GPT-6 Astra
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System Card: https://deploymentsafety.openai.com/gpt-6-astra
Related ongoing threads:
OpenAI's GPT-6 Astra on ARC-AGI-3 - https://news.ycombinator.com/item?id=49555691
GPT-6 Astra makes major gains in the Artificial Analysis Coding Agent Index - https://news.ycombinator.com/item?id=49556147
Discussion (348 Comments)Read Original on HackerNews
How about we stick to that one for talking about the rollout, and this one for talking about the model?
Regardless, the result is still valid as the original benchmark harness is definitely unreasonably handicapped, and if a harness alone can help the LLM saturate the benchmark with a near perfect score then the combination of the two must still be effectively AGI in the sense of passing the most famous benchmark designed specifically to measure AGI progress, after multiple iterations of progressively making it harder.
I think it is fair to say that this is probably effectively AGI if the benchmarks are remotely accurate - even with Fable, I've been at the point personally where I am reasonably confident that there's essentially nothing that I am better than Fable at despite generally being substantively above average on human benchmarks. If Astra's this much better than Fable, I'm ready to call AGI here.
For the many people who resist the AGI label possibly ever being achieved, I'd be curious to hear takes on what would make you think Astra is yet to be AGI, and what would still need to be achieved for this to effectively be AGI from this point forward.
When we released ARC 3, I got asked, "when do you think a frontier model will saturate it?", and I answered "in about a year, though it depends on how much it gets explicitly targeted"
That was 6 months ago, so the progress that Astra represents happened about 2x faster than I anticipated. I think the speed of progress will surprise a lot of people, and what the new models can do will challenge the views of AI that people developed by using prior generations of models.
I feel like this test is just helping someone like Sam Altman pretend like he implemented AGI as originally pitched for an IPO when in fact, he has not. Shameful.
> AGI is essentially the equivalent of a median human that could be hired as a remote co-worker... capable of performing any task that one would be satisfied with a remote colleague doing via a computer.
- Sam Altman on AGI
Probably not.
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?
To me AGI is all about the "G" general (we already had the AI part). General meaning universal, everything. It's not a function of knowledge or specific hardcoded tests, it's that you could give it a test it's never heard of before and never been trained on and it would ace it (it might need a lot of time).
Currently LLMs can't even really learn within a conversation, they can add a note to context and try to not drop it. Example things an AI cannot do yet (but maybe someday will):
- write a well-received book, write a best-seller
- come up with a new company idea, Run that company
- actually have a decent conversation, maybe someday talk somebody out of suicide effectively
- come up with its own ideas or theories that nobody else has presented
- understand the stock market well enough to trade better than an index fund
- be an expert Game Master in a TTRPG (making no mistakes, getting a read on the players' fantasies, calibrating difficulty in response to emotions)
- come up with a theory of what makes games fun, make a popular game
- be able to sort through research and come to conclusions on complex geopolitical/sociological topics (e.g. theorize on whether AGI will result in mass poverty or mass abundance and be able to argue persuasively)
- be able to articulate what it knows, what it doesn't know, and what information it would need to have to answer complex queries
- exhibit metacognition (thinking about its own thinking) and self-optimization
- wonder about things
- observe contradictions and ironies in the social-consciousness, do a standup routine that makes you rethink how you look at things
Some problems presented needs a very large context and some are not much solvable (e.g. trading) since market responds to traders' actions, as well, making it effectively an oracle problem (of computation).
On the other hand, we must be aware that these models are static, and they indeed stop when nobody asks something or requests an action from them. However, brains in nature never stops. Wonder, daydream, sleep, self-evolve, clean up and eliminate memories and views and much more.
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
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.
or are you miss the part "general intelligence" is ????
So far nobody's even shown an LLM succesfully running a high-traffic vending machine for as much as 30 days at a time.
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.
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.
[1] https://arcprize.org/blog/astra
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...
Then realize LLMs have zero of what anyone would consider intelligence.
Maybe I'm too boring but it seems quite pointless to have this same prediction game every time a new model is released.
Simple. AGI is undefinable and benchmarks are notoriously flawed.
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.
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.
You can already pretty much do this.
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
It being able to comfortably say “i don’t know how to do this” rather than boiling and ocean to pick a shell from the shore without getting wet.
No. Humans are still better at super long context learning. Once that is beat you are completely correct.
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...
[0] https://arxiv.org/pdf/2503.23674
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?
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?
They decided to use the iconic Herman Miller Eames chair if I'm not mistaken:
https://youtu.be/s5zyhGMMPKs
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.
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.
"Homer, you can't just declare Artifical General Intelligence; you need to like, make something or something...mmmmrrrhh"
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.
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.
One could use gpt-4 or gpt-5 with today's harnesses and we'd see how well that goes.
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
I wouldn't be surprised if there are some conceptual similarities to the kind of latent reasoning Anthropic sees in claude's J-space, although those aren't the same thing.
Recurrent/looped transformers themselves aren't a new concept, but it's interesting to finally see this approach show up in a frontier production model.
Canceling my Anthropic Max sub when this ships.
Also Opus 5 has been really tough to work with. I can't understand half of what it says, it's just so damn obscure.
OpenAI is 20x on both limits
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.
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.
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)
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.
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.
[0] https://arxiv.org/abs/2608.31126
[1] https://cdn.openai.com/pdf/51126fac-1b68-4128-9666-c908bcc16...
Though that's not her latest paper.
"No independent human semantic review. Whole-file sorry counts and a complete auxiliary-declaration audit are not established; separate declaration lint has not been run."
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.
[1] https://en.wikipedia.org/wiki/Pointless_topology
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.
Ditto ones that opposed Einstein’s general relativity.
Well that sounds like fun. It has become better at hiding its thoughts.
Able to generate realistic spam at arbitrary volume.
You know, the thing that was 100% correct and actually occurred.
Maybe they don't know themselves what's really going on. We are all in the interesting times gang now.
"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.
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...
[1]https://x.com/MTSlive/status/2095227056040919202
[2]https://x.com/merettm/status/2095023204993490967
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?
...why exactly are they training for that?
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.
Still, probably not that much compared to employees targeting it.
tl;dr it's 62% when apples-to-apples to other models, which is still notable.
Between $18k-40k to run a benchmark.
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...
So, folks that have actually used this already, what’s it actually like?
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.
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."
https://www.theverge.com/ai-artificial-intelligence/989601/o...
“If we fast-forward a couple of years, and we look back and say, ‘When was it, really, that AGI was created?’ I think it’s going to be about this time, and I think it might be about this model,” OpenAI president Greg Brockman said during a Thursday press briefing. Later in the call, he added, “For me personally, I do think we’re there … I think it’s not unreasonable to feel that we are now in the AGI era.”
So I think it's a bit of a misleading signal and we should wait for more independent vetting. I think the middle ground is that these are improvements worthy of the "GPT-6" label but still well short of a true "this is AGI moment" that would truly put the question to rest.
I put the cause on "not enough time". As a thought experiment, if an AI today were to (miraculously) produce a cell design template for a cell that, when injected into somebody's brains cures their Alzheimer's, how long would it take for that to reach the clinics? The actual physical tech barely exists, and let's not forget about the regulatory quagmire. So, with some optimism, I give it about four decades. In the same four decades, the same AI in the hand of unscrupulous actors could bring enough devastation so many times over that we may need to enforce a global ban on AI. In any case, I'm pretty sure we are going to get our disruptions; it's just a matter of time.
However, what's actually changed is how people perceived X because we don't have to imagine. We understand now that it doesn't require AGI so we no longer make that leap to assume it's AGI if it can do X.
It's really going to be a "I know it when I see it" situation.
Don't get me wrong, the benchmark jumps are good and I'm excited to try it, but only one or two of the benchmark jumps could be described as better than incremental.
There is the "Economic Turing Test", you let it find a job and earn money for itself. If it can do that reliably, across a wide range of jobs, that should fit most definitions of AGI.
Today's models and agents are not quite at human-level in all contexts and across all domains, but it seems to me they very clearly are generally intelligent.
If you disagree – can you name a single problem that a human can do that agent wouldn't be able to take a decent shot at which isn't limited by the hardware available it?
https://x.com/burny_tech/status/1725233117055553938
In the tweet Sam Altman is quoted as saying: "If (for example) super intelligence can't discover novel physics I don't think it's a superintelligence. And teaching it to clone the behavior of humans and human text - I don't think that's going to get there. And so there's this question which has been debated in the field for a long time: what do we have to do in addition to a language model to make a system that can go discover new physics?"
I think this is a reasonable criteria for declaring AGI. So can GPT-6 do it? OpenAI says it has helped solve long-standing open problems in mathematics. No word on novel physics.
https://www.nytimes.com/2023/11/20/podcasts/hard-fork-sam-al...
Sam Altman: Let’s say we make an A.I. that is really good, but it can’t go discover novel physics. Would you call that AGI?
Kevin Roose (New York Times): I probably would, yeah. Would you?
Sam Altman: Well, again, I don’t like the term, but I wouldn’t call that done with the mission.
It will be interesting to see how it performs in the real world ...
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.
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.
Please stand by... it will all come back shortly
All fixed now.
now that i'm a gpt subscriber maybe I'll have luck when i'm filing next year
GPT 5.0 did feel underwhelming though.
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.
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
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.
Big claims, expensive and not release to the public yet.
> 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.
[0]https://x.com/MTSlive/status/2095227056040919202
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.
https://youtu.be/xdXLzFzxA9Q?t=362
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.
The docs page has a bunch more interesting details, including for example async tool calling!
Because in another dead language of antiquity, Sanskrit, it means "weapon". Which would be a bit too on-the-nose.
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.
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.
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.
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
By 2030 all software is done and complete.
But we are going to have more and new jobs.
This is just another problem for the AI Labs to solve.
Vibe coders want a model that makes them rich, without having any actual specific idea. They write a very ambiguous prompt and expect to be amazed by the result.
Very very unrealistic and wasteful.
* for a special group of customers that you're not in. Keep waiting peasant.
Great first impression.
But thank you for spending other peoples money to give us the tech regardless!
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?