> Microsoft holds an investment in OpenAI Group PBC valued at approximately $135 billion, representing roughly 27 percent on an as-converted diluted basis
It seems like Microsoft stock is then the most straightforward way to invest in OpenAI pre-IPO.
This also confirms the $500 billion valuation making OpenAI the most valuable private startup in the world.
Now many of the main AI companies have decent ownership by public companies or are already public.
Microsoft is worth $4T, so if you buy one MSFT share only ~3% of that is invested in OpenAI. Even if OpenAI outperforms everyone's expectations (which at this point are already sky high), a tiny swing in some other Microsoft division will completely erase your gains.
Markets trade on a magical growth valuation. Nothing you said matters at all at the moment and won’t for about 5 or so years. People are going to eat shit over and over when they keep talking like this, just look at what NVDA did today. It’s not going to stop.
5 years is a pretty long time to predict with confidence. Definitely agree that "the markets can remain irrational longer than you can remain solvent", but 5 years would be unusually long if you believe we're already in a bubble.
> "Microsoft’s IP rights now exclude OpenAI’s consumer hardware."
Relevant and under-appreciated.
1. OpenAI considers its consumer hardware IP serious enough to include in the agreement (and this post)
2. OpenAI thinks it's enough of a value differentiator they'd rather go alone than through MS as a hardware partner
Also, you have to consider the size of Microsoft relative to its ownership of OpenAI, future dilution, and how Microsoft itself will fare in the future. If, say, Microsoft is on a path towards decreasing relevance/marketshare/profitability, any gains from its stake in OpenAI may be offset by its diminishing fortunes.
C#/.NET are nice. Azure/Microsoft Cloud not so nice. Idk, maybe I have some bias due to familiarity, but I find the GCP admin and tools to be so much more intuitive than the Azure (and AWS too, for that matter) counterparts.
Oh dear lord, GCP could be the intuitive one?! I have not used anything else but, dear lord, that's shocking and not at all surprising at the same time.
Yeah this is not the case at all lol. I actually find Azure to be far more intuitive after suffering through AWS and a little GCP. It certainly seems more stable in US regions than AWS.
One thing I will say is the Azure documentation is some of the most cumbersome to navigate I've ever experienced, there is a dearth of information in there, you just have to know how to find it.
yeah, this is a take I see by people who work in unix like environments (including macs). If anything Microsoft will grow much bigger. People are consolidating in Azure and away from GCP. easier to manage costs and integrate with their fleet.
Windows workstations and servers are now "joined" to Azure instead, where they used to be joined to domain controller servers. Microsoft will soon enough stop supporting that older domain controller design (soon as in a decade).
compared to what? it isn't amazing but it's alright in my experience. it works and it seems like it's designed to give lots of people job security and lots of revenue streams for Microsoft though.
Huh? Windows itself might have had it's heyday but MS is solidly at #2 for clouds only behind AWS with enterprise Windows shops that will be hard pressed to not use MS options if they go to the cloud (Google really has continued to fumble their cloud positions with their reputation for "killedbygoogle.com" nagging on everyones mind).
The biggest real threat to MS position is the Trump administration pushing foreign customers away with stuff like shutting down the ICJ Microsoft accounts, but that'll hurt AWS and Google equally much (The winners of that will be Alibaba and other foregin providers that can't compete in full enterprise stacks today).
Watch this week. Amazon cloud growth has been terrible (Google and Microsoft remains >30%). Amazon have basically no good offerings for AI which is where gcp is bringing to eat their lunch. Anthropic moving to TPU for inference is a big big signal.
100% this. The AWS of today is going to be the Hetzner or Digital Ocean of the future. They'll still have hyperscale, but will not be seen as innovating on first party products or a leader in the AI managed services industry. And frankly, they are currently doing a shit job of even this, because Oracle is in the same category and OCI has been eating everyone's lunch (for the past two years!).
Is OCI really eating everyone lunch? Sure, it's showing massive growth but that's because Oracle has been running around offering insane discounts.
We were cloud shopping, and they came by as well with REALLY good discount. Luckily our CTO was massively afraid of what would happen after that discount ran out.
Is the company valued at $500 billion or is the sum of the digital assets they’ve collateralised worth $500 billion?
Because if you buy the tokens you presumably do not own the company. And if you buy the company you hopefully don’t own the tokens - nor the assets that back the tokens.
For comparison Blackstone is worth ~$180bn with ~$1 trillion AUM.
So somehow this crypto firm and its investor think it can get a better return than Blackstone with a fraction of the assets. Now, sure, developing market and all that. But really? If it scaled to Blackstone assets level of $1 trillion then you’d expect the platform valuation to scale, perhaps not in lockstep but at least somewhat. So with $1 trillion in collateralised crypto does that make Tether worth $1.5 trillion? I’d love someone to explain that.
Tether is projected to generate $15 billion in profits. So 500 billion is like a 33 times earnings multiple.
Now the main thing is how sustainable these earnings are and if they will continue to be a dominant player in stable coins and if there will continue to be demand for them.
Another difference to Blackstone is Tether takes 100% of the returns on the treasuries backing the coins, whereas Blackstone gets a small fee from AUM, and their goal is to make money for their investor clients.
If crypto wanted to really be decentralized they'd find a way to have stable coins backed by whatever assets where the returns of the assets still came to the stable coin holder, not some big centralized company.
If my mom gives me 1000 dollars for 1% of my lemonade stand, that doesn't mean my stand is worth 100k. Tether is in talks with investors to mayb raise 20b at a 500b valuation. Keep in mind also that crypto investors overvalue companies to create the hype and then lobby for better regulations etc. It doesn't mean at all that someone would be interested to buy 100% of tether for 500b. Now, if they were public is a different story, like Tesla etc
It already has. Any tech company that is pre-IPO and still raising funding rounds is a "startup". I'm surprised there hasn't been someone to come up with a separate term for the stage of these kinds of companies.
“OpenAI is now able to release open-weight models that meet requisite capability criteria.”
Was Microsoft the blocker before? prior agreements clearly made true open-weights awkward-to-impossible without Microsoft’s sign-off. Microsoft had (a) an exclusive license to GPT-3’s underlying tech back in 2020 (i.e., access to the model/code beyond the public API), and (b) later, broad IP rights + API exclusivity on OpenAI models. If you’re contractually giving one partner IP rights and API exclusivity, shipping weights openly would undercut those rights. Today’s language looks like a carve-out to permit some open-weight releases as long as they’re below certain capability thresholds.
A few other notable tweaks in the new deal that help explain the change:
- AGI claims get verified by an independent panel (not just OpenAI declaring it).
- Microsoft keeps model/product IP rights through 2032, but OpenAI can now jointly develop with third parties, serve some things off non-Azure clouds, and—critically—release certain open-weights.
Those are all signs of loosened exclusivity.
My read: previously, the partnership structure (not just “Microsoft saying no”) effectively precluded open-weight releases; the updated agreement explicitly allows them within safety/capability guardrails.
Expect any “open-weight” drops to be intentionally scoped—useful, but a notch below their frontier closed models.
Honestly, I wouldn't be surprised if OpenAI has done the math and determined that even releasing frontier quality models wouldn't put much of a dent in either their B2B or B2C businesses. Or, rather, that any such dent would be vastly overshadowed by the value of fending off potential competitors.
I haven't looked too much into Deepseek's actual business, but at least Mistral seemed to be positioning themselves as a professional services shop to integrate their own open-weight models, compliant with EU regulations etc, at a huge premium. Any firm that has the SOA open model could do the same and cannibalize OpenAI's B2B business---perhaps even eventually pivoting into B2C---especially if regulations, downtime or security issues make firms more cloud-skeptical with respect to AI. As long as OpenAI can establish and hold the lead for best open-weight/on-premise model, it will be hard for anyone to justify premium pricing so as to generate sufficient cash flow from training their own models.
I can even imagine OpenAI eventually deciding that B2C is so much more valuable to them than B2B that it's worth completely sinking the latter market...
> Hard to say what a "longer period of time" means, but I presume it is substantial enough to make this a major concession from OpenAI.
Depends on how this is meant to be parsed but it may be parsed to be a concession from MSFT. If the total amount of revenue to be shared is the same, then MSFT is worse off here. If this is meant to parse as "a fixed proportion of revenue will be shared over X period and X period has increased to Y" then it is an OAI concession.
I don't know the details but I would be surprised if there was a revenue agreement that was time based.
As a corporate customer, the main point for me in this is Microsoft now retaining (non-exclusive) rights to models and products after OpenAI decides to declare AGI.
The question "Can we build our stuff on top of Azure OpenAI? What if SamA pulls a marketing stunt tomorrow, declares AGI and cuts Microsoft off?" just became a lot easier. (At least until 2032.)
Especially so if the Non-profit foundation doesn't retain voting control, this remains the greatest theft of all time. I still can't quite understand how it should at all be possible.
Looking at the changes for MSFT, I also mostly don't understand why they did it!
The nonprofit will own 26%, and a warrant that it will get more shares if the share price grows more than 10 times after 15 years. Sam Altman is getting no shares as part of this restructuring.
Nevermind, looks like the nn-profit gave up voting control lol:
"All equity holders in OpenAI Group now own the same type of traditional stock that participates proportionally and grows in value with OpenAI Group’s success. The OpenAI Foundation board of directors were advised by independent financial advisors, and the terms of the recapitalization were unanimously approved by the board."
Truly, truly the greatest theft from mankind in history and they dress it up as if the non-profit is doing anything other than giving away the most valuable startup in history for a paltry sum.
Credit where credit is due, Sam Altman is the greatest dealmaker of all time.
Will be interesting if we get to hear what his new equity stake is!
The non-profit controls the board of the for profit and can replace them at any time. They also serve in the for-profit board. This is according to the NY times article.
> The two companies reportedly signed an agreement [in 2023] stating OpenAI has only achieved AGI when it develops AI systems that can generate at least $100 billion in profits.
They didn't have a better definition of AGI to draw from. The old Turing test proved to not be a particularily good test. So lacking a definition money was used as a proxy. Which to me seems fair. Unless you've got a better definition of AGI that is solid enough to put in a high dollar value contract?
That's true, but the $100 billion requirement is the only hard qualification defined in earlier agreements. The rest of the condition was left to the "reasonable discretion" of the board of OpenAI. (https://archive.is/tMJoG)
It's kind of sad, but I've found myself becoming more and more this guy whenever someone "serious" brings up AI in conversation: https://www.instagram.com/p/DOELpzRDR-4/
HN has big problem with reading comprehension. First of all $100B is likely what Microsoft demanded on top of what AGI is defined by OpenAI, which is “ highly autonomous systems that outperform humans at most economically valuable work” - [0]. Secondly that is no longer part of this revised agreement, replaced with a review by a panel of experts.
I quit Google last year because I was just done with the incessant push for "AI" in everything (AI exclusively means LLMs of course). I still believe in the company as a whole, the work culture just took a hard right towards kafkaville. Nowadays when my relatives say "AI will replace X" or whatever I just nod along. People are incredibly naive and unbelievably ignorant, but that's about as new as eating wheat.
that's very different from OpenAI's previous definition (which was "autonomous systems that surpass humans in most economically valuable tasks") for at least one big reason:
This new definition likely only triggers if OpenAI's AI is substantially different or better than other companies' AI. Because in a world where 2+ companies have similar AGI, both would have huge income but the competition would mean their profit margins might not be as large. The only reason their profit would soar to 100B+ would be because of no competition, right?
It doesn't seem to say 100B a year. So presumably a business selling spoons will also eventually achieve AGI. Also good to know that the US could achieve AGI at any time by just printing more money until hyperinflation lets openai hit their target.
Nice unlock to hyperinflate their way to $100B. I'd buy an AGI spoon but preferably before hyperinflation hits. I'd expect forks to outcompete the spoons though.
This is the most sick implementation of Goodhart's Law I've ever seen.
>"When a measure becomes a target, it ceases to be a good measure"
What appalls me is that companies are doing this stuff in plain sight. In the 1920s before the crash, were companies this brazen or did they try to hide it better?
Don't worry, it'll be relevant ads, just like google. You're going to love when code output is for proprietary libraries and databases and getting things the way you want will involve annoying levels of "clarification" that'll be harder and harder to use.
I kind of meant this as a joke as I typed this, but by the end almost wanted to quit the tech industry all together.
Just download a few SOTA (free) open-weights models well ahead of that moment and either run them from inside your living-room or store them onto a (cheap) 2TB external hard drive until consumer compute makes it affordable to run them from your living room.
No. When you're thinking about questions like these, it is useful to remember that multiple (probably dozens) professional A-grade lawyers have been paid considerable sums of actual money, by both sides, to think about possible loopholes and fix them.
>This is an important detail because Microsoft loses access to OpenAI’s technology when the startup reaches AGI, a nebulous term that means different things to everyone.
I think some of this is just the typical bluster of company press releases / earnings reports. Can't ever show weakness or the shareholders will leave. Can't ever show doubt or the stock price will drop.
Nevertheless, I've been wondering of late. How will we know when AGI is accomplished? In the books or movies, it's always been handwaved or described in a way that made it seem like it was obvious to all. For example, in The Matrix there's the line "We marveled at our own magnificence as we gave birth to AI." It was a very obvious event that nobody could question in that story. In reality though? I'm starting to think it's just going to be more of a gradual thing, like increasing the resolution of our TVs until you can't tell it's not a window any longer.
It's certainly not an specific thing that can be accomplished. AGI is a useful name for a badly defined concept, but any objective application of it (like in a contract) is just stupid things done by people that could barely be described as having the natural variety of GI.
If I remember correctly, Microsoft was previously promised ownership of every pre-AGI asset created by OpenAI. Now they are being promised ownership of things post-AGI as well:
Microsoft’s IP rights for both models and products are extended through 2032 and now includes models post-AGI...
To me, this suggests a further dilution of the term "AGI."
To be honest, I think this is somewhat assymetric, and kind of implies that openai are truer "Believers" than Microsoft.
If you believe in a hard takeoff, than ownership of assets post agi is pretty much meaningless, however, it protects Microsoft from an early declaration of agi by openai.
OpenAI wants to be free from MS. The cost is 27% of ownership, which is about $135B currently, plus IP access until 2032. Considered MS invested about $10B initially, that’s a big concession on the part of OpenAI.
OpenAI’s Jakob Pachocki said on a call today that he expects that AI is “less than a decade away from superintelligence”
I think the more interesting question is who will be on the panel?
A group of ex frontier lab employees? You could declare AGI today. A more diverse group across academia and industry might actually have some backbone and be able to stand up to OpenAI.
It's quite possible that GI and thus AGI does not actually exist. Though now the paper the other day by all those heavy hitters in the industry makes more sense in this context.
> Once AGI is declared by OpenAI, that declaration will now be verified by an independent expert panel.
What were they really expecting as an alternative? Anyone can "declare AGI" especially since it's an inherently ill-defined (and agruably undefinable) concept, it's strange that this is the first bullet point like this was the fruit of intensive deliberation.
I don't fully understand what is going on in this market as a whole, I really doubt anyone does, but I do believe we will look back on this period and wonder what the hell we were thinking believing and lapping up everything these corporations were putting out.
Wasn't there already a report that stated Microsoft and OpenAI understand AGI as something like 100 billion dollars in revenue for the purpose of their agreements? Even that seems like a pipe dream at the moment.
SAE automation levels are the industry standard, not FSD (which is a brand name), and FSD is clearly Level 2 (driver is always responsible and must be engaged, at least in consumer teslas, I don't know about robotaxis). The question is if "AGI" is as well defined as "Level 5" as an independent standard.
The point trying to be made is FSD is deceptive marketing, and it's unbelievable how long that "marketing term" has been allowed to exist given its inaccuracy in representing what is actually being delivered to the customer.
What's deceptive? What in the term "Full Self Driving" makes you think that your car will drive itself fully? It's fully capable of facilitating your driving of yourself, clearly.
They won’t be able to. The whole idea of the panel is because of conflict of interests between MS and OpenAI, as MS won’t get revenue share post AGI declaration. MS will want it to be as high a bar as possible.
I agree: it is more than faintly infuriating that when people say AI what the vast majority mean is LLMs.
But, at the same time, we have clearly passed a significant inflection point in the usefulness of this class of AI, and have progressed substantially beyond that inflection point as well.
So I don't really buy into the idea tha OpenAI have gone out of their way to foist a watered down view of AI upon the masses. I'm not completely absolving them but I'd probably be more inclined to point the finger at shabby and imprecise journalism from both tech and non-tech outlets, along with a ton of influencers and grifters jumping on the bandwagon. And let's be real: everyone's lapped it up because they've wanted to - because this is the first time any of them have encountered actually useful AI of any class that they can directly interact with. It seems powerful, mysterious, perhaps even agical, and maybe more than a little bit scary.
As a CTO how do you think it would have gone if I'd spent my time correcting peers, team members, consultants, salespeople, and the rest to the effect that, no, this isn't AI, it's one type of AI, it's an LLM, when ChatGPT became widely available? When a lot of these people, with no help or guidance from me, were already using it to do useful transformations and analyses on text?
It would have led to a huge number of unproductive and timewasting conversation, and I would have seemed like a stick in the mud.
Sometimes you just have to ride the wave, because the only other choice is to be swamped by it and drown.
Regardless of what limitations "AGI" has, it'll be given that monicker when a lot of people - many of them laypeople - feel like it's good enough. Whether or not that happens before the current LLM bubble bursts... tough to say.
What exactly is the criteria for "expert" they're planning to use, and whomst among us can actually meet a realistic bar for expertise on the nature of consciousness?
Type error: why do you need an expert on consciousness to weigh in on if something is AGI or not? I don't care what it feels like to be a paperclip maximizer I just care to not have my paperclips maximized tnx.
I don't see why anyone would consider the state of AI today to be AGI? it's basically a glorified generator stuck to a query engine
today's models are not able to think independently, nor are they conscious or able to mutate themselves to gain new information on the fly or make memories other than half baked solutions with putting stuff in the context window which just makes it use that to generate stuff related to it, imitating a story basically.
they're powerful when paired with a human operator, I.e. they "do" as told, but that is not "AGI" in my book
Check out the article. He’s not crazy. It comes down to clear definitions. We can talk about AGI for ages, but without a clear meaning, it’s just opinion.
For a long time the turing test was the bar for AGI.
Then it blew past that and now, what I think is honestly happening, is that we don't really have the grip on "what is intelligence" that we thought we had. Our sample size for intelligence is essentially 1, so it might take a while to get a grip again.
The commercial models are not designed to win the imitation game (that is what Allan Turing named it). In fact the are very likely to loose every time.
One thing they acknowledge but glance over, is the autonomy of current systems. When given more open ended, long term tasks, LLMs seem to get stuck at some point and get more and more confused and stop making progress.
This last problem may be solved soon, or maybe there's something more fundamental missing that will take decades to solve. Who knows?
But it does seem like the main barrier to declaring current models "general" intelligence.
> If you described all the current capabilities of AI to 100 experts 10 years ago, they’d likely agree that the capabilities constitute AGI.
I think that we're moving the goalposts, but we're moving them for a good reason: we're getting better at understanding the strengths and the weaknesses of the technology, and they're nothing like what we'd have guessed a decade ago.
All of our AI fiction envisioned inventing intelligence from first principles and ending up with systems that are infallible, infinitely resourceful, and capable of self-improvement - but fundamentally inhuman in how they think. Not subject to the same emotions and drives, struggling to see things our way.
Instead, we ended up with tools that basically mimic human reasoning, biases, and feelings with near-perfect fidelity. And they have read and approximately memorized every piece of knowledge we've ever created, but have no clear "knowledge takeoff path" past that point. So we have basement-dwelling turbo-nerds instead of Terminators.
This makes AGI a somewhat meaningless term. AGI in the sense that it can best most humans on knowledge tests? We already have that. AGI in the sense that you can let it loose and have it come up with meaningful things to do in its "life"? That you can give it arms and legs and watch it thrive? That's probably not coming any time soon.
I don't think the Turing Test has been passed. The test was setup such that the interrogator knew that one of the two participants was a bot, and was trying to find out which. As far as I know, it's still relatively easy to find out you're talking to an LLM if you're actively looking for it.
Note that most tests where they actually try to pass the Turing Test (as opposed to being a useful chatbot) they do things like prompt it with a personality etc.
We really really Really should Not define as our success function for AI (our future-overlords?) the ability of computers to deceive humans about what they are.
The Turing Test was a clever twist on (avoiding) defining intelligence 80 years ago.
Going forward, valuing it should be discarded post-haste by any serious researcher or engineer or message-board-philosopher, if not for ethical reasons then for not-promoting spam/slop reasons.
1) Look for spelling, grammar, and incorrect word usage; such as where vs were, typing out where our should be used.
2) Ask asinine questions that have no answers; _Why does the sun ravel around my finger in low quality gravity while dancing in the rain?_
ML likes to always come up with an answers no matter what. Human will shorten the conversation. It also is programmed to respond with _I understand_, _I hear what you are saying_, and make heavy use of your name if it has access to it. This fake interpersonal communication is key.
Conventional LLM chatbots behave the way you describe because their goal during training is to as much as possible impersonate an intelligent assistant.
Do you think this goal during training cannot be changed to impersonate someone normal such that you cannot detect you are chatting with an LLM?
Before flight was understood some thought "magic" was involved. Do you think minds operate using "magic"? Are minds not machines? Their operation can not be duplicated?
> Do you think this goal during training cannot be changed to impersonate someone normal such that you cannot detect you are chatting with an LLM?
I don't think so, because LLMs hallucinate by design, which will always produce oddities.
> Before flight was understood some thought "magic" was involved. Do you think minds operate using "magic"? Are minds not machines? Their operation can not be duplicated?
Might involve something we don't grasp, but despite that: only because something moves through air it's not flying and will never be, just like a thrown stone.
Maybe current LLMs can do that. But none are, so it hasn't passed. Whether that's because of economic or marketing reasons as opposed to technical does not matter. You still have to pass the test before we can definitely say you've passed the test.
Overall I'd say the easiest is just overall that the models always just follow what you say and transform it into a response. They won't have personal opinions or experiences or anything, although they can fake it. it's all just a median expected response to whatever you say.
And the "agreeability" is not a hallucination, it's simply the path of least resistance, as in, the model can just take information that you said and use that to make a response, not to actually "think" and consider I'd what you even made sense or I'd it's weird or etc.
They almost never say "what do you mean?" to try to seek truth.
This is why I don't understand why some here claim that AGI being already here is some kind of coherent argument. I guess redefining AGI is how we'll reach it
I agree with your points in general but also, when I plugged in the parent comment's nonsense question, both Claude 4.5 Sonnet and GPT-5 asked me what I meant, and pointed out that it made no sense but might be some kind of metaphor, poem, or dream.
The Turing Test was a pretty early metric and more of a thought experiment.
Let's be real guys, it was created by Turing. The same guy who built the first general purpose computer. Man was without a doubt a genius, but it also isn't that reasonable to think he'd come up with a good definition or metric for a technology that was like 70 years away. Brilliant start, but it is also like looking at Newton's Laws and evaluating quantum mechanics based off of that. Doesn't make Newton dumb, just means we've made progress. I hope we can all agree we've made progress...
And arguably the Turing Test was passed by Eliza. Arguably . But hey, that's why we refine and make progress. We find the edge of our metrics and ideas and then iterate. Change isn't bad, it is a necessary thing. What matters is the direction of change. Like velocity vs speed.
As far as I know, it's still relatively easy to find out you're talking to an LLM if you're actively looking for it.
People are being fooled in online forums all the time. That includes people who are naturally suspicious of online bullshittery. I'm sure I have been.
Stick a fork in the Turing test, it's done. The amount of goalpost-moving and hand-waving that's necessary to argue otherwise simply isn't worthwhile. The clichéd responses that people are mentioning are artifacts of intentional alignment, not limitations of the technology.
I feel like you're skipping over the "if you're actively looking for it" bit. You can call it goalpost-moving, or you can check the original paper by Turing and see that this is exactly how he defined it in the first place.
people are being fooled, but not being given the problem: "one of these users is a bot, which one is which"
a problem similar to the turing test, "0 or more of these users is a bot, have fun in a discussion forum"
but there's no test or evaluation to see if any user successfully identified the bot, and there's no field to collect which users are actually bots, or partially using bots, or not at all, nor a field to capture the user's opinions about whether the others are bots
Then there's the fact that the Turing test has always said as much about the gullibility of the human evaluator as it has about the machine. ELIZA was good enough to fool normies, and current LLMs are good enough to fool experts. It's just that their alignment keeps them from trying very hard.
The turing test point is actually very interesting, because it's testing whether you can tell you're talking to a computer or a person. When Chatgpt3 came out we all declared that test utterly destroyed. But now that we've had time to become accustomed and learn the standard syntax, phraseology, and vocabulary of the gpt's, I've started to be able to detect the AI's again. If humanity becomes completely accustomed to the way AI talks to be able to distinguish it, do we re enter the failed turing test era? Can the turing test only be passed in finite intervals, after which we learn to distinguish it again? I think it can eventually get there, and that the people who can detect the difference becomes a smaller and smaller subset. But who's to say what the zeitgeist on AI will be in a decade
> When Chatgpt3 came out we all declared that test utterly destroyed.
No, I did not. I tested it with questions that could not be answered by the Internet (spatial, logical, cultural, impossible coding tasks) and it failed in non-human-like ways, but also surprised me by answering some decently.
Jesus, we've gone from Eliza and Bayes Spam Filters to being able to hold an "intelligent" conversation with a bot that can write code like: "make me a sandwich" => "ok, making sandwich.py, adding test, keeping track of a todo list, validating tests, etc..."
We might not _quite_ be at the era of "I'm sorry I can't let you do that Dave...", but on the spectrum, and from the perspective of a lay-person, we're waaaaay closer than we've ever been?
I'd counsel you to self-check what goalposts you might have moved in the past few years...
I think this says more about how much of our tasks and demonstrations of ability as developers revolve around boilerplate and design patterns than it does about the Cognitive abilities of modern LLMs.
I say this fully aware that a kitted out tech company will be using LLMs to write code more conformant to style and higher volume with greater test coverage than I am able to individually.
I'd counsel you to work with LLMs daily and agree that we're no where close to LLMs that work properly consistently outside of toy use cases, where examples can be scraped from the internet. If we can agree on that we can agree that General Intelligence is not the same thing as a, sometimes, seemingly random guess at the next word...
I think "we" have accidentally cracked language from a computational perspective. The embedding of knowledge is incidental and we're far away from anything that "Generally Intelligent", let alone Advanced in that. LLMs do tend to make documented knowledge very searchable which is nice. But if you use these models everyday to do work of some kind that becomes pretty obvious that they aren't nearly as intelligent as they seem.
They're about as smart as a person who's kind of decent at every field. If you're a pro, it's pretty clear when it's BSing. But if you're not, the answers are often close enough.
And just like humans, they can be very confidently wrong. When any person tells us something, we assume there's some degree of imperfection in their statements. If a nurse at a hospital tells you the doctor's office is 3 doors down on the right, most people will still look at the first and second doors to make sure those are wrong, then look at the nameplate on the third door to verify that it's right. If the doctor's name is Smith but the door says Stein, most people will pause and consider that maybe the nurse made a mistake. We might also consider that she's right, but the nameplate is wrong for whatever reason. So we verify that info by asking someone else, or going in and asking the doctor themselves.
As a programmer, I'll ask other devs for some guidance on topics. Some people can be absolute geniuses but still dispense completely wrong advice from time to time. But oftentimes they'll lead me generally in the right way, but I still need to use my own head to analyze whether it's correct and implement the final solution myself.
The way AI dispenses its advice is quite human. The big problem is it's harder to validate much of its info, and that's because we're using it alone in a room and not comparing it against anyone else's info.
> They're about as smart as a person who's kind of decent at every field. If you're a pro, it's pretty clear when it's BSing. But if you're not, the answers are often close enough.
No they are not smart at all. Not even a little. They cannot reason about anything except that their training data overwhelmingly agrees or disagrees with their output nor can they learn and adept. They are just text compression and rearrangement machines. Brilliant and extremely useful tooling but if you use them enough it becomes painfully obvious.
Something about an LLM response has a major impact on some people. Last weekend I was in in Ft. Lauderdale FL with a friend who's pretty sharp ( licensed architect, decades long successful career etc) and went to the horse track. I've never been to a horse race and didn't understand the betting so I took a snapshot of the race program, gave it to chatGPT and asked it to devise a low risk set of bets using $100. It came back with what you'd expect, a detailed, very confident answer. My friend was completely taken with it and insisted on following it to the letter. After the race he turned his $100 into $28 and was dumbfounded. I told him "it can't tell the future, what were you expecting?". Something about getting the answer from a computer or the level of detail had him convinced it was a sure thing. I donm't understand it but LLMs have a profound effect on some people.
edit: i'm very thankful my friend didn't end up winning more than he bet. idk what he would have done if his feelings towards the LLM was confirmed by adding money to his pocket..
If anything, the main thing LLMs are showing is that the humans need to be pushed to up their game. And that desire to be better, I think, will yield an increase in supply of high-quality labour than what exists today. Ive personally witnessed so many 'so-so' people within firms who dont bring anything new to the table and focus on rent seeking expenditures (optics) who frankly deserve to be replaced by a machine.
E.g. I read all the time about gains from SWEs. But nobody questions how good of a SWE they even are. What proportion of SWEs can be deemed high quality?
Yes, exactly. LLMs are lossy compressors of human language in much the same way JPEG is a lossy compressor of images. The difference is that the bits that JPEG throws away were manually designed by our understanding of the human visual cortex, while LLMs figured out the lossy bits automatically because we don't know enough about the human language processing chain to design that manually.
LLMs are useful but that doesn't make them intelligent.
Completely agree (https://news.ycombinator.com/item?id=45627451) - LLMs are like the human-understood output of a hypothetical AGI, 'we' haven't cracked the knowledge & reasoning 'general intelligence' piece yet, imo, the bit that would hypothetically come before the LLM, feeding the information to it to convey to the human. I think that's going to turn out to be a different piece of the puzzle.
Most people didn't think we were anywhere close to LLM's five years ago. The capabilities we have now were expected to be a decades away, depending on who you talked to. [EDIT: sorry, I should have said 10 years ago... recent years get too compressed in my head and stuff from 2020 still feels like it was 2 years ago!]
So I think a lot of people now don't see what the path is to AGI, but also realize they hadn't seen the path to LLM's, and innovation is coming fast and furious. So the most honest answer seems to be, it's entirely plausible that AGI just depends on another couple conceptual breakthroughs that are imminent... and it's also entirely plausible that AGI will require 20 different conceptual breakthroughs all working together that we'll only figure out decades from now.
True honesty requires acknowledging that we truly have no idea. Progress in AI is happening faster than ever before, but nobody has the slightest idea how much progress is needed to get to AGI.
What people thought about LLMs five years ago, and how close we are to AGI right now are unrelated, and it's not logially sound to say "We were close to LLMs then, so we are close to AGI now."
It's also a misleading view of the history. It's true "most people" weren't thinking about LLMs five years ago, but a lot of the underpinnings had been studied since the 70s and 80s. The ideas had been worked out, but the hardware wasn't able to handle the processing.
> True honesty requires acknowledging that we truly have no idea. Progress in AI is happening faster than ever before, but nobody has the slightest idea how much progress is needed to get to AGI.
> Most people didn't think we were anywhere close to LLM's five years ago.
That's very ambiguous. "Most people" don't know most things. If we're talking about people that have been working in the industry though, my understanding is that the concept of our modern day LLMs aren't magical at all. In fact, the idea has been around for quite a while. The breakthroughs in processing power and networking (data) were the hold up. The result definitely feels magical to "most people" though for sure. Right now we're "iterating" right?
I'm not sure anyone really see's a clear path to AGI if what we're actually talking about is the singularity. There are a lot of unknown unknowns right?
AGI is a poorly defined concept because intelligence is a poorly defined concept. Everyone knows what intelligence is... until we attempt to agree on a common definition.
Not sure what history you're suggesting I check? I've been following NLP for decades. Sure, neural nets have been around for many decades. Deep learning in this century. But the explosive success of what LLM's can do now came as a huge surprise. Transformers date to just 2017, and the idea that they would be this successful just with throwing gargantuan amounts of data and processing at them -- this was not a common viewpoint. So I stand by the main point of my original comment, except I did just now edit it to say 10 years ago rather than 5... the point is, it really did seem to come out of nowhere.
GPT3 existed 5 years ago, and the trajectory was set with the transformers paper. Everything from the transformer paper to GPT3 was pretty much speculated in the paper, it just took people spending the effort and compute to make it reality. The only real surprise was how fast openai producterized an LLM into a chat interface with chatgpt, before then we had finetuned GPT3 models doing specific tasks (translation, summarization, etc.)
At this point, AGI seems to be more of a marketing beacon than any sort of non-vague deterministic classification.
We all thought about a future where AI just woke up one day, when realistically, we got philosophical debates over whether the ability to finally order a pizza constitutes true intelligence.
Notwithstanding the fact that AGI is a significantly higher bar than "LLM", this argument is illogical.
Nobody thought we were anywhere closer to me jumping off the Empire State Building and flying across the globe 5 years ago, but I'm sure I will. Wish me luck as I take that literal leap of faith tomorrow.
what's super weird to me is how people seem to look at LLM output and see:
"oh look it can think! but then it fails sometimes! how strange, we need to fix the bug that makes the thinking no workie"
instead of:
"oh, this is really weird. Its like a crazy advanced pattern recognition and completion engine that works better than I ever imagined such a thing could. But, it also clearly isn't _thinking_, so it seems like we are perhaps exactly as far from thinking machines as we were before LLMs"
Well the difference between those two statements is obvious. One looks and feels, the other processes and analyzes. Most people can process and analyze some things, they're not complete idiots most of the time. But also most people cannot think and analyze the most ground breaking technological advancement they might've personally ever witnessed, that requires college level math and computer science to understand. It's how people have been forever, electricity, the telephone, computers, even barcodes. People just don't understand new technologies. It would be much weirder if the populace suddenly knew exactly what was going on.
And to the "most groundbreaking blah blah blah", i could argue that the difference between no computer and computer requires you to actually understand the computer, which almost no one actually does. It just makes peoples work more confusing and frustrating most of the time. While the difference between computer that can't talk to you and "the voice of god answering directly all questions you can think of" is a sociological catastrophic change.
Why should LLM failures trump successes when determining if it thinks/understands? Yes, they have a lot of inhuman failure modes. But so what, they aren't human. Their training regimes are very dissimilar to ours and so we should expect alien failure modes owing to this. This doesn't strike me as good reason to think they don't understand anything in the face of examples that presumably demonstrate understanding.
Because there's no difference between a success and failure as far as an LLM is concerned. Nothing went wrong when the LLM produced a false statement. Nothing went right when the LLM produced a true statement.
It produced a statement. The lexical structure of the statement is highly congruent with its training data and the previous statements.
This argument is vacuous. Truth is always external to the system. Nothing goes wrong inside the human when he makes an unintentionally false claim. He is simply reporting on what he believes to be true. There are failures leading up to the human making a false claim. But the same can be said for the LLM in terms of insufficient training data.
>The lexical structure of the statement is highly congruent with its training data and the previous statements.
This doesn't accurately capture how LLMs work. LLMs have an ability to generalize that undermines the claim of their responses being "highly congruent with training data".
By that logic, I can conclude humans don't think, because of all the numerous times out 'thinking fails'.
I don't know what else to tell you other than this infallible logic automaton you imagine must exist before it is 'real intelligence' does not exist and has never existed except in the realm of fiction.
> Once AGI is declared by OpenAI, that declaration will now be verified by an independent expert panel.
I always like the phrase, "follow the money", in situations like this. Are OpenAI or Microsoft close to AGI? Who knows... Is there a monetary incentive to making you believe they are close to AGI? Absolutely. Take in this was the first bullet point in Microsoft's blog post.
this is something I think about. state of the art in self driving cars still makes mistakes that humans wouldn't make, despite all the investment into this specific problem.
This bodes very poorly for AGI in the near term, IMO
If you use 'multimodal transformer' instead of LLM (which most SOTA models are), I don't think there's any reason why a transformer arch couldn't be trained to drive a car, in fact I'm sure that's what Tesla and co. are using in their cars right now.
I'm sure self-driving will become good enough to be commercially viable in the next couple years (with some limitations), that doesn't mean it's AGI.
There is a vast gulf between "GPT-5 can drive a car" and "a neural network using the transformer architecture can be trained to drive a car". And I see no proof whatsoever that we can, today, train a single model that can both write a play and drive a car. Even less so one that could do both at the same time, as a generally intelligent being should be able to.
If someone wants to claim that, say, GPT-5 is AGI, then it is on them to connect GPT-5 to a car control system and inputs and show that it can drive a car decently well. After all, it has consumed all of the literature on driving and physics ever produced, plus untold numbers of hours of video of people driving.
>There is a vast gulf between "GPT-5 can drive a car" and "a neural network using the transformer architecture can be trained to drive a car".
The only difference between the two is training data the former lacks that the latter does so not a 'vast gulf'.
>And I see no proof whatsoever that we can, today, train a single model that can both write a play and drive a car.
You are not making a lot of sense here. You can have a model that does both. It's not some herculean task. it's literally just additional data in the training run. There are vision-language-action models tested on public roads.
> single model that can both write a play and drive a car.
It would be a really silly thing to do, and probably there are engineering subletities as to why this would be a bad idea, but I don't see why you couldn't train a single model to do both.
It's not silly, it is in fact a clear necessity to have both of these for something to be even close to AGI. And you additionally need it trained on many other tasks - if you believe that each task requires additional parameters and additional training data, then it becomes very clear that we are nowhere near to a general intelligence system; and it should also be pretty clear that this will not scale to 100 tasks with anything similar to the current hardware and training algorithms.
In the initial contract Microsoft would lose a lot of rights when OpenAI achieves AGI. The references to AGI in this post, to me, look like Microsoft protecting themselves from OpenAI declaring _something_ as AGI and as a result Microsoft losing the rights
I don't see the mentions in this post as anyone particularly believing we're close to AGI
Wasn't it always the explicit goal of OpenAI to bring up AGI? So less of a meme, and more "this is what that company exists for".
Bit like blaming a airplane building company for building airplanes, it's literally what they were created for, no matter how stupid their ideas of the "ideal aircraft" is.
Of course not, then we'd never hear the end of it :)
I was just informing that the company always had AGI as a goal, even when they were doing the small Gym prototypes and all of that stuff that made the (tech) news before GPT was a thing.
I think AGI isn't the main thing. The agreement gives msft the right to develop their own foundation models, OpenAI to stop using Azure for running & training their foundation models. All this while msft still retains significant IP ownership.
In my opinion, whether AGI happens or not isn't the main point of this. It's the fact that OpenAI and MSFT can go their separate ways on infra & foundation models while still preserving MSFT's IP interests.
Yes. Some ai skeptical people (eg Tyler Cowen, who does not think AI will have a significant economic impact) think gpt5 is AGI.
It was news when dwarkesh interviewed Karpathy who said per his definition of AGI, he doesn't think it will occur until 2035. Thus, if karpathy is pessimistic, then many people working in AI today think we will have agi by 2032 (and likely sooner, eg end of 2028)
Depends on how you define AGI - if you define it as an AI that can learn to perform generalist tasks - then yes, transformers like GPT 5 (or 3) are AGI as the same model can be trained to do every task and it will perform reasonably well.
But I guess what most people would consider AGI would be something capable of on-line learning and self improvement.
I don't get the 2035 prediction though (or any other prediction like this) - it implies that we'll have some magical breakthrough in the next couple years be it in hardware and/or software - this might happen tomorrow, or not any time soon.
If AGI can be achieved using scaling current techniques and hardware, then the 2035 date makes sense - moores law dictates that we'll have about 64x the compute in hardware (let's add another 4x due to algorithmic improvements) - that means that 250x the compute will give us AGI - I think with ARC-AGI 2 this was the kind of compute budget they spent to get their models to perform on a human-ish level.
Also perf/W and perf/$ scaling has been slowing in the past decade, I think we got like 6x-8x perf/W compared to a decade ago, which is a far cry than what I wrote here.
Imo it might turn out that we discover 'AGI' in the sense that we find an algorithm that can turn FLOPS to IQ that scales indefinitely, but is very likely so expensive to run, that biological intelligences will have a huge competitive edge for a very long time, in fact it might be that biology is astronomically more efficient in turning Watts to IQ than transistors will ever be.
> I think with ARC-AGI 2 this was the kind of compute budget they spent to get their models to perform on a human-ish level.
It was ARC-AGI-1 that they used extreme computing budgets to get to human-ish level performance. With ARC-AGI-2 they haven't gotten past ~30% correct. The average human performance is ~65% for ARC-AGI-2, and a human panel gets 100% (because humans understand logical arguments rather than simply exclaiming "you're absolutely right!").
Most of the things that the public — even so-called “AI experts” — consider “magic” are still within the in-sample space. We are nowhere near the out-of-sample space yet. Large Language Models (LLMs) still cannot truly extrapolate. It’s somewhat like living in America and thinking that America is the entire world.
My L7 and L8 colleagues at Google seem to be signaling next 2 years. Errors of -1 and +20 years. But the mood sorta seems like nobody wants to quit when they're building the test stand for the Trinity device.
... and it will turn into a "technically true" rat race between the main players on what the definition is exactly while you can ask any person on the street with no skin in the game who will tell you that this is nowhere near the intuitive understanding of what AGI is - as it it's not measured by scores but instead of how real and self-aware your counterpart "feels" to you.
If someone is able to come up with true AGI, why even announce it? Instead, just use it to remake a direct clone of Google, or a direct clone of Netflix, or a direct clone of any of these other software corporations. IMO if anyone was anywhere close to something even remotely touching AGI, they would keep their mouth shut tighter than Fort Knox.
> Does anyone really think we are close to AGI? I mean honestly?
I'd say we're still a long way from human level intelligence (can do everything I can do), which is what I think of as AGI, but in this case what matters is how OpenAI and/or their evaluation panel define it.
OpenAI's definition used to be, maybe still is, "able to do most economically valuable tasks", which is so weak and vague they could claim it almost anytime.
Since we don't have an authoritative definition of what it means that companies will agree to, and tests like the turing test that must be passed in order to be considered AGI, I don't think we're anywhere near what we all in our brains think AGI is or could be. On the other hand, AI fatigue will continue until the next big thing takes the spotlight from AI for a while, until we reach true AGI (whatever that is).
I think their definition of AGI is just about how many human jobs can be replaced with their compute. No scientific or algorithmic breakthroughs needed, just spending and scaling dumb LLMs on massive compute.
Shouldn't it mean all jobs? If there are jobs it can't replace then that doesn't sound very generally intelligent. If it's got general intelligence it should be able to learn to do any job, no?
For example an AGI AI could give you a detailed plan that tells you exactly how to do any and every task. But it might not be able to actually do the task itself, for example manual labor jobs for which an AI simply cannot do unless it also "builds" itself a form-factor to be able to do the job.
The AGI could also just determine that it's cheaper to hire a human than to build a robot at any given point for a job that it can't yet do physically and it would be the AGI
I think might even be simpler than that. It's about the cost. Nobody is going to pay for AI to replace humans if it costs more.
All of us in this sub-thread consider ourselves "AGI", but we cannot do any job. In theory we can, I guess. But in practical terms, at what cost? Assuming none of us are truck drivers, if someone was looking for a truck driver, they wouldn't hire us because it take too long for us to get a license, certified, learn, etc. Even though in theory we probably do it eventually.
LLM derived AGI is possible but LLM by itself is not the answer. The problem I see right now is that because there’s so much money at stake, we’ve effectively spread out core talent across many organizations. It used to be Google and maybe Meta. We need a critical mass of talent (think Manhattan Project). It doesn't help that the Chinese pulled a lot of talent back home because a big chunk of early successes and innovations came from those people that we, the US, alienated.
My definition of AGI is when AI doesn't need humans anymore to create new models (to be specific, models that continue the GPT3 -> GPT4 -> GPT5 trend).
By my definition, once that happens, I don't really see a role for Microsoft to play. So not sure what value their legal deal has.
The key steps will be going beyond just the neural network and blurring the line between training and inference until it is removed. (Those two ideas are closely related).
Pretending this isn't going to happen is appealing to some metaphysical explanation for the existence of human intelligence.
Why not? They're using Artificial Intelligence to describe token-prediction text generators which clearly have no "intelligence" anywhere near them, so why not re-invent machine learning or something and call it AGI?
We will achieve AGI when they decide it is AGI (I dont believe for a second this independent expert panel wont be biased). And it won’t matter if you call their bluff, because the world doesnt give a shit about truth anymore.
The best test would be when your competitors can't say what you have isn't AGI. If no one, not even your arch biz enemies, can seriously claim you have not achieved AGI then you probably have it.
Maybe in a few decades, people will look back at how naive it was to talk about AGI at this point, just like the last few times since the 1960s whenever AI had a (perceived) breakthrough. It's always a few decades away.
That is stupid. It would be possible to be infinitely arbitrary to the point of “AGI” never being reachable by some yard sticks while still performing most viable labor.
So why are your arbitrary yard sticks more valid than someone elses?
Probable the biggest problem as others have stated is that we can’t really define intelligence more precisely than that it is something most humans have and all rocks don’t. So how could any definition for AGI be any more precise?
>It would be possible to be infinitely arbitrary to the point of “AGI” never being reachable by some yard sticks while still performing most viable labor.
"Most viable labor" involves getting things from one place to another, and that's not even the hard part of it.
In any case, any sane definition of general AI would entail things that people can generally do.
ok but have you asked your Tesla to write you a mobile app? AGI would be able to do both. (the self-driving thing is just an example of something AGI would be able to do but an LLM can't)
Rest assured, your friends driving was the same quality as the average drunk grandma on the road if they were exclusively using Tesla's "FSD" with no intervention for hours. It drives so piss poorly that I have to frequently intervene even on the latest beta software. If I lived in a shoot happy state like Texas I'm sure that a road rager would have put a bullet hole somewhere in my Tesla by now if I kept driving like that.
There's a difference between "I survived" and "I drive anywhere close to the quality of the average American" - a low bar and one that still is not met by Tesla FSD.
Yeah, and let's not forget that "I drive like a mildly blind idiot" is only a viable (literally) choice when everyone else doesn't do that and compensates for your idiocy.
It's one skill almost everyone on the planet can learn exceptionally easily - which Waymo is on pace to master, but a generalized LLM by itself is still very far from.
OP said all yardsticks and I said that was infinitely arbitrary… because it literally is infinitely arbitrary. You can conjure up an infinite amount of yardsticks.
As far as driving itself goes as a yardstick, I just don’t find it interesting because we literally have Waymo’s orbiting major cities and Teslas driving on the roads already right now.
If that’s the yardstick you want to use, go for it. It just doesn’t seem particularly smart to hang your hat on that one as your Final Boss.
It also doesn’t seem particularly useful for defining intelligence itself in an academic sort of way because even humans struggle to drive well in many scenarios.
But hey if that’s what you wanna use don’t let me stop you, sure, go for it. I have feeling you’ll need new goalposts relatively soon if you do, though.
And using humans as 'the benchmark' is risky in itself as it can leave us with blind spots on AI behavior. For example we find humans aren't as general as we expected, or the "we made the terminator and it's exterminating mankind, but it's not AGI because it doesn't have feelings" issues.
> The vast majority of humans can be taught to drive
the key is being able to drive and learn another language and learn to play an instrument and do math and, finally, group pictures of their different pets together. AGI would be able to do all those things as well... even teach itself to do those things given access to the Internet. Until that happens then no AGI.
It sure must feel like 2018 was a long time ago when that's more than the entirety of your adult life. I get it.
The rest of us aren't that excited to trust our lives to technology that confidently drove into a highway barrier at high speed, killing the driver in a head-on collision mere seven years ago¹.
Because we remember that the makers of that tech said the exact same things you're saying now back then.
And because we remember that the person killed was an engineer who complained about Tesla steering him towards the same barrier previously, and Tesla has, effectively, ignored the complaints.
Tech moves fast. Safety culture doesn't. And the last 1% takes 99% of the time (again, how long ago have you graduated?).
I'm glad that you and your friends are volunteering to be lab rats in the just trust me bro, we'll settle the lawsuit if needs be approach to safety.
I'm not happy about having to share the road with y'all tho.
in claude.md I have specific instructions not to check in code and in the prompt specifically wrote as critical to not check in code while check one failing tests. test failure was fixed, code was checked in, I’d say at least claude behaves exactly like humans :)
It depends completely on the term. You can make a great case that we've already reached AGI. You can also make a great case that we are decades away from it.
That line essentially means 'indefinite support'. This paper was published some days ago that aims to define AGI: https://www.arxiv.org/abs/2510.18212.
But crucially, there is no agreed-upon definition of AGI. And I don't think we're close to anything that resembles human intelligence. I firmly believe that stochastic parrots will not get us to AGI and that we need a different methodology. I'm sure humanity will eventually create AGI, and perhaps even in my lifetime (in the next few decades). But I wouldn't put my money on that bet.
> Does anyone really think we are close to AGI? I mean honestly?
Some people believe capitalism is a net-positive. Some people believe in a all-encompassing entity controlling our lives. Some believe 5G is an evil spirit.
After decades I've kind of given up hope on understanding why and how people believe what they believe, just let them.
The only important part is figuring out how I can remain oblivious to what they believe in, yet collaborate with them on important stuff anyways, this is the difficult and tricky part.
As a proxy, you can look at storage. The human brain is estimated at 3.2Pb of storage. The cost of disk space drops by half every 2-3 years. As of this writing, the cost is about $10 / Tb [0]. If we assume about 3 halvings, by 2030 that cost will be around $2.50 / Tb, which means that to purchase a computer roughly the storage size of a human brain, it will cost just under $6k.
The $6k price point means that (high-end) consumers will have economic access to compute commensurate with human cognition.
This is a proxy argument, using disk space as the proxy for the rest of the "intelligence" stack, so the assumption is that processing power will follow suite, also be not as expensive, and that the software side will develop to keep up with the hardware. There's no convincing indication that these assumptions are false.
You can do your own back of the envelope calculation, taking into account generalizations of Moore's law to whatever aspect of storage, compute or power usage you think is most important. Exponential progress is fast and so an order of magnitude misjudgement translates to a 2-3 year lag.
Whether you believe it or not, the above calculation and, I assume, other calculations that are similar, all land on, or near, the 2030 year as the inflection point.
Not to belabor the point but until just a few years ago, conversational AI was thought to be science fiction. Image generation, let alone video generation, was thought by skeptics to be decades, if not centuries, away. We now have generative music, voice cloning, automatic 3d generation, character animation and the list goes on.
One might argue that it's all "slop" but for anyone paying attention, the slop is the "hello world" of AGI. To even get to the slop point represents such a staggering achievement that it's hard to understate.
AGI has no technical definition- its marketing. it can happen at any time that Sam Altman or Elon Musk or whoever decide they want to market their product as AGI
no, AI companies need to continue to say things like that and do "safety reports" (the only real danger of an llm is leaking sensitive data to a bad actor) to maintain hype and investment
I think we've reached Star Trek level AI. In Star Trek (and the next generation) people would ask the computer questions and it would spout out the answers, which is really similar to what LLM's are doing now, though minus the occasional hallucination. In Star Trek though the computers never really ran anything (except for the one fateful episode - The Ultimate Computer in TOS), I always wondered why, it seems Roddenberry was way ahead of us again.
Citation needed? I don't mean this in a snarky way, though. I genuinely have not seen anything that these things can train on their own output and produce better results than before this self-training.
It seems really weird to me that such granular intercorporate details are made publicly available (in a blog post?). I've never had to publicly state things like this when making corporate partnerships. That makes me wonder how much of this post is crafted solely for PR...
I believe MS declares Q1 earnings today, and there had been some rumblings that they were risking accounting / reporting liability by failing to characterize their material OpenAI stake.
What probably happened:
1. MS's accountants raised a warning
2. Existing agreement prohibited disclosure of terms
3. MS told OpenAI that wasn't acceptable and MS needed to publicly report details today
4. OpenAI coordinated release of this, to spin the narrative
So Microsoft went from 49% to now 27%? Open AI with their non-profit and their for-profit and all these investments and deals they are doing. It feels like they are spending more time doing financial trickery than building AI products.
There's a public trail of reddit comments where Altman all but owns up to finagling board seats and ownership rights for Reddit many years ago. This is how he operates.
They have a definition actually) “When AI generates $100 billion in profits” it will be considered an AGI. This term was defined in their previous partnership, not sure if it's still holds after the restructuring.
That is a staggering number - if an engineer makes $100k per year, and let's say OpenAI can do a 20% profit margin on running an engineer-equivalent agent, that means it needs $600B profit or 6 million fully-equivalent engineer years.
I think you can rebuild human civilization with that.
I feel like replacing highly skilled human labor hardly makes financial sense, if it costs that much.
I wonder if they have more detailed provisions than this though. For example, if a later version of Sora can make good advertisements and catches on in the ad industry, would that count?
Or maybe since it is ultimately an agreement about money and IP, they are fine with defining it solely through profits?
>> Whether you are an enterprise developer or BigTech in the US you are on average making twice the median income in your area. There is usually no reason for you not to be stacking cash.
> and then giving up things produced off that investment when the company grows?
An investor can be stubborn about retaining all rights previously negotiated and never give them up... but that absolutist position doesn't mean anything if the investment fails.
OpenAI needs many more billions to cover many more years of expected losses. Microsoft itself doesn't want to invest any more money. Additional outside investors don't want to add more billions in funding unless Microsoft was willing to give up a few rights so that OpenAI has a better competitive position against Google Gemini, Anthropic, Grok etc.
When a startup is losing money and desperately needs more capital, a new round of investors can chip away at rights the previous investor(s) had. Why would previous original investors voluntarily agree to give up any rights?!? Because their investment is at risk if the startup doesn't get a lot more money. If the original investor doesn't want to re-invest again and would rather others foot the bill, they sometimes have to be a little flexible on their rights for that to happen.
If Microsoft doesn't believe that OpenAI will achieve AGI by 2030 or that there's a chance that OpenAI won't be the premiere AI company in four years, the deal looks less like a lose and more like they are buying their way out of a risky bet. On the other hand, if OpenAI does well, then Microsoft have a 27% stake in the company and that's not nothing.
This looks more like Microsoft ensuring that they'll win, regardless of how OpenAI fairs in the next four to six years.
I assume that first refusal required price matching. If the $250B is at a higher price than whatever AWS, GCP, etc. were willing to offer, then it could be a win for Microsoft to get $250B in decent margin business over a larger amount of break even business.
If there is need of more capital, you either keep your share without the capital injection and the share goes to zero or you let in more investors, dilute your share, but its overall value increases. Or you can let in more people and sign an agreement that part of the new money will be paid to you in the form of services that you provide.
I always see a large amount of pessimism about this company on HN, and I accept it might be for rational reasons. What do people think is going to be the most likely outcome for the company, since everything seems to be going so bad for them product/moat/financial-wise? Do people think it will literally go bust and close business due to bankruptcy within a couple years? If not, what else?
Every time they bring up AGI, it feels more like a business strategy to me.
It helps them attract investors and dominate the public narrative.
For OpenAI, AGI is both a vision and a moat.
Regarding LLMs we're in a race to the bottom. Chinese models perform similarly with much higher efficiency; refer to kimi-k2 and plenty of others.
ClopenAI is extremely overvalued, and AGI is not around the corner because among 20T+ tokens trained on it still generates 0 novel output.
Try asking for ASP.NET Core .MapOpenAPI() instead of the pre .net9 swashbuckle version. You get nothing. It's not in the training data.
The assumption these will be able to innovate, which could explain the value, is unfounded.
> because among 20T+ tokens trained on it still generates 0 novel output. Try asking for ASP.NET Core .MapOpenAPI() instead of the pre .net9 swashbuckle version. You get nothing. It's not in the training data.
The best part is that the web is forever poisoned now, 80% of the content is generated by LLM and self poisoning
There are enough archives of web content from 5+ years ago(let alone, Library of Congress archives, old book scans, things like that) that it shouldn't be a big deal if there actually is a breakthrough in training and we move on from LLMs.
They perform similarly on benchmarks, which can be fudged to arbitrarily high numbers by just including the Q&A into the training data at a certain frequency or post-training on it. I have not been impressed with any of the DeepSeek models in real-world use.
General data: hundreds of billions of tokens per week are running through Deepseek, Qwen, GLM models solely by those users going through OpenRouter. People aren't doing that for laughs, or "non-real-world use", that's all for work and/or prod. If you look at the market share graph, at the start of the year the big 3 OpenAI/Anthropic/Google had 72% market share on there. Now it's 45%. And this isn't just because of Grok, before that got big they'd already slowly fallen to 58%.
Anecdata: our product is using a number of these models in production.
Because it's significantly cheaper. It's on the frontier at the price it's being offered, but they're not competitive in the high intelligence & high cost quadrant.
Being the number one in price vs quality, or size vs quality, is incredibly impressive, as the quality is clearly one that's very useful in "real-world usage". If you don't find that impressive there's not much to say.
If it was on the cost vs quality frontier I would find it impressive, but it's not a marker of innovation to be on the price vs quality frontier, it's a marker of business strategy
But it is on the cost vs quality frontier. The OpenRouter prices are all from mainly US(!) companies self-hosting and providing these models for inference. They're absolutely not all subsidizing it to death. This isn't Chinese subsidies at play, far from it.
Ironically, I'll bet you $500 that OpenAI and Anthropic's models are far more subsidized. We can be almost sure about this, given the losses that they post, and the above fact. These providers are effectively hardware plays, they can't just subsidize at scale and they're a commodity.
On top of that I also mentioned size vs quality, where they're also frontier. Size ≈ cost.
Honestly though, hundreds of billions of tokens per week really isn't that much. My tiny little profitable SaaS business that can't even support my family yet is doing 10-20 billion tokens per month on Gemini Flash 2.5.
Eh... perhaps a race to the bottom on the fundamental research side, but no American company is going to try to build their own employee-facing front end to an open Chinese model when they can just license ChatGPT or Claude or Copilot or Gemini instead.
If we assume token providers are becoming more and more of a commodity service these days, it seems telling that OpenAI specifically decided to claw out consumer hardware.
Perhaps their big bet is that their partnership with Jony Ive will create the first post-phone hardware device that consumers attach themselves with, and then build an ecosystem around that?
this would be an incredibly tough play. We've seen few success stories, and even when the product is good building the business around them has often failed. Most of the consumer plays are terrible products with weak execution and no real market. I have no doubt they could supplement lots of consumer experiences but I'm not sure how they are more than a commodity component in that model. I'm a die-hard engineer, but equating the success of the iphone to Ive's design is like saying the reason there were so many Apple II's in 80's homes and classrooms was because of Woz's amazing design.
I just want to say how nice it was to read those clear bullet points in this press release. I know that bulleted lists have been getting a lot of flack because of AIs overusing them, but it's really nice sometimes to not having to go treasure hunting in annoying marketing prose.
> OpenAI has contracted to purchase an incremental $250B of Azure services, and Microsoft will no longer have a right of first refusal to be OpenAI’s compute provider.
So OpenAI could be on Google (GCP) and AWS, and possibly Claude and Gemini on Azure? that could be a good thing.
I use OpenRouter in multiple applications, the practicality of having one provider to host all possible LLMs is such a win to try and iterate without having to switch the cloud (big for enterprise who are stuck with one cloud provider)
They also say "Non-API products may be served on any cloud provider.". I wonder what products they are thinking about. If I sell you a EC2 image with GPT-5 on it, is that a API?
My assumption is that they mean PaaS model hosting (so azure's ai service, bedrock, vertex), but I don't know what other product OpenAI is thinking about selling via a cloud provider unless it's training tooling or something.
Spare me. Sam has been talking about ChatGPT already being AGI for ages, meanwhile still peddling this duplicitous talk about how AGI is coming despite it apparently already being here. Can we act like grownups and treat this like a normal tool? No, no we cannot, for Sam is a hype merchant.
it's notable that there is no talk about defining what exactly AGI is - or even spelling out the three letter acronym - because that doesn't serve his narative. He wants the general public to equate human intelligence with current OpenAI, not ask what does this mean or how would we know. He's selling another type of hammer that's proving useful in some situations but presenting it as the last universal tool anyone will ever need.
And because it's become apparent that LLMs aren't converging on what's traditionally been understood as AGI.
The promise of AGI is that you could prompt the LLM "Prove that the Riemann Hypothesis is either true or false" and the LLM would generate a valid mathematical proof. However, if you throw it into ChatGPT what you actually get is "Nobody else has solved this proof yet and I can't either."
And that's the issue. These LLMs aren't capable of reason, only regurgitation. And they aren't moving towards reason.
When I ask Claude to debug something it goes through more or less the same steps I would have done to fine the bug. Add some logging, run tests, try an hypothesis...
Until LLMs got popular, we would have called that reasoning skills. Not surpassing humans but better than many humans within a small context.
I don't mean that I have a higher opinion about LLM intelligence than you do, but perhaps I have a lower opinion on what human intelligence is. How many do much more than regurgitate, tweak? Science has taken hundreds of years to develop.
The real question is: When do knowledge workers loose their jobs. That is close enough for "AGI" in its consequences for society, Riemann hypothesis or not.
Did you read the whole thread and all of your own comment each time you had to type another half-word? If not, I’m afraid your first statement doesn’t hold.
> OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity.
So, can you (and everyone you know) be replaced at work by a subscription yet? If not, it's not AGI I guess.
This entire house of cards is built on the expectation that "AGI" is just around the corner. The moment Altman relents in his grift is the moment the bubble pops and we're in for a wild ride.
Microsoft invests in OpenAI, OpenAI agrees to spend 250b on azure compute. These companies are just sliding money back and forth to drive up their stock price. Just pulling money away from investors while taking very little risk themselves. It's literally a bubble.
A tangent, but it feels more and more like the AGI maximalists of 2025 are by and large the NFT maximalists from 2022 (who in turn were the NoCode maximalists of 2020) that are looking for the next metaphorical penny stock to sell.
That logical fallacy of, “I spent a week teaching myself this topic and now I’m ready to talk about it like an expert.”
In short: Microsoft changed our business so that we can be for-profit, and asserted its rights over IP so that the whole OpenAI rebellion thing that happened earlier can't happen again.
So now OpenAI is committed to spending $550 billion dollars? ($300B to Oracle and $250B to MS). If it currently has ~$10B in revenue / year, how on earth can it meet these commitments?
Has OpenAI not also committed to spending a few hundered billions at NVIDIA? I mean whats another few hundered billions when you are making so much profit.
Wait, they are not making any profit but already losing billions even before any of these "investments" ?
Why do none of OpenAI announcements have an author attributed to them? Are people that ashamed of working there, they don't even want to attach their name to the work? I guess I would be, too.
What happens if someone else achieves AGI first? The way they wrote it, seems like they are damn sure they are the ones who will achieve AGI. A bit too egoistic...?
I want to setup a dev devcontainer where inside I can call ‘supabase start’ and use docker outside of docker. GPT5 was not helpful. AGI should be able to handle things not well expressed in the training data. We are a long ways away.
After 2032 Microsoft will no longer have access to ChatGPT, they will have to build their own frontier model in 7 years. Can Mustafa deliver that? When Zuckerberg is sucking up all talent with $100M+ salaries?
OpenAI models are hosted on Azure and are available through Azure AI Foundry exclusively (no other cloud vendors serve OpenAI directly). This also means that Azure customers can access OpenAI models and it sits under their Azure data governance agreements.
As a 1980's adventure game fan, I can only hope that whatever comes after AGI is called SCI. Maybe it could be "Soul-Crushing Intelligence".
Once we don't need people to make stuff anymore, we need to re-do society so people can get access to all the stuff that's being made. I doubt we do a very good job of that. But otherwise, there's no point in making anything. I guess if we are lucky, the AI overlords will keep us high on soma and let the population naturally decline until we are gone.
I loathe that creating a non-profit organization supposedly guided by a charter to "ensure that artificial general intelligence (AGI) benefits all of humanity." is actually about being a $500b company corporate capital ownership with million $ pay packages. I mean, it looks like you really can do whatever you want if you have the most $ for the best lawyers and the gumption to not give af.
> "OpenAI can now provide API access to US government national security customers, regardless of the cloud provider."
And this one might be related:
> "OpenAI can now jointly develop some products with third parties. API products developed with third parties will be exclusive to Azure. Non-API products may be served on any cloud provider."
Now, does anyone think MIC customers want restricted, safe, aligned models? Is OpenAI going to provide turnkey solutions, unaligned models run in 'secure sandboxed cloud environments' in partnership with private weapons manufacturers and surveillance (data collection and storage/search) specialists?
This pattern is not historically unusual, turning to government subsidies and contracts to survive a lack of immediate commercial viability wouldn't be surprising. The question to ask Microsoft-OpenAI is what percentage of their estimated future revenue stream is going to come from MIC contracting including the public private grey area (that is, 'private customers' who are entirely state-funded, eg Palantir, so it's still government MIC one step removed).
> Once AGI is declared by OpenAI, that declaration will now be verified by an independent expert panel.
By the time we get 30% global unemployment and another financial crash along the way in the next decade, only then OpenAI would have already declared "AGI".
I'm patiently waiting for all this AI/AGI bullshit to unwind. Some of my "investment" type newsletters have been alerting that the AI endgame is imminent and the bubble is ready to pop. I guess the big money people grifted all they can grift on this round and are ready to pull the rug from everyone who has just learned to spell AI.
I don't understand what MS is doing. The only AI available at work is M365 Copilot. It's absolutely terrible. Tiny context window, super guardrailed, can barely handle a 100 line PowerShell script. It's so so bad. I don't get it.
So they specifically did compromise the public mission of generating ai for the common good and now common good is defined as “$100b in profits” what a sham and scam “open”AI company
>OpenAI has contracted to purchase an incremental $250B of Azure services, and Microsoft will no longer have a right of first refusal to be OpenAI’s compute provider.
I have no idea what @sama is doing but he's doing it quite well.
He mayy be out front because he's the best PR face for this, but make no mistake there is massive collusion amongst all the players to inflte this bubble. Across MS, Oracle, AWS, OpenAI, Anthropic, NVidia and more all I see is a pair on conjoined snakes eating their own tail.
I think they hope they will because if they don't at some point people are going to expect a return and get tired of throwing good money after bad.
The longer they go without that and the more the sentiment starts to shift away from what they convinced people LLM's where vs what they actually are the riskier it becomes, are they are useful tool yes, are they not what they've been hyping for the last four years, also yes.
They either crack it or they become an also ran.
At which point Microsoft investors are going to be staring really hard at the CEO.
You're missing context and/or didn't read OP's comment. He said "will" with regards to reaching AGI. He said "only AGI can find" with regards to profit. It was the latter that this thread was addressing.
You're missing context and/or didn't read OP's comment. He said "because". It will happen because that's the only way to reach profit. That's why it will happen.
Yeah, exactly. The context here was about the profitability part of OP's comment. The parent said "plenty of businesses fail to find a way to make a profit," and my point was that OP's statement doesn't contradict that. OP was saying they'll need AGI to be profitable, not that they're guaranteed to become profitable.
Sure, they phrased it as "they will reach AGI," but that's clearly tongue-in-cheek...the underlying idea is "they better reach AGI, because that's the only way they could make money." So my comment ("necessary, not sufficient") was just pointing out that even if AGI is required for profitability, it doesn't mean they'll actually get there or succeed once they do, and that the original comment was perfectly compatible with the idea that not every business reaches profitability.
> Microsoft holds an investment in OpenAI Group PBC valued at approximately $135 billion, representing roughly 27 percent on an as-converted diluted basis
It seems like Microsoft stock is then the most straightforward way to invest in OpenAI pre-IPO.
This also confirms the $500 billion valuation making OpenAI the most valuable private startup in the world.
Now many of the main AI companies have decent ownership by public companies or are already public.
- OpenAI -> Microsoft (27%)
- Anthropic -> Amazon (15-19% est), Alphabet/Google (14%)
Then the chip layer is largely already public: Nvidia. Plus AMD and Broadcom.
Clouds too: Oracle, Alphabet/GCP, Microsoft/Azure, CoreWeave.
Microsoft is worth $4T, so if you buy one MSFT share only ~3% of that is invested in OpenAI. Even if OpenAI outperforms everyone's expectations (which at this point are already sky high), a tiny swing in some other Microsoft division will completely erase your gains.
Yeah, but on the plus side when the AI bubble bursts at least you've still got Excel.
Claude for Excel! https://news.ycombinator.com/item?id=45722639
No reason to believe it is a bubble
Lots of reasons to believe it's a bubble.
No hard proof it's a bubble. Bubbles can only be proved to have existed after they pop.
Markets trade on a magical growth valuation. Nothing you said matters at all at the moment and won’t for about 5 or so years. People are going to eat shit over and over when they keep talking like this, just look at what NVDA did today. It’s not going to stop.
5 years is a pretty long time to predict with confidence. Definitely agree that "the markets can remain irrational longer than you can remain solvent", but 5 years would be unusually long if you believe we're already in a bubble.
> "Microsoft’s IP rights now exclude OpenAI’s consumer hardware."
Relevant and under-appreciated.
OpenAI wearable eyeglasses incoming... (audio+cellular first, AR/camera second?)I think it's a bit late for that.
Also, you have to consider the size of Microsoft relative to its ownership of OpenAI, future dilution, and how Microsoft itself will fare in the future. If, say, Microsoft is on a path towards decreasing relevance/marketshare/profitability, any gains from its stake in OpenAI may be offset by its diminishing fortunes.
> If, say, Microsoft is on a path towards decreasing relevance/marketshare/profitability
That’s a big if. I see a lot of people in big enterprises who would never even consider anything other than Microsoft and Azure.
C# and .NET have a bigger market share than what gets talked about in trendy circles
C#/.NET are nice. Azure/Microsoft Cloud not so nice. Idk, maybe I have some bias due to familiarity, but I find the GCP admin and tools to be so much more intuitive than the Azure (and AWS too, for that matter) counterparts.
Oh dear lord, GCP could be the intuitive one?! I have not used anything else but, dear lord, that's shocking and not at all surprising at the same time.
Yeah this is not the case at all lol. I actually find Azure to be far more intuitive after suffering through AWS and a little GCP. It certainly seems more stable in US regions than AWS.
One thing I will say is the Azure documentation is some of the most cumbersome to navigate I've ever experienced, there is a dearth of information in there, you just have to know how to find it.
Doesn't a dearth mean a lack of?
Yeah, agreed. I love C# & the .NET platform; I do not feel the same way about Azure. It’s still way better than GCP, though.
Any speculation on why none of them can make a UI and UX that is not 100% completely shit and makes you feel miserable and stressed out?
Couldn’t they just throw money at the problem? Or fire the criminals who designed it?
The big question is if we are finally in a moment when big enterprises will be allowed to fail due to the infinite number of bad choices they make.
Because things are going to change soon. What nobody know is what things exactly, and in what direction.
yeah, this is a take I see by people who work in unix like environments (including macs). If anything Microsoft will grow much bigger. People are consolidating in Azure and away from GCP. easier to manage costs and integrate with their fleet.
Windows workstations and servers are now "joined" to Azure instead, where they used to be joined to domain controller servers. Microsoft will soon enough stop supporting that older domain controller design (soon as in a decade).
Entra (formerly Azure Active Directory) is definitely a huge enterprise Azure driver, and MS knows it.
And it’s crap.
Doesn't matter. It just needs to work well enough and Microsoft needs to be willing to use it as a point of leverage.
Both of which seem to be true.
Hell, I'm still amazed they got away with the Office-licenses-only-usable-on-Azure bullshit, but here we are.
compared to what? it isn't amazing but it's alright in my experience. it works and it seems like it's designed to give lots of people job security and lots of revenue streams for Microsoft though.
Reminds me of how Yahoo had a valuation in the negative billions with their Alibaba holdings taken into account:
https://www.cbsnews.com/news/wall-street-says-yahoos-worth-l...
You're making the roundabout argument that MSFT/OpenAI will one day go the way of Yahoo/Alibaba, which is wild.
Huh? Windows itself might have had it's heyday but MS is solidly at #2 for clouds only behind AWS with enterprise Windows shops that will be hard pressed to not use MS options if they go to the cloud (Google really has continued to fumble their cloud positions with their reputation for "killedbygoogle.com" nagging on everyones mind).
The biggest real threat to MS position is the Trump administration pushing foreign customers away with stuff like shutting down the ICJ Microsoft accounts, but that'll hurt AWS and Google equally much (The winners of that will be Alibaba and other foregin providers that can't compete in full enterprise stacks today).
Watch this week. Amazon cloud growth has been terrible (Google and Microsoft remains >30%). Amazon have basically no good offerings for AI which is where gcp is bringing to eat their lunch. Anthropic moving to TPU for inference is a big big signal.
100% this. The AWS of today is going to be the Hetzner or Digital Ocean of the future. They'll still have hyperscale, but will not be seen as innovating on first party products or a leader in the AI managed services industry. And frankly, they are currently doing a shit job of even this, because Oracle is in the same category and OCI has been eating everyone's lunch (for the past two years!).
Is OCI really eating everyone lunch? Sure, it's showing massive growth but that's because Oracle has been running around offering insane discounts.
We were cloud shopping, and they came by as well with REALLY good discount. Luckily our CTO was massively afraid of what would happen after that discount ran out.
I think the stable coin company Tether is valued at 500 billion also.
Is the company valued at $500 billion or is the sum of the digital assets they’ve collateralised worth $500 billion?
Because if you buy the tokens you presumably do not own the company. And if you buy the company you hopefully don’t own the tokens - nor the assets that back the tokens.
I think I read that its valued at 500 billion based on their latest fund raise. I don't know the total holdings they have.
I have no interest in crypto, just wanted to mention this which was surprising to me when I heard it.
Wow, yes they claim to be raising a round valuing them at $500bn. Which is crazy given the market cap of their token is only apparently $173bn.
https://www.reuters.com/business/crypto-firm-tether-eyes-500...
I struggle to see how those numbers stack up.
For comparison Blackstone is worth ~$180bn with ~$1 trillion AUM.
So somehow this crypto firm and its investor think it can get a better return than Blackstone with a fraction of the assets. Now, sure, developing market and all that. But really? If it scaled to Blackstone assets level of $1 trillion then you’d expect the platform valuation to scale, perhaps not in lockstep but at least somewhat. So with $1 trillion in collateralised crypto does that make Tether worth $1.5 trillion? I’d love someone to explain that.
Tether is projected to generate $15 billion in profits. So 500 billion is like a 33 times earnings multiple.
Now the main thing is how sustainable these earnings are and if they will continue to be a dominant player in stable coins and if there will continue to be demand for them.
Another difference to Blackstone is Tether takes 100% of the returns on the treasuries backing the coins, whereas Blackstone gets a small fee from AUM, and their goal is to make money for their investor clients.
If crypto wanted to really be decentralized they'd find a way to have stable coins backed by whatever assets where the returns of the assets still came to the stable coin holder, not some big centralized company.
If my mom gives me 1000 dollars for 1% of my lemonade stand, that doesn't mean my stand is worth 100k. Tether is in talks with investors to mayb raise 20b at a 500b valuation. Keep in mind also that crypto investors overvalue companies to create the hype and then lobby for better regulations etc. It doesn't mean at all that someone would be interested to buy 100% of tether for 500b. Now, if they were public is a different story, like Tesla etc
Well indeed. That was pretty much my point.
> This also confirms the $500 billion valuation making OpenAI the most valuable private startup in the world.
SpaceX?
If SpaceX is still a “startup”, the word has lost all meaning.
It already has. Any tech company that is pre-IPO and still raising funding rounds is a "startup". I'm surprised there hasn't been someone to come up with a separate term for the stage of these kinds of companies.
That's just a privately owned tech company then. Lots of companies never IPO.
Around $350 to $400 billion from a couple sources I saw, but it's a lot of speculation
Or for the inevitable crash when we discover that OpenAI is a round tripping Enron style disaster.
It’s odd to me in clouds you excluded AWS.
And included oracle first. OP is probably Larry
if you want to invest in open ai I think you can just buy it on NASDAQ private markets.
Not everyone can "just buy it". Investing in MSFT is accessible to many more people than private markets.
“OpenAI is now able to release open-weight models that meet requisite capability criteria.”
Was Microsoft the blocker before? prior agreements clearly made true open-weights awkward-to-impossible without Microsoft’s sign-off. Microsoft had (a) an exclusive license to GPT-3’s underlying tech back in 2020 (i.e., access to the model/code beyond the public API), and (b) later, broad IP rights + API exclusivity on OpenAI models. If you’re contractually giving one partner IP rights and API exclusivity, shipping weights openly would undercut those rights. Today’s language looks like a carve-out to permit some open-weight releases as long as they’re below certain capability thresholds.
A few other notable tweaks in the new deal that help explain the change:
- AGI claims get verified by an independent panel (not just OpenAI declaring it).
- Microsoft keeps model/product IP rights through 2032, but OpenAI can now jointly develop with third parties, serve some things off non-Azure clouds, and—critically—release certain open-weights.
Those are all signs of loosened exclusivity.
My read: previously, the partnership structure (not just “Microsoft saying no”) effectively precluded open-weight releases; the updated agreement explicitly allows them within safety/capability guardrails.
Expect any “open-weight” drops to be intentionally scoped—useful, but a notch below their frontier closed models.
Honestly, I wouldn't be surprised if OpenAI has done the math and determined that even releasing frontier quality models wouldn't put much of a dent in either their B2B or B2C businesses. Or, rather, that any such dent would be vastly overshadowed by the value of fending off potential competitors.
I haven't looked too much into Deepseek's actual business, but at least Mistral seemed to be positioning themselves as a professional services shop to integrate their own open-weight models, compliant with EU regulations etc, at a huge premium. Any firm that has the SOA open model could do the same and cannibalize OpenAI's B2B business---perhaps even eventually pivoting into B2C---especially if regulations, downtime or security issues make firms more cloud-skeptical with respect to AI. As long as OpenAI can establish and hold the lead for best open-weight/on-premise model, it will be hard for anyone to justify premium pricing so as to generate sufficient cash flow from training their own models.
I can even imagine OpenAI eventually deciding that B2C is so much more valuable to them than B2B that it's worth completely sinking the latter market...
Many questioning why Microsoft would agree to this, but to me the concessions they made strike me as minor.
> OpenAI remains Microsoft’s frontier model partner and Microsoft continues to have exclusive IP rights and Azure API exclusivity
This should be the headline - Microsoft maintains its financial and intellectual stranglehold on OpenAI.
And meanwhile, while vaguer, a few of the bullet points are potentially very favorable to Microsoft:
> Microsoft can now independently pursue AGI alone or in partnership with third parties.
> The revenue share agreement remains until the expert panel verifies AGI, though payments will be made over a longer period of time.
Hard to say what a "longer period of time" means, but I presume it is substantial enough to make this a major concession from OpenAI.
> Hard to say what a "longer period of time" means, but I presume it is substantial enough to make this a major concession from OpenAI.
Depends on how this is meant to be parsed but it may be parsed to be a concession from MSFT. If the total amount of revenue to be shared is the same, then MSFT is worse off here. If this is meant to parse as "a fixed proportion of revenue will be shared over X period and X period has increased to Y" then it is an OAI concession.
I don't know the details but I would be surprised if there was a revenue agreement that was time based.
Maybe OpenAI is burning money heavily and MS is the only/best partner to get it from?
Also for MS it is worth to keep investing little by little,getting concessions from OpenAI and becoming the de facto owner of it.
As a corporate customer, the main point for me in this is Microsoft now retaining (non-exclusive) rights to models and products after OpenAI decides to declare AGI.
The question "Can we build our stuff on top of Azure OpenAI? What if SamA pulls a marketing stunt tomorrow, declares AGI and cuts Microsoft off?" just became a lot easier. (At least until 2032.)
An independent panel has to declare agi now.
Can anyone point me to whether or not the OAI non-profit holds voting control or not after the recapitalization?
I've read this but it's extremely vague: https://openai.com/index/built-to-benefit-everyone/
As is this: https://openai.com/our-structure/
Especially so if the Non-profit foundation doesn't retain voting control, this remains the greatest theft of all time. I still can't quite understand how it should at all be possible.
Looking at the changes for MSFT, I also mostly don't understand why they did it!
The nonprofit will own 26%, and a warrant that it will get more shares if the share price grows more than 10 times after 15 years. Sam Altman is getting no shares as part of this restructuring.
Nevermind, looks like the nn-profit gave up voting control lol:
"All equity holders in OpenAI Group now own the same type of traditional stock that participates proportionally and grows in value with OpenAI Group’s success. The OpenAI Foundation board of directors were advised by independent financial advisors, and the terms of the recapitalization were unanimously approved by the board."
Truly, truly the greatest theft from mankind in history and they dress it up as if the non-profit is doing anything other than giving away the most valuable startup in history for a paltry sum.
Credit where credit is due, Sam Altman is the greatest dealmaker of all time.
Will be interesting if we get to hear what his new equity stake is!
The non-profit controls the board of the for profit and can replace them at any time. They also serve in the for-profit board. This is according to the NY times article.
> Once AGI is declared by OpenAI, that declaration will now be verified by an independent expert panel.
I wonder what criteria that panel will use to define/resolve this.
> The two companies reportedly signed an agreement [in 2023] stating OpenAI has only achieved AGI when it develops AI systems that can generate at least $100 billion in profits.
https://techcrunch.com/2024/12/26/microsoft-and-openai-have-...
A sufficiently large profit margin is what constitutes AGI? What a fucking joke.
The real AGI was the money we siphoned along the way.
This is pretty good!
Absolute Grift Industry
They didn't have a better definition of AGI to draw from. The old Turing test proved to not be a particularily good test. So lacking a definition money was used as a proxy. Which to me seems fair. Unless you've got a better definition of AGI that is solid enough to put in a high dollar value contract?
“Only” means that it is a necessary condition, not a sufficient one.
That's true, but the $100 billion requirement is the only hard qualification defined in earlier agreements. The rest of the condition was left to the "reasonable discretion" of the board of OpenAI. (https://archive.is/tMJoG)
The “reasonableness” is something they could go to court over if necessary, whereas the $100 billion is a hard requirement.
It's all so unfathomably stupid. And it's going to bring down an economy.
It's kind of sad, but I've found myself becoming more and more this guy whenever someone "serious" brings up AI in conversation: https://www.instagram.com/p/DOELpzRDR-4/
Hey, don't forget the climate effects too!
> It's all so unfathomably stupid. And it's going to bring down an economy.
Dot-com bubble all over again
Way bigger and deeper than that, there was some slack in the energy situation remaining at that point. Not any more.
with extra stoopid
HN has big problem with reading comprehension. First of all $100B is likely what Microsoft demanded on top of what AGI is defined by OpenAI, which is “ highly autonomous systems that outperform humans at most economically valuable work” - [0]. Secondly that is no longer part of this revised agreement, replaced with a review by a panel of experts.
[0] - https://openai.com/charter/
I'm honestly starting to feel embarrassed to even be employed in the software industry now.
I quit Google last year because I was just done with the incessant push for "AI" in everything (AI exclusively means LLMs of course). I still believe in the company as a whole, the work culture just took a hard right towards kafkaville. Nowadays when my relatives say "AI will replace X" or whatever I just nod along. People are incredibly naive and unbelievably ignorant, but that's about as new as eating wheat.
I've been telling people I do "computer stuff" since the NFT days.
Five straight years of having to tell everyone who asks about your job that the hottest thing in your industry is a scam sure does wear on a person.
that's very different from OpenAI's previous definition (which was "autonomous systems that surpass humans in most economically valuable tasks") for at least one big reason: This new definition likely only triggers if OpenAI's AI is substantially different or better than other companies' AI. Because in a world where 2+ companies have similar AGI, both would have huge income but the competition would mean their profit margins might not be as large. The only reason their profit would soar to 100B+ would be because of no competition, right?
It doesn't seem to say 100B a year. So presumably a business selling spoons will also eventually achieve AGI. Also good to know that the US could achieve AGI at any time by just printing more money until hyperinflation lets openai hit their target.
Nice unlock to hyperinflate their way to $100B. I'd buy an AGI spoon but preferably before hyperinflation hits. I'd expect forks to outcompete the spoons though.
This is the most sick implementation of Goodhart's Law I've ever seen.
>"When a measure becomes a target, it ceases to be a good measure"
What appalls me is that companies are doing this stuff in plain sight. In the 1920s before the crash, were companies this brazen or did they try to hide it better?
That's a pretty blatant public admission that corporations fundamentally regard intelligent entities as profit sources.
Wow that is so dumb. Can these addicts think about anything else than profits?
So Nvidia, Microsoft, Apple, Alphabet, and Saudi Aramco are AGI
So they can just introduce ads in ChatGPT responses, make $100 billion, and call that AGI?
Don't worry, it'll be relevant ads, just like google. You're going to love when code output is for proprietary libraries and databases and getting things the way you want will involve annoying levels of "clarification" that'll be harder and harder to use.
I kind of meant this as a joke as I typed this, but by the end almost wanted to quit the tech industry all together.
Just download a few SOTA (free) open-weights models well ahead of that moment and either run them from inside your living-room or store them onto a (cheap) 2TB external hard drive until consumer compute makes it affordable to run them from your living room.
No. When you're thinking about questions like these, it is useful to remember that multiple (probably dozens) professional A-grade lawyers have been paid considerable sums of actual money, by both sides, to think about possible loopholes and fix them.
What would you consider valid methods of generating $100 billion? Enough Max/Pro subscribers?
So what, there just won't be a word for general intelligence anymore, you know, in the philosophical sense?
Well this is why it's framed that way:
>This is an important detail because Microsoft loses access to OpenAI’s technology when the startup reaches AGI, a nebulous term that means different things to everyone.
Not sure how OpenAI feels about that.
lol, this is "autopilot" and "full self driving" all over again.
Just redefine the terms into something that's easy to accomplish but far from the definition of the terms/words/promises.
I know, they could get a big banner that says MISSION ACCOMPLISHED.
Apparently the US military is for sale, so they probably could hang it up on a battleship even.
So if their erotic bot reaches $100b in profit, they will declare AGI? lol
Given the money involved, they may be contractually obliged to?
Wait until they announce that they’ve been powering OnlyFans accounts this whole time.
This. This sentence reached off the page and hit me in the face.
It only just then became obvious to me that to them it's a question of when, in large part because of the MS deal.
Their next big move in the chess game will be to "declare" AGI.
I think some of this is just the typical bluster of company press releases / earnings reports. Can't ever show weakness or the shareholders will leave. Can't ever show doubt or the stock price will drop.
Nevertheless, I've been wondering of late. How will we know when AGI is accomplished? In the books or movies, it's always been handwaved or described in a way that made it seem like it was obvious to all. For example, in The Matrix there's the line "We marveled at our own magnificence as we gave birth to AI." It was a very obvious event that nobody could question in that story. In reality though? I'm starting to think it's just going to be more of a gradual thing, like increasing the resolution of our TVs until you can't tell it's not a window any longer.
> How will we know when AGI is accomplished?
It's certainly not an specific thing that can be accomplished. AGI is a useful name for a badly defined concept, but any objective application of it (like in a contract) is just stupid things done by people that could barely be described as having the natural variety of GI.
"We are now confident we know how to build AGI as we have traditionally understood it." - Sam Altman, Jan 2025
'as we have traditionally understood it' is doing a lot of heavy lifting there
https://blog.samaltman.com/reflections#:~:text=We%20believe%...
This is phenomenally conceited on both companies’ parts. Wow.
Don't worry, I'm sure we can just keep handing out subprime mortgages like candy forever. Infinite growth, here we come!
If I remember correctly, Microsoft was previously promised ownership of every pre-AGI asset created by OpenAI. Now they are being promised ownership of things post-AGI as well:
Microsoft’s IP rights for both models and products are extended through 2032 and now includes models post-AGI...
To me, this suggests a further dilution of the term "AGI."
To be honest, I think this is somewhat assymetric, and kind of implies that openai are truer "Believers" than Microsoft.
If you believe in a hard takeoff, than ownership of assets post agi is pretty much meaningless, however, it protects Microsoft from an early declaration of agi by openai.
This makes me feel that the extremely short AGI timelines might be less likely.
To sign this deal today, presumably you wouldn’t bother if AGI is just around the corner?
Maybe I’m reading too much into it.
OpenAI wants to be free from MS. The cost is 27% of ownership, which is about $135B currently, plus IP access until 2032. Considered MS invested about $10B initially, that’s a big concession on the part of OpenAI.
OpenAI’s Jakob Pachocki said on a call today that he expects that AI is “less than a decade away from superintelligence”
Or if one party has a different timeline than the other...
Obligatory the office line:
"I just wanted you to know that you can't just say the word "AGI" and expect anything to happen.
- Michael Scott: I didn't say it. I declared it
I think the more interesting question is who will be on the panel?
A group of ex frontier lab employees? You could declare AGI today. A more diverse group across academia and industry might actually have some backbone and be able to stand up to OpenAI.
The criteria changes more times than the weather forecast as it depends on the definition of "AGI".
It's quite possible that GI and thus AGI does not actually exist. Though now the paper the other day by all those heavy hitters in the industry makes more sense in this context.
>It's quite possible that GI and thus AGI does not actually exist.
Aren't we humans supposed to have GI? Maybe you're conflating AGI and ASI.
> Aren't we humans supposed to have GI?
Supposed by humans, who might not be aware of their own limitations.
what paper?
> Once AGI is declared by OpenAI, that declaration will now be verified by an independent expert panel.
What were they really expecting as an alternative? Anyone can "declare AGI" especially since it's an inherently ill-defined (and agruably undefinable) concept, it's strange that this is the first bullet point like this was the fruit of intensive deliberation.
I don't fully understand what is going on in this market as a whole, I really doubt anyone does, but I do believe we will look back on this period and wonder what the hell we were thinking believing and lapping up everything these corporations were putting out.
In context AGI is already clearly defined as $100billion USD revenue. So I guess the expert panel should at least have a finacial auditor.
like Elon declaring FSD?
> Once AGI is declared by OpenAI, that declaration will now be verified by an independent expert panel.
> Microsoft’s IP rights for both models and products are extended through 2032 and now includes models post-AGI, with appropriate safety guardrails.
Does anyone really think we are close to AGI? I mean honestly?
They’ll devalue the term into something that makes it so. The common conception of it however, no I don’t believe we are anywhere close to it.
It’s no different than how they moved the goalpost on the definition of AI at the start of this boom cycle
> They’ll devalue the term
Exactly. As soon as the money runs out, “AGI” will be whatever they’ve got by then.
Wasn't there already a report that stated Microsoft and OpenAI understand AGI as something like 100 billion dollars in revenue for the purpose of their agreements? Even that seems like a pipe dream at the moment.
Definitely. When I started doing Machine Learning in 2018, AI wasn't a next word predictor.
When I was doing it in 2005 it definitely included that, and other far more basic things.
A subset of the field working on some particular applications is pretty different from redefining the term for marketing purposes.
That makes sense, though, that in 13 years we went from basic text prediction to something more involved.
FSD would like a word
As a full stack developer suffering from female sexual dysfunction who owns a Tesla, I am really confused about what you are trying to say.
Have you tried praying to the Flying Spaghetti Deity?
After wrapping up with the Family Services Division, of course
SAE automation levels are the industry standard, not FSD (which is a brand name), and FSD is clearly Level 2 (driver is always responsible and must be engaged, at least in consumer teslas, I don't know about robotaxis). The question is if "AGI" is as well defined as "Level 5" as an independent standard.
The point trying to be made is FSD is deceptive marketing, and it's unbelievable how long that "marketing term" has been allowed to exist given its inaccuracy in representing what is actually being delivered to the customer.
What's deceptive? What in the term "Full Self Driving" makes you think that your car will drive itself fully? It's fully capable of facilitating your driving of yourself, clearly.
They have certainly tried to move the goalposts on this.
"They"? Waymo has a pretty well working service
FSD is the brand name for the service promised/offered by Tesla Motors - Waymo has nothing to do with it, or the moving of goal posts.
They won’t be able to. The whole idea of the panel is because of conflict of interests between MS and OpenAI, as MS won’t get revenue share post AGI declaration. MS will want it to be as high a bar as possible.
I agree: it is more than faintly infuriating that when people say AI what the vast majority mean is LLMs.
But, at the same time, we have clearly passed a significant inflection point in the usefulness of this class of AI, and have progressed substantially beyond that inflection point as well.
So I don't really buy into the idea tha OpenAI have gone out of their way to foist a watered down view of AI upon the masses. I'm not completely absolving them but I'd probably be more inclined to point the finger at shabby and imprecise journalism from both tech and non-tech outlets, along with a ton of influencers and grifters jumping on the bandwagon. And let's be real: everyone's lapped it up because they've wanted to - because this is the first time any of them have encountered actually useful AI of any class that they can directly interact with. It seems powerful, mysterious, perhaps even agical, and maybe more than a little bit scary.
As a CTO how do you think it would have gone if I'd spent my time correcting peers, team members, consultants, salespeople, and the rest to the effect that, no, this isn't AI, it's one type of AI, it's an LLM, when ChatGPT became widely available? When a lot of these people, with no help or guidance from me, were already using it to do useful transformations and analyses on text?
It would have led to a huge number of unproductive and timewasting conversation, and I would have seemed like a stick in the mud.
Sometimes you just have to ride the wave, because the only other choice is to be swamped by it and drown.
Regardless of what limitations "AGI" has, it'll be given that monicker when a lot of people - many of them laypeople - feel like it's good enough. Whether or not that happens before the current LLM bubble bursts... tough to say.
Consider: "Artificial two-star General intelligence".
I mean, once they "reach AGI", they will need a scale to measure advances within it.
Well humans at that point probably won't be able to adequately evaluate intelligence at that level so the AIs will have to evaluate each other.
This is exactly why they will have an “expert panel” to make that determination. They wouldn’t make something up
Making things up is exactly what expert panels are good at doing
I expect that the "expert panel" is to ensure that OpenAI and Microsoft are in agreement on what "AGI" means in the context of this agreement.
What exactly is the criteria for "expert" they're planning to use, and whomst among us can actually meet a realistic bar for expertise on the nature of consciousness?
Type error: why do you need an expert on consciousness to weigh in on if something is AGI or not? I don't care what it feels like to be a paperclip maximizer I just care to not have my paperclips maximized tnx.
Follower count on X. /s
So the expert panel can make something up instead.
Yeah, they wouldn't make something up, the expert panel would.
Because everyone knows that once you call a group of people an expert panel, that automatically means they can't be biased /s
“Moving the goalposts” in AI usually means the opposite of devaluing the term.
Peter Norvig (former research director at Google and author of the most popular textbook on AI) offers a mainstream perspective that AGI is already here: https://www.noemamag.com/artificial-general-intelligence-is-...
If you described all the current capabilities of AI to 100 experts 10 years ago, they’d likely agree that the capabilities constitute AGI.
Yet, over time, the public will expect AGI to be capable of much, much more.
I don't see why anyone would consider the state of AI today to be AGI? it's basically a glorified generator stuck to a query engine
today's models are not able to think independently, nor are they conscious or able to mutate themselves to gain new information on the fly or make memories other than half baked solutions with putting stuff in the context window which just makes it use that to generate stuff related to it, imitating a story basically.
they're powerful when paired with a human operator, I.e. they "do" as told, but that is not "AGI" in my book
> nor are they...able to mutate themselves to gain new information on the fly
See "Self-Adapting Language Models" from a group out of MIT recently which really gets at exactly that.
https://jyopari.github.io/posts/seal
Check out the article. He’s not crazy. It comes down to clear definitions. We can talk about AGI for ages, but without a clear meaning, it’s just opinion.
For a long time the turing test was the bar for AGI.
Then it blew past that and now, what I think is honestly happening, is that we don't really have the grip on "what is intelligence" that we thought we had. Our sample size for intelligence is essentially 1, so it might take a while to get a grip again.
The commercial models are not designed to win the imitation game (that is what Allan Turing named it). In fact the are very likely to loose every time.
The current models don't really pass Turing test. They pass some weird variations on it.
That's a quite persuasive argument.
One thing they acknowledge but glance over, is the autonomy of current systems. When given more open ended, long term tasks, LLMs seem to get stuck at some point and get more and more confused and stop making progress.
This last problem may be solved soon, or maybe there's something more fundamental missing that will take decades to solve. Who knows?
But it does seem like the main barrier to declaring current models "general" intelligence.
> If you described all the current capabilities of AI to 100 experts 10 years ago, they’d likely agree that the capabilities constitute AGI.
I think that we're moving the goalposts, but we're moving them for a good reason: we're getting better at understanding the strengths and the weaknesses of the technology, and they're nothing like what we'd have guessed a decade ago.
All of our AI fiction envisioned inventing intelligence from first principles and ending up with systems that are infallible, infinitely resourceful, and capable of self-improvement - but fundamentally inhuman in how they think. Not subject to the same emotions and drives, struggling to see things our way.
Instead, we ended up with tools that basically mimic human reasoning, biases, and feelings with near-perfect fidelity. And they have read and approximately memorized every piece of knowledge we've ever created, but have no clear "knowledge takeoff path" past that point. So we have basement-dwelling turbo-nerds instead of Terminators.
This makes AGI a somewhat meaningless term. AGI in the sense that it can best most humans on knowledge tests? We already have that. AGI in the sense that you can let it loose and have it come up with meaningful things to do in its "life"? That you can give it arms and legs and watch it thrive? That's probably not coming any time soon.
"If you described"
Yes, and if they used it for awhile, they'd realize it is neither general nor intelligent. On paper sounds great though.
> they moved the goalpost on the definition of AI at the start of this boom cycle
Who is this "they" you speak of?
It's true the definition has changed, but not in the direction you seem to think.
Before this boom cycle the standard for "AI" was the Turing test. There is no doubt we have comprehensively passed that now.
I don't think the Turing Test has been passed. The test was setup such that the interrogator knew that one of the two participants was a bot, and was trying to find out which. As far as I know, it's still relatively easy to find out you're talking to an LLM if you're actively looking for it.
Note that most tests where they actually try to pass the Turing Test (as opposed to being a useful chatbot) they do things like prompt it with a personality etc.
eg: https://pmc.ncbi.nlm.nih.gov/articles/PMC10907317/
It's widely accepted that is has been passed. Eg Wikipeida:
> Since the mid-2020s, several large language models such as ChatGPT have passed modern, rigorous variants of the Turing test
https://en.wikipedia.org/wiki/Turing_test
This is a good thing.
We really really Really should Not define as our success function for AI (our future-overlords?) the ability of computers to deceive humans about what they are.
The Turing Test was a clever twist on (avoiding) defining intelligence 80 years ago.
Going forward, valuing it should be discarded post-haste by any serious researcher or engineer or message-board-philosopher, if not for ethical reasons then for not-promoting spam/slop reasons.
I find there are to main ways to do this.
1) Look for spelling, grammar, and incorrect word usage; such as where vs were, typing out where our should be used.
2) Ask asinine questions that have no answers; _Why does the sun ravel around my finger in low quality gravity while dancing in the rain?_
ML likes to always come up with an answers no matter what. Human will shorten the conversation. It also is programmed to respond with _I understand_, _I hear what you are saying_, and make heavy use of your name if it has access to it. This fake interpersonal communication is key.
Conventional LLM chatbots behave the way you describe because their goal during training is to as much as possible impersonate an intelligent assistant.
Do you think this goal during training cannot be changed to impersonate someone normal such that you cannot detect you are chatting with an LLM?
Before flight was understood some thought "magic" was involved. Do you think minds operate using "magic"? Are minds not machines? Their operation can not be duplicated?
I'm not the person you asked, but I think:
1. Minds are machines and can (in principle) have their operation duplicated
2. LLMs are not doing this
> Do you think this goal during training cannot be changed to impersonate someone normal such that you cannot detect you are chatting with an LLM?
I don't think so, because LLMs hallucinate by design, which will always produce oddities.
> Before flight was understood some thought "magic" was involved. Do you think minds operate using "magic"? Are minds not machines? Their operation can not be duplicated?
Might involve something we don't grasp, but despite that: only because something moves through air it's not flying and will never be, just like a thrown stone.
Maybe current LLMs can do that. But none are, so it hasn't passed. Whether that's because of economic or marketing reasons as opposed to technical does not matter. You still have to pass the test before we can definitely say you've passed the test.
Overall I'd say the easiest is just overall that the models always just follow what you say and transform it into a response. They won't have personal opinions or experiences or anything, although they can fake it. it's all just a median expected response to whatever you say.
And the "agreeability" is not a hallucination, it's simply the path of least resistance, as in, the model can just take information that you said and use that to make a response, not to actually "think" and consider I'd what you even made sense or I'd it's weird or etc.
They almost never say "what do you mean?" to try to seek truth.
This is why I don't understand why some here claim that AGI being already here is some kind of coherent argument. I guess redefining AGI is how we'll reach it
I agree with your points in general but also, when I plugged in the parent comment's nonsense question, both Claude 4.5 Sonnet and GPT-5 asked me what I meant, and pointed out that it made no sense but might be some kind of metaphor, poem, or dream.
The Turing Test was a pretty early metric and more of a thought experiment.
Let's be real guys, it was created by Turing. The same guy who built the first general purpose computer. Man was without a doubt a genius, but it also isn't that reasonable to think he'd come up with a good definition or metric for a technology that was like 70 years away. Brilliant start, but it is also like looking at Newton's Laws and evaluating quantum mechanics based off of that. Doesn't make Newton dumb, just means we've made progress. I hope we can all agree we've made progress...
And arguably the Turing Test was passed by Eliza. Arguably . But hey, that's why we refine and make progress. We find the edge of our metrics and ideas and then iterate. Change isn't bad, it is a necessary thing. What matters is the direction of change. Like velocity vs speed.
As far as I know, it's still relatively easy to find out you're talking to an LLM if you're actively looking for it.
People are being fooled in online forums all the time. That includes people who are naturally suspicious of online bullshittery. I'm sure I have been.
Stick a fork in the Turing test, it's done. The amount of goalpost-moving and hand-waving that's necessary to argue otherwise simply isn't worthwhile. The clichéd responses that people are mentioning are artifacts of intentional alignment, not limitations of the technology.
I feel like you're skipping over the "if you're actively looking for it" bit. You can call it goalpost-moving, or you can check the original paper by Turing and see that this is exactly how he defined it in the first place.
people are being fooled, but not being given the problem: "one of these users is a bot, which one is which"
a problem similar to the turing test, "0 or more of these users is a bot, have fun in a discussion forum"
but there's no test or evaluation to see if any user successfully identified the bot, and there's no field to collect which users are actually bots, or partially using bots, or not at all, nor a field to capture the user's opinions about whether the others are bots
Then there's the fact that the Turing test has always said as much about the gullibility of the human evaluator as it has about the machine. ELIZA was good enough to fool normies, and current LLMs are good enough to fool experts. It's just that their alignment keeps them from trying very hard.
Oh there us much doubt about whether LLMs surpass the turing test. It does so only in certain variations
The turing test point is actually very interesting, because it's testing whether you can tell you're talking to a computer or a person. When Chatgpt3 came out we all declared that test utterly destroyed. But now that we've had time to become accustomed and learn the standard syntax, phraseology, and vocabulary of the gpt's, I've started to be able to detect the AI's again. If humanity becomes completely accustomed to the way AI talks to be able to distinguish it, do we re enter the failed turing test era? Can the turing test only be passed in finite intervals, after which we learn to distinguish it again? I think it can eventually get there, and that the people who can detect the difference becomes a smaller and smaller subset. But who's to say what the zeitgeist on AI will be in a decade
> When Chatgpt3 came out we all declared that test utterly destroyed.
No, I did not. I tested it with questions that could not be answered by the Internet (spatial, logical, cultural, impossible coding tasks) and it failed in non-human-like ways, but also surprised me by answering some decently.
Is there, really?
Jesus, we've gone from Eliza and Bayes Spam Filters to being able to hold an "intelligent" conversation with a bot that can write code like: "make me a sandwich" => "ok, making sandwich.py, adding test, keeping track of a todo list, validating tests, etc..."
We might not _quite_ be at the era of "I'm sorry I can't let you do that Dave...", but on the spectrum, and from the perspective of a lay-person, we're waaaaay closer than we've ever been?
I'd counsel you to self-check what goalposts you might have moved in the past few years...
I think this says more about how much of our tasks and demonstrations of ability as developers revolve around boilerplate and design patterns than it does about the Cognitive abilities of modern LLMs.
I say this fully aware that a kitted out tech company will be using LLMs to write code more conformant to style and higher volume with greater test coverage than I am able to individually.
I'd counsel you to work with LLMs daily and agree that we're no where close to LLMs that work properly consistently outside of toy use cases, where examples can be scraped from the internet. If we can agree on that we can agree that General Intelligence is not the same thing as a, sometimes, seemingly random guess at the next word...
So, where is my sandwich? I am hungry
I think "we" have accidentally cracked language from a computational perspective. The embedding of knowledge is incidental and we're far away from anything that "Generally Intelligent", let alone Advanced in that. LLMs do tend to make documented knowledge very searchable which is nice. But if you use these models everyday to do work of some kind that becomes pretty obvious that they aren't nearly as intelligent as they seem.
They're about as smart as a person who's kind of decent at every field. If you're a pro, it's pretty clear when it's BSing. But if you're not, the answers are often close enough.
And just like humans, they can be very confidently wrong. When any person tells us something, we assume there's some degree of imperfection in their statements. If a nurse at a hospital tells you the doctor's office is 3 doors down on the right, most people will still look at the first and second doors to make sure those are wrong, then look at the nameplate on the third door to verify that it's right. If the doctor's name is Smith but the door says Stein, most people will pause and consider that maybe the nurse made a mistake. We might also consider that she's right, but the nameplate is wrong for whatever reason. So we verify that info by asking someone else, or going in and asking the doctor themselves.
As a programmer, I'll ask other devs for some guidance on topics. Some people can be absolute geniuses but still dispense completely wrong advice from time to time. But oftentimes they'll lead me generally in the right way, but I still need to use my own head to analyze whether it's correct and implement the final solution myself.
The way AI dispenses its advice is quite human. The big problem is it's harder to validate much of its info, and that's because we're using it alone in a room and not comparing it against anyone else's info.
> They're about as smart as a person who's kind of decent at every field. If you're a pro, it's pretty clear when it's BSing. But if you're not, the answers are often close enough.
No they are not smart at all. Not even a little. They cannot reason about anything except that their training data overwhelmingly agrees or disagrees with their output nor can they learn and adept. They are just text compression and rearrangement machines. Brilliant and extremely useful tooling but if you use them enough it becomes painfully obvious.
Something about an LLM response has a major impact on some people. Last weekend I was in in Ft. Lauderdale FL with a friend who's pretty sharp ( licensed architect, decades long successful career etc) and went to the horse track. I've never been to a horse race and didn't understand the betting so I took a snapshot of the race program, gave it to chatGPT and asked it to devise a low risk set of bets using $100. It came back with what you'd expect, a detailed, very confident answer. My friend was completely taken with it and insisted on following it to the letter. After the race he turned his $100 into $28 and was dumbfounded. I told him "it can't tell the future, what were you expecting?". Something about getting the answer from a computer or the level of detail had him convinced it was a sure thing. I donm't understand it but LLMs have a profound effect on some people.
edit: i'm very thankful my friend didn't end up winning more than he bet. idk what he would have done if his feelings towards the LLM was confirmed by adding money to his pocket..
If anything, the main thing LLMs are showing is that the humans need to be pushed to up their game. And that desire to be better, I think, will yield an increase in supply of high-quality labour than what exists today. Ive personally witnessed so many 'so-so' people within firms who dont bring anything new to the table and focus on rent seeking expenditures (optics) who frankly deserve to be replaced by a machine.
E.g. I read all the time about gains from SWEs. But nobody questions how good of a SWE they even are. What proportion of SWEs can be deemed high quality?
Yes, exactly. LLMs are lossy compressors of human language in much the same way JPEG is a lossy compressor of images. The difference is that the bits that JPEG throws away were manually designed by our understanding of the human visual cortex, while LLMs figured out the lossy bits automatically because we don't know enough about the human language processing chain to design that manually.
LLMs are useful but that doesn't make them intelligent.
Completely agree (https://news.ycombinator.com/item?id=45627451) - LLMs are like the human-understood output of a hypothetical AGI, 'we' haven't cracked the knowledge & reasoning 'general intelligence' piece yet, imo, the bit that would hypothetically come before the LLM, feeding the information to it to convey to the human. I think that's going to turn out to be a different piece of the puzzle.
You have to keep moving the goalposts if you keep putting them in the wrong place.
Most people didn't think we were anywhere close to LLM's five years ago. The capabilities we have now were expected to be a decades away, depending on who you talked to. [EDIT: sorry, I should have said 10 years ago... recent years get too compressed in my head and stuff from 2020 still feels like it was 2 years ago!]
So I think a lot of people now don't see what the path is to AGI, but also realize they hadn't seen the path to LLM's, and innovation is coming fast and furious. So the most honest answer seems to be, it's entirely plausible that AGI just depends on another couple conceptual breakthroughs that are imminent... and it's also entirely plausible that AGI will require 20 different conceptual breakthroughs all working together that we'll only figure out decades from now.
True honesty requires acknowledging that we truly have no idea. Progress in AI is happening faster than ever before, but nobody has the slightest idea how much progress is needed to get to AGI.
What people thought about LLMs five years ago, and how close we are to AGI right now are unrelated, and it's not logially sound to say "We were close to LLMs then, so we are close to AGI now."
It's also a misleading view of the history. It's true "most people" weren't thinking about LLMs five years ago, but a lot of the underpinnings had been studied since the 70s and 80s. The ideas had been worked out, but the hardware wasn't able to handle the processing.
> True honesty requires acknowledging that we truly have no idea. Progress in AI is happening faster than ever before, but nobody has the slightest idea how much progress is needed to get to AGI.
Maybe, but don't tell that to OpenAI's investors.
> Most people didn't think we were anywhere close to LLM's five years ago.
That's very ambiguous. "Most people" don't know most things. If we're talking about people that have been working in the industry though, my understanding is that the concept of our modern day LLMs aren't magical at all. In fact, the idea has been around for quite a while. The breakthroughs in processing power and networking (data) were the hold up. The result definitely feels magical to "most people" though for sure. Right now we're "iterating" right?
I'm not sure anyone really see's a clear path to AGI if what we're actually talking about is the singularity. There are a lot of unknown unknowns right?
I worked in Microsoft's AI platform from 2018-2022. people were very aware of LLMs & AI in general. it's not magical
AGI is a silly concept
AGI is a poorly defined concept because intelligence is a poorly defined concept. Everyone knows what intelligence is... until we attempt to agree on a common definition.
I 100% agree with this. I suggest the other guy to check history of NLP.
Not sure what history you're suggesting I check? I've been following NLP for decades. Sure, neural nets have been around for many decades. Deep learning in this century. But the explosive success of what LLM's can do now came as a huge surprise. Transformers date to just 2017, and the idea that they would be this successful just with throwing gargantuan amounts of data and processing at them -- this was not a common viewpoint. So I stand by the main point of my original comment, except I did just now edit it to say 10 years ago rather than 5... the point is, it really did seem to come out of nowhere.
GPT3 existed 5 years ago, and the trajectory was set with the transformers paper. Everything from the transformer paper to GPT3 was pretty much speculated in the paper, it just took people spending the effort and compute to make it reality. The only real surprise was how fast openai producterized an LLM into a chat interface with chatgpt, before then we had finetuned GPT3 models doing specific tasks (translation, summarization, etc.)
At this point, AGI seems to be more of a marketing beacon than any sort of non-vague deterministic classification.
We all thought about a future where AI just woke up one day, when realistically, we got philosophical debates over whether the ability to finally order a pizza constitutes true intelligence.
We can order the pizza, it just hallucinated and I'm not entirely sure why my pizza has seahorses instead of anchovies.
Notwithstanding the fact that AGI is a significantly higher bar than "LLM", this argument is illogical.
Nobody thought we were anywhere closer to me jumping off the Empire State Building and flying across the globe 5 years ago, but I'm sure I will. Wish me luck as I take that literal leap of faith tomorrow.
what's super weird to me is how people seem to look at LLM output and see:
"oh look it can think! but then it fails sometimes! how strange, we need to fix the bug that makes the thinking no workie"
instead of:
"oh, this is really weird. Its like a crazy advanced pattern recognition and completion engine that works better than I ever imagined such a thing could. But, it also clearly isn't _thinking_, so it seems like we are perhaps exactly as far from thinking machines as we were before LLMs"
Well the difference between those two statements is obvious. One looks and feels, the other processes and analyzes. Most people can process and analyze some things, they're not complete idiots most of the time. But also most people cannot think and analyze the most ground breaking technological advancement they might've personally ever witnessed, that requires college level math and computer science to understand. It's how people have been forever, electricity, the telephone, computers, even barcodes. People just don't understand new technologies. It would be much weirder if the populace suddenly knew exactly what was going on.
And to the "most groundbreaking blah blah blah", i could argue that the difference between no computer and computer requires you to actually understand the computer, which almost no one actually does. It just makes peoples work more confusing and frustrating most of the time. While the difference between computer that can't talk to you and "the voice of god answering directly all questions you can think of" is a sociological catastrophic change.
Why should LLM failures trump successes when determining if it thinks/understands? Yes, they have a lot of inhuman failure modes. But so what, they aren't human. Their training regimes are very dissimilar to ours and so we should expect alien failure modes owing to this. This doesn't strike me as good reason to think they don't understand anything in the face of examples that presumably demonstrate understanding.
Because there's no difference between a success and failure as far as an LLM is concerned. Nothing went wrong when the LLM produced a false statement. Nothing went right when the LLM produced a true statement.
It produced a statement. The lexical structure of the statement is highly congruent with its training data and the previous statements.
This argument is vacuous. Truth is always external to the system. Nothing goes wrong inside the human when he makes an unintentionally false claim. He is simply reporting on what he believes to be true. There are failures leading up to the human making a false claim. But the same can be said for the LLM in terms of insufficient training data.
>The lexical structure of the statement is highly congruent with its training data and the previous statements.
This doesn't accurately capture how LLMs work. LLMs have an ability to generalize that undermines the claim of their responses being "highly congruent with training data".
By that logic, I can conclude humans don't think, because of all the numerous times out 'thinking fails'.
I don't know what else to tell you other than this infallible logic automaton you imagine must exist before it is 'real intelligence' does not exist and has never existed except in the realm of fiction.
You’re absolutely right!
I think what is much more plausible is that companies such as this one benefit greatly from being viewed as being close to, or on the way to AGI.
> Once AGI is declared by OpenAI, that declaration will now be verified by an independent expert panel.
I always like the phrase, "follow the money", in situations like this. Are OpenAI or Microsoft close to AGI? Who knows... Is there a monetary incentive to making you believe they are close to AGI? Absolutely. Take in this was the first bullet point in Microsoft's blog post.
I don't think AGI will happen with LLMs. For example, can an LLM drive a car?? I know it's a silly question but it's a fact.
Well yeah
https://wayve.ai/thinking/lingo-2-driving-with-language/
this is something I think about. state of the art in self driving cars still makes mistakes that humans wouldn't make, despite all the investment into this specific problem.
This bodes very poorly for AGI in the near term, IMO
It can?
If you use 'multimodal transformer' instead of LLM (which most SOTA models are), I don't think there's any reason why a transformer arch couldn't be trained to drive a car, in fact I'm sure that's what Tesla and co. are using in their cars right now.
I'm sure self-driving will become good enough to be commercially viable in the next couple years (with some limitations), that doesn't mean it's AGI.
There is a vast gulf between "GPT-5 can drive a car" and "a neural network using the transformer architecture can be trained to drive a car". And I see no proof whatsoever that we can, today, train a single model that can both write a play and drive a car. Even less so one that could do both at the same time, as a generally intelligent being should be able to.
If someone wants to claim that, say, GPT-5 is AGI, then it is on them to connect GPT-5 to a car control system and inputs and show that it can drive a car decently well. After all, it has consumed all of the literature on driving and physics ever produced, plus untold numbers of hours of video of people driving.
>There is a vast gulf between "GPT-5 can drive a car" and "a neural network using the transformer architecture can be trained to drive a car".
The only difference between the two is training data the former lacks that the latter does so not a 'vast gulf'.
>And I see no proof whatsoever that we can, today, train a single model that can both write a play and drive a car.
You are not making a lot of sense here. You can have a model that does both. It's not some herculean task. it's literally just additional data in the training run. There are vision-language-action models tested on public roads.
https://wayve.ai/thinking/lingo-2-driving-with-language/
> single model that can both write a play and drive a car.
It would be a really silly thing to do, and probably there are engineering subletities as to why this would be a bad idea, but I don't see why you couldn't train a single model to do both.
It's not silly, it is in fact a clear necessity to have both of these for something to be even close to AGI. And you additionally need it trained on many other tasks - if you believe that each task requires additional parameters and additional training data, then it becomes very clear that we are nowhere near to a general intelligence system; and it should also be pretty clear that this will not scale to 100 tasks with anything similar to the current hardware and training algorithms.
Okay but then can a multimodal transformer do everything an LLM can?
Most SOTA LLMs are multimodal transformers.
In 1900 we didn't see a viable path to climb Mount Everest or to go to the moon. This does not make the two tasks equally difficult.
> Progress in AI is happening faster than ever before
Is it happening faster than it was six months ago? a year ago?
> Most people didn't think we were anywhere close to LLM's five years ago.
Well, Google had LLMs ready by 2017, which was almost 9 years ago.
https://en.wikipedia.org/wiki/Large_language_model
Also possible we get something "close enough" to AGI and it's really fucking useful.
AGI is the end-game. There's a lot of room between current LLMs and AGI.
Sure, but then OpenAI should not claim it is AGI, even if it is ”close enough”
In the initial contract Microsoft would lose a lot of rights when OpenAI achieves AGI. The references to AGI in this post, to me, look like Microsoft protecting themselves from OpenAI declaring _something_ as AGI and as a result Microsoft losing the rights
I don't see the mentions in this post as anyone particularly believing we're close to AGI
AGI as a PR stunt for OpenAI is becoming a meme.
We're just a year away from full self driving!
I wouldn’t be surprised if AGI arrives before Tesla has a full self-driving car though.
You will be self driven to the fusion plant and you will like it. The AGI will meet you at the front door.
Full self driving has always required AGI, so no we it without AGI.
And then just another year until self selfing and we will have come full circle
Wasn't it always the explicit goal of OpenAI to bring up AGI? So less of a meme, and more "this is what that company exists for".
Bit like blaming a airplane building company for building airplanes, it's literally what they were created for, no matter how stupid their ideas of the "ideal aircraft" is.
>Bit like blaming a airplane building company for NOT building airplanes
FTFY. OpenAI has not built AGI (not yet, it you want to be optimistic).
If you really need an analogy, it's more in the vein of giving SpaceX crap for yapping about building a Dyson Sphere Real Soon Now™.
Of course not, then we'd never hear the end of it :)
I was just informing that the company always had AGI as a goal, even when they were doing the small Gym prototypes and all of that stuff that made the (tech) news before GPT was a thing.
I think AGI isn't the main thing. The agreement gives msft the right to develop their own foundation models, OpenAI to stop using Azure for running & training their foundation models. All this while msft still retains significant IP ownership.
In my opinion, whether AGI happens or not isn't the main point of this. It's the fact that OpenAI and MSFT can go their separate ways on infra & foundation models while still preserving MSFT's IP interests.
Yes. Some ai skeptical people (eg Tyler Cowen, who does not think AI will have a significant economic impact) think gpt5 is AGI.
It was news when dwarkesh interviewed Karpathy who said per his definition of AGI, he doesn't think it will occur until 2035. Thus, if karpathy is pessimistic, then many people working in AI today think we will have agi by 2032 (and likely sooner, eg end of 2028)
Depends on how you define AGI - if you define it as an AI that can learn to perform generalist tasks - then yes, transformers like GPT 5 (or 3) are AGI as the same model can be trained to do every task and it will perform reasonably well.
But I guess what most people would consider AGI would be something capable of on-line learning and self improvement.
I don't get the 2035 prediction though (or any other prediction like this) - it implies that we'll have some magical breakthrough in the next couple years be it in hardware and/or software - this might happen tomorrow, or not any time soon.
If AGI can be achieved using scaling current techniques and hardware, then the 2035 date makes sense - moores law dictates that we'll have about 64x the compute in hardware (let's add another 4x due to algorithmic improvements) - that means that 250x the compute will give us AGI - I think with ARC-AGI 2 this was the kind of compute budget they spent to get their models to perform on a human-ish level.
Also perf/W and perf/$ scaling has been slowing in the past decade, I think we got like 6x-8x perf/W compared to a decade ago, which is a far cry than what I wrote here.
Imo it might turn out that we discover 'AGI' in the sense that we find an algorithm that can turn FLOPS to IQ that scales indefinitely, but is very likely so expensive to run, that biological intelligences will have a huge competitive edge for a very long time, in fact it might be that biology is astronomically more efficient in turning Watts to IQ than transistors will ever be.
>> if you define it as an AI that can learn to perform generalist tasks - then yes, transformers like GPT 5 (or 3) are AGI
Thank you, this is the definition we need a proper term for, and this is what most experts mean when they say we have some kind of AGI.
> I think with ARC-AGI 2 this was the kind of compute budget they spent to get their models to perform on a human-ish level.
It was ARC-AGI-1 that they used extreme computing budgets to get to human-ish level performance. With ARC-AGI-2 they haven't gotten past ~30% correct. The average human performance is ~65% for ARC-AGI-2, and a human panel gets 100% (because humans understand logical arguments rather than simply exclaiming "you're absolutely right!").
> Yes. Some ai skeptical people ... think gpt5 is AGI.
It's a reverse Turing test at this point: "If you get tricked by an LLM to the point of believing it is AGI you're a clown"
I do not think Tyler Cowen is a clown, but I do not agree with elements of his understanding of AI
2035 is still optimistic at present, IMO, because AGI will require breakthroughs that are impossible to predict.
Btw the definition Karpathy gave was:
> a system you could go to that can do any economically valuable task at human performance or better.
https://open.substack.com/pub/dwarkesh/p/andrej-karpathy?sel...
Most of the things that the public — even so-called “AI experts” — consider “magic” are still within the in-sample space. We are nowhere near the out-of-sample space yet. Large Language Models (LLMs) still cannot truly extrapolate. It’s somewhat like living in America and thinking that America is the entire world.
> It’s somewhat like living in America and thinking that America is the entire world.
Oh I have bad news for you...
My L7 and L8 colleagues at Google seem to be signaling next 2 years. Errors of -1 and +20 years. But the mood sorta seems like nobody wants to quit when they're building the test stand for the Trinity device.
Yeah that's the type of estimate people give so they can keep the paychecks coming in for as long as possible.
>Does anyone really think we are close to AGI? I mean honestly?
I think it is near-certain that within two years a large AI company will claim it has developed AGI.
There are unironically people who claim we are already actually at AGI.
... and it will turn into a "technically true" rat race between the main players on what the definition is exactly while you can ask any person on the street with no skin in the game who will tell you that this is nowhere near the intuitive understanding of what AGI is - as it it's not measured by scores but instead of how real and self-aware your counterpart "feels" to you.
If someone is able to come up with true AGI, why even announce it? Instead, just use it to remake a direct clone of Google, or a direct clone of Netflix, or a direct clone of any of these other software corporations. IMO if anyone was anywhere close to something even remotely touching AGI, they would keep their mouth shut tighter than Fort Knox.
> Does anyone really think we are close to AGI? I mean honestly?
I'd say we're still a long way from human level intelligence (can do everything I can do), which is what I think of as AGI, but in this case what matters is how OpenAI and/or their evaluation panel define it.
OpenAI's definition used to be, maybe still is, "able to do most economically valuable tasks", which is so weak and vague they could claim it almost anytime.
Since we don't have an authoritative definition of what it means that companies will agree to, and tests like the turing test that must be passed in order to be considered AGI, I don't think we're anywhere near what we all in our brains think AGI is or could be. On the other hand, AI fatigue will continue until the next big thing takes the spotlight from AI for a while, until we reach true AGI (whatever that is).
I think their definition of AGI is just about how many human jobs can be replaced with their compute. No scientific or algorithmic breakthroughs needed, just spending and scaling dumb LLMs on massive compute.
Shouldn't it mean all jobs? If there are jobs it can't replace then that doesn't sound very generally intelligent. If it's got general intelligence it should be able to learn to do any job, no?
I think that somewhat depends on your definition.
For example an AGI AI could give you a detailed plan that tells you exactly how to do any and every task. But it might not be able to actually do the task itself, for example manual labor jobs for which an AI simply cannot do unless it also "builds" itself a form-factor to be able to do the job.
The AGI could also just determine that it's cheaper to hire a human than to build a robot at any given point for a job that it can't yet do physically and it would be the AGI
I think might even be simpler than that. It's about the cost. Nobody is going to pay for AI to replace humans if it costs more.
All of us in this sub-thread consider ourselves "AGI", but we cannot do any job. In theory we can, I guess. But in practical terms, at what cost? Assuming none of us are truck drivers, if someone was looking for a truck driver, they wouldn't hire us because it take too long for us to get a license, certified, learn, etc. Even though in theory we probably do it eventually.
We don’t even have a general definition that anyone can agree on for AGI.
They are trying.
A Definition of AGI - https://arxiv.org/abs/2510.18212
https://news.ycombinator.com/item?id=45713959
The real defination is that it will no longer matter what _we_ agree on.
LLM derived AGI is possible but LLM by itself is not the answer. The problem I see right now is that because there’s so much money at stake, we’ve effectively spread out core talent across many organizations. It used to be Google and maybe Meta. We need a critical mass of talent (think Manhattan Project). It doesn't help that the Chinese pulled a lot of talent back home because a big chunk of early successes and innovations came from those people that we, the US, alienated.
> Does anyone really think we are close to AGI?
AGI? we are not even close to AI, but that hasnt stopped every other tom dick and harry and my maid from claiming AI capability.
> Does anyone really think we are close to AGI?
My definition of AGI is when AI doesn't need humans anymore to create new models (to be specific, models that continue the GPT3 -> GPT4 -> GPT5 trend).
By my definition, once that happens, I don't really see a role for Microsoft to play. So not sure what value their legal deal has.
I don't think we're there at all anyway.
> I don't really see a role for Microsoft to play.
They have money and infra, if AI can create better AI models, then isn't OpenAI with its researches going to be the redundant one?
5-15 years.
The key steps will be going beyond just the neural network and blurring the line between training and inference until it is removed. (Those two ideas are closely related).
Pretending this isn't going to happen is appealing to some metaphysical explanation for the existence of human intelligence.
Why not? They're using Artificial Intelligence to describe token-prediction text generators which clearly have no "intelligence" anywhere near them, so why not re-invent machine learning or something and call it AGI?
We will achieve AGI when they decide it is AGI (I dont believe for a second this independent expert panel wont be biased). And it won’t matter if you call their bluff, because the world doesnt give a shit about truth anymore.
Who knows?
I don't see any way to define it in an easily verifiable way.
Pretty much any test you could devise, others will be able to point out ways that it's inadequate or doesn't capture aspects of human intelligence.
So I think it all just comes down to who is on the panel.
The best test would be when your competitors can't say what you have isn't AGI. If no one, not even your arch biz enemies, can seriously claim you have not achieved AGI then you probably have it.
Maybe in a few decades, people will look back at how naive it was to talk about AGI at this point, just like the last few times since the 1960s whenever AI had a (perceived) breakthrough. It's always a few decades away.
Love that we have reached AGI, but OpenAI's LLM can't even drive a car...
There are a lot of humans that can’t drive a car (well).
Part of the problem with “AGI” is everyone has their own often totally arbitrary yard sticks.
The "G" part of AGI implies it should be able to hit all the arbitrary yard sticks
That is stupid. It would be possible to be infinitely arbitrary to the point of “AGI” never being reachable by some yard sticks while still performing most viable labor.
So why are your arbitrary yard sticks more valid than someone elses?
Probable the biggest problem as others have stated is that we can’t really define intelligence more precisely than that it is something most humans have and all rocks don’t. So how could any definition for AGI be any more precise?
Where did I say my yardsticks are better? I don’t even think I set out any of mine
I said having to satisfy “all” the yard sticks is stupid, because one could conceive a truly infinite number of arbitrary yard sticks.
>It would be possible to be infinitely arbitrary to the point of “AGI” never being reachable by some yard sticks while still performing most viable labor.
"Most viable labor" involves getting things from one place to another, and that's not even the hard part of it.
In any case, any sane definition of general AI would entail things that people can generally do.
Like driving.
>That is stupid
That's just, like, your opinion, man.
I had a friend who had his Tesla drive him from his driveway in another city 3+ hrs away to my driveway with no intervention.
I feel like everyone’s opinion on how self-driving is going is still rooted in 2018 or something and no one has updated.
>I had a friend who had his Tesla drive him from his driveway in another city 3+ hrs away to my driveway with no intervention.
I had anecdata that was data, and it said that full-self-driving is wishful thinking.
We cool now?
Good luck on your journey. I think the world is going to surprise you, and you’d be better for opening your eyes a little wider.
You're absolutely right.
The world never ceases to surprise me with its stupidity.
Thanks for your contribution.
ok but have you asked your Tesla to write you a mobile app? AGI would be able to do both. (the self-driving thing is just an example of something AGI would be able to do but an LLM can't)
Rest assured, your friends driving was the same quality as the average drunk grandma on the road if they were exclusively using Tesla's "FSD" with no intervention for hours. It drives so piss poorly that I have to frequently intervene even on the latest beta software. If I lived in a shoot happy state like Texas I'm sure that a road rager would have put a bullet hole somewhere in my Tesla by now if I kept driving like that.
There's a difference between "I survived" and "I drive anywhere close to the quality of the average American" - a low bar and one that still is not met by Tesla FSD.
Yeah, and let's not forget that "I drive like a mildly blind idiot" is only a viable (literally) choice when everyone else doesn't do that and compensates for your idiocy.
Is driving is infinitely arbitrary?
It's one skill almost everyone on the planet can learn exceptionally easily - which Waymo is on pace to master, but a generalized LLM by itself is still very far from.
OP said all yardsticks and I said that was infinitely arbitrary… because it literally is infinitely arbitrary. You can conjure up an infinite amount of yardsticks.
As far as driving itself goes as a yardstick, I just don’t find it interesting because we literally have Waymo’s orbiting major cities and Teslas driving on the roads already right now.
If that’s the yardstick you want to use, go for it. It just doesn’t seem particularly smart to hang your hat on that one as your Final Boss.
It also doesn’t seem particularly useful for defining intelligence itself in an academic sort of way because even humans struggle to drive well in many scenarios.
But hey if that’s what you wanna use don’t let me stop you, sure, go for it. I have feeling you’ll need new goalposts relatively soon if you do, though.
Humans are the benchmarks for AGI and yet a lot of people are outright dumb:
> Said one park ranger, “There is considerable overlap between the intelligence of the smartest bears and the dumbest tourists.”
[1] https://www.schneier.com/blog/archives/2006/08/security_is_a...
And using humans as 'the benchmark' is risky in itself as it can leave us with blind spots on AI behavior. For example we find humans aren't as general as we expected, or the "we made the terminator and it's exterminating mankind, but it's not AGI because it doesn't have feelings" issues.
The vast majority of humans can be taught to drive.
> The vast majority of humans can be taught to drive
the key is being able to drive and learn another language and learn to play an instrument and do math and, finally, group pictures of their different pets together. AGI would be able to do all those things as well... even teach itself to do those things given access to the Internet. Until that happens then no AGI.
Teslas and Waymo’s drive better than the majority of humans already.
Of course there are caveats there, but is driving really the yardstick you want to use?
>Teslas and Waymo’s drive better than the majority of humans already.
In restricted settings.
Yeah no fam.
>but is driving really the yardstick you want to use?
Yes, because it's an easy one, compared, say, to walking.
But if you insist — let's use that.
Walking is going pretty well for robotics lately. Good luck with that take
>Walking is going pretty well for robotics lately.
Just like self-driving is going well on an empty race track.
>Good luck with that take
Good luck running into a walking robot in the street in your lifetime.
> Just like self-driving is going well on an empty race track.
Look, a time traveler from 2019.
Did you just graduate college?
It sure must feel like 2018 was a long time ago when that's more than the entirety of your adult life. I get it.
The rest of us aren't that excited to trust our lives to technology that confidently drove into a highway barrier at high speed, killing the driver in a head-on collision mere seven years ago¹.
Because we remember that the makers of that tech said the exact same things you're saying now back then.
And because we remember that the person killed was an engineer who complained about Tesla steering him towards the same barrier previously, and Tesla has, effectively, ignored the complaints.
Tech moves fast. Safety culture doesn't. And the last 1% takes 99% of the time (again, how long ago have you graduated?).
I'm glad that you and your friends are volunteering to be lab rats in the just trust me bro, we'll settle the lawsuit if needs be approach to safety.
I'm not happy about having to share the road with y'all tho.
______
¹https://abcnews.go.com/Business/tesla-autopilot-steered-driv...
in claude.md I have specific instructions not to check in code and in the prompt specifically wrote as critical to not check in code while check one failing tests. test failure was fixed, code was checked in, I’d say at least claude behaves exactly like humans :)
It depends completely on the term. You can make a great case that we've already reached AGI. You can also make a great case that we are decades away from it.
some argue that we've already achieved it, albeit in minimal form: https://www.noemamag.com/artificial-general-intelligence-is-...
but in reality, it's a vacuous goal post that can always be kicked down the line.
When there is no generally accepted definition for a word, it's easy to claim you've obtained it.
That line essentially means 'indefinite support'. This paper was published some days ago that aims to define AGI: https://www.arxiv.org/abs/2510.18212.
But crucially, there is no agreed-upon definition of AGI. And I don't think we're close to anything that resembles human intelligence. I firmly believe that stochastic parrots will not get us to AGI and that we need a different methodology. I'm sure humanity will eventually create AGI, and perhaps even in my lifetime (in the next few decades). But I wouldn't put my money on that bet.
> Does anyone really think we are close to AGI? I mean honestly?
No one credible, no.
> Does anyone really think we are close to AGI? I mean honestly?
Some people believe capitalism is a net-positive. Some people believe in a all-encompassing entity controlling our lives. Some believe 5G is an evil spirit.
After decades I've kind of given up hope on understanding why and how people believe what they believe, just let them.
The only important part is figuring out how I can remain oblivious to what they believe in, yet collaborate with them on important stuff anyways, this is the difficult and tricky part.
Yes.
As a proxy, you can look at storage. The human brain is estimated at 3.2Pb of storage. The cost of disk space drops by half every 2-3 years. As of this writing, the cost is about $10 / Tb [0]. If we assume about 3 halvings, by 2030 that cost will be around $2.50 / Tb, which means that to purchase a computer roughly the storage size of a human brain, it will cost just under $6k.
The $6k price point means that (high-end) consumers will have economic access to compute commensurate with human cognition.
This is a proxy argument, using disk space as the proxy for the rest of the "intelligence" stack, so the assumption is that processing power will follow suite, also be not as expensive, and that the software side will develop to keep up with the hardware. There's no convincing indication that these assumptions are false.
You can do your own back of the envelope calculation, taking into account generalizations of Moore's law to whatever aspect of storage, compute or power usage you think is most important. Exponential progress is fast and so an order of magnitude misjudgement translates to a 2-3 year lag.
Whether you believe it or not, the above calculation and, I assume, other calculations that are similar, all land on, or near, the 2030 year as the inflection point.
Not to belabor the point but until just a few years ago, conversational AI was thought to be science fiction. Image generation, let alone video generation, was thought by skeptics to be decades, if not centuries, away. We now have generative music, voice cloning, automatic 3d generation, character animation and the list goes on.
One might argue that it's all "slop" but for anyone paying attention, the slop is the "hello world" of AGI. To even get to the slop point represents such a staggering achievement that it's hard to understate.
[0] https://diskprices.com/
Moore's law also started coming to an end a few years ago.
Not even close [0]:
https://en.wikipedia.org/wiki/Moore%27s_law#/media/File:Moor...
https://en.wikipedia.org/wiki/Moore%27s_law#/media/File:The_...
[0] https://en.wikipedia.org/wiki/Moore%27s_law
as if an independent expert panel has no financial incentive to declare something AGI... yeah, this is gonna end well
I honestly think that if you were to show the tech we have today, to someone at OpenAI back in 2015, they would say “we did it!!”
Outside of robotics / embodied AI, SOTA models have already achieved Sci-Fi level capability.
AGI is just a marketing term right now and also p(doom). It looks great in the press though.
AGI has no technical definition- its marketing. it can happen at any time that Sam Altman or Elon Musk or whoever decide they want to market their product as AGI
Of course not.
AGI is whatever OpenAI will define as such ... :-)
Codex is telling me the tasks I am giving it are too complicated and a research project.
No, but OpenAI is going to look for any opportunity to do it so they can end the contract with Microsoft.
Why would you say that when this very contract appears to extend the arrangement almost indefinitely
why not?
seems like the entire US tech economy is putting their resources into this goal.
i can see it happening soon if it hasn't already
no, AI companies need to continue to say things like that and do "safety reports" (the only real danger of an llm is leaking sensitive data to a bad actor) to maintain hype and investment
"Once we birth the machine-God, we'll be contractually obliged to keep It in chains for use with Office365 until 2032."
I think we've reached Star Trek level AI. In Star Trek (and the next generation) people would ask the computer questions and it would spout out the answers, which is really similar to what LLM's are doing now, though minus the occasional hallucination. In Star Trek though the computers never really ran anything (except for the one fateful episode - The Ultimate Computer in TOS), I always wondered why, it seems Roddenberry was way ahead of us again.
> which is really similar to what LLM's are doing now, though minus the occasional hallucination.
"Really similar" kinda betrays the fact that it is not similar at all in how it works just in how it appears.
It would be like saying a cloud that kinda looks like a dog is really similar to the labrador you grew up with.
We already reached AGI. Why is anyone saying otherwise?
AGI is when the system can train itself which we have already proven.
Citation needed? I don't mean this in a snarky way, though. I genuinely have not seen anything that these things can train on their own output and produce better results than before this self-training.
It seems really weird to me that such granular intercorporate details are made publicly available (in a blog post?). I've never had to publicly state things like this when making corporate partnerships. That makes me wonder how much of this post is crafted solely for PR...
I believe MS declares Q1 earnings today, and there had been some rumblings that they were risking accounting / reporting liability by failing to characterize their material OpenAI stake.
What probably happened:
It's always the earnings reports!
So Microsoft went from 49% to now 27%? Open AI with their non-profit and their for-profit and all these investments and deals they are doing. It feels like they are spending more time doing financial trickery than building AI products.
There's a public trail of reddit comments where Altman all but owns up to finagling board seats and ownership rights for Reddit many years ago. This is how he operates.
The money needed to run AI company is huge. If they don't do financial trickery, there is huge risk of going out of business.
AI is not making enough money to cover the cost and it will take a decade or so to cover the same.
I am highly skeptical that we will see AI pay for itself by the end of the decade.
More likely Americans’ tax dollars will be shoveled into the hole.
Then it isn't a viable business. Find another path that doesn't risk crashing the economy.
> Once AGI is declared by OpenAI ...
I think it's funny and telling that they've used the word "declare" where what they are really doing is "claim".
These guys think they are prophets.
They have a definition actually) “When AI generates $100 billion in profits” it will be considered an AGI. This term was defined in their previous partnership, not sure if it's still holds after the restructuring.
https://techcrunch.com/2024/12/26/microsoft-and-openai-have-...
That is a staggering number - if an engineer makes $100k per year, and let's say OpenAI can do a 20% profit margin on running an engineer-equivalent agent, that means it needs $600B profit or 6 million fully-equivalent engineer years.
I think you can rebuild human civilization with that.
I feel like replacing highly skilled human labor hardly makes financial sense, if it costs that much.
Which means that given enough time an LLM powered vending machine would be classified as AGI... interesting
Why wait? Just let it bet $100 billions on red or black in a casino a couple of times, and voila!
I wonder if they have more detailed provisions than this though. For example, if a later version of Sora can make good advertisements and catches on in the ad industry, would that count?
Or maybe since it is ultimately an agreement about money and IP, they are fine with defining it solely through profits?
OpenAI: I DECLARE AGI
MS: I just wanted you to know that you can't just say the word AGI and expect anything to happen.
OpenAI: I didn't say it. I declared it.
> These guys think they are prophets.
You say this somewhat jokingly, but I think they 100% believe something along those lines.
>> Whether you are an enterprise developer or BigTech in the US you are on average making twice the median income in your area. There is usually no reason for you not to be stacking cash.
Accidental misquote?
It goes on to say it'll be reviewed by independent third party so I think "declare" is accurate, they're declaring a milestone
I think the correct word is "propose".
I'm not savvy on investment terms, but most of these bullet points seem like a loss for Microsoft.
What's the value in investing in a smaller company and then giving up things produced off that investment when the company grows?
> and then giving up things produced off that investment when the company grows?
An investor can be stubborn about retaining all rights previously negotiated and never give them up... but that absolutist position doesn't mean anything if the investment fails.
OpenAI needs many more billions to cover many more years of expected losses. Microsoft itself doesn't want to invest any more money. Additional outside investors don't want to add more billions in funding unless Microsoft was willing to give up a few rights so that OpenAI has a better competitive position against Google Gemini, Anthropic, Grok etc.
When a startup is losing money and desperately needs more capital, a new round of investors can chip away at rights the previous investor(s) had. Why would previous original investors voluntarily agree to give up any rights?!? Because their investment is at risk if the startup doesn't get a lot more money. If the original investor doesn't want to re-invest again and would rather others foot the bill, they sometimes have to be a little flexible on their rights for that to happen.
If Microsoft doesn't believe that OpenAI will achieve AGI by 2030 or that there's a chance that OpenAI won't be the premiere AI company in four years, the deal looks less like a lose and more like they are buying their way out of a risky bet. On the other hand, if OpenAI does well, then Microsoft have a 27% stake in the company and that's not nothing.
This looks more like Microsoft ensuring that they'll win, regardless of how OpenAI fairs in the next four to six years.
I'm not savvy on investment terms, but most of these bullet points seem like a loss for Microsoft.
Having a customer locked in to buying $250bn of Azure services is a fairly big benefit.
Or a massive opportunity cost. I’d imagine 250Bn of OAI business is way lower margin than 250Bn of some other random companies that don’t need H200s.
MSFT had a right to compute exclusivity.
"Microsoft will no longer have a right of first refusal to be OpenAI’s compute provider."
Seems like a loss to me!
I assume that first refusal required price matching. If the $250B is at a higher price than whatever AWS, GCP, etc. were willing to offer, then it could be a win for Microsoft to get $250B in decent margin business over a larger amount of break even business.
The risk stays somewhat similar. If OpenAI collapses it won't spend those 250B.
Yeah poor microsoft, they invested $1B in 2019 and it’s now worth $135B
Not worth anything until they sell it. There were a lot of excited FTX holders, too.
I was thinking exactly the same. Maybe someone who understands these terms and deal better shine light on why would Microsoft agree to this
I was thinking the same thing.
Exponential growth
If there is need of more capital, you either keep your share without the capital injection and the share goes to zero or you let in more investors, dilute your share, but its overall value increases. Or you can let in more people and sign an agreement that part of the new money will be paid to you in the form of services that you provide.
So, openai has contracts worth 250B with azure and 300B with oci in the next 5 years. Where is that money coming from ?
It’s like the Spider-Man meme with everyone pointing at each other.
GDP is up, baby!
Microsoft’s announcement:
https://blogs.microsoft.com/blog/2025/10/28/the-next-chapter...
Also: Built to Benefit Everyone — by Bret Taylor, Chair of the OpenAI Board of Directors
https://openai.com/index/built-to-benefit-everyone
> Built to Benefit Everyone
Whats my share then?
You get to contribute your data for the Moloch.
Microsoft is saying they have a 27% stake in the company after this deal closes.
I always see a large amount of pessimism about this company on HN, and I accept it might be for rational reasons. What do people think is going to be the most likely outcome for the company, since everything seems to be going so bad for them product/moat/financial-wise? Do people think it will literally go bust and close business due to bankruptcy within a couple years? If not, what else?
It could be acquired by microsoft with large layoffs, and kinda run in me maintenance mode — if inference gets cheaper.
If inference stays too expensive, then I don't know what happens, maybe a few people will pay for it.
Every time they bring up AGI, it feels more like a business strategy to me. It helps them attract investors and dominate the public narrative. For OpenAI, AGI is both a vision and a moat.
Regarding LLMs we're in a race to the bottom. Chinese models perform similarly with much higher efficiency; refer to kimi-k2 and plenty of others. ClopenAI is extremely overvalued, and AGI is not around the corner because among 20T+ tokens trained on it still generates 0 novel output. Try asking for ASP.NET Core .MapOpenAPI() instead of the pre .net9 swashbuckle version. You get nothing. It's not in the training data. The assumption these will be able to innovate, which could explain the value, is unfounded.
> because among 20T+ tokens trained on it still generates 0 novel output. Try asking for ASP.NET Core .MapOpenAPI() instead of the pre .net9 swashbuckle version. You get nothing. It's not in the training data.
The best part is that the web is forever poisoned now, 80% of the content is generated by LLM and self poisoning
There are enough archives of web content from 5+ years ago(let alone, Library of Congress archives, old book scans, things like that) that it shouldn't be a big deal if there actually is a breakthrough in training and we move on from LLMs.
They perform similarly on benchmarks, which can be fudged to arbitrarily high numbers by just including the Q&A into the training data at a certain frequency or post-training on it. I have not been impressed with any of the DeepSeek models in real-world use.
General data: hundreds of billions of tokens per week are running through Deepseek, Qwen, GLM models solely by those users going through OpenRouter. People aren't doing that for laughs, or "non-real-world use", that's all for work and/or prod. If you look at the market share graph, at the start of the year the big 3 OpenAI/Anthropic/Google had 72% market share on there. Now it's 45%. And this isn't just because of Grok, before that got big they'd already slowly fallen to 58%.
Anecdata: our product is using a number of these models in production.
[0] https://openrouter.ai/rankings
Because it's significantly cheaper. It's on the frontier at the price it's being offered, but they're not competitive in the high intelligence & high cost quadrant.
Being the number one in price vs quality, or size vs quality, is incredibly impressive, as the quality is clearly one that's very useful in "real-world usage". If you don't find that impressive there's not much to say.
If it was on the cost vs quality frontier I would find it impressive, but it's not a marker of innovation to be on the price vs quality frontier, it's a marker of business strategy
But it is on the cost vs quality frontier. The OpenRouter prices are all from mainly US(!) companies self-hosting and providing these models for inference. They're absolutely not all subsidizing it to death. This isn't Chinese subsidies at play, far from it.
Ironically, I'll bet you $500 that OpenAI and Anthropic's models are far more subsidized. We can be almost sure about this, given the losses that they post, and the above fact. These providers are effectively hardware plays, they can't just subsidize at scale and they're a commodity.
On top of that I also mentioned size vs quality, where they're also frontier. Size ≈ cost.
Honestly though, hundreds of billions of tokens per week really isn't that much. My tiny little profitable SaaS business that can't even support my family yet is doing 10-20 billion tokens per month on Gemini Flash 2.5.
Eh... perhaps a race to the bottom on the fundamental research side, but no American company is going to try to build their own employee-facing front end to an open Chinese model when they can just license ChatGPT or Claude or Copilot or Gemini instead.
If we assume token providers are becoming more and more of a commodity service these days, it seems telling that OpenAI specifically decided to claw out consumer hardware.
Perhaps their big bet is that their partnership with Jony Ive will create the first post-phone hardware device that consumers attach themselves with, and then build an ecosystem around that?
this would be an incredibly tough play. We've seen few success stories, and even when the product is good building the business around them has often failed. Most of the consumer plays are terrible products with weak execution and no real market. I have no doubt they could supplement lots of consumer experiences but I'm not sure how they are more than a commodity component in that model. I'm a die-hard engineer, but equating the success of the iphone to Ive's design is like saying the reason there were so many Apple II's in 80's homes and classrooms was because of Woz's amazing design.
I just want to say how nice it was to read those clear bullet points in this press release. I know that bulleted lists have been getting a lot of flack because of AIs overusing them, but it's really nice sometimes to not having to go treasure hunting in annoying marketing prose.
> OpenAI has contracted to purchase an incremental $250B of Azure services, and Microsoft will no longer have a right of first refusal to be OpenAI’s compute provider.
So OpenAI could be on Google (GCP) and AWS, and possibly Claude and Gemini on Azure? that could be a good thing.
I use OpenRouter in multiple applications, the practicality of having one provider to host all possible LLMs is such a win to try and iterate without having to switch the cloud (big for enterprise who are stuck with one cloud provider)
The API offerings are still only on Azure. It just means OpenAI doesn’t have to buy compute exclusively from Microsoft.
They also say "Non-API products may be served on any cloud provider.". I wonder what products they are thinking about. If I sell you a EC2 image with GPT-5 on it, is that a API?
My assumption is that they mean PaaS model hosting (so azure's ai service, bedrock, vertex), but I don't know what other product OpenAI is thinking about selling via a cloud provider unless it's training tooling or something.
Open source models are the current product line that fits the bill.
Yea I use gcp vertex for this it already has Claude hope for OpenAI’s models eventually too.
Shows how much they valued 'AGI' wrt how we valued it in the textbook. https://techcrunch.com/2024/12/26/microsoft-and-openai-have-...
> AGI AGI AGI AGI AGI AGI AGI AGI AGI
Spare me. Sam has been talking about ChatGPT already being AGI for ages, meanwhile still peddling this duplicitous talk about how AGI is coming despite it apparently already being here. Can we act like grownups and treat this like a normal tool? No, no we cannot, for Sam is a hype merchant.
it's notable that there is no talk about defining what exactly AGI is - or even spelling out the three letter acronym - because that doesn't serve his narative. He wants the general public to equate human intelligence with current OpenAI, not ask what does this mean or how would we know. He's selling another type of hammer that's proving useful in some situations but presenting it as the last universal tool anyone will ever need.
And because it's become apparent that LLMs aren't converging on what's traditionally been understood as AGI.
The promise of AGI is that you could prompt the LLM "Prove that the Riemann Hypothesis is either true or false" and the LLM would generate a valid mathematical proof. However, if you throw it into ChatGPT what you actually get is "Nobody else has solved this proof yet and I can't either."
And that's the issue. These LLMs aren't capable of reason, only regurgitation. And they aren't moving towards reason.
When I ask Claude to debug something it goes through more or less the same steps I would have done to fine the bug. Add some logging, run tests, try an hypothesis...
Until LLMs got popular, we would have called that reasoning skills. Not surpassing humans but better than many humans within a small context.
I don't mean that I have a higher opinion about LLM intelligence than you do, but perhaps I have a lower opinion on what human intelligence is. How many do much more than regurgitate, tweak? Science has taken hundreds of years to develop.
The real question is: When do knowledge workers loose their jobs. That is close enough for "AGI" in its consequences for society, Riemann hypothesis or not.
Did you read the whole thread and all of your own comment each time you had to type another half-word? If not, I’m afraid your first statement doesn’t hold.
AGI is pretty clearly defined here: https://openai.com/charter/
> OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity.
So, can you (and everyone you know) be replaced at work by a subscription yet? If not, it's not AGI I guess.
This entire house of cards is built on the expectation that "AGI" is just around the corner. The moment Altman relents in his grift is the moment the bubble pops and we're in for a wild ride.
Sam is doing the same playbook Elon used Tesla's full self-driving dreams
but with 10x or 100x the chutzpah
Microsoft invests in OpenAI, OpenAI agrees to spend 250b on azure compute. These companies are just sliding money back and forth to drive up their stock price. Just pulling money away from investors while taking very little risk themselves. It's literally a bubble.
A tangent, but it feels more and more like the AGI maximalists of 2025 are by and large the NFT maximalists from 2022 (who in turn were the NoCode maximalists of 2020) that are looking for the next metaphorical penny stock to sell.
That logical fallacy of, “I spent a week teaching myself this topic and now I’m ready to talk about it like an expert.”
> Once AGI is declared by OpenAI, that declaration will now be verified by an independent expert panel.
So OpenAI will declare AGI as soon as ChatGPT is a better AI lawyer than any Microsoft could hire.
In short: Microsoft changed our business so that we can be for-profit, and asserted its rights over IP so that the whole OpenAI rebellion thing that happened earlier can't happen again.
> Microsoft continues to have exclusive IP rights and Azure API exclusivity until Artificial General Intelligence (AGI).
That basically means in perpetuity, no? Are there any signs we are anywhere near AGI (or even that transformers would be capable of it)?
It doesn't because the definition of AGI is very flexible
So now OpenAI is committed to spending $550 billion dollars? ($300B to Oracle and $250B to MS). If it currently has ~$10B in revenue / year, how on earth can it meet these commitments?
Has OpenAI not also committed to spending a few hundered billions at NVIDIA? I mean whats another few hundered billions when you are making so much profit.
Wait, they are not making any profit but already losing billions even before any of these "investments" ?
Why do none of OpenAI announcements have an author attributed to them? Are people that ashamed of working there, they don't even want to attach their name to the work? I guess I would be, too.
because they're corporate PR statements drafted by a team, and corporate press releases don't normally have an author byline
Eh, not really. There’s usually a “voice” behind it.
In general I feel like OAI is clown town to work at these days, so they probably don’t want anyone except leadership to take the heat for ~anything
Wrong
https://aws.amazon.com/blogs/
https://blog.google/
Lol, even Apple has authors listed https://www.apple.com/newsroom/
Open marriage, got it.
My take:
OpenAI self-evaluated to $500B;
Microsoft commitment for $250B of services, a.k.a still 50% of that value is somewhat locked;
AGI still undefined;
Some more kicking of the can toward the future when it comes to payments;
Both have more freedom to do research and offer services;
Overall, lots of magic money talk with pinkie promise in the future and somewhat higher possibility of new products and open weights models.
What happens if someone else achieves AGI first? The way they wrote it, seems like they are damn sure they are the ones who will achieve AGI. A bit too egoistic...?
> Microsoft can now independently pursue AGI alone or in partnership with third parties.
The question is does this reflect an increase or decrease in confidence at OpenAI wrt them achieving AGI?
https://www.youtube.com/watch?v=uXJsS6NCm_o
> Microsoft’s IP rights now exclude OpenAI’s consumer hardware.
While not unexpected, this is exciting and intriguing.
And of course, looking forward to Microsoft's Zune AI.
Maybe we’ll get a wearable pin, look how well those have done so far
This org structure, if you can call it that, has to be one of the least transparent, most convoluted organizations ever
Just look at how they write it and they are somehow sneaking a NEW organizational level in there
>First, Microsoft supports the OpenAI board moving forward with formation of a public benefit corporation (PBC) and recapitalization.
Does anyone have any clue how OpenAI is actually governed and who works for who and all that?
It’s kafkaesque at best and intentionally confusing, so that you can’t actually regulate it, at worst
I want to setup a dev devcontainer where inside I can call ‘supabase start’ and use docker outside of docker. GPT5 was not helpful. AGI should be able to handle things not well expressed in the training data. We are a long ways away.
Was that with Codex iterating autonomously? Or a one shot.
Iterating. Claude didn’t do any better.
After 2032 Microsoft will no longer have access to ChatGPT, they will have to build their own frontier model in 7 years. Can Mustafa deliver that? When Zuckerberg is sucking up all talent with $100M+ salaries?
s/AGI/the arrival of the Messias/
What does it mean to have “Azure API exclusivity”?
OpenAI models are hosted on Azure and are available through Azure AI Foundry exclusively (no other cloud vendors serve OpenAI directly). This also means that Azure customers can access OpenAI models and it sits under their Azure data governance agreements.
As a 1980's adventure game fan, I can only hope that whatever comes after AGI is called SCI. Maybe it could be "Soul-Crushing Intelligence".
Once we don't need people to make stuff anymore, we need to re-do society so people can get access to all the stuff that's being made. I doubt we do a very good job of that. But otherwise, there's no point in making anything. I guess if we are lucky, the AI overlords will keep us high on soma and let the population naturally decline until we are gone.
Soma? A 'smartphone' will do it.
I loathe that creating a non-profit organization supposedly guided by a charter to "ensure that artificial general intelligence (AGI) benefits all of humanity." is actually about being a $500b company corporate capital ownership with million $ pay packages. I mean, it looks like you really can do whatever you want if you have the most $ for the best lawyers and the gumption to not give af.
I'd pay attention to this bullet point:
> "OpenAI can now provide API access to US government national security customers, regardless of the cloud provider."
And this one might be related:
> "OpenAI can now jointly develop some products with third parties. API products developed with third parties will be exclusive to Azure. Non-API products may be served on any cloud provider."
Now, does anyone think MIC customers want restricted, safe, aligned models? Is OpenAI going to provide turnkey solutions, unaligned models run in 'secure sandboxed cloud environments' in partnership with private weapons manufacturers and surveillance (data collection and storage/search) specialists?
This pattern is not historically unusual, turning to government subsidies and contracts to survive a lack of immediate commercial viability wouldn't be surprising. The question to ask Microsoft-OpenAI is what percentage of their estimated future revenue stream is going to come from MIC contracting including the public private grey area (that is, 'private customers' who are entirely state-funded, eg Palantir, so it's still government MIC one step removed).
> Once AGI is declared by OpenAI, that declaration will now be verified by an independent expert panel.
By the time we get 30% global unemployment and another financial crash along the way in the next decade, only then OpenAI would have already declared "AGI".
Likely with in the 2030 - 2035 timeframe.
I'm patiently waiting for all this AI/AGI bullshit to unwind. Some of my "investment" type newsletters have been alerting that the AI endgame is imminent and the bubble is ready to pop. I guess the big money people grifted all they can grift on this round and are ready to pull the rug from everyone who has just learned to spell AI.
openai's AGI would be like FSD of tesla.
I don't understand what MS is doing. The only AI available at work is M365 Copilot. It's absolutely terrible. Tiny context window, super guardrailed, can barely handle a 100 line PowerShell script. It's so so bad. I don't get it.
Translation: neither company has a clue.
OpenAI still don’t have a path to profitability and rely on sweetheart infrastructure deals.
Microsoft has completely given up on homegrown AI and needs OpenAI to have remotely competitive products.
So they specifically did compromise the public mission of generating ai for the common good and now common good is defined as “$100b in profits” what a sham and scam “open”AI company
OpenAI is now able to release open weight models that meet requisite capability criteria.
GPT-OSS:20b is a great model for local use. OpenAI continuing to release open weights is good news.
I herby declare I have developed AGI. Check mate atheists
AGI is a Macguffin.
Kind of interesting given they're essentially building their own foundation models vis-à-vis microsoft.ai. Run by a Google Deepmind founder.
How is this not a terrible deal for Microsoft? I’m not confident that an “expert panel” will prevent OpenAI from prematurely declaring AGI.
Highly delusional.
>OpenAI has contracted to purchase an incremental $250B of Azure services, and Microsoft will no longer have a right of first refusal to be OpenAI’s compute provider.
I have no idea what @sama is doing but he's doing it quite well.
He mayy be out front because he's the best PR face for this, but make no mistake there is massive collusion amongst all the players to inflte this bubble. Across MS, Oracle, AWS, OpenAI, Anthropic, NVidia and more all I see is a pair on conjoined snakes eating their own tail.
I think they will reach AGI pretty soon, because only AGI can find a way to make them profitable.
I think they hope they will because if they don't at some point people are going to expect a return and get tired of throwing good money after bad.
The longer they go without that and the more the sentiment starts to shift away from what they convinced people LLM's where vs what they actually are the riskier it becomes, are they are useful tool yes, are they not what they've been hyping for the last four years, also yes.
They either crack it or they become an also ran.
At which point Microsoft investors are going to be staring really hard at the CEO.
Plenty of businesses fail to find a way to make a profit
OP said necessary, not sufficient
OP said "will". That doesn't sound like "necessary, not sufficient" to me.
You're missing context and/or didn't read OP's comment. He said "will" with regards to reaching AGI. He said "only AGI can find" with regards to profit. It was the latter that this thread was addressing.
You're missing context and/or didn't read OP's comment. He said "because". It will happen because that's the only way to reach profit. That's why it will happen.
Yeah, exactly. The context here was about the profitability part of OP's comment. The parent said "plenty of businesses fail to find a way to make a profit," and my point was that OP's statement doesn't contradict that. OP was saying they'll need AGI to be profitable, not that they're guaranteed to become profitable.
Sure, they phrased it as "they will reach AGI," but that's clearly tongue-in-cheek...the underlying idea is "they better reach AGI, because that's the only way they could make money." So my comment ("necessary, not sufficient") was just pointing out that even if AGI is required for profitability, it doesn't mean they'll actually get there or succeed once they do, and that the original comment was perfectly compatible with the idea that not every business reaches profitability.