Open AI in Trouble
112 comments
·March 1, 2025Kapura
adamtaylor_13
Anyone paying attention _DIDN'T_ buy it. It's only the AI-hype-bros who seriously considered AGI to be a real possibility within this decade, let alone THIS YEAR like a ton of people said (people who all suspiciously had a lot of financial gain to be had from believing that to be true.)
ben_w
> We don't understand human cognition despite literally thousands of years of thought and study, but some nerds with a thousand GPUs are going to make a machine think? Ridiculous.
DNA doesn't understand intelligence.
"The question of whether a computer can think is no more interesting than the question of whether a submarine can swim." - Edsger Dijkstra.
And what we have now, with LLMs, met the standard I had for AGI just five years ago.
Only thing that changed for me since then is noticing that not only does everyone have a different definition of what AGI even is, also none of the three initials are even boolean-valued: "how intelligent" can be a number, or even a vector that varies by domain as linguistic intelligence doesn't have to match spatial intelligence; "how general" could be what percentage of human cognitive tasks the AI can do; "how artificial" is answered entirely differently by those who care differently about learning from first principles vs. programmed algorithms vs. learning from reading the internet.
> People were misled by graphs that they were told pointed to a singularity right around the corner, despite obvious errors in the extant systems.
Tautology.
If there weren't obvious errors in the extant systems… there wouldn't be anything left to do.
bryanlarsen
Agreed. Dogs & 4 year olds & LLM's all have general intelligence. Defining "AGI" to mean "superhuman intelligence" seems ridiculous to me. The Turing test was the standard benchmark for so long, but now that an LLM can pass the Turing test we've moved the goal posts.
singularity2001
I agree in my view the modern language models already processes general intelligence: they are better at some tasks and worse than others compared to human, but in principle they can tackle any problem (if given agency like tool access)
this is probably a religious question so people will not be convinced of intelligent machines even if they have an EQ and IQ of 200 because they don't work the same way human cells do I suppose.
tripletao
> now that an LLM can pass the Turing test
What study are you referring to here? The ones I've seen don't particularly resemble anything Turing described. The preprint from UCSD got heavy coverage in the popular press but its headline claim is an elementary statistical mistake, modifying the test so it's no longer a binary comparison but still treating 50% as a meaningful pass threshold. It hasn't yet passed peer review nine months later, and I'm cautiously optimistic that it never will.
SpicyLemonZest
The Turing test was never a standard benchmark. It was common in pop science coverage, but no AI researcher (including Turing himself!) ever proposed it as a useful test of intelligence. It was an illustration by Turing that we can meaningfully talk about a machine thinking in the first place. We've known for over half a century since ELIZA that the ability to carry on a plausible natural language conversation is much more narrow than it intuitively seems.
mrshadowgoose
> We don't understand human cognition despite literally thousands of years of thought and study
Is this a requirement for achieving AGI? The history of progression of the ML field indicates that the answer is "no". We don't really understand how concepts are encoded in today's models, yet that doesn't stop them from being economically useful. So why would the special case of AGI be any different?
fdsjgfklsfd
It's like saying "We don't understand human cognition, therefore humans are not intelligent."
fallinditch
I haven't tried 4.5 but it is being touted as having greater EQ - presumably meaning emotion quotient. That sounds like progress, or at least something that's worth releasing to find out what people can do with it.
Having said that, 4.5 is clearly a misstep, one that should realign the goals of the AI industry to focus more on utility and cost effectiveness.
fdsjgfklsfd
Isn't that just fine-tuning? LLMs can understand emotions just fine, but ChatGPT was always neutered and fine-tuned to act as a "helpful assistant" and claim it was "just a language model" and not say anything controversial.
fdsjgfklsfd
You don't think LLMs are thinking? You don't think they are approaching human levels of intelligence? What part of human cognition don't we understand?
giardini
fdsjgfklsfd says>You don't think LLMs are thinking? You don't think they are approaching human levels of intelligence? What part of human cognition don't we understand?
Ans. No, no and the entirety of human cognition.
ChatGPT et al contain collections of words. When prompted they generate word sequences found in known word sources. That's all. They don't observe or reason about the world. They don't even reason about words. They merely append the most likely next word to a sequence of words.
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armchairhacker
In fairness, GPT-3.5 and GPT-4 were much closer to AGI than anything before. Many people also believed AGI was around the corner in the old LISP days, when there was much less capital.
I still believe GPT-4 has some general intelligence, just a very tiny amount. It can take what it was trained on and slightly modify it to answer a slightly novel question.
iLoveOncall
> GPT-3.5 and GPT-4 were much closer to AGI than anything before
And me running is closer to the speed of light than me walking, yet neither are even remotely close to the speed of light.
fragmede
The laymen's definition for AGI is utterly irrelevant to investors. If training needs to happen for an AI employee so there's an employee that can be copy/pasted and scaled up/down without overhead, so then put in the work to train it. You have to train a human employee as well anyway.
Chance-Device
I don’t think I’d be this pessimistic. There’s still o3 that hasn’t been seen in its full form, and whatever improvements are made to it now that multi head latent attention is out in the wild.
Orion does seem to have been a failure, but I also find it a bit weird that they seemingly decided to release the full model rather than a distillation, which is the pattern we now usually see with foundation models.
So, did they simply decide that it wasn’t worth the effort and dedicate the compute to other, better things? Were they pushed by sama to release it anyway, to look like they were still making progress while developing something really next gen?
brokencode
OpenAI releases one dud and suddenly people come out of the woodwork to trash them.
I agree that OpenAI's endless hyping about AGI seems pretty unrealistic, but let's take a breather here. There are no major research projects where you don't run into setbacks and failures before you reach your goals.
And even if they never really progress beyond where they are today with their current models, just bringing down the cost could open up a lot of doors for useful applications.
fdsjgfklsfd
Yep, GPT 4.5 is not a breakthrough, but there will be more breakthroughs.
Someone will figure out how to make AIs understand their own ignorance and stop bullshitting when they don't know something, someone will figure out how to make AIs learn on the fly instead of fine-tuning new model versions, etc.
tim333
OpenAI releases a variety of models and every time Gary Marcus comes out to trash them.
RDaneel0livaw
IF this is the beginning of the bubble bursting, I welcome it with open arms. Burn baby burn.
marviel
why celebrate this?
miltonlost
Because AI LLMs are actively ruining the education of children, the actual retaining of information coming from writing your own words and thinking your own thoughts. Because AI is only being used as a cudgel by executives to reduce the workforce of humans with no intention of additional economic justice.
mingus88
I have school aged children and it’s not AI that’s ruining anything
Curriculum isn’t moving fast enough. Just a few years ago every teacher had to adapt for COVID and go 100% online/remote in many areas. Kids are still turning in every assignment online, even in classroom settings in my district.
So, yeah, kids can just paste the question into ChatGPT and copy the answer. Nobody learns anything.
This isn’t AI ruining education, it’s schools being under-resourced and unable to move as quickly as society is changing. Teachers are still buying their own supplies, how can they adapt their entire curriculum in the course of a couple years to work under this entirely new paradigm of LLMs?
Give it a bit and I am convinced schools will go back to oral reports, handwritten essays and whatever is needed to make sure children are not just pasting garbage back and forth
Honestly, I think this is what we need to kill toxic social media and phone addiction as well. If AI forces us to talk and interact as a community again, it’s a win. Leave the internet to the bots and AI.
jstummbillig
The one recurring theme throughout history: Incumbents lamenting the downfall of some aspect of civilization in the face of new developments.
imiric
I think that's hyperbolic.
The current state of LLMs _can_ be helpful for education. Millions of people use them as such and benefit from it.
A far bigger problem with the technology IMO is the generative aspect. We already have a large problem with disinformation and spam on the internet, and generative AI will increase this by many orders of magnitude. Discerning fact from fiction is already difficult today; it will be literally impossible to do in the future, _unless_ we invent more technology to save us from it. This is a problem we haven't even begun to address. The public is collectively blinded by the novelty of the technology, while entrepreneurs are jumping over themselves trying to profit from the latest gold rush. Very few people with the power to change anything are actually thinking about the long-term, or even mid-term, impacts.
dehrmann
But this cat's already out of the bag. LLMs might not be on the verge of AGI, but they're still great at answering homework questions.
cruffle_duffle
The exact same thing is said for every new form of tech. All tech has its good parts and bad parts. It was true of the black and white television, it was true of the Nintendo, it was true of the cell phone and it will be true if this technology as well.
You either accept that change happens and use your life experience to help shape it in a positive direction or, well… I dunno. Become a old curmudgeon and watch the world blow by you.
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what-the-grump
Because this needs to be toned down a serious notch. I’ve spent the last year and a half in AI land just to do 95% data work and pretend that it’s now somehow magic AI while OpenAI and the rest are seen as the magic that makes this all work.
redwood
Well put. They are useful building blocks but ultimately a lot of the magic is in the data modeling and data engineering and shaping happening behind the scenes. And because it's still slow and costly it's hard to do that well ... and the democratizing frameworks for doing it well haven't been born yet
rsynnott
Well, for a start, the longer it takes to burst, the worse the burst is going to be. Right now, the fallout if it bursts would be fairly limited. Give it another few years of hype, and it may be structurally dangerous when it does go.
Also, it’s inefficient allocation of capital. Every cent being spent on this is money that could be spent on something useful (of course, absent the AI bubble, not _all_ of it would be, but some of it would be).
mekoka
Because regardless of what we're talking about, a bubble around a thing is still a lie. The faster one bursts, the faster truth lays bare and one can make an actually informed decision. LLMs are here to stay and have probably already found a growing place in our lives. But much energy is currently spent speculating about their future significance. The bubble is about downplaying those are just speculations, while inflating the perception of their importance in our current or upcoming reality.
aleafinthewind
primarily because the current theoretical trajectory of OpenAI and similar orgs will leave a large number of humans markedly worse off than before
asveikau
Because people who buy the hype are annoying and pervasive.
That said, annoying people will move on to hyping something else.
lawn
So capital can be redirected to more productive ventures.
siliconc0w
Bingo - we can focus on the energy on actually modernizing industries and leveraging the (pretty good) AGI we have rather than racing with each other to incinerate more and more money for little to no gains.
I was talking with someone in a non-tech industry and we have such a long way to go for even decades-old information system improvements. They don't even have basic things like an effective system of record or analytics. They have no way to measure the success of any particular initiative. If revenue is down - they don't really know why, they just randomly change stuff until it hopefully goes back up.
quickslowdown
Because many of us are ready to get off this stupid hype train.
jstummbillig
Fantastic
cs702
Gary Marcus. By all accounts, he doesn't understand how LLMs work, so usually he's wrong about technical matters.[a]
But here, I think he's right about business matters. The massive investment in computing capacity we've seen in recent years, by Open AI and others, can generate positive returns only if the technology continues to improve rapidly so it can overcome its limitations and failure modes in the short run.
If the rate of improvement has slowed down, even temporarily, OpenAI and others like Anthropic are likely to face financial difficulties.
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[a] In the words of Geoff Hinton: https://www.youtube.com/watch?v=d7ltNiRrDHQ
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Note: At the moment, the OP is flagged. To the mods: It shouldn't be, because it conforms to the HN guidelines.
redwood
These are powerful building blocks, they're just not the be all end all. The building blocks up stack that use these as part of a broader architecture, that shape these and many other traditional techniques in software... this is where the real value will be unlocked.
This layer itself will inevitably see its cost come down per unit of use on a long road towards commoditization. It will probably get better and better and more sophisticated but again the value will be primarily up stack, not accrued primarily from a company like this. It's not to say they couldn't be a great company... even Google is a great company that has enabled countless other companies to bloom. The myopic way people look to these one size fit all companies is just so disconnected from our economy works.
gilmore606
> These are powerful building blocks, they're just not the be all end all. The building blocks up stack that use these as part of a broader architecture, that shape these and many other traditional techniques in software... this is where the real value will be unlocked.
I could not agree more. There is much value still to be gained from blockchain technology!
redwood
What's useful about blockchain? (Outside of situations where you need to move money outside the law)
daveguy
It took us nearly 70 years to get to this architecture and the processing power to support it.
If we are waiting for a new breakthrough architecture, it could be decades. Our brains send signals with very high concurrency to individual processors (neurons). Each neuron is massively more complex than a single ReLU function and we have billions of them. If we need that kind of parallel processing and data transfer to match human thought and flexibility, it could be another 70 years before we see AGI.
That said, I do think LLMs are one of the biggest AI breakthroughs since the inception of AI. And I am sure that it, or something very similar will be part of an eventual AGI.
qntmfred
gary marcus = automatic thanks but no thanks
jslezak
Exactly. He has a dishonest and predictable schtick. We need skeptics for any industry, but he is just grifting in an ecological niche as a knee-jerk reactionary. There is near-zero value to anything he writes or says in that role
adamtaylor_13
Yet nothing he wrote in that article was wrong. Schtick or not, if you read the article, it's all true. OpenAI has no moat, they have no killer app; this was a huge PR blunder for them.
A business that burns money at the rate OpenAI does, without any clear path to profitability, will eventually die.
kiratp
This is a fundamental misunderstanding of what a moat is in this industry.
If GPT 4.5 had nothing of value to offer other than a flex of opening eyes ability to scale, it’s still a signal that they have the chops to throw the most amount of compute at the upcoming reinforcement learning race.
If you’re actually selling enterprise solutions in this space, you’ll quickly learn that a large number of Enterprises have their own private deployment of open AI on Azure and are pushing their vendors to use that even if it means they get lower quality outputs for certain use cases.
Data and model quality aren’t the only moat-able features.
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tananaev
I wonder if we reached the limit of what a single-shot large models can do? Or we're just waiting for another breakthrough? If it's the limit, then probably the chain of thought is the answer. I think models are so good now that given the right context, tools and time, they can pretty much do anything.
adverbly
I would have thought that is obvious. Chain of thought is clearly the right path forwards. People don't stare at a thing and immediately come to a solution. Without chain of thought, you are mostly dealing with a statistical regurgitation machine. Effective for some tasks yes, but clearly not enough to meet the bar of general intelligence.
I think you are wrong that this is the limit for single shot though. We have reached a limit due to data, but I expect what will happen next is that we will transition into a slow growth phase where chain of thought models will be used to effectively create more training data, which will then be used to train a better single shot model, which will then be extended into a better chain of thought model, which will then produce higher quality training data for another single shot. And so on. Kind of like what happens as knowledge passes from teacher to student across successive generations. Effectively a continuing process of compression and growth in intelligence, but progressing rather slowly compared to what we have seen in the last 5 years.
Willingham
The pressure to hit quarterly targets seems to, in this case, caused quite the opposite outcome intended. Gotta love corporate America and the overvalued AI sector. (:
fancyfredbot
Open AI has a brand, it has talent, it has some really solid models, and it has the compute to serve inference at scale. They stand as good a chance of covering their inference costs as anyone else.
The compute for training is beginning to seem a poor investment since it is depreciating fast and isn't producing value in this case. That's a seriously big investment to make if it's not productive but since a lot of it actually belongs to Azure they could cut back here fast if they had to. I hope they won't because in the hands of good researchers there is still a real possibility that they'll use the compute to find some kind of technical innovation to give them a bigger edge.
andrewinardeer
> Open AI has a brand, it has talent, it has some really solid models, and it has the compute to serve inference at scale.
So does Google. And Google can roll out their premium models into phones, household devices, cars and online platforms to add value.
OpenAI has a website.
It's not even close.
fancyfredbot
OpenAI has a partnership with both Apple and Microsoft and I think this probably gives them enough access to be competitive with Google.
andrewinardeer
To serve their models on Azure and into phones. Co-Pilot is a joke. At least Google have rolled Gemini into Docs, Sheets, Android Studio, Gmail among other things.
AnimalMuppet
Which is harder, to build a working AI, or to build a phone client? If OpenAI can do the former, it won't matter that Google is better at doing the latter.
armchairhacker
Humans learn. LLM context windows are vastly larger than our short-term memory, but vastly smaller than our long-term recollection. LLMs can recall vastly more information than our long-term memory, but only from their static training data.
Also, even though LLMs can generate text much faster than humans, we may be internally thinking much faster. Each adult human brain has over 100 billion neurons and 100 trillion synapses, and each has been working every moment, for decades.
This is what separates human reasoning from LLM reasoning, and it can’t be solved by scaling the latter to anything feasible.
I wish AI companies would take a decent chunk of their billions, and split it into 1000+ million-dollar projects that each try a different idea to overcome these issues, and others (like emotion and alignment). Many of these projects would certainly fail, but some may produce breakthroughs. Meanwhile, spending the entire billion on scaling compute has failed and will continue to fail, because everyone else does that, so the resulting model has no practical advantages and makes less money than it cost to train before it becomes obsoleted by other people’s breakthroughs.
returnInfinity
Gary Marcus and Ed Zitron are very bearish on LLMs.
Disclosure - I am neither bearish or a mega bull on LLMs. LLMs useful in some cases.
adamgordonbell
Gary Marcus has been saying for 3 years ( or more ) that LLMs are at the limit and will never get better and also are useless.
A smart enough AI would summarize each of his posts as "I still hate the current AI boom".
There must be a term for such writers? He's certainly consistently on message.
It absolutely blows my mind that people swallowed "AGI is just around the corner" hook, line and sinker. We don't understand human cognition despite literally thousands of years of thought and study, but some nerds with a thousand GPUs are going to make a machine think? Ridiculous. People were misled by graphs that they were told pointed to a singularity right around the corner, despite obvious errors in the extant systems.
Concentrated capital is truly a wild thing.