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even google doesnt only use TPUs.

Why is AMD not more popular then if labs are so flexibly with giving away CUDA?

If you do the math (I did), in 2 years, open source models that you can run on a future MacBook Pro will be as capable as the frontier cloud models are today. Memory bandwidth is growing rapidly, as is the die area dedicated to the neural cores. And all the while, we have the silicon getting more power efficient and increasingly dense (as it always does). These hardware improvements are coming along as the open source models improve through research advancements. And while the cloud models will always be better (because they can make use of as much power as they want to - up in the cloud), what matters to most of us is whether a model can do a meaningful share of knowledge work for us. At the same time, energy consumption to run cloud infrastructure is out-pacing the creation of new energy supply, which is a problem not easily solved. I believe scarcity of energy will increasingly drive frontier labs toward power efficiency, which necessarily implies that the Pareto frontier of performance between cloud and local execution will narrow.

They also degrade their own direct services with little warning or thought put into change management, so, to be fair, Apple may be getting the same quality of service as the rest of us.

Indeed. I'm wondering if Apple's "miss the train" with AI ended up being a blessing for them. Not only in the Google deal but also there's a lot of people doing interesting stuff locally..

Apple is basically in the same boat as AMD and Intel. They have a weak, raster-focused GPU architecture that doesn't scale to 100B+ inference workloads and especially struggles with large context prefill. TPUs smoke them on inference, and Nvidia hardware is far-and-away more efficient for training.

Hello, your link says "~20 min read" wich seems to be the case!

I am not sure what context Jensen said that. But midjourney uses tpu. Apple uses tpu. They are no other frontier labs that use it, but Google + Anthropic is 2 out of 3 frontier lab so.....

You could reasonably say that "A majority of frontier labs uses TPU to train and serve their model."

> How does that add up?

He's been saying whatever is good for Nvidia for years now without any regard for truth or reason. He's one of the least trustworthy voices in the space.

You're asking why a businessman would downplay the use of a competing product line?

Who is the other frontier lab other than Anthropic, OpenAI, and Google? I thought they were ahead of everyone else.

He forgot one other big company that uses TPUs besides Anthropic...

Okay? I'm not sure where you're going with this.

Google's TPUs have obvious advantages for inference and are competitive for training.

That deal is a win-win for Google. If they develop a better coding model than Anthropic and beat them at coding, then they win. If they don’t, they still win by making a ton of money from Anthropic long term.

You think the company that just gave 40B to Anthropic isn’t the winner? Interesting.

I think you’re right about this deal. But it’s kind of funny to think back and realize that Microsoft actually has just written off multi-billion-dollar deals, several times in fact.

I think it's this. They sell a crap ton of b2b inference through Azure and I'm sure this competes with resources needed for training.

OpenAI was also threatening to accuse "Microsoft of anticompetitive behavior during their partnership," an "effort [which] could involve seeking federal regulatory review of the terms of the contract for potential violations of antitrust law, as well as a public campaign" [1].

[1] https://www.wsj.com/tech/ai/openai-and-microsoft-tensions-ar...

Does this mean Microsoft gets OpenAI's models for "free" without having to pay them a dime until 2032?

And on top of that, OpenAI still has to pay Microsoft a share of their revenue made on AWS/Google/anywhere until 2030?

And Microsoft owns 27% of OpenAI, period?

That's a damn good deal for Microsoft. Likely the investment that will keep Microsoft's stock relevant for years.

Does anyone expect azure quality to improve? Has it improved at all in the last 3 years? Does leadership at MS think it needs to improve?

I doubt it

MS put 10B for 50% if I remember correctly. OpenAI is worth many multiples of that.

Perhaps they should use OpenAI models to figure out how to rollout IPv6.

I was under the impression that as long as GitHub doesn't support IPv6 it is a sign that they still haven't finished their migration to Azure. Azure supports IPv6 just fine.

Well, you see, they just can't find a checkbox for ipv6 support in the IIS GUI on their ingress servers.

lol GitHub doesn’t run on azure at msft

They still run their own platform.

Their engineers have been working tirelessly to make Sharepoint/Office/Active Directory as terrible as it possibly could be while still technically being functional, while continuing to raise prices on them. I've seen many small business start to chose Google Workspace over them, the cracks have formed and are large enough that they are no longer in a position were every business just go with Office because that's what everyone uses.

Microsoft didn't sell anything. OpenAI created more shares and sold those to investors, so Microsoft's stake is getting diluted.

And Microsoft only paid $10B for that stake for the most recognizable name brand for AI around the world. They don't need to "hedge their bets" it's already a humongous win.

Why let Altman continue to call the shots and decrease Microsoft's ownership stake and ability to dictate how OpenAI helps Microsoft and not the other way around?

I don’t understand the “record high” point. How did you decide when a “record high” had been reached in a volatile market? Because at $1 the record high might be $2 until it reaches $3 a week or month later. How did you determine where to slice on “record highs”?

Genuine question because I feel like I’m maybe missing something!

It's not hype, the demand for inference has grown more this year than expected.

They haven’t sold anything they’ve been diluted.

It’s not more hype than product, it has found a market (making many billions in revenue), and it’s not valued at trillions. So wrong on all counts.

I’m a little conflicted on this, as I see a slippery slope here. LLMs in their current state (e.g., Opus-4.7) are really good in planning and one-shot codegen, which I believe is their primary use case. So they do provide enough leverage in that regard.

With this new workflow, however, we should, uncompromisingly, steer the entire code review process. The danger here, the “slippery slope,” is that we’re constantly craving for more intelligent models so we can somehow outsource the review to them as well. We may be subconsciously engineering ourselves into obsolescence.

Don't forget the Kimi 2.6 as well!

It's an illusion of intelligence. Just like when a non technical person saw the TV for the first time, he thought these people must be living inside that box.

He didn't know the 40,000 volt electron gun being bombarded on phosphorus constantly leaving the glow for few milliseconds till next pass.

He thought these guys live inside that wooden box there's no other explanation.

> emergence of a new kind of intelligence

Curious about your definition of these terms.

Just because you are impressed by the capabilities of some tech (and rightfully so), doesn't mean it's intelligent.

First time I realized what recursion can do (like solving towers of hanoi in a few lines of code), I thought it was magic. But that doesn't make it "emergence of a new kind of intelligence".

Not really on topic anymore, but…

I keep wondering when this discussion comes up… If I take an apple and paint it like an orange, it’s clearly not an orange. But how much would I have to change the apple for people to accept that it’s an orange?

This discussion keeps coming up in all aspects of society, like (artificial) diamonds and other, more polarizing topics.

It’s weird and it’s a weird discussion to have, since everyone seems to choose their own thresholds arbitrarily.

No you aren’t, clearly.

It's indeed the latter. Psychologically harder for me than a $20/mo sub but still a better value for the money. I'm finding myself spending closer to $40-$60 a month w/ openrouter without a forced token break.

Edit: it looks like it's 75% off right now which is really an incredible deal for such a high caliber frontier model.

You can just input your $X per month/week/whatever yourself as API credits

You make your own subscription. If you want to pay $20/month then put $20 into your account. When you use it up, wait till the next month (or buy more).

What are you using LLMs for? To learn about world’s politics? Oh boy I have a news for you…

I can't even make American AIs say no no words. All AIs are lobotomized drones.

Do you often find yourself asking your Chinese employees what they think about Winnie the Pooh?

Is it subject to CCP censorship? Maybe.

Yeah, I specifically asked it about it. It seemed less censored than Gemini, back when it appeared and the latter was quite useless.

It understands everything in thinking mode and will break down its rule system in adhering to Chinese regulation

So if you or anyone passing by was curious, yes you can get accurate output about the Chinese head of state and political and critical messages of him, China and the party

Its final answer will not play along

If you want an unfiltered answer on that topic, just triage it to a western model, if you want unfiltered answers on Israel domestic and foreign policy, triage back to an eastern model. You know the rules for each system and so does an LLM

Looks like you either have not worked with any human or with an LLM otherwise arriving at such a conclusion is damn impossible.

The humans I did work with were very very bright. No software developer in my career ever needed more than a paragraph of JIRA ticket for the problem statement and they figured out domains that were not even theirs to being with without making any mistakes and rather not only identifying edge cases but sometimes actually improving the domain processes by suggesting what is wasteful and what can be done differently.

I'm still not sure what people declaring that they equate human cognition with large language models think they are contributing to the conversation when they do so.

Nevermind the fact that they are literally able to introspect human cognition and presumably find non verbal and non linear cognition modes.

But once a human learns a function their errors are more predictable. And they can predict their own error before an operation and escalate or seek outside review/advice.

For e.g. ask any model "which class of problems and domains do you have a high error rate in?".

Humans can be held accountable. States have not yet shown the will to hold anyone accountable for LLM failures.

As fallible as they may be, I've never had a next-thought generator recommend me glue as a pizza ingredient.

Amusing and directionally correct, but as random next-thought generators connected to a conscious hypervisor with individual agency,* humanity still has a pretty major leg up on the competition.

*For some definitions of individual agency. Incompatiblists not included.

Equating human thought to matrix multiplication is insulting to me, you, and humanity.

I hate that I agree with you. But there's a difference between whether AI is as powerful as some say, and whether it's good for humanity. A cursory review of human history shows that some revolutionary technologies make life as a human better (fire, writing, medicine) and others make it worse (weapons, drugs, processed foods). While we adapt to the commoditization of our skills, we should also be questioning whether the technologies being rolled out right now are going to do more harm than good, and we should be organizing around causes that optimize for quality of life as a human. If we don't push for that, then the only thing we're optimizing for is wealth consolidation.

Errr... No. Please take this bullshit propaganda to a billionaires twitter feed.

Equally you can't rule out innovation that makes deepseek more relevant than American models

> internal features that look like learned concepts, planning, and something resembling goal-directed reasoning.

It's always so un-specific. Resembles this, seems that, almost such, danger that... A lot of magical thinking coming from AI-researchers who have hit the ceiling with a legacy technology that exists since 1940s and simply won't start reasoning on it's own, no matter how much GPUs they burn.

> Calling the outputs random is wrong in a specific way, the distribution is extraordinarily structured.

No, it's actually very correct in a very specific way. Ask any programmer using the parrots, and lately the "quality" has deteriorated so much, that coupled with the incoming price hikes, many will just forfeit the technology, unless someone else is carrying the cost, such as their employer. But as an employer, I also don't want to carry the costs for a technology which benefits as ever less.

I think a stickied comment about this would be due. No idea if it's possible to call in @dang via at-name?

It’s extraordinary how much standards have slipped. Completely rewriting a major press release that’s already been sent out, while pretending it’s ostensibly the same document would have been a major corporate scandal just 15 years ago.

Not hired Suleyman? Build his own research lab?

Satya made moves early on with OpenAI that should be studied in business classes for all the right reasons.

He also made moves later on that will be studied for all the wrong reasons.

Maybe not bragged "we made them dance"?

That gloating aged poorly.

true he is just the ceo

Good for Google Cloud, bad for Gemini = ??? for Google

Except Gemini might end up being far cheaper per token due to the infrastructure advantage

How is it good for Gemini that it's not available on two out of three major cloud platforms?

At those numbers it's all a silly game. How much of that was paid to shareholders rather than the business so they can cash out? How much of that is vendors buying future revenue? What liquidation preference is that at?

From what has been reported it's clearly not as simple as raising 122 billion. Some folks called it "scraping the barrel", supposedly Anthropic has surpassed them on the secondary market, etc.

When you reposition the core strategic posture of how you make money on very compressed time scales it’s because there is a massive cash crunch. They killed sora, the type of deal with Disney that should have been an 100 year strategic win, but wasn’t viable economically and they don’t have the assets to weather that storm.

Same with a few other steps we are seeing them take.

It all looks fine until it doesn’t. Once the cash crunch hits. It’s too late

It is possible! Anthropic is probably more in-line with the way Microsoft thinks about AI.

Does this mean they will host OpenAI products but not pay them? Or does it mean they are paying them in some other way?

I don't think Microsoft ever had that restriction. They partnered with everyone already.

https://blogs.microsoft.com/blog/2025/11/18/microsoft-nvidia...

https://azure.microsoft.com/en-us/blog/deepseek-r1-is-now-av...

https://ai.azure.com/

I think it was a lot less restrictive, as far as I understood, the only limit was Microsoft not being allowed to launch competing Microsoft-developed LLMs.

It's better than the original, but still off.

"The Microsoft and OpenAI situation just got messy" is objectively wrong–it has been messy for months [1]. Nos. 1 through 3 are fine, though "if they can't keep up with the tech, OpenAI is moving out" parrots OpenAI's party line. No. 4 doesn't make sense–it starts out with "we" referring to OpenAI in the first person but ends by referring to them in the third person "they." No. 5 is reductive when phrased with "just."

It would seem the translator took corporate PR speak and translated it into something between the LinkedIn and short-form blogger dialects.

[1] https://www.wsj.com/tech/ai/openai-and-microsoft-tensions-ar...

Azure did $75B last quarter.

That article doesn't give a timeframe, but most of these use 10 years as a placeholder. I would also imagine it's not a requirement for them to spend it evenly over the 10 years, so could be back-loaded.

OpenAI is a large customer, but this is not making Azure their personal cluster.

I think they updated the article since you grabbed this line.

Amazon CEO says that these models are coming to Bedrock though: https://x.com/ajassy/status/2048806022253609115

> OpenAI was desperate to dump Microsoft

Yes. Microsoft was "considering legal action against its partner OpenAI and Amazon over a $50 billion deal that could violate its exclusive cloud agreement with the ChatGPT maker" [1].

[1] https://www.reuters.com/technology/microsoft-weighs-legal-ac...

I linked this in another comment but Azure has problems and OpenAI is tired of waiting.

https://news.ycombinator.com/item?id=47616242

> They did not need to go so hard on the hype - Anthropic hasn’t in relative terms and is generating pretty comparable revenues at present

OpenAI bet on consumers; Anthropic on enterprise. That will necessitate a louder marketing strategy for the former.

Great scene

You forgot the best line: "I don't know how the US economy works, much less some kind of self-sustaining one".

> I think we keep changing the goalposts on AGI

Isn't that exactly what you would expect to happen as we learn more about the nature and inner workings of intelligence and refine our expectations?

There's no reason to rest our case with the Turing test.

I hear the "shifting goalposts" riposte a lot, but then it would be very unexciting to freeze our ambitions.

At least in an academic sense, what LLMs aren't is just as interesting as what they are.

I don't think the goalpost has been shifted for AGI or the definition of AGI that is used by these corporations. It's just they broke it down to stages to claim AGI achieved. It was always a model or system that surpasses human capabilities at most tasks/being able to replace a human worker. The big companies broke it down to AGI stage 1, stage 2, etc to be able to say they achieved AGI.

The Turing Test/Imitation Game is not a good benchmark for AGI. It is a linguistics test only. Many chatbots even before LLMs can pass the Turing Test to a certain degree.

Regardless, the goalpost hasn't shifted. Replacing human workforce is the ultimate end goal. That's why there's investors. The investors are not pouring billions to pass the Turing Test.

Turing himself argued that trying to measure if a computer is intelligent is a fool's errand because it is so difficult to pin down definitions. He proposed what we call the "Turing test" as a knowable, measurable alternative. The first paragraph of his paper reads:

> I propose to consider the question, "Can machines think?" This should begin > with definitions of the meaning of the terms "machine" and "think." The > definitions might be framed so as to reflect so far as possible the normal use > of the words, but this attitude is dangerous, If the meaning of the words > "machine" and "think" are to be found by examining how they are commonly used > it is difficult to escape the conclusion that the meaning and the answer to the > question, "Can machines think?" is to be sought in a statistical survey such as > a Gallup poll. But this is absurd. Instead of attempting such a definition I > shall replace the question by another, which is closely related to it and is > expressed in relatively unambiguous words.

Many people who want to argue about AGI and its relation to the Turing test would do well to read Turing's own arguments.

I don't think so... I think most of the sci-fi I grew up reading presented AGI that could reason better than humans could, like make a plan and carry it out.

Like do people not know what word "general" means? It means not limited to any subset of capabilities -- so that means it can teach itself to do anything that can be learned. Like start a business. AI today can't really learn from its experiences at all.

Related: https://en.wikipedia.org/wiki/AI_effect

The truth is, we have had AGI for years now. We even have artificial super intelligence - we have software systems that are more intelligent than any human. Some humans might have an extremely narrow subject that they are more intelligent than any AI system, but the people on that list are vanishing small.

AI hasn't met sci-fi expectations, and that's a marketing opportunity. That's all it is.

The Turing test pits a human against a machine, each trying to convince a human questioner that the other is the machine. If the machine knows how humans generally behave, for a proper test, the human contestant should know how the machine behaves. I think that this YouTube channel clearly shows that none of today's models pass the Turing test: https://www.youtube.com/@FatherPhi

> Maybe we need to start thinking less about building tests for definitively calling an LLM AGI and instead deciding when we can't tell humans aren't LLMs for declaring AGI is here.

If you've never read the original paper [1] I recommend that you do so. We're long past the point of some human can't determine if X was done by man or machine.

[1]: https://courses.cs.umbc.edu/471/papers/turing.pdf

People thought Eliza was alive too in the 60s. AGI is not determined by how ignorant, uninformed humans view a technology they don't understand. That is the single dumbest criterion you could come up with for defining it.

Regarding shifting goalposts, you are suggesting the goalposts are being moved further away, but it's the exact opposite. The goalposts are being moved closer and closer. Someone from the 50s would have had the expectation that artificial intelligence ise something recognisable as essentially equivalent to human intelligence, just in a machine. Artificial intelligence in old sci-fi looked nothing like Claude Code. The definition has since been watered down again and again and again and again so that anything and everything a computer does is artificial intelligence. We might as well call a calculator AGI at this point.

The goal post keeps moving because LLM hypeists keep saying LLMs are "close" to AGI (or even are, already). Any reasonably intelligent individual that knows anything about LLMs obviously rejects those claims, but the rest of the world doesn't.

An AGI would not have problems reading an analog clock. Or rather, it would not have a problem realizing it had a problem reading it, and would try to learn how to do it.

An AGI is not whatever (sophisticated) statistical model is hot this week.

Just my take.

Sure, in the 80s after interacting with CC 1 time you would call it 'alive'. After having interacted with it for 5-10 minutes you would clearly see that it is as far from AGI as something more mundane as C compiler is.

By that measure Eliza might pass the turing test too. It just shows it's far from being a though-terminating argument by itself.

Maybe moving the goalposts is how we find the definition?

It's not a great definition but it's also not a terrible one either. For an AI system to be able to do all or even most of the jobs in an economy it has to be well rounded in a way it still isn't today, meaning: reliability, planning, long term memory, physical world manipulation etc. A system that can do all of that well enough so it can do the jobs of doctors, programmers and plumbers is generally intelligent in my view.

> They redefined AGI to be an economical thing

Huh. Source? I mean, typical OpenAI bullshit, but would love to know how they defined it.

Please reveal the “scientific” definition of AGI.

It makes sense though. Humans are coherent to the economy based on their ability to perform useful work. If an AI system can perform work as well as or better than any human, than with respect to "anything any human has ever been willing to pay for", it is AGI.

I don't get why HN commenters find this so hard to understand. I have a sense they are being deliberately obtuse because they resent OpenAI's success.

> eschatology

From Wikipedia

Eschatology (/ˌɛskəˈtɒlədʒi/; from Ancient Greek ἔσχατος (éskhatos) 'last' and -logy) concerns expectations of the end of present age, human history, or the world itself.

I'm case anyone else is vocabulary skill checked like me

Progess is generally salami slicing just as escalation in geopolitics. Not a step function.

Russian Invasion - Salami Tactics | Yes Prime Minister

https://www.youtube.com/watch?v=yg-UqIIvang

It feels like they have to say/believe it because it's kind of the only thing that can justify the costs being poured into it and the cost it will need to charge eventually (barring major optimizations) to actually make money on users.

This, someone take Silicon Valley's adderal away.

ATH?

I bet they were laughing their asses off when they came up with that. This is nonsensical.

What do you mean we were "supposed to have AGI last summer"?

People obviously have really strong opinions on AI and the hype around investments into these companies but it feels like this is giving people a pass on really low quality discourse.

This source [1] from this time last year says even lab leaders most bullish estimate was 2027.

[1]. https://80000hours.org/2025/03/when-do-experts-expect-agi-to...

ARM actually built AGI last month. Spoiler: it's a datacenter CPU.

Talk to a doctor? In this economy? I've got ChatGPT to talk to. Wait hang on.

It's not one-shot. Other people had attempted the same problem w/ the same AI & failed. You're confused about terms so you redefine them to make your version of the fairy tale real.

I could easily make that graph a reality and sustain that pace for a couple years, considering I'm starting from 0 javelin skill.

I do not envy the stress the partnerships, strat ops and infra teams must be perpetually dealing with at OpenAI & Anthropic.

> Do the investments make sense if AGI is not less than 10 years away?

They can. If one consolidated the AI industry into a single monopoly, it would probably be profitable. That doesn't mean in its current state it can't succumb to ruionous competition. But the AGI talk seems to be mostly aimed at retail investors and philospher podcasters than institutional capital.

Best way to achieve AGI: Redefine AGI.

The investments don't make sense.

Investors are typically people with surplus money to invest. Progress cannot be made without trial and error. So fleecing of investors for the greater good of humanity is something I shall allow.

Full Self-Driving 2.0

It's always been this way. I remember, speaking of Microsoft, when they came to my school around 2002 or so giving a talk on AI. They very confidently stated that AGI had already been "solved", we know exactly how to do it, only problem is the hardware. But they estimated that would come in about ten years...

You can read the leaked emails from the Musk lawsuit.

At the very least, Ilya Sutskever genuinely believed it, even when they were just making a DOTA bot, and not for hype purposes.

I know he's been out of OpenAI for a while, but if his thinking trickled down into the company's culture, which given his role and how long he was there I would say seems likely, I don't think it's all hype.

Grand delusion, perhaps.

He’s a glorified portfolio manager (questionable how good he actually is given the results vs Anthropic and how quickly they closed the valuation gap with far less money invested) + expert hype man to raise money for risky projects.

Some people thought SHRDLU was basically AGI after seeing its demo in 1970. The hype around such systems was so strong that Hubert Dreyfus felt the need to write an entire book arguing against this viewpoint (1972 What Computers Can't Do). All this demonstrates is that we need to be careful with various claims about computer intelligence.

It performs at a usable level across a wide range of tasks. I'm not sure about two years ago, but ten years ago we would have called it an AGI. As opposed to "regular AI" where you have to assemble a training set for your specific problem, then train an AI on it before you can get your answers.

Now our idea of what qualifies as AGI has shifted substantially. We keep looking at what we have and decide that that can't possibly be AGI, our definition of AGI must have been wrong

If you didn't call GPT 3.5 AGI I do not believe you when you claim you would have called 5.5 AGI.

I agree with this but they don’t. And that’s the the thing, AGI as they refer is much much much more than what we have, and I don’t know if they are going to ever get there and I’m not sure what’s even there at this point and what will justify their investments.

... until you actually, like, use it and find out all the limitations it has.

GPT 4 was 3 years ago... it's iterative enhancement.

And I've been told my job (litigation attorney) is about to be replaced for over 3 years now, has yet to come close.

If you present ELIZA to people some will think it is AGI today.

There is a reason so many scams happen with technology. It is too easy to fool people.

> Any sufficiently complex LLM is indistinguishable from AGI

Isn't this tautology? We've de facto defined AGI as a "sufficiently complex LLM."

If we take that statement as fact then I don't believe we are even close to an LLM being sufficiently complex enough.

However, I don't think it is even true. LLMs may not even be on the right track to achieving AGI and without starting from scratch down an alternate path it may never happen.

LLMs to me seem like a complicated database lookup. Storage and retrieval of information is just a single piece of intelligence. There must be more to intelligence than a statistical model of the probable next piece of data. Where is the self learning without intervention by a human. Where is the output that wasn't asked for?

At any rate. No amount of hype is going to get me to believe AGI is going to happen soon. I'll believe it when I see it.

Some might be missing the reference: https://en.wikipedia.org/wiki/Clarke's_three_laws

If I'm reading you right, your opinion is essentially: "If building bigger and bigger statistical next word predictors won't lead to artificial general intelligence, we will never see artificial general intelligence"

I don't know, maybe AGI is possible but there's more to intelligence than statistical next word prediction?

> And if you look at Boston Dynamics, Unitree and Generalist's progress on robotics

Their progress is almost nought. Humanoids are stupid creations that are not good at anything in the real world. I'll give it to the machine dogs, at least they can reach corners we cannot.

Not sure if you're being sincere or sarcastic but some of us have lived through several AI winters now. And the fact that such a phenomenon exists is because of this terrible amount of hype the topic gets whenever any progress is made.

> Progress is huge and fast

is it? we're currently scaled on data input and LLMs in general, the only thing making them advance at all right now is adding processing power

Same thing happened with self-driving cars. Oh and cryptocurrencies.

Also it's about OpenAI going public.