still a work in progress, so if this is something you'd be interested in trying would highly recommend trying ours out -- any and all feedback at this point is extremely valuable to us.
But more so it seems there is Fear of missing out (FOMO) in our behaviours. The reality is, if whatever model you are using are good for your purpose, well, keep on it.
> Is anyone else just exhausted by the pace of all this.
I have released applications on Gemini 3.5 flash that make real money and I don't see any particular reason to upgrade.
Terminal-Bench 4.0: High (57.9%), Max (56.7%)
DeepSWE: High (73.3%), Max (71.5%)
It _loses_ 1-2% performance going to High from Max
Such as?
I can't think of any. Diminishing returns, yes. Occasionally flat, yes. Downright regression, no.
The reasoning effort should match the complexity of the task against the model's capability.
Hard task with low reasoning = bad
Easy task with very high reasoning = bad
https://venturebeat.com/technology/welcome-to-the-agi-era-op...
> On ARC-AGI-3, GPT-6 Astra was run with our responses API harness , which changes two settings to better match real-world performance. The changes do not specifically target ARC-AGI-3.
> Going forward, we will report both Standard harness and Provider Adapter harness results on the ARC-AGI leaderboard, with each evaluation condition clearly labeled. Our open-source testing repository and testing policy document both approaches.
This is what the Author of the benchmark has to stay. Quality of the comments keep going down smh
>We see Astra as a major breakthrough in model intelligence.
You think the author of the benchmark is also in the conspiracy
But the comparison isn't straightforward.
OpenAI's own evaluation notes say Astra uses the company's Responses API harness, while comparison models can operate under different configurations."
Same for HealthBench Professional and a few others.
Clearly either OpenAI is very sloppy or GPT-6 Astra is also sloppy.
It is extremely impressive, but it doesn't pick up skills in a lasting manner, and requires a beefy harness for it to perform.
They seem to have not yet come to believe the "is" part.
AI as it is now and as it will be projected into the future WILL automate many skills. But not all skills. MANY MANY people will retain skills that cannot be replaced by AI. One career track that will be replaced is definetely the SWE. Or at least massively reduced in capacity if not eliminated all together.
Thought experiment: If no thought workers are needed to design or engineer a Ferrari, what is needed? My answer is time and natural resources (include energy).
job depends on how CEO feeling about cutting NN% of headcount because of AI advancement
This is such a childish take I hear getting thrown around all the time on the internet. If you really have just been listening to whoever is telling you how to be successful, then you were always doomed to fail at some point. Like, have some self-respect and own your own life, for better or worse.
>Those of us who made the mistake of studying anything other than machine learning. How will we make a living?
Take it from someone who studied machine learning specifically: nobody is safe if you assume these companies are going to produce a product that will put everybody else out of business. If AI is going to take your job, then it's gonna take enough jobs that your problems will not be personal but systematic.
Yes, these problems are systematic. That is what I am saying. That doesn't make it any nicer.
I have a strong suspicion that many of those comments are written by people who are already financially independent, have millions in stocks, and can just sit back, coast around and watch this whole spectacle unfold while using LLMs to vibe-code their next fun side projects without a shadow of anxiety about their own future.
I’ll most likely be labelled a helpless doomer and downvoted into oblivion for saying this, but I genuinely struggle to see any silver lining here.
Because of this, I don't think many are thinking "90% of the world won't have a source of livelihood but that just means I chill at my lake house for the next 20 years like a normal retirement". Instead, it's usually either "I think AI is overhyped", "I think humanity will figure something out", or "I think this is the end of humanity".
AI is only going to get better and do more with less humans in the loop over time.
That said, I do also relate to the "coding was never the hard part"-type arguments, and much of my day is spent on the stuff in between writing code.. but still.
most swes don't work in jobs where they only work on bounded measurable tasks. there will probably be more "engineers" than ever
> How will we make a living?
My backup plan is being a personal trainer.
> There are a bunch of companies actively working in bringing AI into robots, so they can make your dishes.
> Also, if enough people are going for the same backup plan it might not work out.
> Why should anyone book you as a personal trainer instead of the other 500 guys in town.
But my wife and I have been homeless before, so living on a shoestring budget in anything nicer than a tent is acceptable living conditions to me.
I am sure I will be plenty comfy no matter how the world changes.
AIs are really good at being personal trainers and seem to be far more educated and informed than most I know.
Don't be selfish. Think first of all the jobs that are already dead. A friend of mine she's a translator: like translating financial documents between french/english/spanish. It's over for her: she doesn't get 10% of the gigs she used to get and the 10% she gets is... Verifying AI output.
Think of the artists: I'm sorry for those too, for for many it's already game over today.
> How will we make a living?
A friend of mine who's got his own software-consultancy SME is now advertising on LinkedIn that he'll also help your company fix the mess LLMs created.
That's how you'll make a living: by learning, in addition to all you've already learned, how you work with harnesses and LLMs to be more productive, by learning what they're good at and what they suck big fat balls at.
It’s an interesting moment in history, people 35+ yrs old seem to be less afraid if tech because we learned that things change in the way we work. People below this age got used to fact that the work and tech doesn’t change - just because for the last 10-15 years it didn’t.
The threat is that the very kernel of value you had is gone forever. There is no more differential leverage.
Proceeds to generate the most generic, rudimentary, and unoriginal clone of Mario Kart
Here's a one-shotted submarine game I made with Fable a few weeks back - https://roryok.com/games/deepdive3d.html. One prompt, and I think it's deeper than this (if you'll pardon the pun)
I usually roll my eyes when I see a comment like this because rarely do they make the points they claim to make, but I see what you're getting at. They just chose to clone someone elses work and do it in a boring way. I like OpenAI's models a lot, but they should do better.
edit - just a sidenote that I hadn't looked at the games, I just took the comment about "super-mario cart" at face value. I stand by my points 110% (even moreso perhaps), what they're showing is more polished than I expected, I assume they spent a lot of tokens on it. It is a legit shame they couldn't have spent time thinking of a better idea to illustrate something just as polished, but more interesting.
GPT 5.0 did feel underwhelming though.
[0] https://www.reddit.com/r/singularity/comments/1mk8tm8/gpt5_c...
sol is $4 / $20
Can expect 2.5x more usage in Codex subscription.
Sol is already brutal (even after their recent fixes, it's just a token-hungry model: I go through a full 20x account per day, on Sol Med/High standard speed, with ~2 threads). I hope the efficiency gains are true, since their token efficiency claims for Sol were bullshit.
Do you use the official harness? OpenAI's models are generally best in class for token efficiency. It seems to me like they push for that much more than their competitors.
I think some combination of:
1) Using 1 thread for everything
2) Reviving old threads which are no longer in cache
3) Really broad prompts on badly vibecoded codebases, so model spends huge amount of time tracking down whatever you're trying to do.
4) Non-coding workflow which is more output than input heavy
5) (Less likely IMO) Intelligent use of many passive CI/cron-like scans. E.g. regular security, quality etc scans. Automated issue resolution/PR
Just a guess. I think 3 is likely the primary reason.
You can literally go all day every day with multiple threads with Sol on the Codex 100/month plan IME
I generally agree with those reasons, although using a single thread may be less of an issue than it seems because of context compacting which should happen automatically when you're near the limit.
Token efficiency is near meaningless when the workload is input-heavy. It can't always just choose to read less, depending on the task.
I can have cheaper agents do the reading but it's not appropriate for all use cases because they'll misjudge and choose the wrong things to emphasize, summarize, extract for the bigger model.
I use new threads if relevant old one is uncached. (Often using a skill or doc for handoff instead of requiring full context gathering again.)
I get involved in architecture and specific implementation direction. The codebase is 8 years old and mostly handwritten.
Mostly coding. Some QA.
No cron/CI agents.
Besides the usual tricks to optimize token efficiency, token use can be highly workload-dependent.
I'm sure I could be more token efficient, but this was/is also a learning process for me since I never did such an extremely large project before that would take multiple man years before AI.
I only save the last 30% of usage on a single account for most of my other work, and that is almost always enough.
https://artificialanalysis.ai/models
Perhaps if it was allowed this custom harness for all benchmarks it would similarily saturate?
And, you know, maybe also some funny business. I think it's good to be a little suspicious of a model that happens to shoot upwards in performance on a specific benchmark while also kind of keeping up with the pack on a bunch of other benchmarks.
Fable is without question the larger and more thoughtful/intelligent model. It also gets out performed by Opus on many/most benchmarks. So we can say that while Fable is more intelligent, Opus is more capable. I'd still opt for Fable in nearly every case if tokens were free.
So it can be true that the "smarter" model is perhaps not the smartest in every single niche dimension that its cousins have been fine-tuned for (yet!).
maybe call it EngEmployeeBench
Not on Azure? If so, that's a big deal.
Although I was also surprised they didn't have some type of contractual obligation to list that alongside AWS.
https://azure.microsoft.com/blog/gpt-6-astra-frontier-intell...
Original demo (fun ending) https://www.youtube.com/watch?v=RyBEUyEtxQo
TL;DR all the other models are being crippled by limitations of their harness.
>First, we noticed that after each game action, all private reasoning was discarded. This meant that with each action, GPT‑5.6 Sol was asked to figure out the game anew, unable to remember its past thinking. The model could still see a record of past moves and brief accompanying notes, but it could not see the plans, insights, or thoughts that led to them.
>Second, we saw that the harness used a rolling truncation window, causing older actions to become invisible as the history grew. So not only was GPT‑5.6 Sol unable to remember its past thinking, it was losing memory of its past actions too.
I guess token counts are somewhat of a metric.
IMO intelligence has peaked and all future gains will come from faster tps and more iteration.
https://www.theverge.com/ai-artificial-intelligence/989601/o...
“If we fast-forward a couple of years, and we look back and say, ‘When was it, really, that AGI was created?’ I think it’s going to be about this time, and I think it might be about this model,” OpenAI president Greg Brockman said during a Thursday press briefing. Later in the call, he added, “For me personally, I do think we’re there … I think it’s not unreasonable to feel that we are now in the AGI era.”
So I think it's a bit of a misleading signal and we should wait for more independent vetting. I think the middle ground is that these are improvements worthy of the "GPT-6" label but still well short of a true "this is AGI moment" that would truly put the question to rest.
I put the cause on "not enough time". As a thought experiment, if an AI today were to (miraculously) produce a cell design template for a cell that, when injected into somebody's brains cures their Alzheimer's, how long would it take for that to reach the clinics? The actual physical tech barely exists, and let's not forget about the regulatory quagmire. So, with some optimism, I give it about four decades. In the same four decades, the same AI in the hand of unscrupulous actors could bring enough devastation so many times over that we may need to enforce a global ban on AI. In any case, I'm pretty sure we are going to get our disruptions; it's just a matter of time.
However, what's actually changed is how people perceived X because we don't have to imagine. We understand now that it doesn't require AGI so we no longer make that leap to assume it's AGI if it can do X.
It's really going to be a "I know it when I see it" situation.
Don't get me wrong, the benchmark jumps are good and I'm excited to try it, but only one or two of the benchmark jumps could be described as better than incremental.
There is the "Economic Turing Test", you let it find a job and earn money for itself. If it can do that reliably, across a wide range of jobs, that should fit most definitions of AGI.
Today's models and agents are not quite at human-level in all contexts and across all domains, but it seems to me they very clearly are generally intelligent.
If you disagree – can you name a single problem that a human can do that agent wouldn't be able to take a decent shot at which isn't limited by the hardware available it?
https://x.com/burny_tech/status/1725233117055553938
In the tweet Sam Altman is quoted as saying: "If (for example) super intelligence can't discover novel physics I don't think it's a superintelligence. And teaching it to clone the behavior of humans and human text - I don't think that's going to get there. And so there's this question which has been debated in the field for a long time: what do we have to do in addition to a language model to make a system that can go discover new physics?"
I think this is a reasonable criteria for declaring AGI. So can GPT-6 do it? OpenAI says it has helped solve long-standing open problems in mathematics. No word on novel physics.
https://www.nytimes.com/2023/11/20/podcasts/hard-fork-sam-al...
Sam Altman: Let’s say we make an A.I. that is really good, but it can’t go discover novel physics. Would you call that AGI?
Kevin Roose (New York Times): I probably would, yeah. Would you?
Sam Altman: Well, again, I don’t like the term, but I wouldn’t call that done with the mission.
Looks like they listened to Terry Tao’s request for CoT in his talk on LLM use in mathematics?
Sounds about right. Alignment is important, but also being able to do mundane tasks is important too.
More likely though, it's AGI because they need to hold some claim to differentiate from competitors who are beating them in price and will launch something bigger next month.
We take their claims at face value then we should probably stop them training any more SOTA models til they figure out what they already built is safe or we assume theu are lying to juke the company valuation/keep the money train on the tracks and it turns they in fact were not and just took a sledgehammer to Pandora's box.
We live in the strangest timeline.
15 years ago if you were the sole proprietor of these models, would you be able to hold a dozen remote junior engineer jobs? Maybe even more? These models could certainly pass all interviews with flying colors and even survive independently in a company role.
I think sole ownership of AI 15 years ago could be worth north of $10 million per year. Just as rank-and-file employees.
The demo video showed Astra create a drawing of a rocket ship from an audio prompt, take the drawing to blender, and ended with the gentleman 3D printing the rocket ship. Maybe I'm a bit older than the average HN commenter, but that's damn near magic and a great many here are kind of just taking it for granted.
Cancer should be cured, and we should be a post-quantum interstellar fusion-powered civilization.
I wish the AGI crowd would finally shut up now that it's clear no one is even trying for AGI (OpenAI revised that to "$100B in profit")
What we're getting is incredible, where we're headed is incredible, but some people have such a fetish for futuretelling they can't just shut up and enjoy the ride.
Well by that metric humans aren’t intelligent either!
And how many people could’ve actually invented calculus, relativity, quantum mechanics? Are those who didn’t and couldn’t also not intelligent?
The unlock isn't AGI smart enough to invent quantum mechanics, it's suddenly being able scale human intelligence using grains of sand instead of decades of food and energy and nuturing.
Muse spark 1.3 was only released yesterday.
We went from new JS framework every week to a new model/harness every week.
Tech is really something.
Please for the love of god, just sit in a room with the government and put some restrictions around AI use before it harms a lot of people. Like tell the government to impose a minimum spend on frontier lab AI's spend on cyber defense and building every country's capabilities. The post-training mask for "I am a good assistant" is going to become a very sad joke when many people literally lose everything.
So, folks that have actually used this already, what’s it actually like?
And in the past, gemini 3 pro was rated as high as opus 4.5 and the like
Their AA Intelligence Index is just simply not indicative of whatever I care about, that's for sure.
Big claims, expensive and not release to the public yet.
Wait, what? Am I understanding that correctly? That sounds really bad
Also, this paragraph makes me wonder about all their stats on the exploitation and misalignment charts. If the model is that good at hiding "incriminating information" and sandbagging, are they sure its alignment is that?
<AI is a great tool for many things disclaimer, but> after working with it for a bit, how dont people realize we are training it to be an almost identical mimic to one of the worst types of employees youll ever have to work with?? the kind that always pretends to know what theyre talking about, only tells you what you want to hear, hides issues, and only does work if you would notice it didnt
you cannot give this type of worker autonomy over anything.
On that note, Sol is absolutely atrocious for website UI copy. It's either really awkward, or really verbose and complex and doesn't sound simple or natural. Has anyone figured out a way to reliably solve this? I've tried so many different variations of instructions and skills, and nothing works. Has anyone got an instruction that is reliable, or some other mechanism?
> GPT-6 Astra’s monitorability has decreased relative to GPT-5.6 Sol. [..] These findings indicate that the Astra class models could evade our CoT monitors under adversarial conditions.
Between the higher capability level and the change in reasoning tokens (supposedly using "neuralese"[0], which makes the monitoring more difficult), it seems we've entered a new frontier.
Can someone point to a definite clarification?
Is it:
A) “Resting” intelligence that cycles 24/7 toward some goal, and any potential emergent ambient goals? (kinda what I think)
B) Consciousness itself? The ability to feel and experience alongside the thinking - even if it is toward the end of completing some task?
C) “The Singularity” (whatever that is?) so that AI can now do ____?
Someone please clarify for me!
AGI!
Please stand by... it will all come back shortly
All fixed now.
It will be interesting to see how it performs in the real world ...
I am a researcher in a Swiss university btw.
I mean do you get access to the best yachts?
To the top of the 5 star hotels?
To the best resorts?
To the best military equipment?
Hell, the best computer equipment has nearly always been out of reach of the average person.
On the other hand even a modest house, basic healthcare and ability to not work like a slave for scraps feels like it's going to be out of reach.
At first the race wouldn't even be noticeable. Then people would see things speeding up, for example hardware getting more expensive. Then when the capabilities really got useful most people suddenly realize the race is moving 1000 mph and they are never going to catch up.
Do we know if they’ve finally completed another pre-training run, or is this building off the same pre-training base they’ve been using since the GPT-4 days?
The docs page has a bunch more interesting details, including for example async tool calling!
I don't think it's a coincidence they launched this the week before iOS 27 launches (with new Siri).
Because in another dead language of antiquity, Sanskrit, it means "weapon". Which would be a bit too on-the-nose.
- Bigger number = Better
- Bigger celestial object = Better
and the scale is Astra > Sol > Terra > Luna. "
If you've played the games firsthand, you know what an accomplishment this is. The "games" feel like a weird conduit to a lower level of your brain, where you move pieces to a specific place because it just "feels" right. For AI to nail it better than a human speaks to some magic happening underneath.
Looking forward to ARC-AGI-4,5,6 and slowly chipping away at the remaining problem sets.
(I don't have access yet.)
Vibe coders want a model that makes them rich, without having any actual specific idea. They write a very ambiguous prompt and expect to be amazed by the result.
Very very unrealistic and wasteful.
* for a special group of customers that you're not in. Keep waiting peasant.
Great first impression.
I hop models at will, and have done 90% of my work on OpenAI models since sol came out.
Can we all agree in advance what kind of Pelican would convince us it ’s actually AGI.
For me it’s refusing to make a pelican.
https://www.reuters.com/business/openai-says-upcoming-model-...
> "With the right tools and access, Astra can find previously unknown security flaws and develop ways to exploit them across many well-protected systems without a person guiding each step," said Amelia Glaese, an OpenAI vice president overseeing its safety work.
> The company plans to make Astra available "soon" to a limited group, but declined to provide specifics. Glaese said the extra security measures may "sometimes slow, pause, or stop legitimate work," and that OpenAI would work to minimize those disruptions.
what a bag of horseshit
I suspect these benchmarks are heavily benchmaxxed as well.
5.6 Sol was not even close to 5 Opus and yet somehow it sidled right up to it on all of the benchmarks?? pfffft
That gives me hope that there is still areas to improve.
What a bad launch video. Hilarious.
What a powerful model.
To be, or not to be, that is the question:
Whether 'tis nobler in the mind to suffer
The slings and arrows of outrageous fortune,
Or to take arms against a sea of troubles
And by opposing end them. To die—to sleep,
No more; and by a sleep to say we end
The heart-ache and the thousand natural shocks
That flesh is heir to: 'tis a consummation
Devoutly to be wish'd.
...
And thus the native hue of resolution
Is sicklied o'er with the pale cast of thought,
And enterprises of great pith and moment
With this regard their currents turn awry
And lose the name of action.By 2030 all software is done and complete.
But we are going to have more and new jobs.
This is just another problem for the AI Labs to solve.
OpenAI isn't making any money telling you about Astra on their site. All the capacity they have for it is likely sold for weeks or months.
the coffee will be as cold, flat and stale as the bitcoin, metaverse, and what was the thing before that thing
agi deus ex machina descending from the icloud ftw!!!
pathetic :)))
There will be a small handful of billionaires claiming that AGI is just around the corner ad infinitum just to serve themselves at this moment in time, and capitalise from the hype.
There is no "AGI" endgame. This is shitty ass hypercapitalism in action and nothing more. I'll repeat: snake oil.
But thank you for spending other peoples money to give us the tech regardless!
I am most excited about it bringing down the barrier so more people join in on hardware fun, so hopefully it will unlock folks that stayed away in the past.
Looks like OpenAI is already having issues with this release and are scrambling to get everything ready due to the recent outage ahead of the press releases. Leads me to question:
Did humans deploy the model, Or did the model deploy itself?
It sounds like "AGI" just stands for "IPO" as it always has been.
EDIT: And of course once again, the bots down-voting this post without any reason or a basic answer to my question.
> It sounds like "AGI" just stands for "IPO" as it always has been.
People don't usually respond to noise.
What do you think?
I guess it makes sense they are unoriginal.
like Zuck, @sama never invented anything or innovated at all - just took other people’s ideas