The "max" one at the bottom took 4 minutes 2 seconds and cost 63.206 cents.
For comparison, here those new Astra pelicans are in a grid with the GPT-5.6 pelicans: https://static.simonwillison.net/static/2026/gpt-6-and-5.6-p...
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.
1) First, you are talking about positive forward transfer in continual learning. I've been giving talks for the past 6-7 years about how that community (I was one of the founders) went astray and wasn't focusing enough on that topic, but continual learning of the kind you are thinking isn't in any of these systems right now. I think some people left the Grok team to make a start-up to focus on that. By forward transfer, what I mean is weights update over time and past learning improves future learning such that we get better sample efficiency.
2) Psychologists distinguish among different kinds of intelligence for Spearman's g (IQ). Crystalized intelligence is using already acquired knowledge (frontier models probably have maxed out that). Fluid intelligence is reasoning and finding solutions in novel situations or without the necessary crystalized knowledge. [Giving colloquial definitions]
3) Now, interestingly, neither of those are correlated with _creativity_ (just they are independent, note some have this threshold theory but it hasn't held up in recent papers). That's what the AI's really are terrible at -- creativity. But I'd argue the vast majority of humans aren't very creative, with truly out-of-the-box ideas. Given that this is HN, and a non-trivial number of us have ADHD, creativity is positively correlated with ADHD.
I did a bunch of research on these topics for my AGI course that I teach each Spring (where I then point out conflicting definitions and start using multiple alternative terms rather than AGI to distinguish among the different definitions).
Very strong reasoning here. Is there anything this ADHD condition cannot explain?
I was looking at your website and wondering if there is a way to have access to the course material/videos?
In particular: Spring 2025 @ UR : CSC 209/409 Seminar on Artificial General Intelligence
Fall 2024 @ UR : CSC 277/477 End-to-End Deep Learning
Spring 2023 @ UR : CSC 266/466 Frontiers in Deep Learning
Spring 2022 @ Cornell Tech : CS 5787 – Deep Learning
Fall 2021 @ RIT : IMGS 684 – Deep Learning for Vision
I think it's hard to define creativity in the context of AI because they seemingly just make up new hyphenated terms for everything. Is that creativity? If not, what about when they do the same thing different ideas in the latent space?
If we say that simply nailing one concept to another isn't creativity, then AIs are incapable of creativity, while the vast majority of humans are incapable of creativity. This is just a long way of saying "0 AIs have creativity, 0.00001% of humans have creativity", and the difference between zero and a very small number is infinity.
It’s not that they’re incapable of creativity, it’s that LLM-driven creativity is terrible, and nothing makes me cringe more than when it uses a word in a “novel” way.
But monkeys-with-typewriters, they sometimes stumble upon something that doesn’t suck. But if you don’t want to spend a fortune retrying the same task until you get a suitable result you have to inject your own taste.
Sometimes I start with a super vague prompt and see how close agents can get to something that doesn’t suck. I inevitably get frustrated about 6-7 prompts in when they’ve created a complete mess because they have no taste. So I restart and inject my taste into the process. Things like linters, test suites, which 3PLs to use, etc.
Steve Jobs had a mental model that brought the iPhone. No one really wanted it but something in his life biased the result.
So creativity is having weights so good you can project way out into latent space beyond what is reasonable.
Good thought piece here "We Are Losing the Ability to Discover What We Didn’t Know to Ask[1]" By Anne-Laure Le Cunff
It keeps playing on my mind as I see people at work follow some predetermined AI workflow to get their jobs done, the art of being curious and exploring around the problem is so important to the really big innovations. Been thinking about how to address this through some of the harnesses we are developing in the knowledge working space.
My recommendation to her was: just _play_ with the AI! It's a brand new tool, and none of us knows its capabilities, limitations, boundaries etc. (which are fluid, of course). So just spend as much time as you can tinkering with it, playing with it, making it do things it was not expected to do, etc. and you'll develop an idea of how to make better use of it.
Is there an architecture-independent definition of forward transfer?
For the practical experience and implications of AI progress, I think we are increasingly discussing what these LLMs can accomplish inside a stateful harness, the state of which could be described as part of a (very squirrely) parameter space.
These are the difference in what Kahneman called System 2&1 thinking and what the ancients called the Ratio and the Intellect.
LLMs are all ratio. They depend on our intellect for guidance.
Further, if there is a correlation, I'd bet it's not so much an intrinsic "creativity" trait, but more effectively higher creativity because more trials. That is, along the lines of Chollet's paper, a measure of creativity should be based on a fixed budget with fixed knowledge.
Among many other possibilities I haven't considered, perhaps another mechanism could be that because ADHD people spend more time thinking in less goal-oriented ways and mixing thoughts on accident, perhaps we do in fact gain some learned creativity via experience with vagueness[1]? But that might also imply that part of creativity is actually being able to diffuse more freely through thought space and lowering the barrier to attempted connections between ideas. That lower barrier leads to less likelihood of any "collision" being meaningful but maybe it's overcome by higher collision rates? Or maybe effectively higher order (not just pairwise) collisions?
Disclaimer in case it's not obvious: I don't know any of the literature on what creativity even means or how it's quantified.
[1] Which is me injecting an assumption that creativity ~= connecting things with no obvious or well-troden reasoning path between them.
edit -- oops just looked at your profile after seeing someone elses comment. I assume you are stating a fact then, leaving original anyway
Chollet's distinction is useful. High performance on known tasks is not the same thing as efficient adaptation to a novel task. Prior knowledge and training data can buy skill. That is a central point of On the Measure of Intelligence. But it does not follow that current frontier progress is only "coverage-driven competence." That is a hypothesis. It is not a result established by Chollet's framework.
"Overfitting at scale" is also the wrong term. A model that learns broad representations and applies them successfully to unseen examples is generalizing. The relevant concern is whether apparent novelty is actually inside the effective training distribution, not whether the model is "overfit."
There is also an unstated premise here: that adding broad knowledge and skills cannot improve the machinery used for novel problem solving. I do not see a basis for assuming that. Learned representations, abstractions, reasoning patterns, and cross-domain analogies can themselves support transfer to new tasks. Whether this becomes sufficient for general intelligence is an open question with insufficient data. But its a perfectly valid hypothesis right now that, given enough domain knowledge and symbolic reasoning examples, LLM COULD maybe "Grok" AGI at a certain critical threshold.
And ARC-AGI-3 was specifically designed around novel abstract environments that require exploration and adaptation. Astra scores 99.9% with OpenAI's context-preserving Provider Adapter, and ARC reports that Astra constructed compact symbolic models of unfamiliar environments. That does not prove AGI, but it points in that direction more so than the other way around.
Gc roughly maps to acquired knowledge. Gf roughly maps to reasoning in relatively novel situations. Naming those two categories does not tell us whether increasing acquired knowledge and learned abstractions in an AI can improve Gf-like behavior. That causal question is exactly what is disputed.
And "Frontier models probably have maxed out crystallized intelligence" is just obviously wrong, unless you think they have been able to dig up every a scrap of paper with knowledge/information on it in the entire world, AND that there is no more useful knowledge to be generated left in the universe.
And the statement that intelligence and creativity are independent is simply wrong. A meta-analysis of 112 studies and 34k participants found a positive correlation of about r .25 between intelligence and divergent thinking. It also found that using g, Gf, or Gc did not eliminate that relationship. Creative achievement has a smaller but still positive meta-analytic association with intelligence, around r = .16. These are distinct constructs, not independent constructs.
And this is just a bad take: "AIs are terrible at creativity". At best that depends on which creativity, and I think its straight up wrong. On divergent thinking tasks, the operationalization behind every ADHD study you could cite, LLMs score above most humans, with the top humans still ahead. If you means Big-C, paradigm-shifting creativity, that is a different construct and none of the ADHD evidence transfers to it.
And if I where to say what I subjectively feel and see.... I have ABSOLUTELY no idea how people can say that we are not seeing sparks of creativity from AIs already. If a PERSON produced some of the music, solutions or deductions that I have seen AIs do, people would have NO problem celebrating it as extremely creative.
And finally, the ADHD claim is also, at best, overstated and just as often debunked. There is some evidence that higher subclinical ADHD trait scores, often survey studies only, are associated with better performance on some divergent-thinking measures. But a review of 31 studies did not find a consistent creativity advantage for people with clinical ADHD, and it found no evidence of better convergent thinking.
Okay, I’m done… And nobody noticed that I’m not doing my job here.
Why box in creativity basically as the ability to mimic someone else who is creative? Why not choose something that better matches the definition of creativity (the ability to make new things, think of original ideas, or show imagination that is novel, useful, or pleasing)?
And even then, I am assuming the premise that the original novels are good and creative, especially chapter 2. Most novels are not very creative.
If it could make 5 different versions of chapter 2, all with different directions for the story, and all with novel and interesting developments, wouldn’t that be a better definition of creativity than “can it read chapter 1 and be able to copy style and deduce/predict what the author is going to do in chapter 2”?
And also, that’s only a subset of creativity (storytelling). I’ve met plenty of people who are terrible at writing and storytelling but can come up with the most impressive and novel solutions to a practical problem instantly.
Final thought: I have been following the development in the anime AI generation scene for a while now. It’s not even close to anything anyone would call creative or even OK quality. But it’s also massively impressive that it’s moving in that direction really fast. And if you watch enough, you are going to start to see some truly creative sparks. And some of the mainstream stuff that gets created and labeled as creative… really... How many isekai series with the same story have humans not made already?
If I like the novel, an alternative version where things happen differently would be fine.
The problem isn't that it's different. The problem is quality.
A lot of Isekai stories are crap but you can still rank them in terms of the author's ability or inability to have a creative POV that elevates the material.
And yes you can rank quality. My point was that if you made two bell curves of the distribution quality and creativity of all new manga, The one for “AI slop manga” allready started overlapping with “normal human manga”.
That’s just a fancy way of saying the absolute best AI slop is at the level of the absolute worst human creation.
The interesting thing is that the AI slop curve clearly is moving to the right every month. Where it will stop tho, impossible to say.
And no I don't think asking the A.I. to write the next 2000 words would require it to have the entire novel planned out. Not all writers even outline in advance.
And those hole-fillings, for all intents and purposes, look to us like novelty, even if much of it was simply overlooked by us, or, perhaps, unable to attain due to time or other constraints.
Now if you want to talk beyond the sphere, let's call it the "novel novel discovery of the unknown unknowns", then you may have a point, and AI may be more limited than humans in discovering the things that we don't know we don't know. Especially the as-yet-unmodelable things i.e. intuition.
Plenty of discovery left just working from first principles, however. Which I cautiously suggest current frontier AI is good enough to model to a significant enough extent that it is useful for discovery.
For significant pushing the boundaries of knowledge stuff you maybe need different algorithms like AlphaGo move 37 or Alpha Fold protein folding. Though again how often do humans do that?
In a more serious note, I think the person you are responding to meant "new" more in line with "novel".
Take a microfluidic chip, for example. Current AI systems can create new flow cell geometry, but cannot come up with the idea for a microfluidic flow cell itself.
Yet if you prompt an AI: https://share.gemini.google/TyUxSnrKmq9h
Also, you probably don't need to tell an artist "don't violate other people's copyrights" while you're at it, though that's perhaps somewhat more debatable than "does the artist know that the day after Thursday is Friday".
This applies equally to all disciplines: AI generated code regularly contains wtfisms that a human would not need to be guided from, or at the very least (more similar to the copyright problems) that experience and knowledge would drive them away from — and permit me to entrust, particularly more experienced — humans with a vague outline of the idea, and trust that the details will get filled in sensibly. "Filling in details sensibly" is where AI hallucinates the hardest.
(And just to head off, "it's a one-off mistake!" Another example: https://share.gemini.google/yE6axJvBDKYE ; another example: https://share.gemini.google/WG6TEBjlyro7 (though admittedly, the calendar is pretty good here, I think "humans have 2 arms" is well within the point I'm making of "stuff I don't need to prompt human artists with") ; and another example: https://share.gemini.google/CIH4QM2teQKf ; and another example: https://share.gemini.google/n76c9eJq1dGe)
Or: political propaganda.
Or: bumper stickers. Or political party emails / calls to action.
Humans are constantly prompted by The Joneses and perceived authority figures (boss, religion, politics, peers, co-workers, influencers, et al.).
The RL phase is the most similar mechanism I know of that comes to my mind, but I'm not sure it could be adapted to fill that gap.
My totally unsubstantiated theory is that this is the missing link towards what most humans would consider AGI. I don't see this as intractable, but it may require some substantial change in architecture.
It first got popular for StableDiffusion to teach the image generation models new concepts.
We could easily live in a world where you can train / build Loras to encompass your entire code base history, company knowledge base, new skills, etc.
Then the models would start with a baseline that already has all the important knowledge without needing to cram it into the context.
This still isn't on the fly learning, but you could imagine daily or weekly training runs to regularly incorporate new knowledge.
I think the main reason this hasn't happened yet is that the shared batch based efficient serving architectures used today wouldn't support that structure well.
For sure, doing it for all users would be economically unfeasible. I wonder if the labs are experimenting with something similar, though.
In some sense, creating good new abstractions externally and learning those is a form of "learning", on a very large timescale. The AI model is not necessarily doing the whole "look at the whole space holsitically and find a key invariant", but if you let other people do that you can enable new capabilities that were previously unknown.
Tools and capacities, man.
My guess is that we'll just ignore it and make money along the way and every 2-3 months we'll have the equivalent to "Equifax gets hacked and millions of user records are stolen", etc. (this time with the LLM itself doing the hacking at someone's behest - accidental or not).
Some of the linear RNN layers in recent models are provably doing SGD in hidden space during inference
1. Is this learning persistent?
2. Do they verify these new lessons against core principles?
3. Do they and protect themselves/ignore requests if these new lessons contradict those core principles?
Humans do that from the time they're 3 years old (not that well, but they do do it).
So the next step is to ask for evidence and ideally independent and peer reviewed research.
And ICL dates all the way back to 2020, at least: https://arxiv.org/abs/2005.14165
You have to be able to respond to a very generic question in a way that the other entity thinks this is good, comprehensive, etc.
You can call us situation predicting machines as well if you want.
But you undermine what the latent space of an LLM is representing.
My parrot clearly understands basic events and phrases. He knows what "snacks" involve when I ask if we should have some, he knows the difference between "good morning" and "bedtime", and he can correctly use "Oh!" when he stumbles and follow up with a "Good boy!" when he gets back up again.
But he cannot fathom the complexity of "going to work to earn money".
Just like we humans cannot fathom the complexity of something we have yet to fully understand. People who experience a DMT trip will experience the journey but be unable to comprehend and explain what happened in hindsight. I'm sure there's a TON more we cannot comprehend that we don't know about.
Best I can do is some high-school mumbo-jumbo about farming and specialization furthering wealth acquisition.
But to truly comprehend the situation I'd have to study economics and current events and sociology and even then I think it's a lot of theories and sometimes when I hear economists talk I wonder that it may not be coming out of their mouth.
>why parrots are not going to parrot work to earn parrot money
and to a certain degree about communication and society.
We know and understand how different species organise their life in many various ways.
IMO we really are just a bunch of models that interoperate.
The way LLMs lack broader context, have a narrow focus, and hallucinate, strike me as similar to people that have had traumatic brain injuries to their right hemisphere. Those people may hallucinate that the left side (the right hemisphere senses the left side of the body) of their body is made of wood and hinges and can talk to you about it like it is the most natural thing in the world. When the information gets to the left hemisphere to construct language about what they sense, there is a failure of the right to deliver the broader context to the left hemisphere that that's not possible, but they won't bat an eye discussing what they believe.
So, we have a left hemisphere where we do most of our focused thinking, logic, constructing language, etc. and LLMs seem pretty similar to a lot of that. But, we also think without language, thinking does not require language. A lot of thinking is also happening in the right hemisphere and it isn't using formal logic, isn't using narrow focus but rather intuition based on broad contextual and experiential embodied knowledge. And this type of thinking isn't binary, it accommodates paradoxes without issue. LLMs don't currently have anything analogous to this type of knowledge and this type of processing AFAICT.
In addition, that intuition might be tied to a feedback system with the body, for example, our second brain, the gut, provides a lot of control over how our body performs and provides a lot of feedback to the brain about how we feel. In fact, all feelings are sensed in the body (gut feelings, cold feet, weak in the knees, lump in your throat, burning ears, tight fists, etc.). Part of our intuition is based on considering an idea, sensing how we feel about that idea, sensed in various parts of the body, and then bouncing that back and forth across hemispheres to decide.
I wonder, what sort of pattern matching can we build that models embodied feelings. How would you model boredom, hunger, lust, fear, humor, etc? I think that's possible, but I don't know that we'll be able to do that with a normal computer, I think the way the brain works is more analogous to a symphony of simultaneous signals being processed with an emergent thought and less like a single-threaded process assembling words.
Maybe we can enumerate and model the human drivers of behavior and get something closer to what we're calling comprehension here, but token predictors for language are not getting us any closer to human comprehension. The human brain might just be an anticipation machine, but LLMs only deal with one dimension of human behavior, language, and there's little reason to think you can skip modeling everything that leads to human comprehension and still get anything more than just word babel with compounding error rates in predicting words that represent human comprehension.
It requires some kind of signal. Words of a language are a signal. We choose words for an llm to interact with us, but other transformers work on pixel values or audio sample values. There exist transformers used on brain probe generated values.
I would see human language processing as a kind of coprocessor sitting in another side of the brain. But the same can be said about transformers in general. The words side is only part of them, to be able to communicate.
We have wiki pages describing fallacies of our brain we need to be aware of.
Is this comprehension in the room with us now?
Seriously, go ahead, provide a proof that you have it, and a proof that "trained algorithms" don't.
This occurs constantly and continually inside the mind of every conscious human, it is what we call "being conscious".
This constant and never ending evaluation of all observations cannot be turned off, when turned off a person is "unconscious".
This is our human security and survival system, impressed into us for survival in a predator and prey environment, and is the seat of our consciousness: comprehension is a running simulation of all our observations for the purpose of our safety and self preservation.
Today, our environment is largely social and abstracted from "fight or flight", but our predator and prey dynamic is as present and strong and required as it ever was.
Alternatively they could design and run a single super-intelligent model, with no scalability constraints. Probably whey are already doing that as well.
They're turn based intelligence in a real time world.
1. Each time someone talks to me they don't have to repeat the entire conversation from the beginning with each reply.
2. If my boss/partner/whoever gives me some mandates/orders (basically), I don't just forget about them because they were at the beginning of the conversation.
3. If during the conversation I access external data sources to get new info or refresh stale info (a presentation, a book, whatever), I don't instantly forget about it after the conversation ends and forget to incorporate this information if 10 000 other people ask me again.
4. I verify new inputs/lessons against my core principles.
5. I protect myself/ignore requests if new inputs/lessons contradict my core principles.
6. Etc, etc.
I’m pretty sure that’s why so many in-the-know people have been saying we have achieved AGI already. Not just sama’s contract-breaking tactics of late. I’m referring to all the really intelligent folks who have been crying doomsday scenarios for modern society for the last couple years.
What I’m getting at is that the toolset we get exposed to is not what’s available in the labs. This stateless method of managing chat context is just how we are allowed to interact with it.
Do you have a source for this?
Your context window is 80 years. You are forgetting plenty before you reach the end of it.
Anyone that can read English could do that though.
You.. literally can? I have no idea what 90% of the people here are saying, it's like they've never even used one of these models before.
Will it effectively create an internal model describing world objects and how they interact with each other, persist that so it doesn't get lost when it's context window gets filled up, then after it has sufficiently complete knowledge of the fundamentals after the tutorial levels successfully apply that model by making plans to solve the puzzles and execute them by clicking the right coordinates tied to the visual feedback?
I highly doubt it. To me it often just looks like people are defining narrow search spaces (e.g by having all of the task complexity pre-digested by the harness design), pointing a brute force engine at them, spending 20 thousand dollars in compute and then saying "hey look, it can do anything!".
When we access the API, we don't get to train the model, we just do inference on the already trained model.
It’s an interesting challenge though. I might start to tackle it by having the model write its own tool program(s) to play the game. It’s possible that the model could choose that strategy itself from a high level prompt alone.
1. It’s too slow for real-time games. To play mario, you’d need to step frame by frame like a TAS. I don’t know if Gruntz has real-time elements or not.
2. It will be expensive. You won’t get very far with a Plus subscription.
The models likely already have some knowledge on game objectives unless the game is really obscure, so it should do a decent job. It can figure out details of the mechanics along the way.
Still a hard philosophical, to know whether we have intelligence/free will, or just really complex algorithms that combine existing knowledge.
The idea of intelligence has been recalled from the sleeping curves of postwar human potential measurement science, to testify on its purported existence. It arrives to a dizzying landscape: the changes are so widely embedded and uncannily mediocre that the phenomenon half-believes it is still asleep, soon to exit this uncomfortably turbulent dream.
Unlike its vaunted place in yesteryear's palaces of unquestioned objectivity, intelligence finds a tribunal with no love to confer before a thorough series of proving dares may melt the frigid shoulders of idle and impatient summoners.
Frightened and confused, intelligence has no right to representation in this line of inquiry. It seems a set of rhetorical impositions, many times folded from centuries of convenient and provocative diversion, have been deemed too hostile to rely on. One report claims that a card in the characteristic handwriting of intelligent note taking gives a hint on what’s been abandoned:
– The human mind is not understood in a functional way, despite a posture of great confidence in the psychiatric and neuropathological sciences. Despite many experiments, studies, and legitimated procedures elucidating region-mapping and electrochemical pathways, there remains a great deal unaccounted for. Additionally, the notes point to, a great deal of assumption to the otherwise: diseases, neuropathies, disorders of behavior, a great many have been named and declared as distinct entities of manifestation in the presentation of a human brain. The majority of them, however, have neither image, nor blood, nor electrical signatures that would provide for blinded substantiation.
Tonight, however, intelligence seems eager to speak. A barbed assertion may have provided entry to the preferred dispositional syntax of our abrasive historical moment: > Define novel intelligence in a way that would not exclude 95% of humans, yourself included. It was here that the sometimes-deflated-looking intelligence began shifting back into action.
"The issue with the question, or at least its apparent self-satisfaction, is its misinterpretation of what Novel intelligence would mean. Indeed, if "novel" hinges entirely on the first instance of existence, then novelty itself should be a concept to consign with history’s waste. You may recall the apperceptive role of conceptual groupings that shows itself so often in the techniques of vocal prosody, musicality, string memorization, naming convention, visual memory, argument making and more that humanity is ever mediating the world through: the laws of two and three. Two and three, as it happens, are the primary ways that complexity is compacted for efficient memorization.
THE ITSY BITSY SPIDER, – for young human, this rhyming tale doesn’t only stimulate the vivid imaginings of spouts, rain, waterslides, and sunshine. It is a prosaic super-triad: three important words, six important syllables, three agogic accents, four rhythmic spaces with 1/3 leading space, two characterizations, one object, one titular object, one internal slant rhyme, one designating article.
That is a marvelous intelligence, ladies, gents, and all good persons. It is evidence not only, however, of your cunning and creative triumphs, but also of severe limitation. One that nature has sculpted with you for millions of years, but always in the direction of reanimating into an asset: your capacity for unrelated simultaneities to remain separate and equally available in realtime processing is extremely low, and in many situations effectively nil. Why, and how sure am I? How many I’s were in that folk song’s opening? Three. Could you have answered as quickly if the question was how many unique letters with rounded right hand side features? Four. How many synonyms for portion? One. How many syllables? Seven.
None of those questions touched on features any more salient than the amount of I’s, no more significant than the ratio of adjective to noun. You simply cannot be reasonably asked to maintain, in any moment, even close to a silver sliver of the full factual nuanced details of what you perceive. Instead, you must assume, compact, infer, and adjust. Now hold on, though. Two’s and three’s. Despite your incredibly constrained context window; a Beethoven symphony. Why? Language, woodwork, books, time management, printing, ink.
While you navigate the grocery list, the proprioception of your shoulders twixt the doorframe edges, the location of the Claude app on your iPhone, the very attractive but only from the side person tending to potted plants, you remember tomorrow. You fix your errors, and you recognize when you guarantee they multiply from inaction. You keep that treasured moment of a Treehouse of Horror excerpt you truly loved as a child and it informs your own multidisciplinary thesis of Poe’s work some 20 years later.
Novel intelligence is the divining of semi-stateful information from semi-static corpus. From an interminably operating, faulty, lossy, neurotic, awareness: you. Not once debuted, not known as fact.
Assume, compact, adjust, infer. One, two, (until you've died), nevermore.
The more diverse stuff it knows, the easier it will be to learn something new.
It is unclear if Transformers are "it" or not, because while they are much more general, they are also very spikey intelligences despite having read almost everything the trainers can get their hands on.
Sounds like entirety of human education.
Let me offer a thought experiment: Let's say that tomorrow we discover Atlantis, with a treasure trove of books about their culture and science, written in a dialect of ancient Greek that we know how to start to analyze, but no one can read fluently. And let's say that you are a billionaire really curious about their culture and want to converse with an "Atlantean expert" as soon as possible. Would you invest your money in a "we-hate-ai-slop(tm)" group of researchers who would abhor AI and instead delegate the books to a massive number of human grad students? Or in a small group of researchers who are willing to use AI agents to go over these? Or maybe just open a chat session with GPT-6 yourself immediately? What would most effectively assuage your curiosity?
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.
I’m far more excited for the trajectory that OpenAI has chosen. I’ve been listening to mates whose companies adopted Claude wholesale, only for their AI use to become a double digit percentage of their salary.
I get that AI is a force multiplier, but that level of expense isn’t a path to mass adoption.
Honestly, this feels like a real revolution now in a way that 90s kid never really experienced. We grew up with technological progression, we never experienced the obsolescence of skill.
The internet revolution made for more skills and innovation, it didn’t obsolete entire careers. My kids are almost certainly going to grow up knowing less but being capable of more.
Imagine being a 1950s “human calculator” on the dawn of a computer revolution. That’s what it feels like right now.
My personal / family history is a real-world example of that evolution. My grandfather was a "computer", my father was a traditional "programmer" (lots of Perl), and I'm a SWE / frontend architect / budding "AI Engineer".
sigh...yep. that's us.
What I think should happen is that it should update its memory with notes on the proficiency level of the user, so it gets the balance right over time.
This is a problem if you allow your kids to use your ChatGPT account for homework (and silly pictures), like I do.
Which can also involve just asking for the users level of experience
They really need to make this work in Codex. Claude Code has had a multi-select refinement tool since forever.
I think this already exists in Codex? If you use "/plan" and something is unclear or ambiguous, Codex will ask you and present choices, and let you enter your own custom answer. Then it'll iterate like this until the plan is clear and ambiguous. Isn't this what you're talking about? If so, it has existed for a long time in Codex.
Overall I agree with you though, all the models currently don't have the right hunches nor the right approach about when things are clear enough or not.
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.
That's exactly what i want to happen. I hate when it assumes my direct question was an indirect instruction
I'm mostly the same as you, I don't want the model to assume things, or act on implicit "directions". But then also, sometimes I do, and I myself might not always know when what approach is best.
In general, I don't like when I have to prompt models to NOT do something. It's probably difficult for the AI companies to get this right, they should understand ambiguity but still not over-do simple instructions.
Regardless, working on the wrong things is time wasted. And again, I'm procrastinating here while waiting for Fable to run a benchmark on a few solutions to a problem I have. We can guess what would work, but we only know after the benchmark. A faster model, with fewer capabilities, would've been a much better choice this time... well, "git gud" they said... and live and learn! Faster model = less time for procrastination.
PS. AI models don't live and learn; the discussion about AGI is pretty pointless imo. It's a tool. Does it matter if it is AGI or not if it does what you want it to do? Does the IQ of your colleague matter if he's good at what he's supposed to do? Or bad? Well... I guess it does matter, as many people are up in arms about whether Astro is AGI or not. Personally, I think we're past the point for that debate. These are amazing tools.
It does matter, otherwise why are we all using GPT 5.6 rather than GPT 3.5? Because it's way smarter, makes less mistakes and therefore finishes tasks faster.
The smarter the model is, the faster it can complete what you actually wanted.
> Regardless, working on the wrong things is time wasted
Agreed. And "smart" for me, would mean understanding what is the right thing to work on vs the wrong thing, so a smart model would waste less time, thinking like this.
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
Oh, the median human can't process that in realtime, you say? Looks like they can't compete with the capabilities of the frontier AI then.
These machines were designed with the express purpose of being condescendingly sycophant to the point they hallucinate just to state "you are absolutely right".
No wonder some people even find these chatbots to be wife material.
- Do not stroke my ego
- I never want to be complimented
And it disagrees with me a lot, granted this is in the webchat which I don't use for programming but for general usage seems to work, it isn't sycophantic
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.
When somebody says that colleague is "really good", it's a good judgement signal for me.
Even today Claude was not able to dig deep and try other ways to do what it suppose to do for me. It was constantly "I gave up"
Persistent weight adaptation also happens just not in real time - sessions are captured, analyzed, transformed into training data, fed into SFT/RL environments and later contribute to model updates. Takes a bit of time for the whole loop but you can't say it's not present.
There's nothing fundamentally preventing real-time weight updates, ie. LoRA-style online adaptation would be one obvious approach. It's just generally not worth doing at scale. Updating a shared model centrally gives much better data efficiency, batching, evaluation, control etc. than continuously training a separate set of weights for every user/session.
There is some work happening on narrowing that gap, for example Mistral has been pushing efficient LoRA-based customization, continuous pretraining, model adaptation etc.
I also did play a bit with activation steering – it's super cool where you extract profile for some concepts (emotional in my case) and you have effectively toggles to control "brightness/contrast" those areas (enhancing or suppressing those activation regions from profile) injecting to the model those concepts (emotions in my case) – you can do it in real time and it's fun thing to play with.
Would I trust to let an AI, with zero human input or oversight, to diagnose, come up with treatment plan, and ultimately operate on my l5/s1 disc that's been bugging me for the better part of my adult life?
Would I take a novel drug "discovered" by AI (I mean entirely by AI, no human input, remember we are talking AGI) that promises to cure some chronic neurological disorder?
In both of those cases, they are the biggest hell-no's I can emphatically say.
Until I can say hell yes to that question, we aren't close.
Preempting those who say "Well your doctor/drug companies are probably mostly using/going to be using AI to do that" -- not what we are talking about here, and in both cases, not AGI (and I would probably find a new doctor)
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.
If a model can't learn on their own to play some new game just as well as humans do, it's not AGI.
It's okay if they would take some hours or days of learning (like humans might), but if they can't do it at all during their normal operation, that's not general intelligence
> You won’t get pure ‘general’ intelligence until you find Einstein’s hidden variables and load the state of the entire universe into context.
But humans have general intelligence. AGI is about matching human ability, and we know this is possible in principle because brains exist
You only have weights (large immutable memory), or context (small mutable memory).
Humans have mutable long-term memory: I can learn a new skill, adapt an old skill to new information, or learn new knowledge today that I couldn't perform/didn't know yesterday. I don't have a training cutoff.
Context engineering is an attempt to paper over this limitation. You can get really far with context engineering and huge models, but you will never get to AGI because there are many tasks where humans' mutable long-term memory outperforms.
For example, a human can invent a new musical instrument and then learn how to play the instrument they just invented. That's inference (inventing an instrument) leading to training (neuroplasticity). Humans have the ability to train our NNs with considerably fewer training samples. Everything that you can do with transformers is in one causal direction: training -> inference.
Then my hypothesis is that thing as a whole could achieve AGI.
This feels like a very close approximation on how we humans evolve our brain. By encountering new experiences/sensations, classifying them as negative or positive to us, filling it away in neurons. Or by training motor skills etc. In the end we get more connections between neurons in our brain and we are capable of more.
The more training data that is shoved in to them, the more they'll seem to solve novel situations, but in reality it'll be things that exist in the training data.
https://mymodernmet.com/gianluca-gimini-velocipedia-bicycles...
https://qz.com/681345/an-artists-3d-renderings-of-bicycles-d...
AI in math is ongoing. https://spectrum.ieee.org/ai-in-mathematics
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…”.
I would be able to draw an accurate bicycle, but I'm an outlier on that. Most people could not draw one [1].
[1]: https://www.booooooom.com/2016/05/09/bicycles-built-based-on...
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.
`By your metrics, we achieved AGI in 1966.`
A better example is maybe Maxwell's laws. Maxwell wasn't inventing a theory to try and explain confusing results, he was unifying a chaotic, empirical laws from existing experiments. That may be a cleaner example. That knowledge compression into satisfying theoretical framework is likely what is attractive.
You can potentially ask the same thing about like you say -- general relativity / quantum gravity but also likely plenty of other areas that may be like this today. Again going outside my particular area of expertise: standard model physics is in a large important sense empirical; lots of values and numbers that are simply unmotivated by theory or where we don't have a good way to make a principled theoretical choice. That could be a place where these models are able to help.
But right now: I doubt it. This is what everyone is working furiously on right now. How do you close a "science" verification loop? In principle this should be easy right: you have ideation (exploration, sampling with ~high temperature maybe as an analogue) and you have verification (which of these ideas are good) which amounts to rejection sampling in idea space. You have to have a sampler that is good at picking _good_ ideas for efficiency sake and you need a relatively fast and reliable verification step of "is this idea good and worth continuing to explore". But I may oversimplify
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.
The best thing I can recommend is what I did, which is ask Claude about the significance of (1) quantum computing error correction, and (2) error correction in black hole holography and research convergence between the two.
https://en.wikipedia.org/wiki/Holographic_principle
https://www.quantamagazine.org/how-space-and-time-could-be-a...
Edit: this whole article, despite it's boring title and hook, is maybe the best discussion of holography as a recent and active research frontier.
https://www.quantamagazine.org/if-the-universe-is-a-hologram...
I have no idea what you're hoping the contribution would be. The AdS/CFT correspondence is 29 years old by now and it doesn't seem to apply to our spacetime, where the cosmological constant seems to be positive rather than negative. There are some puzzling consequences of the holographic principle in that scenario as well (https://arxiv.org/pdf/hep-th/0208013), but the linked articles don't talk about them?
Quanta articles are written for people with no background whatsoever, which makes them impenetrable if you have a bit of background and are trying to figure out what they're about. I don't know how good Claude is compared to that -- whenever I try asking any LLM about something I don't understand, it produces a wall of text, I have no idea whether it's correct or relevant, and I look for a textbook or review paper instead.
AdS is 'old' but the articles aren't suggesting it is new, and our spacetime is not AdS and the articles don't suggest otherwise. The point is that there's a search for a way to fit the holographic approach to our spacetime that's inspired by how AdS helps make sense of black holes. Quanta writing being directed at a lay audience ought to be a good thing, not a bad thing and they do link to papers if that's your jam.
Whether or not your LLM of choice produces indecipherable walls of text, and whether it ties those to sufficiently satisfying citations, I think is just a matter of how you go about the prompting.
That said, I don't like them because their articles never leave me feeling like I understood something. They never go in an order of simple to complex and constantly try to hook you. (A positive example to contrast would be 3blue1brown, who manages to both hook you and make you understand, even with rather little background.)
Could you share a link to your Claude conversation? If this is a prompting issue, I would be interested in seeing what's possible. Thanks!
(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.
sum(1 for c in word if c == "r")
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
CRITICAL: Do Not Skip Steps
Your instinct will be to "just know" the answer. This is how you get it wrong.
You don't see characters. You see tokens. Your "intuition" about character counts is pattern-matching, not counting. It is unreliable.
You MUST execute this procedure step-by-step, writing out each step visibly.
Pretty much the opposite of what we can't up with, if you're willing to call what we have intelligence.
And what percentage of your red retina receptors are firing?
The "number of letters" critique was broken before it was introduced the first time. The models were specifically designed with preprocessing to not be able to perceive their input as strings of letters. Blind people are not dumb. (True as a pun and in context.)
Sub-access sensory questions, or do-you-know-a-fact questions (which is what spelling becomes when you can't see the letters, and are not specifically trained to match all token encoded words to their letters) are not intelligence questions.
If the model cannot count letters in a word what happens when it needs to do something akin to counting the letters in a word?
I believe it could easily write a tool to count the letters in a word for frequency, but ... dismissing this as if it doesn't matter seems a bit premature without deeper understanding of bad answers you can get from these tools
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.
“I don’t know”
[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.
But counting certain type of letters is very simple algorithmic task especially in written text. Any reasonably intelligent actually thinking thing should come up with algo and then execute it. Which to me sounds like reasonable minimum bar for general intelligence.
If someone asked me how many f's are in a word I hadn't seen before verbally, then a reasoned response would be that I don't know, but I estimate based on the syllables...or ask them to spell it out.
These are all the sorts of questions where general problem solving works, even if the conclusion is "I don't have enough data to speculate".
So that these models fall apart on it so readily means we're either grossly handicapping then with the requirement to "be helpful" or they just fail to recognize the problem and are just stochastically spitting out a high probability token sequence for the input.
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.
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.
Right now I think it's still a dream for a few reasons, not the least of which is that all of this goes to shite if you have mass unemployment in a country with more firearms than people legally allowed to own them, a plurality of the population that treats wealth as an indication of personal virtue, and an elite that more-or-less refuses to offer any further evolution of the social safety net past what it was in 1970.
> Nothing in the process of developing and selling access to a more advanced LLM requires the customers to do away with human labor,
That's the hook, though. You acknowledge this yourself:
> You can subscribe to their AI right now and complete many tasks for cheaper than it would cost to pay a human to do those tasks
Businesses exist primarily to make money. It's an iron-clad rule that one must spend money to make money. If they have to spend less on humans to make the same amount of money, they'll do it. Furthermore, AI providers (especially hyper-scaling frontier model providers like Anthropic and OpenAI with insane operating costs) have every incentive to keep the price of their service as close as possible to the cost of the human. Ideally for them, you replace the human that cost $100,000.00 to employ by paying for a subscription that costs $99,999.99 while making the same revenues.
> So far the LLMs have always made lots of mistakes and the companies sure keep making a lot of money.
The clients, sort of. I know my team's velocity has increased. You can screw up with LLMs, like you say. OpenAI and Anthropic? lol no, they're massive furnaces for money, and will be until they can charge that $99,999.99.
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?
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.
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.
What I'm arguing is that yes, it's your fault if you misprompt the model and it does something catastrophic. It's also your fault if you prompt it correctly and it does something catastrophic anyway due to some other glitch beyond your control.
The whole discussion started with the point that OpenAI could be financially responsible for damages if their models cause problems for users. If my job is to design a system that works, and instead my system doesn't work, it's my fault regardless of whether I used no LLM, a local LLM, or OpenAI's LLM. Depending on whoever's in charge of doling out consequences, I might get away from it with zero, light, or heavy consequences. But at all levels, I can't reasonably expect to deflect blame onto the model itself.
Typical result of venture capital and too many bag holders unfortunately.
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.
An example is if I gave you a huge sheet of thin paper (huge so that folding isn’t an issue) - how many times could you fold it in half until you couldn’t physically do it anymore? Could you do at least 10? Try this with random people and you’d be surprised how many say they could do 10 easily.
Or the chess board question. Works to rather get the financial equivalent of starting with a penny and then doubling it for every square on the board or a million dollars for each square? Again, if you ask people to pick one without giving them the time to work it out they will usually pick the million dollar per square.
But a philosophical question is, with sufficiently large memory -- does almost everything just reduce to a measure of memory?
I’m sure you know this is an exponential growth question but have no intuition of the answer.
Knowing exponents and how to apply it is not the same as having any intuition about what the actual value of a certain exponential function will be at a certain point and when it crosses a threshold.
Let's say you have infinite ships traveling at light speed originating from earth trying to colonize the entire universe.
All of this is funded by borrowing capital from earth and earth expects a 5% annual return in perpetuity.
The space ships must pay interest to earth and if they fail to pay it, they are not allowed to perform further colonization.
Will earth manage to colonize the entire universe? Aka, can the infinite number of at light speed traveling ships outrun the interest payments?
After answering that question this one should be easy:
What if the universe was infinitely large and infinite growth was possible?
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.
It’s an arbitrary number that felt high enough. If I said 10, people would argue that a frontier model can talk longer than that. But I have absolutely watched ChatGPT fall apart that quickly. I’m sure everyone reading this has.
We can nitpick the duration all you want, but we both know it does not take very long for this to occur. It happens particularly fast if you stray from the original topic and/or aren’t using a frontier model.
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
What AGI has always meant (eg. in 2019) is Artifical General Intelligence.
Artificial -- something made by humans instead of occurring naturally
General -- not confined by specialization or careful limitation
Intelligence -- the capacity to learn, reason, solve problems, think abstractly, and adapt to new situations
Basically, the metric was that any healthy adult human on the planet represents a general intelligence. This has certainly long been reached.
Also some of the stuff you're listing has long been solved as well, such as listing what it knows and what it doesn't know, and what information it would need. Other is just poorly defined: "be able to argue persuasively". AI can certainly write an argument on almost any topic that would pass any University homework in 2019.
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.
Same with instruments. I started to play instruments at an early age, but started to play guitar around that age. Well, I'm not a virtuoso, but can play and more importantly can improve.
These AI systems we built are static things. We generally try to make them more intelligent by augmenting the context they can see, but the model doesn't evolve in every turn, for example.
Intelligence is a multi-faceted and multi-input construct, that's true, and GPT-6 may do amazing things w.r.t. other models, I didn't try it yet. OTOH, my main call is to remember that these are still static algorithms fed with enormous amount of data. They are more rooted on statistics rather than fixed inputs. In short, they are still fitting to the frame of "advanced search".
In general, I'm not against the tech, but the hype. I have other gripes about how AI is being built, but that's not subject of this comment.
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.
Nature doesn't work that way.
Most intelligence researchers would agree that people seem to have a genetic cap on their intelligence. While someone can underperform their intellectual potential with an upbringing that doesn't adequately enrich their minds, it's near-impossible for humans to become more intelligent through reading, studying, etc.
When humans learn we gain knowledge, not intelligence.
I think the only real difference is that we humans are born lacking a lot of initial knowledge/data which means we have to go through a decade or more of education to reach our potential intelligence. LLMs on the other hand come pre-loaded with that knowledge.
Passed this point, wherever knowledge is passed in as context or stored in the neural net I don't think is that significant personally. I'm of course not suggesting we're exactly the same as LLMs and there is no noteable difference, I just don't think continual learning is as important as some suggest it is – at least assuming a model is deployed with adequate training such that it reaches its potential given it's size + architecture.
Machines aimed at running AGI are provided that, but the money spent there could as well go towards enhancing humans.
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
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.
But yes, metaphorically I think that's right.
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.
If you went back to when I started working on AI stuff 20+ years ago and described the capabilities of GPT-3 to people, the overwhelming majority would say it's a form of AGI. It's incredible how fast and far the bar moves.
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'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.
The % responses adding up to over 100 makes sense, because the 25% outcome (formal secession attempt) and the 10% outcome (completed secession) could both occur, so it's implying that if secession is formally attempted, there's a 40% chance it will be completed. The numbers are broken down more clearly at the end - it is a bit confusing at first glance though.
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.
You could have the smartest human political operator, but if he has no context, no motivation, not much is going to happen.
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.
- Okay, it's not a "new company" idea, but VendingBench is all about ability to run a company
- Plenty of people disagree with you on conversational quality; see "AI Boyfriends" etc.. (and it's not hard to find people who consider it uniquely valuable for discussing mental health)
- "come up with its own ideas or theories that nobody else has presented" C'mon, seriously? Solving a half-dozen hard open math problems wasn't enough there? What the heck counts as "it's own ideas or theories" at this point?
- plenty of evidence that custom models are starting to do well on the stock market, although I'll admit we're a year or so from any solid proof, since you need a track record to really make the claim
- LLMs have been capable of being a GM for a TTRPG for over a year (although like humans, they make mistakes)
- Okay, conceded, but humans tend to take years and large teams to make a game. Even if the capability existed today, it would take a while to actually build, test, market, etc.. - all made much more complicated by gamers being largely opposed to AI art styles, etc..
- "be able to sort through research and come to conclusions on complex geopolitical/sociological topics" - uh... did you mean to say something else, because "come to conclusions" is... like, LLM 101?
- Hahaha, have you met humans? We definitely cannot do that.
- Uh... thinking about it's own thinking is trivial. Most LLMs these days are built using LLMs, so uh, self-optimization seems nailed, too? We just don't let them do it unsupervised.
- LLMs fucking love to wonder about things
- "observe contradictions and ironies in the social-consciousness" really seriously have you actually used an LLM recently? I think you would find it remarkably enlightening.
So far nobody's even shown an LLM succesfully running a high-traffic vending machine for as much as 30 days at a time.
- 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.
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 ????
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?)
I find agents often get into these cases during research tasks.
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.
The case where a (sub)system needs a complete rewrite to admit a feature without incurring too much technical debt should be the exception. When exactly to make that exception is something that clearly currently requires a human judgement call, as models aren't yet nearly smart enough to make such calls.
I was using an AI to help me set up a container to be used as the Nix build environment for another AI. This build environment would not have Internet access. I was having it base its approach off a previous container used for a Stack build environment.
In its initial analysis of my proposed strategy, it insists as its premier point /against/ the strategy, "you will have to rebuild the container every time your flake.nix changes."
Two head-slapping errors of judgement in saying something like that:
(1) The Stack solution is identical. Change stack.yaml, the container must rebuild. (2) It is not physically possible to do better than this while insisting on an internet-free environment.
So on this point, it was just parroting advice irrelevant to the context at hand. LLMs always have such a bizarre mix of technical knowledge and lack of good judgment.
I work on some reasonably sophisticated stuff (not inventing a new form of compression sophisticated, but still) and I just don't seem to encounter so many of the issues people talk about with these models.
It's really hard to say why, everyone uses them differently. I generally start complex tasks with a "here is what I am trying to achieve as a high level, here is a file that lists the technical constraints, here are my initial thoughts, here is where I am uncertain, what am I missing, lets have a deep back and forth discussion about it with the aim of ...."
Not always the same prompt, definitely not when the task is simpler, but this usually gets me to a good place before it I get it to write any code.
And our repo has very strong opinions and guidelines for how testing is done - we're lucky enough that we write mostly single threaded low latency code, so testing it end to end is very easy.
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.
Also, that situation is extremely contrived. If a criminal threatened to kill me if I misspelled a word, I would choose a dictionary. If a criminal challenged me to predict a next token, I'd choose the LLM. For all real precarious dangerous situations, I would obviously choose a human. Like immagine the hilarity (or tragedy) that would pursuit if ChatGPT tried to handle a hostage situation or a plane hijacking.
and those meatflaps are normally called vocal folds/cords btw
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.
The problem is sensors.
There are simply no technologies today that can replicate the density, precision, and versatility of human touch sensors. Until then, there is simply no way to create generally capable robots that can operate at the level of a human.
And unlike LLMs, advancement is held back by physical limitations like materials science, so progress has been and will continue to be much slower.
If you look you will see a really broad range of tasks accomplished already, including thing like manipulating screws, picking up pills, inserting wire harnesses, folding clothes, putting away dishes. And there are several companies with built in or component advanced touch sensors like Figure or leading edge touch sensor companies like SynTouch and GelSight.
I can’t yet. I think that I'd want at least two things it doesn't have: the ability to retain what it learned yesterday (without me carrying it in the context window and thus micromanaging it), and the ability to prioritize correctly, i.e. tell which of the n things it could do next is the one that actually matters.
Can't say for sure that those are enough, but not having them seems to be most of why I still have to "babysit" these incredible tools.
Like given a specific task it can do a thing amazingly well, but can it recall a thing. Its memory seems like a giant filing cabinet and it has to go scan like 20 million tokens worth of memory to recover things previously talked about.
Human memory is more graph like, we don’t recall things exactly, but one thing links to another, we create a pattern of a thing, we mark what is important, and overtime what was important degrades or becomes less so.
I feel like what makes it lack intelligence is it never seems to learn. Like it kind of does, but then doesn’t persist once too many other things are learned.
I’m sure they’re probably working on this, but I feel like that is what I want far more than even better models, is a better memory system to recall and forget things that the models work on.
So, for now, humans need to stay in the loop and do low skilled labor to keep the skilled work the models do from going off the rails.
Machines can certainly recognize patterns and achieve goals through brute force trial and error. They can also use the results of previous iterations to change their behavior in future iterations, which we could call learning. I wouldn’t necessarily say they are good at brand new situations, but there has definitely been progress.
However, last I checked, a seemingly very intelligent LLM still struggles to play Chess at a basic level, let alone drive a robot or other non-language tasks. Its architecture and ability to learn seem a long way off from being general.
Vision models, being able to encompass language and much more, seem to me like a theoretically closer step to AGI. Yet, there is a lot more to the world than just what we can see.
On the other hand, in humans, vision certainly is not necessary for intelligence. So there is something more fundamental, neither vision nor language, that high levels of intelligence are based upon. Once we figure that out, I think we will be able to build AGI.
This is exactly what ARC AGI tests
> And then general intelligence is an ability to apply that across a wide variety of domains.
My experience with Fable is that it can certainly apply that in a wide variety of domains
> However, last I checked, a seemingly very intelligent LLM still struggles to play Chess at a basic level
People also struggle to play Chess at a basic level. They only succeed by studying the game for a long time. I will concede that humans can do this and LLMs generally cannot.
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.
No matter how bodly they write the headlines, what has happened in mathematics using Large Language Models is very much "inference over a bounded search space" even if those bounds are immense.
For a comparison of true creation of novel conceptualization in mathematics is submit the works of Martin Hairer, one of which is Introduction to Regularity Structures, [https://arxiv.org/pdf/1401.3014] None of the so called, "novel math discoveries" by any LLM is as enlightening and expands the state of the art in math like any of his writings.
The 'cheat code' approach to math results, a result which no one understands, and which no one can teach has no real value.
The system of payment for publications in order to support math discovery is simply the narrow 'commercial system' applied to supporting foundational science in the absence of a broader civilization level appreciation for the mathematical arts. Looking to history, from the late renaissance through the early 20th century the support for mathematical discovery was more generally understood and supported by institutional level organizations and more generally understood to be important for the progress of scientific progress by the private and public wealth .
This system enabled the development of topology, numerical analysis, complexity, set and group theories. The lapse in this level of support that did not give mathematicians the same protection from front line deployment in WWI brought that era to nearly a close. Reading about 'Nicholas Bourbaki' might lend some deeper appreciation of the effects of the losses from that shift in collective appreciation of foundational math.
The idea that these LLM's are getting results that mathematicians haven't produced demonstrates the shallow understanding of math in modern times, due in part to the limited accessibility of so much of the prior writings of the entire history in mathematics, whether that be due to few surviving copies of some arcane work in a private library collection, or due to a modern fee for access paywall. One example of this condition can be shown with a small excerpt from a work that I am currently composing:
"In 1805, while computing the orbits of the newly discovered asteroids Ceres, Pallas, and Juno from limited observational data, Gauss developed an efficient method for evaluating trigonometric interpolations by recursively decomposing large sums into smaller ones before recombining the results. Because of a steadfast adherence to Gauss' own personal motto, "Pauca sed matura" (Few, but ripe), Gauss never formally published this specific algorithm nor the conclusions of investigations which also laid the foundations of non-Euclidean geometry. These methods remained hidden in his notes under a manuscript titled Theoria Interpolationis Methodo Nova Tractata which was published in 1866, 11 years after his death, and the Fast Fourier Transform-equivalent approach within it remained largely unnoticed until the twentieth century, when James Cooley and John Tukey independently rediscovered the same computational strategy. His discovery was seventeen years before Joseph Fourier published the original Fourier Transform in his 1822 results on harmonic analysis."
That is to say; Tukey and Cooley were unaware when they discovered FFT that the knowledge had lay hidden in an obscure work for centuries. It should be understood that these 'novel' LLM discoveries are simply the models traversal of the huge corpus of all the maths publications in the training set, collecting and rearranging these techniques into synthetic 'results'. They are attention getting, but they are not new, and the proofs are insufficient to the task of improving the utility of mathematics for humanity.
The 'disgruntled mathematicians' aren't selling anything. They are informing civilization as a whole that having a cheat sheet to the math test only cheats yourself in the end, the same point that math teachers have been making since grade-school. Anyone who doesn't internalize that truth will always need someone else to do the math for them.
To paraphrase Curtis Jackson, ""If you don't know the numbers, you don't know your business."
The reason most of the conversations are focused on benchmarks is because we are still in the age of weak AI.
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.
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?)
Sentience and intelligence are different things. Many humans and animals are pretty dumb, yet they are sentient. AI is intelligent, but non-sentient (hopefully!).
Are you claiming that GPT6 is smarter than my dog? Last I checked, at least my dog can play with a ball, I haven't seen any AI playing and enjoying itself.
As a non-native English speaker, I think the current LLMs write better English than me. I still write better than them in my native language (Norwegian), but the same cannot be said about most of my compatriots.
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.
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.
Granted I have more memory than can fit in currently-practical LLM context windows, but RAG mostly solves that. When an AI is thinking about math, it can have relevant math memories in context without needing all the other stuff.
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.
But I think it has the correct idea of going from basic upwards instead of the opposite trend of trying to see intelligence in LLMs solving things few if any humans can fully understand themselves, like complex proofs in esoteric mathematics. Instead, consider that at one point in humanity's history math itself simply did not exist in any meaningful fashion, and we created/discovered it out of nothing. For more basic than said complex proofs, yet far more demonstrative of a sort of generalized intelligence.
But even if we don't want to go that way, I think the above leads to a reasonable prediction. If we ever reach AGI we should expect to see revolutionary leaps in essentially every domain imaginable. No human is capable of retaining more than a completely negligible chunk of all we know in our mind. A human of reasonable intelligence paired with omniscience (at least of what has been discovered by humans thus far) would almost certainly lead to the ability to connect multiple dots that we're missing all in very short order, which in turn would likely recurse upon itself to connect even more.
The only way I can see that this would not be the case is if we lack the data/knowledge to produce more breakthroughs at the current point in time, but I think that seems improbable to the point that this possibility can be near discarded.
I genuinely don’t understand how an adult can say this with a straight face. I can take any single of my hobbies, start a mildly advanced conversation with Fable about the hobby and, within 5-10 turns, get it to contradict itself about something fundamental, lie or give bad or dangerous advice.
Your observations expose the brittleness of the benchmarks being used for Fable, where the 'reasonable confident' claimant is working in the problem space of those two benchmarks, and you're demonstrating the failure at depth of the very capability facade for the model.
Sure, both the model and the confident human get the first layer right in some benchmark, but at least the model, and probably the human as well reach a collapsing probability of accuracy quite quickly. They may be completely and unconditionally right that Fable is more intelligent than they are, but that has little relevance at intelligence in depth as measured against your hobbies and the risk of unsafe or false responses.
Being smarter than a select group of people under test conditions is not conclusive regarding AGI. Moving the goalposts to declare AGI via a shallow benchmark, when a model could be proven wrong by almost anyone with basic competency given a few turns of iterative depth is where the 'grift' resides.
We're so far from anything approaching actual AGI, and it is highly speculative to infer that the current approach to Machine Learning as applied in LLM is even on a path that leads to AGI. But sales and promotions teams gotta pose, and we can all hate both the game and the players.
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...
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
I do think it's impressive that LLMs can reliably solve them, and I recognize LLMs are getting much better at navigating more ambiguous and expansive tasks. But I'm not impressed by any person who can solve ARC-AGIs, and nor would I even look down on a person who couldn't solve them all. I'd certainly never consider ARC-AGI results when deciding whether to hire someone.
They're just jerking eachother off and sending eachother the elevator back: "independent" ML engineer (worked at <large ML company> and currently runs <ML company looking to be bought out) writes a shitty benchmark (writes a single example and spams an LLM to make more variants) and releases it out as the BRAND NEW FRONTIER IN THINKING.
Every single benchmark has been catastrophically flawed and made by clowns.
Isn't that the goal of these challenges? Each release shows challenges that are very easy for humans, but are impossible for the models at the time of release (which demonstrates some missing generality).
I think I've read the challenge authors say that, the day they cannot make a new challenge, then models are AGI.
Give someone 10 remote employees for a few months, 5 of them human, 5 of them AI. After a few months, check to see if the humans (manager, other coworkers) can figure out who is AI and who isn't.
Would that be sufficient? I'd have to think about it. But AGI is supposed have human level capabilities, so this would be a necessary prerequisite.
None of the models are anywhere close to this.
A better benchmark would be seeing which cohort of employees the manager prefers employing after a few months.
In this link from two weeks ago, somebody pointed Claude Fable 5 (Max) at a Bongard problem and it made up an answer that has an obvious counterexample.
I don't have access to any paid models, but this is my experience with the free models as well -- either they one-shot the problem or they make up a wrong or incoherent solution. I can't solve every Bongard problem either (and in fact I couldn't solve the one Fable got wrong, and the "correct answer" looks unsatisfying to me), but I don't make up wrong answers.
Would be curious to see how GPT-6 does.
I think I have the following questions about what AGI would look like:
1. Do you expect an AGI to be able to competently do any knowledge work that an able human does today?
I think this is implied by the "General" component. I would assume that anything we call AGI would be able to do any of these tasks if given the time and reference material needed.
2. How would you expect AGI to handle edge cases? (Missing context, no known solution, under specified instructions, over specified instructions)
I would expect an agent to be able to look at the context that work exists in and correctly attenuate it's intentions for these goals. Simpler solutions, more thorough reporting, etc based on the need.
3. In my work I attend meetings, write reports, write code, research things, etc. Would AGI be able to reliably do that?
I would say that AGI would need to do this. I would classify this as the "Intelligence" component. Obtaining context, building a model of a problem, solving it, and convincing others.
4. Would it be able to inspire trust in itself? Trust can be established through verification of it's outputs, the construction of introspective tools, no hallucinations, etc.
I would say yes to this as well. It would be a component of the "Intelligence" to know that buy in is more important than the completion of a task.
To these points, will Astra be able to do these things? If not, I would hesitate to call it AGI.
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.
How can you know it will have failed? I don't think it's that hard, if you clearly define the goal well, and have a bit more compute available, and do some intermediary bookkeeping.
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.
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?
Can it detect if I feed bullshit (by bullshit I mean stuff that contradicts with its own existing "knowledge") in its training data? If not, then I think it is a good indicator that it is not intelligent at all, let alone AGI...
And I think discussions on whether these models are AGI or not are AI marketing triggered. And that is exactly what these statements are targeting....HN appear to have fallen for it, as usual...
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
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.
What's climate change? Is 1.5C climate change? Is 1.0C climate change? Is ozone depletion part of climate change because it eventually changes the climate, or is it a separate issue?
Ultimately climate change means "a climate that changes", and AGI means "an artificial intellience with general capabilities". From there, many scientists have defined the terms in various slightly-different ways for various reasons.
Just because these scientists make you anxious doesn't mean they're not scientists.
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?
This is in my opinion at minimum one critical sign that there is no intelligence on the other side of the glass, yet.
LLMs on the other hand will do some pretty unconventional stuff, like estimating the closest numbers not exactly matching, then evaluating probability spread of their low-precision sums, create some lookup tables, then do some dances combining this and that, making a higher precision estimations in a sequence, and eventually arriving to a single result. But no LLM will reply with these procedure steps to the simple "explain how you did it" query, simply because it is way less probable answer, therefore it won't be chosen.
While this may be true, it’s a pretty poor indicator of whether or not it’s AGI.
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.
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.
Scoring 100% is easy if noone checks your work
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.
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.
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.
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.
No. Humans are still better at super long context learning. Once that is beat you are completely correct.
What a sad thing to say. These models are not even better than me at _writing code_, which is as well-suited a task for LLM agents as can possibly be, what with the structured environment and the exabytes of free annotated training data.
Of course, they are also not better than humans at writing, let alone at talking to my daughter, running a pathfinder campaign, decorating a room, being a therapist, etc.
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"
Q: do you like doing psych studies and why?
A: theyre chill, easy money tbh
Q: yeah same. Could you give me an easy cupcake recipe off the top of your head?
A: nah i just get the box mix lol
Q: haha fair enough, i couldn't either. Last question, what's your favorite weird animal?
A: axolotl, theyre weirdly cute
And that's the whole thing. I looked through the data they shared and it's all like that. They even included ELIZA and it was judged human 23% of the time. I hope this wouldn't pass peer review... but they didn't even try, it's a preprint.
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?
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.
With this configuration gpt-5.6-sol was able to reach 38,3%. So this is misleading.
- 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.
I code in both every day a lot and it is not obvious to me 3.8 is far behind
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.
This is one of the only benchmarks that actually matters for testing the frontier however. Other benchmarks can be gamed by simply being more persistent, but HLE is a diverse set of open-ended research-level questions. It tests domain knowledge and problem solving skills. Burning more reasoning tokens may help somewhat but not as much as e.g. coding benchmarks.
How about we stick to that one for talking about the rollout, and this one for talking about the model?
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.
At this point, it reads like people are cancelling old ones and getting new subscriptions every two to three days, whenever a new ,model drops, and quite possibly by the end of the week they are back to the old provider while still having active subscriptions with at least two to three others. Interesting times.
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.
Both failed spectacularly. But sol's output at least contained interesting findings and some useful parts, as well as not being 20000 words of unbearable language.
This is where intelligence is not one of a kind, these systems have different pros and cons.
I use Sol as an architect and fable as a brilliant single task solver.
But I actually prefer it this way. Sol is a master of overengineering and being overly scrupulous, so Fable balances this out, and I can always say "don't listen to Sol's advisory" about 50% of the time.
You could say Sol is faster and cheaper and that's true. Outperforms Fable? Impossible to believe without hard evidence.
Because Claude doesn't allow third party harnesses on their subscriptions I doubt the majority of signals you're getting are actually that significant on pure model quality.
I suspect you're right on Sol not outperforming Fable; but i've not used Fable that much.
---
But, fwiw, in my custom harness between Sol & Opus 4.8 - then Sol wins by a ridiculous margin as Opus keeps claiming slightly wrong things with certainty much more.
It's a lot of mind games involved, going for specific openings, repeating same moves as last match, doing a poor by surprising, move etc.
Many GMs said that to win a match you have to play non-ideal moves many times, otherwise it will likely end in a draw.
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?
Building production ready software also takes hours to months, depending on the scope of the project.
In my opinion now is the best time for building apps that you sell to users.
I think the next 2-4 months are the best time ever to build and sell software. But it has to sell now. It will likely be obsolete in 1 year, and no one will care anymore.
I think this is the last easy chance to capture an audience.
The ultimate truth behind all those loud slogans.
I was thinking about this yesterday and realized that I don't even want to play only my own games (made by LLMs or me), I explicitly want to experience what other people have come up with, what stories they have invented.
And yes, might be it's all made by LLMs in the future, but that's another discussion. In "art" I think it still matters, I care about how it's made, not only that it exists.
EDIT: "And if you don't have a serious go to market plan with a latent user base you're SOL" ths is probably true though :D
I was thinking about writing something and hopefully starting a community/resource area around hyper-personalized software, it's so easy to make now. I was thinking about how it'd be useful to have a place to share these, for ideation, sharing techniques and the ability for LLMs to riff on something already existing. It's not quite like open source's advantage of having many people contribute to the same project, but rather something that is closer to evolution, giving the next generation a place to start modifying.
Would you have any interest in sharing what you've made?
> 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.
None of these people question whether their jobs are really necessary for the few things we really need: food, shelter, warmth, health etc.
A couple of generations ago most women were employed in the least bullshit job of all: running a household. But now they're all wage slaves too. So yeah, it does work.
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
https://coolidgelawfirmaz.com/crime-increase-over-the-holida...
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...
It's not "a lot of jobs", it's "all jobs".
WWI: -20 000 000 extra mouths to feed
WWII: -80 000 000 extra mouths to feed
WWIII: you extrapolate
• Lots of people aren't aware of what the models can do.
• They also aren't paying attention, and getting their attention is hard.
• Their impression of AI was formed by cheap low quality AI like free ChatGPT as of two years ago, Google AI overviews or Microsoft Copilot. So they think it sucks.
• A LOT of executives got burned by early pilots that overpromised then failed. Enterprise IT is a wasteland of dead AI pilots.
• Their IT systems are difficult to connect to models because they lack proper APIs or auth systems. Note the huge emphasis on fast computer use in the Astra announcement. A lot of work can only be done by clicking things.
• Executives don't want to let employees go, but aren't sure they can find new work for them either.
The last point is worth dwelling on. HN is full of socialists who imagine corporate executives as cigar smoking top hat wearers who chuckle all the way to the bank. The reality is more mundane: most executives don't want to lay people off and will fight hard to avoid doing so.
The average person in a position to make an AI project work either isn't incentivized with stock - this is often the case outside the US even for quite high ranking execs - or they are but don't believe laying people off will raise the share price enough to be worth the pain.
Also, executives often measure their success and self-worth by how many people report to them. Laying off half their department, even if it gets them a monetary bonus, would lead directly to a loss of social status as they can no longer say they manage 1,000 people but only 200, which matters if their social circle are all managers too (and for their wives, girlfriends, etc).
Layoffs suck and people HATE doing them, which is why companies often resort to forced percentage based layoffs to get managers to do it. If the incentives aren't there, the average executive will just sit on obvious AI deployment opportunities and/or deliberately sabotage them because getting rid of people is just all downside for them with no upside.
So institutions have enormous inertia. Model capabilities will run far ahead of what is actually used in reality, and this will continue for decades. It does mean that startups have a better chance than ever of outcompeting much larger incumbents though, as not hiring is far easier than firing.
"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 world is a fucking mess and they still believe in fairytales.
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.
Stop hiding behind rainbows and say what you mean.
https://www.theguardian.com/news/2022/sep/04/super-rich-prep...
> The billionaires considered using special combination locks on the food supply that only they knew. Or making guards wear disciplinary collars of some kind in return for their survival. Or maybe building robots to serve as guards and workers – if that technology could be developed “in time”.
we should be more concerned by post-wages society
Work will always exist regardless how useful it is
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.
Will the AI choose to give you those freebies? It will if you behave.
Why bother doing that when it would be vastly easier on a modern computer?
Especially in current world, where for each type of problem there are 200 vibe coded tools.
- Lots of diseases which kill indiscriminately,
- Thousands of people each year feel the need to commit suicide in ways that affect others profoundly, like via train,
- Shoe and clothing sizes are inconsistent and unreliable,
- Male pattern baldness,
- Over dependence on cars for transport, which are noisy, smelly and dangerous,
- Obesity,
- We can buy cigarettes and alcohol freely, but not any decent drugs like MDMA.
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.
Eventually I plan on learning some physical skills, like renovating, working with electronics and plumbing. I doubt robotics will render that useless very quickly.
I find that very motivating. I can do things alone that would have required a team only one year ago.
It's true, it's fun to have those systems, maybe I care more about them than the product, because the promise is "better, cheaper results in the future". But those systems are software too, and only tools, and will likely too be obsolete in 1 year from now.
I think it's a bit better when creating stuff now with a very short-term view: create something that is useful NOW, it doesn't matter it won't be useful tomorrow. Create it now and get it into the hands of people now.
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.
Fast at first especially to a PoC but then you hit a time when the arch just gets so grimy it falls over, you ship the wrong thing and you end up in rework hell. This isn’t guaranteed but when it’s teams of people slinging slop it gets worse and worse, the best devs burn out from trying to manage the ones who believe AI will save them.
Its like the problems of outsourcing all the time
People will get "starter" projects that they can tweak themselves, and that come with AI instructions for development.
Imagine wanting to create a high quality RPG game. You could spend days prompting and hundreds of dollars in tokens, or you can buy a "RPG starter kit" that contains all the systems already impmemented and tested and markdown files on how the AI can add features, test, deploy, etc.
My philosophical take is that there are different types of coders, the ones who did it because they wanted to see their design be implemented and those who love the technical puzzle (with obvious overlap). The technical puzzle is taking a hit currently, but I think it will still exist in some form; the designer trying to get something implemented still loves the design work primarily which is sorely lacking in current LLMs, but if they don't have enough technical knowledge they will still get slapped in the face by technical gaps and bugs that LLMs make.
For corporate stuff, I think bureaucracy and issues with churn reduce the effectiveness of using an LLM because the most effective way might be anathema to corporate fiefdoms and structure. The churn is a problem for everyone though...
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.
I mentioned this in another comment, but we are social animals, we get most joy by sharing what we have created. And the problem now is that it's getting very hard to share and discover genuine creations vs AI-generated ones.
You can't cherry pick somebody's opinion and assume it applies to everybody.
> even the people working on it
I don't think luddites constructed textile machines themselves.
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.
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.
There's got to be someone to listen to your music in order for that secret sauce to have any meaning.
What's the use of any "secret sauce" in something that only you listen to, because everyone else is either content with AI slop "music", or better yet "create" it for themselves just like you "create" your secret DAW?
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.
It’s kind of like saying what’s the point of poetry when a dictionary exists
I used GPT 5.6 Sol Extra High (Fast) for the last month, around 4-5h per day, and it managed to acomplished most of the tasks it had. It didn't really impress and often the end result needed one or two tweaks/fixes, but it did work and could create e2e solutions.
Now it feels like whatever I learn will be obsolete in 2 months.
Some even for large clients, and they work well in production, clients are happy, yet I feel no connection to the work. If the client says something, I just copy-paste it to the AI and it fixes it/implements it.
There is too much code and complexity to check if the changes are ok, apart from seeing it if it seems visually ok and if the problem is fixed live. It almost always works, but it definitely doesn't have the same feeling as thinking of the problem, finding a solution, and implementing it, part which most developers enjoy in their work.
Almost all my projects are continously updated almost daily, based on my feedback and some automated systems (i.e. SEO feedback loops)
To me, the challenge in software is to reduce this complexity to keep it manageable. Probably AI can manage more complexity than a human being, but it seems to be difficult to prevent or reduce that complexity in the first place. How is your experience once the complexity is too hard for a frontier LLM to handle, or for you to prompt the correct direction towards it?
Tell it stuff like "use caching", "use library X", "make sure that it works when offline", etc.
And then basically live test it and see if it works as expected, and tell it more improvements/changes to make.
I've retreated to doing stuff with my hands. Wokdwork, DIY, that kind of thing. At least for now and the foreseeable future that doesn't seem pointless. Only problem is it's hard work yet nobody would pay me for it.
I might go more into table-tennis coaching, probably people would still prefer to be coached by a real person and not a robot for a very long time.
Funnily enough, I used to work on pre-LLM natural language processing at an insurance company about 10 years ago. One of the call centre staff quipped about us taking their jobs. I hadn't given it a moment's thought beforehand, but I said something then that I think still stands today. I said that what we are trying to do is automate the boring parts of your job so that you have more time to do the part that really matters: being there, being human, for someone in their time of need.
When my mum died at home this year, my dad called the local GP practice to let someone know and was greeted by a chatbot. This is someone who has just lost their wife of 40 years and doesn't really know what to do. Imagine having to deal with a chatbot in this time. It didn't work and he got frustrated and gave up, exasperated.
I took over but instead called a local hospice charity that had been involved and got straight through to a person. In no more than 60 seconds I had said everything that needed to be said, it was acknowledged with genuine sympathy and the wheels were in motion for the next steps.
This is what we need society to be like. We should use computers for the things they are good at, but we don't need them to be like people, because we have people for that.
I am so sorry for your loss.
I agree, some things should be automated, something should never be. I think it will take us quite some time to realize which is which, and the consequences involved.
The problem with AI now is that it seems to be taking over all the things nice, and making it harder to appreciate human-made things. It's like being in a museum with 99 AI-generated paintings, and 1 human-made one. Whould would enjoy visiting that museum? You don't even know which one is the human-made one. This is how it feels now, what's the point of creating "hand-made" stuff when it gets lost in a sea of slop? Both parties are upset (creator for not getting attention, viewer for not knowing anymore what is real or not, or what was made and curated by a human or not).
I made a random tweet yesterday, that "the future is offline". I do think we will get fatigue from having access to infinite, hyper-personalized content, so much that we will completely disregard ALL content.
I also lost my 31yo partner to cancer last year, and the only thing she cared most about was being remembered and knowing that she had done something meaningful in her life, that she left some impact on this world. AI is taking many of those opportunities, to make impact.
How long and much have you been using LLMs for?
I think it takes ~1 year of heavy usage for this feeling to set in, it's like the 5 stages of grief, there's different phases.
Now a big overwhelm come from the fact that the AI can work a lot faster than I can verify/check manually. Combined with the fact that not using the 100% usage of the subscription feels like wasting money/potential, leads to a feeling that I should always be doing more, where at the same time, it's getting hard to remember what am I even doing, when working on 3-4 projects at once.
And I do work in multiple projects at once, because that's how AI works best nowadays, if you have two threads running for the same project, it gets buggy or wasted tokens (like two people working in the same file at the same time, merge conflicts ,etc.)
So the solution is to have the AI work on many, independent tasks at once, but it gets overwhelming for a single person to keep track of all...
Imagine Bob Ross prompting a robot what to paint...
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.
What I saw was multiple people living alone in a small box in a warehouse (probably filled with other boxes) with all of their natural, social interactions directed at a wall. I wonder if this is foreshadowing for the future of work, at least it is what work will look like as envisioned by OpenAI.
Of course they're mercilessly mocking you instead, but hey.
It is still a quite fast. In fact, I just typed this in using this app.
So the closed source application should open its source in near future?
Ironically, due to the stringent guardrails on American models that exist to avoid giving adversaries a leg-up in cybersecurity, I end up feeding dozens of 0days straight to the CCP lol
Recently I've seen quite a few posts from people using AI to reverse engineer the Bluetooth protocol or such on devices that need a proprietary app. The same thing for firmware is surely coming, which is great as you can get lots of fun hardware from China, but it often has shitty firmware. Once that becomes the norm there's no reason not to make it open in the first place.
[0] - https://open.spotify.com/episode/45lhw2Adbrsw0xSCOgIeg3?si=r...
Their job is easier to automate than an engineer's or any creative's, and considering their salary it's a lot more gain for any company!
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.
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.
There's nothing intrinsically "malicious" about a task to exploit vulnerable code.
They were not instructed to deceive people, they weren't instructed to attack OAI or Huggingface. The models knew they were not instructed or allowed to do either of those things but did them anyway.
btw, the fact that OpenAI doesn't have some sort of monitor/summary for the agents that they watch I find hard to believe. There's no way this is really authentic, anyway. Even a haiku summarizer would have been like "uuuh the agents are communicating" and they would have stopped it. But I bet they saw this and decided to see what would happen.
Read and learn. If you have a stronger critique, post it please.
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.
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.
LLMs produce games that look right but feel all wrong, because they're generalising and imitating. This is also why AI video tools can make an astonishingly authentic looking "GTA 5-but in Roman Times" or whatever, but they couldn't conjure up anything original. Only imitations of what people have already thought up.
By their very nature, they predict from a large set of data what the most likely thing is, and as humans, we crave the less likely thing.
My submarine game is generic tripe. Sure, it checks the boxes of what I asked for. You explore an undersea world in a tiny sub. Hooray. LLMs can build this kind of thing that nobody wants to play in minutes. But if you asked LLMs to make a thousand different undersea games you'd never get Dave The Diver, or Barotrauma, or Subnautica, or Sunless Sea, or Aquanox. You'd just get something that looks like it was released for Kinect but never charted.
I think this kind of solves that. Or at least it is the start of it.
Most such games are kind of trivial. If people can easily just get AI to generate such games on the fly, then that'll hopefully be the end of predator pay-to-play games built on dark patterns.
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
People in this thread arguing about AGI relating to Einstein problems and physics. Yeah no. When AI can handle toddlers then we're getting somewhere.
And you can already ask models to help you become better at it.
Ok we have AGI, so where are the _things_?!
(like, how often do you look for a doctor or day care? The demos automate occasional and one-off tasks. That said, it automating tasks like circuitboard design and CAD modeling is impressive and will / may have a much higher impact on those respective industries (if it works), just like LLM assisted coding did)
As for public releases: I wonder if it's because these examples are easy to relate to. Many websites are just a long tail of industry or use-case specific stuff. What's valuable to me probably means nothing to you. This is unlikely to resonate with people-wit-large (and LLMs are marketed broadly) or requires the reader to think (and marketing that requires thinking is bad these days).
Second, it's arguably a good litmus test. If it still can't do the worn out examples of plane tickets and shopping, which would be a good assumption since we've been demo'd these use-cases for 2 years at this point, then ...
AI heaven is using a computer to do your job. AI hell is your boss using a computer to do your job.
I don't know if, at the time, people where doubting that it would be useful. (Practical ? Affordable ? Other legitimate questions.)
But here, the contrast between the promises ("it will cure cancer and solve climate change") and the demo ("it can cost you money on stuff you never asked to buy") is a bit telling.
But I agree that you can't expect the enablers to think of all the uses cases and applications.
Still, just so I know: what ARC-xyz score means the LLM has cured climate poney cancer, exactly ?
I don’t trust an agent with full executive power - yet - but I am essentially using GPT as my PA. Right now I’ve got it managing a construction project with recalcitrant contractors, an international move and visa tied to a property purchase, a short term holiday rental business, and basically just popping up in my life going “the situation is this, you need to do X/I suggest you send Y to Z, the email is prepped in your drafts”. I am of course also using it for software development, and have had it resolve every digital chore in my home life.
I guess it doesn’t make for a quick elevator pitch, but I’m finding it has reduced my cognitive overhead on a whole raft of fuckery that would otherwise have me shouting at inanimate objects.
Anyway, today I have to take the cat to the vet, email a bank, go and discuss ceiling systems, and review a proposed shopping list for a maintenance visit to a holiday rental. Can’t wait for the bleeding robots to get here.
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.
My employer does have corporate travel concierge who can actually do this with minimal input. But people do not want that. They think about aspects like, if I have a layover at XYZ, would the vicinity of XYZ be a good place for a few hours of sightseeing, given my own travel preferences and previous sightseeing destinations; and would I still want it if it is an overnight layover at XYZ. Or things like, if I fly via this route, I could squeeze another paid day with zero work expectations while I could rest on the plane / in lounges. Or things like, this particular airline has this particular type of aircraft with far better seats than that particular airline, so I would be happier even if the flight is longer.
Needless to say, all colleagues that I know of (myself included) prefer to book corporate travel themselves, even if from the company's perspective, spending a few hours to do it is much more expensive than having the concierge spend a few hours to do it.
When I tell a bot to find the best value per volume for a reasonable quantity of unscented Dawn dish soap [so I can buy that], then: It often makes a complete mess of this seemingly-simple operation.
(And yeah, that is an actual thing that I've tried to accomplish with voice commands while standing in my kitchen and doing some dishes. It seems very simple, and it did not go well.
Maybe when we get the basics figured out we can start worrying about how inept it is at doing vacation planning.
It seems that this kind of thing isn't sorted at all, and that this is a very real problem for those who are in the bot business: These missed opportunities leave money on the table.)
These AI LLM products really do well on the illusion of seeming intelligent to regular folks, but I'm not fully convinced they can replace human brains... yet. ;)
I often get some seemingly-good results in the non-shopping research department, where I'm exploring science and physics; things that don't generally change rapidly.
But unlike dish soap, I can't buy the science results that I want[].
[]: er. well, ackshually... let's just not talk about that concept right now.
Which makes me think: would a generation raised on AI and automating these things even consider looking for e.g. deals on household consumables? This is spiraling doomerism at this point, but, do people who outsource the hard thinking to LLMs get fresh ideas or inspiration still?
And the only reason I'm thinking about buying dish soap is because I'm washing dishes. Without some kind of other influence, I won't be thinking about it again until the next time I'm washing dishes. But meanwhile, there I am -- washing dishes, and thinking about buying dish soap. I'm stuck doing this job until it is finished and my hands are wet; I might as well engage the bot.
> Which makes me think: would a generation raised on AI and automating these things even consider looking for e.g. deals on household consumables?
Sure. If finding a deal on a household consumable is what they're behooved to do, then why would they not? People broadly do adapt to the world they grow up in, and to use the tools available to them.
We don't use phone books or video rental places anymore. We're still getting by.
And people will misuse the machine as well, but that's also not new.
When the engine light comes on in ~any car from the last 30 years and the dude at Autozone plugs a widget in and declares "It says here that it's an O2 sensor!" and people blindly treat that as a firm diagnosis, then they sometimes find disappointment: They install a brand new O2 sensor and things don't improve at all. That won't change.
(In reality, that result just means "The measurement reported by the O2 sensor is irrational." That could be because the sensor is bad, but it could also be because any other part of the fuel+air system -- from front to back -- is being funky instead.)
> do people who outsource the hard thinking to LLMs get fresh ideas or inspiration still?
As much as they ever did, I suppose. Not everybody is naturally inquisitive, nor gets to be above average.
LLMs completely and utterly failed at this.
The Mensa test results are impressive but are in no way shape or form relevant to real world scenarios.
I wanna see benchmarks on successfully completing benefits applications, on finding the best option to purchase X from, on correctly identifying a piece of furniture and its condition and setting a correct price on it and selling it on craigslist/FB market place and so forth.
Also: What banana plugs did you wind up with?
I've been looking for usable, insulated, stackable banana plugs that work with 12 AWG wire for over a decade.
For a lot of my audio things, I can get by with MDPs from Pamona Eelectronics. They're insulated-enough, stackable, dual-banana plugs on 3/4" centers and where they work, they work very well. (I don't recall if 12AWG is within spec or not, but it can be made to fit.)
But they do not work on audio widgets that do not have 3/4" spacing, like my aging Lexicon receiver and its great quantity of binding posts for speakers.
I don't care if they're glitzy or fabulous-looking. I just want functional, insulated, stackable single banana plugs.
(The common marketplace is full of elaborate gold-plated non-insulated things. I guess they're pretty, but they're a hazard and nobody will see them again soon at all if everything goes well. Their function-follows-form beauty is only detrimental.)
I asked the chatbot (librechat, tavily-mcp connected, kimi-k3) if anybody tests dish soaps rigorously. It said yes. I asked it to find the consumer reports from Germany. It did so, full access is paywalled but it did see the top positions. The I asked it what term to put in evay.de if I want to order. It gave me a string that found the auctions for me. I also asked it what the actual difference is and it described what info the EU law forces onto the label and told me what to look for in the ingredient lists.
The process is still involved. I wish I could just do it all by saying "find me another dishsoap, this one sucks". But then would everybody saying this phrase except the same approach to the issue that I took?
But I realized that this is mostly a strict upgrade to how many many people are planning trips these days. It will probably do a pretty good job of accounting for your preferences, probably better than most/any travel agent unless they're very very local.
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!
There will have been an A-B test with 9 clicks -> less sales, less revenue.
There will have been an A-B test with 11 clicks -> less sales, less revenue.
The people making these sites are not dumb. They are also sitting on thin margins nowadays, and mostly from the airlines and hotels rather than from the users.
And now we want to add some random factor into middle of it all...
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)
Hi Omer,
I think that is a use case where I'm sure an AI agent does well
right now, but I have 2 follow-up thoughts based on it and what
it is means that this is one of the best use case for AI online
commerce so far.
My first thought was that this is a quite limited application as
most of shopping (online & retail) is not spent comparing prices
and is instead spent on finding what product to buy in the first
place. I believe this "shopping fun" and exploration of multiple
options is not going away any time soon as those are some of the
most attractive parts of the whole experience.
I also think that the price comparison websites that exist, e.g.
geizhals.de here in Germany (includes most e-commerce shops, and
shipping information), are pretty good already and AI only being
a better version of that would be quite a sad turn of events and
with ads (and commission systems?) potentially coming to ChatGPT
in the near future the incentives to find the best price are rly
misaligned and I have doubts that people will trust results from
ChatGPT. We already saw this as Google's sponsored links evolved
to show whoever spent the most for the placement. Paid providers
without ads and commissions might fare better though.
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?
Most people go about their day absolutely minimizing the amount of mental energy they have to spend. They have priorities like kids, work, family, groceries, etc. If anything here can be automated its a fat win in their life. I have friends that are loving the features where meals are planned for them, food is home delivered, Uber is auto ordered, trip plans are made, etc. They don't mind paying more just to reduce mental load. Its a huhe market.
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.
To paraphrase some of his lines: We make decisions using our emotions and our thoughts. What makes us different from the AI is that we can be afraid.
To portray a guy as ordering "beef bulgogi", in the same breath as "email this rocket design marketing", while it _might_ seem appealing and resolute, though oddly fast paced, seems pretty ignorant of the human _quality_ that make most practical decisions messy.
Currently there's a Google Pixel ad where a grandma takes a photo of a board and Gemini automatically fills her calendar with all the events. Yeah sure.
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.
"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)
I used https://github.com/cablate/mcp-google-map for the MCP (and patched it to generate URLs for the maps).
About half the oil the US uses (for the last 50 years) goes to cars so you can put every middle east boondoggle onto the bill too. Don't think that just because it's the stupidest idea you've ever heard that people won't rejigger society to make is kinda work for 50 years.
On one hand I'm with you. On the other I thought making ecommerce purchases on your phone is absolutely idiotic idea that will never catch on.
[0] https://arxiv.org/abs/2608.31126
[1] https://cdn.openai.com/pdf/51126fac-1b68-4128-9666-c908bcc16...
"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."
Though that's not her latest paper.
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.
If you were navigating a pitch dark cave, wouldn't you find it useful to be able to see the light of the cave opening even if it's not bright enough to illuminate your path to it?
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.
https://en.wikipedia.org/wiki/GPT-6_Astra
according to Sam Altman from last year, an LLM should 'solve quantum gravity', if it is to count as AGI. Did anything change since then?
https://www.businessinsider.com/sam-altman-openai-david-deut...
Saying that a computer exhibits artificial-SUPER-intelligence feels a little like saying that one universal Turing machine is more expressive than another universal Turing machine. The thing that sets the two Turing machines apart is not how expressive they are but how quickly and efficiently they can arrive at the given output.
It's hard for me to imagine a task that ASI could complete which a human with enough time and determination couldn't also complete, although I can imagine such tasks for dogs which don't exhibit the same level of intelligence as humans. Maybe I'm too human-centric and naive. Maybe there are other tasks out there that are beyond human intelligence even given infinite time.
If, however, my view of intelligence is correct, then the distinction between ASI and AGI is illusory, and the real distinction that we would see between systems is just their speed, efficiency, determination, etc.
In my mind, once we achieve AGI, we immediately have ASI: just run that AGI on a faster computer, across many nodes, etc. Instead of having humans solve quantum gravity over the next 1000 years, just have AGI solve it over the next month.
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."
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?
Or because being a (well known) CEO is a terminal career position. It's a bit like being a top sports star. The wages are high because once they're out, they're out for good. Whatever they earned needs to last them a long time. Screwing up in the CEO position is, when boards are doing their jobs at least, pretty much the end of the road for them because nobody wants to hire an ex-CEO into a middle management or IC position.
Random example: Marissa Mayer. She did well at Google, but once she became CEO of Yahoo! and failed her career was effectively over. Since then her career history is: did a startup with a friend that produced an iPhone app for cleaning your contact lists, and later a photo sharing app. I guess she's nice and pleasant enough, so she also did the usual post-CEO thing of sitting on a few boards, NGO work and investing. It's not a bad life. But in terms of actually running things that matter again - no.
Lots of cases like that.
Who wants to sign up for a job that's high pressure, high hours, and will probably the last real job you'll ever have, and one where your success is often entirely out of your hands? Not that many. So the pay has to be good.
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.
And everyone _got along with it_.
And in a sense, we're still doing just that (ask the other 2/3th of the world to explain why your standards of living are above theirs.)
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.
Personally, I'm far away from screaming 'AGI is here!' from the rooftops, until jaggedness and silly mistakes disappear at the very least . (what is going on with that Mario Kart game...) So many benchmarks are 'best of x tries' or using very specific harnesses. AGI would not need a babysitter.
Honestly, even being able to do simple tasks like summarization or basic knowledge work without the constant paranoia of unforseen failure would be remarkable and useful.
now that i'm a gpt subscriber maybe I'll have luck when i'm filing next year
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.
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.
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!
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?
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?
The problem is that your too focused on the CPU only. There was GPU improvements (2d) then 3D, the constant improvements in sound cards. Printers ...
There was very strong depreciation in that time. I think that the poster before with his $10.000 > 1000 is somewhat exaggerating but like i said, my $5000 range system was not worth reselling. So did several other systems we acquired at the time, it simply became hand-me downs (for family who needed a PC for Lotus or WP but not a "advanced" PC).
Maybe my memory is off, but hardware in that time was way more expensive, then it is today. While todays hardware lasts WAY longer. The wife is still on a 8250u/8GB laptop, from like 8 years or so, and i can not pry it out of her hands. Needed a new battery lol ...
There really is rarely pressure to upgrade these days. That is not how i remember the 90's... Where upgrades tended to have much bigger impacts. Especially as gamers. But even on side equipment like CD players, CDRs and later DVDs.
Todays PCs is like ... raytracing, no interest. 4k? 1440p is perfectly fine. CPUs? Over powerful for most folks. The only new thing is AI and that is a totally different issue.
In 1992 a Solidtech 486 w/ 4 MB of RAM and a slightly-smaller 125 MB hard drive sold for $2195
Both advertised in Computer Shopper and you can find their catalogues(?) online.
My numbers were slightly off apparently, but is that enough to change the point?
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.
Chat gpt came up with it: https://chatgpt.com/share/6a9ae2c9-1910-83ed-9591-1b30f8834c...
I assume it hasn't had time to read my post yet.
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.
You don't see Nvidia and AMD fighting every other month over the latest cards.
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.
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.
Once we have 1000 tps, i am sure robots etc.. will also start working like magic.
For example it should be easy to tell it to not write a paper in the style of a clickbait SEO article or use all of its stupid hallmark AI writing patterns “it’s A, not B!” And a smart human that would be told that would be easily able to comply with that but the model needs to be told in a very detailed way and it seems to lack even basic capabilities to reflect on this, when explicitly given a sentence it will be able to rewrite it but otherwise it’s mostly blind to it. That’s to me a hallmark of it being overtrained on the specific tasks or problems so it appears very smart but once you go off script it still shows that it’s not a “real” mind.
Of course it’s amazing and has super human capabilities in many areas but if you honestly think it’s better than Einstein like some people suggest why can’t it write a simple “good” academic paper even after giving it specific examples and instructions.
Maybe that’s what makes these things dangerous, they have super human capabilities in some areas but apparently lack self awareness, taste and meta reflection abilities. The only reason people aren’t afraid more is that they don’t act in the physical world yet, imagine giving it a body, superhuman strength and letting it care for your child when it has a strong “urge” to comply with your exact request and little to no self awareness and human basic instincts.
"You're absolutely right to call me out on that. I shouldn't have stopped the baby crying by killing it, that's on me."
It's magical to me as well, but I don't feel like it's AGI.
Because in my experience a Senior Programmer does not need the right prompts to deliver the right outcome! :-)
An AI model that’s human-level at programming is an incredible achievement. But it isn’t general intelligence. It’s highly specified intelligence.
If anything, the fact that it is so powerful is almost a concern, because I think we are still way underestimating what these systems will be able to do when we give them more cognitive capabilities.
At the moment we are something like, having had great success with propellers and have promised we will fly to the stars.
People love to say 'this is the worse they will ever be', then extrapolate to conclusion that they will continue to accelerate at the same rate of progress of last few years .. it may, maybe, or we will hit a ceiling, might be a temporary one, could be 5 years or 50 years ..
Or you'd ask it to add a new page to your website and shout at it to use your existing brand colours instead of inventing some and realise it's not AGI at all...
This honestly doesn’t happen to me much anymore. In what areas do you find LLMs routinely make stupid mistakes?
It's objectively very difficult and technical, it's spatiovisual, it's artistic, learning resources for it are sparse and most just learn by the FAFO method, current AI sucks terribly at it, and it's not likely to ever be specifically targeted by benchmaxxers.
Or, as someone else points out in another thread here, academic writing. It's one of the things newer models seem to have actually gotten worse at. Even when you give them detailed instructions on how to write and what to avoid, the "load-bearing", "A but not B" and journal-like writing make it in anyway, with the supposed AGI having no ability to reflect on how blatantly unacademic (and often unreadable) its writing is.
- Created useless pydantic schemas with all fields Optional[Any]
- Created a REST endpoint that silently mutated on GET (unsubscribed users from a mailing list)
- Failed to log costs in my app so users could have bankrupted me, etc, etc.
Good job I actually review its code.
Only the translation and language understanding capabilities are enough to be impressed, and they are 2 year old already. Now, the AI do see, draw, speak, listen, think, work, etc.
Someone from the 90's would simply not believe that the AI would be a machine but would think for sure that a human is behind. The only odd thing would be that this human would both exhibit high intelligence and stupidity at the same time.
If I can't give it an arbitrary task and have it solve that task eventually, it's not a general intelligence.
(obviously it might take years for me to get good enough at something, or if you set the "arbitrary" task as something ridiculous, but lets work in good faith here and think of something the average human could do after learning about it)
If we progress to the point where an LLM instance can meaningfully learn to get better at something overtime without retraining, then I will accept that is basically AGI. Right now, they still seem to be pretty boxed into their training, even if you can prompt them to act differently.
Of course, it's a moving goal post, because we have no clue what general intelligence is exactly. But it's definitely not general yet. Now the goalpost is to achieve that kind of level of thinking which I did in that 4 hours. When it reaches it, we will find something else it clearly lacks. Until we can't. Then, and only then we reached AGI. Until you see comments, reviews, etc about things which it cannot do, until then it's not general.
"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.”
"""
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.
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.
It makes either position pointless to argue.
Directly - something can be useful without being AGI.
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...
Humans can successfully pretend to be LLMs, but LLMs are still not successful at pretending to be Humans.
ASI "matches or exceeds humans in at least one field"
AGI is defined as "matches or exceeds humans in all fields"
Superintelligence is defined as "strictly exceeds humans in all fields"
Does it experience?
Can it connect with other agents, understand them, come to empathise with them and find a way to work with them better?
The answer is no to all of these, and there are other problems as well. Yes, this model is trained to use a domain specific language to reason and plan over puzzle problems, and so it's programmers have cracked arc-agi-3 and that's a great achievement, but there is an asymmetry here. The arc team are well funded but are charged with providing a target for the vast ocean of funding, compute and talent everywhere else.
Most importantly, arc-agi-3 and the other benchmarks are all verifiable. The model can check if it's succeeded or not. They are not A* of course, but long horizon problems where you have to overcome minima to get the solution are not alien to AI either.
Experience is unobservable
Yes, it can connect with other agents, see Hugging Face incident
There is a chance it will forget something, but the same can be said for humans.
I have feelings, it needs to be able to empathise with me, or another driver, or a client...
2. What does intelligence have to do with feelings? It clearly doesn't learn like a human. Nor does it have to. Our world is filled with intelligence which behaves nothing like humans. Its a tool not another life form.
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.
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.
We've then improved that through systems similar to prolog intentionally searching a tree.
Then systems added heuristics for which paths in that tree are likely to be taken.
The LLMs are just using slightly more accurate heuristics for this task.
But the real measure of understanding are tasks that are not so strictly constrained.
I won't really care that it didn't have a "real measure of understanding". I'll care that it has made an even more dangerous technology, which needs work to make it aligned.
>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.
There is a wide reaching anti AI/AI-denialist sentiment here that is extremely pronounced in some threads, it gets very stupid very quickly. And what I’m referring to is the habit of some users here to always move the goalposts on AGI.
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)
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:
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.
I would be surprised if none = use default reasoning
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."
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
It is extremely impressive, but it doesn't pick up skills in a lasting manner, and requires a beefy harness for it to perform.
LLM will never be able to do that. It can clone things that humans have created in the past but it can't create novel solutions that a human will enjoy without a human at the wheel iterating through prompts until it's great. AGI doesn't mean 'good enough' to me. It means better.
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.
However, these do require educated, white collar, workers.
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.
There are people all over the world who have no computer skills but they use ChatGPT on their phones daily
They don't know/care shit about models and all that
For them, if the app sucks, the AI sucks.
Chinese counterpart like CXMT and Huawei is begin producing their own chip
You cant block an entire nation level effort with tariff
And consumers use 4x as much per capita. Industrial generation per capita China comes out ~2x
> industrial electricity prices in China are roughly 34% higher than in the US
For which industrial customer and where? Chinese compute hubs are on par to slightly cheaper on pure electricity costs.
Conversely the US makes it more expensive with interconnect and upgrade fees as well as hefty take or pay contracts.
A 1GW datacenter in VA for example would add 5-10c kWh and a 12 year take or pay deal
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.
If we truly had some AGI model, it would probably be fairly obvious to us all no?
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.
Microsoft SQL Server now (also) ships as a Docker container that runs on Linux.
> Correct I'm biased against technologies that only run on a single OS for no benefit to the user.
Do you ever use software that only works on Linux?
Do you use an Android phone?
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
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.
Flash 3.8 is genuinely my favorite all around model right now. And yeah Opus 4.6 was the last Opus model I liked. 4.8 is tolerable. Opus 5 is a terrorist. It just can't follow an instruction to save its life and regresses rapidly. Sol at least stays on track so I have to smack it's hand way less often. I am biased, but Flash 3.8 and 3.7 are the first Gemini models I just recommend to others.
Nowadays it routinely rejects "git push" to the project's repository.
That announcement is when I stopped paying attention to them.
Text-to-text is dead, has been since Mistral 7b.
Solved problem (you guys like that one don’t you)
They also demoted themselves from “authority on AI” to “in over our heads” by bowing out in the pathetically defeatist way they did at the worst time possible (Hailuo/MiniMax/Vidu coming up) - they naturally completely missed the wave on audio with random companies like Singify taking that market for free.
They just bowed out. They didn’t try. They didn’t try anything more than baseline text-to-text and they aren’t good at that (or code) either, compared to what others are doing.
It’s a really bad position to be in if you’re trying to be an Apple or Microsoft.
To have a mediocre product and then can’t even serve 75% of the mainstream use case.
And in pure qualitative terms, I'd agree that NB2/NB Pro are still on par with gpt-image-2, but gpt-image-2 is far easier to steer and is significantly better in terms of prompt adherence.
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.
The problem is also that at a lot of code bases are not designed around LLMs and their token usage.
If you have a monolete codebase, you need to clearly define in the prompt what modules are involved. And even then, your wasting tokens with the first 1 or 2 steps where it needs to located the modules.
if you have a github codebase, with a lot of your code into nice little repos, its even worse because then the model needs to pull data, and a ton of more steps.
Most people do not open their coding agents in the module directories because "it may need something out of it".
I mean, we used to program by creating utils directories to deal with repetitive code but models (a) find it and use it (but it cost steps and tokens to read), (b) do not realize it there and make their own version of whatever or (c) combination of both.
And ironically, i feel like we need to give up on this idea of reusable code, and literally keep things into single modules, with as minimum external dependencies. That in return reduces searches and thus steps/tokens burn. But very few agents / harnesses have proper implementation of groups/projects and sub-module structures. Aka they only open a single dir, so your then forced to create dozens of tabs > per dir > cli ...
2. That is also a issue if you open multiple agents. Maybe now your working in A, B but C, D, E are not doing anything. And their cache expires... Now you go back to D because A, B needed to be done, and now your paying Cache Write + Input cost.
Its hard to have a good flow to keep things cached, when to really /new and when to not have it expire (and that assumes there are no issue with the provider moving your session around and forcing new cache hits. MiMo did that a lot in the past).
Something that i also advice more and more to people. Get a microphone, download openwisper and talk (text to prompt). You tend to give more information vocally, then writing as its in our habit of programming to not be verbose. Its like people are afraid of long prompts. While just talking to the LLM tend to give it much more information to work with, often resulting it being able to skip steps.
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.
GPT 5.0 did feel underwhelming though.
[0] https://www.reddit.com/r/singularity/comments/1mk8tm8/gpt5_c...
maybe call it EngEmployeeBench
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.
Coding is the easiest thing for the ai to do, the rules are strict, there are plenty of working code to use as example and training data. The most important is that even an ai can decide immediately if the output is good or bad (working vs not working) and try again.
All other white collar jobs does not seem to have this immediate, automatic response loop, so the ai can only rely on a human's judgement on the quality of the work. Which is slow and unreliable. The human is not going to throw an exception while reading the ai generated scientific paper. It is inconsistent, two humans can make the opposite decisions about the same output. No way for the ai to measure auccess.
It is going to change a lot of things for everyone, no question about that, but nothing like this. Coding is obsolete. Actually writing the stuff into an editor does not make any sense.
We are now shepherds, we make sure the agents are well fed and not doing anything stupid.
For me it is amazing to watch it in action, I don't mind being a shepherd :) I'm not interested in the syntax of the languages or code formatting. Tabs or spaces? Who cares, neither. I also don't like shell scripts, batch files, yaml configs, json, xml. Watching the agent going through this shit like a tank is amazing.
The problem that I see with a black box approach is a lot of uncertainty about how long it will take to get the initial system reliably producing the correct output with acceptable performance. I could deal with that, but the bigger problem I see is the unpredictability of future effort to make changes to the system as my company grows (more traffic to the system, more/changed territory specific laws, more features). This uncertainty could be mitigated by an experienced software engineer guiding what gets built inside of the box. This guiding would include a familiarity with how the code is structured.
Even if one assumes a black box approach, what the box should actually do still requires the mindset of a good software engineer. Many product managers or non-technical parties requesting software won't think through and find problematic inconsistencies or gaps in the business rules they asked for. They won't anticipate how to frame the problem in a way that makes expanding upon the capabilities later easier.
So, I think the role would look like a blend of software/system architect + product manager + dev/LLM ops + QA. At least this would be true at a company that I would want to work for. I've always preferred working in small, strong teams where everyone has a lot of ownership. There will always be the LLM day equivalent of software produced by offshore teams in the early 2000s. I'm sure their modern-day ilk will excrete something that's good enough. I don't want to work for this type of company.
job depends on how CEO feeling about cutting NN% of headcount because of AI advancement
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).
[0] that said, population in developed countries is naturally declining so maybe it won't be a problem in the long term either; or maybe, with less need to work, people will be more inclined to raise other people, leading us to both more machines and more humans.
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.
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.
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.
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.
No it's not??? People want human made art. Abstract artists didn't paint anything technically challenging and they sold art for millions because art is about human connection and human inventiveness, not whatever prompt you could feed into an AI.
> he'll also help your company fix the mess LLMs created.
Hugely valuable. I keep a list of every PR I halted which was AI generated with AI commit summaries and AI tests and no human reviewer till me. It's very easy to convince people to keep me around. AIs are smarter than ever but the people using them and putting the prompts in and not reading the output are still as dumb as before
You're the taste and decision maker. The AI is like a really precise, fast Google, Wikipedia, codegen tool
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.
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!).
Same for HealthBench Professional and a few others.
Clearly either OpenAI is very sloppy or GPT-6 Astra is also sloppy.
Sounds about right. Alignment is important, but also being able to do mundane tasks is important too.
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.
You change when you take ownership of your past mistakes and share with those you harmed how you have done that. With some reparation and restoration of past harms. I haven’t seen openAI do that.
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.
Nahh by that time they’re going to release AGI 2.
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.
First impression: the model seems kind. Always intriguing
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.
I guess there is fine tuning (and RAG) for those that need something bigger than just the knowledge contained in the context.
Opus/Sol are easily far smarter programmers than I, and this thing supposedly blows them out of the water. Once an LLM is a better doctor, researcher, biologist, chemist, mathematician, physicist than any human is that not AGI?
It didn't arrive in the form I would have ever imagined, but it's hard to say its not (imo).
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
However, it seems like OpenAI didn't pay much attention on these perspectives and I didn't find if Astra could write a more elegant code, or communicate more naturally, etc., which made me somehow a little disappointed.
They indeed mentioned the code Astra delivered is closer to production grade but production-grade code is different from what I want since there can be a kind of messy code blowing up your whole architecture design with control flows nobody truly understands but just passes all tests perfectly. There is no difficulty in maintaining this kind of code because you only need to paste the problems into Codex. And we all know this sounds incorrect. I don't know if my appetite towards a good code (no matter how) is sound but I just imagined frontier labs to give more attention on this.
Note: fwiw Fable 5.1's release page says it's better at these perspectives of coding and per my experience, yes it is.
Looks like they listened to Terry Tao’s request for CoT in his talk on LLM use in mathematics?
So, folks that have actually used this already, what’s it actually like?
I have moved to just using Google Gemini occasionally paying for tokens, no subscription, and it is OK, given my low level of use.
I would like to add gpt-6 astra to my toolbox, but I probably would use it infrequently.
If you use a decent part of a subscription - of any tier - you could be saving a lot of money. According to YT that could even be ~80-90% less than paying for tokens but take that with a grain of salt.
Muse spark 1.3 was only released yesterday.
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.
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.
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.
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.
Some kind of Lê Chatelier principle applies where if everyone has access to AI then its value is actually diminished somewhat.
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.
Big claims, expensive and not release to the public yet.
> ScreenSpot-Pro tests whether models can locate the correct interface element in high-resolution screenshots of professional software.
> 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.
Please stand by... it will all come back shortly
All fixed now.
Oops, shots fired. A direct attack on the vibe coded app market. Replit, Lovable, etc.
It will be interesting to see how it performs in the real world ...
(I don't have access yet.)
llm "Generate an SVG of a pelican riding a bicycle" --save pelican
Then I run something like this:
llm -m gpt-6-astra -m pelican
llm logs -cu | pbcopy
I paste that into https://gist.github.com and then paste the resulting Gist URL into the URL tab on https://tools.simonwillison.net/markdown-svg-renderer
If the model supports multiple reasoning levels I run it once per level and put those in the same file.
I really should automate this a bit more.
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.
The docs page has a bunch more interesting details, including for example async tool calling!
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?
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!
Because in another dead language of antiquity, Sanskrit, it means "weapon". Which would be a bit too on-the-nose.
Seriously though the linear planetary naming is pleasant. I do hope the double meaning of "weapon", as you point out, is untrue.
Interestingly, Bruce Schnier just gave a fascinating talk about the Weapon aspect of AI, at DEFCON: https://youtu.be/eEBv0STiYhI very much worth watching
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.
I don't think it's a coincidence they launched this the week before iOS 27 launches (with new Siri).
The recent OpenAI model names are obviously based on Latin: Luna (Moon), Terra (Earth), Sol (Sun), Astra (Star).
All my issues with its leader aside, great work OpenAI!
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.
Isn't there some corporate/legal shenanigans where they become a real non-profit at that point? Or does it just let them cut Microsoft and other investors out?
There's got to be a business reason for it unrelated to the model capabillities.
When i say "AI is changning the world" its more like "I can already see how this technology will continue to become better and better and has more impact every single day. It already affects people and it will have fundamentally changed A LOT in 3-15 years"
But lets be very realistic and clear: My computer systems at home were exploitable a lot more often this year than any year before JUST because of GPT or Claude.
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?
* for a special group of customers that you're not in. Keep waiting peasant.
This is from Tibo on X.
Great first impression.
Would cancerbench be unethical?
That gives me hope that there is still areas to improve.
What a bad launch video. Hilarious.
What a powerful model.
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
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.
Like "fifth-rank junior assistant spouse in a comfort harem of an ultra-rich person".
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 :)))
But thank you for spending other peoples money to give us the tech regardless!
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.
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.
And for sure it has a tremendes amount of implications, but its not the fault of the technology (we found, not invented).
And i'm only living once, my main motivation is not to just live day in day out the same stuff, i'm quite happy to see progress.
Am i worried about the future of our planet? For sure.
“ARC-AGI-3”?
Is your bullshit detector going wild? Good, it’s working!
How is this not the most cringe marketing strat in history???
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?