AI is going to be a highly-competitive, extremely capital-intensive commodity market that ends up in a race to the bottom competing on cost and efficiency of delivering models that have all reached the same asymptotic performance in the sense of intelligence, reasoning, etc.<p>The simple evidence for this is that everyone who has invested the same resources in AI has produced roughly the same result. OpenAI, Anthropic, Google, Meta, Deepseek, etc. There's no evidence of a technological moat or a competitive advantage in any of these companies.<p>The conclusion? AI is a world-changing technology, just like the railroads were, and it is going to soon explode in a huge bubble - just like the railroads did. That doesn't mean AI is going to go away, or that it won't change the world - railroads are still here and they did change the world - but from a venture investment perspective, get ready for a massive downturn.
There is a pretty big moat for Google: extreme amounts of video data on their existing services and absolutely no dependence on Nvidia and it's 90% margin.
In this case the difference between its and it’s does alter the meaning of the sentence.
I have yet to be convinced the broader population has an appetite for AI produced cinematography or videos. Independence from Nvidia is no more of a liability than dependence on electricity rates; it's not as if it's in Nvidia's interest to see one of its large customers fail. And pretty much any of the other Mag7 companies are capable of developing in-house TPUs + are already independently profitable, so Google isn't alone here.
The value of YouTube for AI isn't making AI videos, it's that it's an incredibly rich source for humanity's current knowledge in one place. All of the tutorials, lectures, news reports, etc. are great for training models.
If you think they are going to catch up with Google's software and hardware ecosystem on their first chip, you may be underestimating how hard this is. Google is on TPU v7. meta has already tried with MTIA v1 and v2. those haven't been deployed at scale for inference.
I don't think many of them will want to, though. I think as long as Nvidia/AMD/other hardware providers offer inference hardware at prices decent enough to not justify building a chip in-house, most companies won't. Some of them will probably experiment, although that will look more like a small team of researchers + a moderate budget rather than a burn-the-ships we're going to use only our own hardware approach.
Well, anthropic just purchased a million TPUs from Google because even with a healthy margin from Google, it's far more cost effective because of Nvidia's insane markup. That speaks for itself. Nvidia will not drop their margin because it will tank their stock price. it's half of the reason for all this circular financing - lowering their effective margin without lowering it on paper.
It's in Nvidia's interest to charge the absolute maximum they can without their customers failing. Every dollar of Nvidia's margin is your own lost margin. Utilities don't do that. Nvidia is objectively a way bigger liability than electricity rates.
The video data is probably good for training models, including text models.
And yes, all their competitors are making custom chips. Google is on TPU v7. absolutely nobody is going to get this right on the first try among their competitors - Google didn't.
<p><pre><code> > There's no evidence of a technological moat or a competitive advantage in any of these companies.
</code></pre>
I disagree based on personal experience. OpenAI is a step above in usefulness. Codex and GPT 5.2 Pro have no peers right now. I'm happy to pay them $200/month.<p>I don't use my Google Pro subscription much. Gemini 3.0 Pro spends 1/10th of the time thinking compared to GPT 5.2 Thinking and outputs a worse answer or ignores my prompt. Similar story with Deepseek.<p>The public benchmarks tell a different story which is where I believe the sentiment online comes from, but I am going to trust my experience, because my experience can't be benchmaxxed.
I still find it so fascinating how experiences with these models are so varied.<p>I find codex & 5.2 Pro next to useless and nothing holds a candle to Opus 4.5 in terms of utility or quality.<p>There's probably something in how varied human brains and thought processes are. You and I likely think through problems in some fundamentally different way that leads to us favouring different models that more closely align with ourselves.<p>No one seems to ever talk about that though and instead we get these black and white statements about how our personally preferred model is the only obvious choice and company XYZ is clearly superior to all the competition.
I use both and ChatGPT will absolutely glaze me. I will intentionally say some BS and ChatGPT will say “you’re so right.” It will hilariously try to make me feel good.<p>But Gemini will put me in my place. Sometimes I ask my question to Gemini because I don’t trust ChatGPT’s affirmations.<p>Truthfully I just use both.
> AI is going to be a highly-competitive, extremely capital-intensive commodity market<p>It already is. In terms of competition, I don't think we've seen any groundbreaking new research or architecture since the introduction of inference time compute ("thinking") in late 2024/early 2025 circa GPT-o4.<p>The majority of the cost/innovation now is training this 1-2 year old technology on increasingly large amounts of content, and developing more hardware capable of running these larger models at more scale. I think it's fair to say the majority of capital is now being dumped into hardware, whether that's HBM and research related to that, or increasingly powerful GPUs and TPUs.<p>But these components are applicable to a lot of other places other than AI, and I think we'll probably stumble across some manufacturing techniques or physics discoveries that will have a positive impact on other industries.<p>> that ends up in a race to the bottom competing on cost and efficiency of delivering<p>One could say that the introduction of the personal computer became a "race to the bottom." But it was only the start of the dot-com bubble era, a bubble that brought about a lot of beneficial market expansion.<p>> models that have all reached the same asymptotic performance in the sense of intelligence, reasoning, etc.<p>I definitely agree with the asymptotic performance. But I think the more exciting fact is that we can probably expect LLMs to get a LOT cheaper in the next few years as the current investments in hardware begin to pay off, and I think it's safe to assume that in 5-10 years, most entry-level laptops will be able to manage a local 30B sized model while still being capable of multitasking. As it gets cheaper, more applications for it become more practical.<p>---<p>Regarding OpenAI, I think it definitely stands in a somewhat precarious spot, since basically the majority of its valuation is justified by nothing less than expectations of future profit. Unlike Google, which was profitable before the introduction of Gemini, AI startups need to establish profitability still. I think although initial expectations were for B2C models for these AI companies, most of the ones that survive will do so by pivoting to a B2B structure. I think it's fair to say that most businesses are more inclined to spend money chasing AI than individuals, and that'll lead to an increase in AI consulting type firms.
> One could say that the introduction of the personal computer became a "race to the bottom." But it was only the start of the dot-com bubble era, a bubble that brought about a lot of beneficial market expansion.<p>I think the comparison is only half valid since personal computers were really just a continuation of the innovation that was general purpose computing.<p>I don't think LLMs have quite as much mileage to offer, so to continue growing, "AI" will need at least a couple step changes in architecture and compute.
I don't think anyone <i>knows</i> for sure how much mileage/scalability LLMs have. Given what we do know, I suspect if you can afford to spend more compute on even longer training runs, you can still get much better results compared to SOTA, even for "simple" domains like text/language.
> in 5-10 years, most entry-level laptops will be able to manage a local 30B sized model<p>I suspect most of the excitement and value will be on edge devices. Models sized 1.7B to 30B have improved incredibly in capability in just the last few months and are unrecognizably better than a year ago. With improved science, new efficiency hacks, and new ideas, I can’t even imagine what a 30B model with effective tooling available could do in a personal device in two years time.
I, personally, use chatGPT for search more than I do Google these days. It, more often than not, gives me more exact results based on what I'm looking for and it produces links I can visit to get more information. I think this is where their competitive advantage lies if they can figure out how to monetize that.
We don’t need anecdotes. We have data. Google has been announcing quarter after quarter of record revenues and profits and hasn’t seen any decrease in search traffic. Apple also hinted at the fact that it also didn’t see any decreased revenues from the Google Search deal.<p>AI answers is good enough and there is a long history of companies who couldn’t monetize traffic via ads. The canonical example is Yahoo. Yahoo was one of the most traffic sites for 20 years and couldn’t monetize.<p>2nd issue: defaults matter. Google is the default search engine for Android devices, iOS devices and Macs whether users are using Safari or Chrome. It’s hard to get people to switch<p>3rd issue: any money that OpenAI makes off search ads, I’m sure Microsoft is going to want there cut. ChatGPT uses Bing<p>4th issue: OpenAIs costs are a lot higher than Google and they probably won’t be able to command a premium in ads. Google has its own search engine, its own servers, its own “GPUs” [sic],<p>5th: see #4. It costs OpenAI a lot more per ChatGPT request to serve a result than it costs Google. LLM search has a higher marginal cost.
I personally know people that used ChatGPT a lot but have recently moved to using Gemini.<p>There’s a couple of things going on but put simply - when there is no real lock in, humans enjoy variety. Until one firm creates a superior product with lock in, only those who are generating cash flows will survive.<p>OAI does not fit that description as of today.
I'm genuinely curious. Why do you do this instead of Google Searches which also have an AI Overview / answer at the top, that's basically exactly the same as putting your search query into a chat bot, but it ALSO has all the links from a regular Google search so you can quickly corroborate the info even using sources not from the original AI result (so you also see discordant sources from what the AI answer had)?
AI is capital intensive because autodiff kinda sucks.
This will remain the case until we have another transformer-level leap in ML technology. I don’t expect such an advancement to be openly published when it is discovered.
>That doesn't mean AI is going to go away, or that it won't change the world - railroads are still here and they did change the world - but from a venture investment perspective, get ready for a massive downturn.<p>I don't know why people always imply that "the bubble will burst" means that "literally all Ai will die out and nothing will remain that is of use". The Dotcom bubble didn't kill the internet. But it was a bubble and it burst nonetheless, with ramifications that spanned decades.<p>All it really means when you believe a bubble will pop is "this asset is over-valued and it will soon, rapidly deflate in value to something more sustainable" . And that's a good thing long term, despite the rampant destruction such a crash will cause for the next few years.
Your premise is wrong in a very important way.<p>The cost of entry is far beyond extraordinary. You're acting like anybody can gain entry, when the exact opposite is the case. The door is closing right now. Just try to compete with OpenAI, let's see you calculate the price of attempting it. Scale it to 300, 500, 800 million users.<p>Why aren't there a dozen more Anthropics, given the valuation in question (and potential IPO)? Because it'll cost you tens of billions of dollars just to try to keep up. Nobody will give you that money. You can't get the GPUs, you can't get the engineers, you can't get the dollars, you can't build the datacenters. Hell, you can't even get the RAM these days, nor can you afford it.<p>Google & Co are capturing the market and will monetize it with advertising. They will generate trillions of dollars in revenue over the coming 10-15 years by doing so.<p>The barrier to entry is the same one that exists in search: it'll cost you well over one hundred billion dollars to try to be in the game at the level that Gemini will be at circa 2026-2027, for just five years.<p>Please, inform me of where you plan to get that one hundred billion dollars just to try to keep up. Even Anthropic is going to struggle to stay in the competition when the music (funding bubble) stops.<p>There are maybe a dozen or so companies in existence that can realistically try to compete with the likes of Gemini or GPT.
> Just try to compete with OpenAI, let's see you calculate the price of attempting it. Scale it to 300, 500, 800 million users.<p>Apparently the DeepSeek folks managed that feat. Even with the high initial barriers to entry you're talking about, there will always be ways to compete by specializing in some underserved niche and growing from there. Competition seems to be alive and well.
"AI is going to be a highly-competitive" - In what way?<p>It is not a railroad and the railroads did not explode in a bubble (OK a few early engines did explode but that is engineering). I think LLM driven investments in massive DCs is ill advised.
Just in time for a Government guaranteed backstop.
Massive upfront costs and second place is just first loser. It’s like building fabs but your product is infinitely copyable. Seems pretty rough.
What exactly is "second" place? No-one really knows what first place looks like. Everyone is certain that it will cost an arm, a leg and most of your organs.<p>For me, I think that, the possible winners will be close to fully funded up front and the losers will be trying to turn debt into profit and fail.<p>The rest of us self hoster types are hoping for a massive glut of GPUs and RAM to be dumped in a global fire sale. We are patient and have all those free offerings to play with for now to keep us going and even the subs are so far somewhat reasonable but we will flee in droves as soon as you try to ratchet up the price.<p>It's a bit unfortunate but we are waiting for a lot of large meme companies to die. Soz!
Not sure why they put so much investment into videoSlop and imageSlop. Anthropic seems to be more focused at least.
Because almost everyone involved in AI race grew up in "winner takes it all" environments, typical for software, and they try really hard to make it reality. This means your model should do everything to just take 90% of market share, or at least 90% of specific niche.<p>The problem is, they can't find the moat, despite searching very hard, whatever you bake into your AI, your competitors will be able to replicate in few months. This is why OpenAI is striking deal with Disney, because copyright provides such moat.
> copyright provides such a moat.<p>Been saying this since the 2016 Alice case. Apple jumped into content production in 2017. They saw the long term value of copyright interests.<p><a href="https://arstechnica.com/information-technology/2017/08/apple-reportedly-ready-to-spend-1-billion-on-original-content-next-year/" rel="nofollow">https://arstechnica.com/information-technology/2017/08/apple...</a><p>Alice changed things such that code monkeys algorithms were not patentable (except in some narrow cases where true runtime novelty can be established.) Since the transformers paper, the potential of self authoring content was obvious to those who can afford to think about things rather than hustle all day.<p>Apple wants to sell AI in an aluminum box while VCs need to prop up data center agrarianism; they need people to believe their server farms are essential.<p>Not an Apple fanboy but in this case, am rooting for their "your hardware, your model" aspirations.<p>Altman, Thiel, the VC model of make the serfs tend their server fields, allow them control of foundation models, is a gross feeling. It comes with the most religious like sense of fealty to political hierarchy and social structure that only exists as hallucination in the dying generations. The 50+ year old crowd cannot generationally churn fast enough.
> your competitors will be able to replicate in few months.<p>Will they really be able to replicate the quality while spending significantly less in compute investment? If not then the moat is still how much capital you can acquire for burning on training?
Striking deals without a proper vision is a waste of resources. And that’s the path OAI is on.
OpenAI is (was?) extremely good at making things that go viral. The successful ones for sure boost subscriber count meaningfully<p>Studio Ghibli, Sora app. Go viral, juice numbers then turn the knobs down on copyrighted material. Atlas I believe was a less successful than they would've hoped for.<p>And because of too frequent version bumps that are sometimes released as an answer to Google's launch, rather than a meaningful improvement - I believe they're also having harder time going viral that way<p>Overall OpenAI throws stuff at the wall and see what sticks. Most of it doesn't and gets (semi) abandoned. But some of it does and it makes for better consumer product than Gemini<p>It seems to have worked well so far, though I'm sceptical it will be enough for long
Because as with the internet 99% of the usage won’t be for education, work, personal development, what have you. It will be for effing kitten videos and memes.
That’s an unusual way of saying uh…adult entertainment
Are the posters of effing kitten videos a customer base with a significant LTV?<p>(The obvious well-paying market would be erotic / furry / porn, but it's too toxic to publicly touch, at least in the US.)
Openrouter stats already mention 52% usage is roleplay.<p>As for photo/video very large number of people use it for friends and family (turn photo into creative/funny video, change photo, etc.).<p>Also I would think photoshop-like features are coming more and more in chatgpt and alike. For example, “take my poorly-lit photo and make it look professional and suitable for linkedin profile”
If only 99% of the Internet was kitten videos and memes
Well, it sure as hell not all 3blue1brown, crr0ww, Feynman, and alike
Because OpenAI stands for AI leader.<p>If Gemini can create or edit an image, chatgpt needs to be able to do this too. Who wants to copy&paste prompts between ai agents?<p>Also if you want to have more semantics, you add image, video and audio to your model. It gets smarter because of it.<p>OpenAI is also relevant bigger than antropic and is known as a generic 'helper'. Antropic probably saw the benefits of being more focused on developer which allows it to succeed longer in the game for the amount of money they have.
> Because OpenAI stands for AI leader.<p>It'll just end up spreading itself too thin and be second or third best at everything.<p>The 500lb gorilla in the room is Google. They have endless money and maybe even more importantly they have endless hardware. OpenAI are going to have an increasingly hard time competing with them.<p>That Gemini 3 is crushing it right now isn't the problem. It's Gemini 4 or 5 that will likely leave them in the dust for the general use case, meanwhile specialist models will eat what remains of their lunch.
>Also if you want to have more semantics, you add image, video and audio to your model. It gets smarter because of it.<p>I think you are confusing generation with analysis. As far I am aware your model does not need to be good at generating images to be able to decode an image.
It is, to first approximation, the same thing. The generative part of genAI is just running the analysis model in reverse.<p>Now there are all sorts of tricks to get the output of this to be good, and maybe they shouldn't be spending time and resources on this. But the core capability is shared.
> <i>Who wants to copy&paste prompts between ai agents?</i><p>An AI!<p>The specialist vs generalist debate is still open. And for complex problems, sure, having a model that runs on a small galaxy may be worth it. But for most tasks, a fleet of tailor-made smaller models being called on by an agent seems like a solidly-precedented (albeit not singularity-triggering) bet.
<p><pre><code> > But for most tasks, a fleet of tailor-made smaller models being called on by an agent seems like a solidly-precedented (albeit not singularity-triggering) bet.
</code></pre>
not an expert by any means, but wouldn't smaller but highly refined models also output more reproducible results?<p>intuitively it sounds akin to the unix model...
I think you're partially right, but I don't think being an AI leader is the main motivation -- that's a side effect.<p>I think it's important to OpenAI to support as many use-cases as possible. Right now, the experience that most people have with ChatGPT is through small revenue individual accounts. Individual subscriptions with individual needs, but modest budgets.<p>The bigger money is in enterprise and corporate accounts. To land these accounts, OpenAI will need to provide coverage across as many use-cases as they can so that they can operate as a one-stop AI provider. If a company needs to use OpenAI for chat, Anthropic for coding, and Google for video, what's the point? If Google's chat and coding is "good enough" and you <i>need</i> to have video generation, then that company is going to go with Google for everything. For the end-game I think OpenAI is playing for, they will need to be competitive in all modalities of AI.
[delayed]
The fact that they do this isn't very bullish for them achieving whatever they define as AGI.
Because for all the incessant whining about "slop," multimodal AI i/o is incredibly useful. Being able to take a photo of a home repair issue, have it diagnosed, and return a diagram showing you what to do with it is great, and it's the same algos that power the slop. "Sorry, you'll have to go to Gemini for that use case, people got mad about memes on the internet" is not really a good way for them to be a mass consumer company.
Because those and world models are the endgame, way way more than text is.
But how much more profitable are they? We see revenue but not profits / spending. Anthropic seems to be growing faster than OpenAI did but that could be the benefit of post-GPT hype.
Because their main use is for advertising/propaganda, which is largely videoSlop & imageSlop even without AI.
The best case I can see is they integrate shopping and steal the best high-intent cash cow commercial queries from G. It's not really about AI, it's about who gets to be the next toll road.
This article doesn’t add anything to what we know already. It’s still an open question what happens with the labs this coming year, but I personally think Anthropic’s focus on coding represents the clearest path to subscriber-based success (typical SaaS) whereas OpenAI has a clear opportunity with advertising. Both of these paths could be very lucrative. Meanwhile I expect Google will continue to struggle with making products that people actually want to use, irrespective of the quality of its models.
What Google AI products do people not want to use? Gemini is catching up to chatpt from a MAU perspective, ai overviews in search are super popular and staggeringly more used than any other ai-based product out there, a Google ai mode has decent usage, and Google Lens has surprisingly high usage. These products together dwarf everyone else out there by like 10x.
>Gemini is catching up to chatpt from a MAU perspective<p>It is far far behind, and GPT hasn't exactly stalled growth either. Weekly Active Users, Monthly visits...Gemini is nowhere near. It's like Google vs Bing. They're comfortably second, but second is well below first.<p>>ai overviews in search are super popular and staggeringly more used than any other ai-based product out there<p>Is it ? How would you even know ? It's a forced feature you can not opt out of or not use. I ignore AI overviews, but would still count as a 'user' to you.
> ai overviews in search are super popular and staggeringly more used than any other ai-based product out there<p>This really is the critical bit. A year ago, the spin was "ChatGPT AI results are better than search, why would you use Google?", now it's "Search result AI is just as good as ChatGPT, why bother?".<p>When they were disruptive, it was enough to be different to believe that they'd win. Now they need to <i>actually be better</i>. And... they kinda aren't, really? I mean, lots of people like them! But for Regular Janes at the keyboard, who cares? Just type your search and see what it says.
Where does google struggle to make products people don’t want to use?
Is it a personal opinion?
Bart was a flop.
Google search is losing market share to other LLM providers.
Gemini adoption is low, people around me prefer OpenAI because it is good enough and known.<p>But on the contrary, Nano Banana is very good, so I don't know.
And in the end, I'm pretty confident Google will be the AI race winner, because they got the engineers, they tech background and the money. Unless Google Adsense die, they can continue the race forever.
If Google is producing very good models and they aren’t gaining much traction, that seems like a pretty bad sign for them, right? If they were failing with bad models, the solution would be easy: math and engineer harder, make better models (I mean, this is obviously very hard but it is a clear path). Failing with good models is… confusing, it indicates there’s some unknown problem.
It’s irrelevant, Google needs to focus on performance enhancements that the enterprise market segment demands - who only operate in the air of objectivity.<p>If they can achieve that they will cut off a key source of blood supply to MSFT+OAI. There is not much money in the consumer market segment from subscribers and entering the ad-business is going to be a lot tougher than people think.
> Gemini adoption is low, people around me prefer OpenAI because it is good enough and known.<p>Gemini is built into Android and Google search. People may not be going to gemini.google.com, but that does not mean adoption is low.
I mean it’s a simple Google search to show that Google isn’t loosing much market share to ChatGPT and most ChatGPT users still use Google.<p><a href="https://searchengineland.com/nearly-all-chatgpt-users-visit-google-461830" rel="nofollow">https://searchengineland.com/nearly-all-chatgpt-users-visit-...</a><p>But even more importantly, it obviously isn’t losing money from advertisers to ChatGPT. You can look at their quarterly results.
what are you talking about Gemini adoption has tripled in a few months alone and have around 18% of marketshare and its accelerating.
Anti Gravity is a flop. I mean it uses Gemini under the hood.<p>But you cannot use it with an API key.<p>If you're on a workspace account, you can't have normal individual plan.<p>You have to have the team plan with $100/month or nothing.<p>Google's product management tier is beyond me.
OK, but Gmail, Google Maps, Google Docs, and Google Search etc are ubiquitous. `Google' has even become a verb. Google might take a shotgun approach, but it certainly does create widely used products.
I will add that there's also Gemini in Chrome. With Chrome being the largest browser by market share, that's a powerful de facto default.
That doesn't negate my original point.
I don't. Google has at least a few advantages:<p>1. Google books, which they legally scanned. No dubious training sets for them. They also regularly scrape the entire internet. And they have YouTube. Easy access to the best training data, all legally.<p>2. Direct access to the biggest search index. When you ask ChatGPT to search for something it is basically just doing what we do but a bit faster. Google can be much smarter, and because it has direct access it's also faster. Search is a huge use case of these services.<p>3. They have existing services like Android, Gmail, Google Maps, Photos, Assistant/Home etc. that they can integrate into their AI.<p>The difference in model capability seems to be marginal at best, or even in Google's favour.<p>OpenAI has "it's not Google" going for it, and also AI brand recognition (everyone knows what ChatGPT is). Tbh I doubt that will be enough.
And they have hardware as well, and their own cloud platform.<p>In my view Google is uniquely well positioned because, contrary to the others, it controls most of the raw materials for Ai.
Google's most significant advantage in this space is its organizational experience in providing services at this scale, as well as its mature infrastructure to support them. When the bubble pops, it's not lights-out or permanently degraded performance.
There is no doubt that OpenAI is taking a lot of risks by betting that AI adoption will translate into revenues in the very short term. And that could really happen imo (with a low probability sure, but worth the risk for VCs? Probably).
It's mathematically impossible what OpenAI is promising. They know it. The goal is to be too big to fail and get bailed out by US taxpayers who have been groomed into viewing AI as a cold war style arms race that America cannot lose.
> The goal is to be too big to fail and get bailed out by US taxpayers<p>I know this is the latest catastrophizion meme for AI companies, but what is it even supposed to mean? OpenAI failing wouldn’t mean AI disappears and all of their customers go bankrupt, too. It’s not like a bank. If OpenAI became insolvent or declared bankruptcy, their intellectual property wouldn’t disappear or become useless. Someone would purchase it and run it again under a new company. We also have multiple AI companies and switching costs are not that high for customers, although some adjustment is necessary when changing models.<p>I don’t even know what people think this is supposed to mean. The US government gives them money for something to prevent them from filing for bankruptcy? The analogy to bank bailouts doesn’t hold.
Bailing out OAI would be entirely unnecessary (crowded field) and political suicide (how many hundreds of billions that could have gone to health care instead?)<p>If it happens in the next 3 years, tho, and Altman promises enough pork to the man, it could happen.
> It's mathematically impossible what OpenAI is promising<p>Citation is needed
on the one hand, i understand you are making a stylized comment, on the other hand, as soon as i started writing something reasonable, i realized this is an "upvote lame catastrophizing takes about" (checking my notes) "some company" thread, which means reasonable stuff will get downvoted... for example, where is there actual scarcity in their product inputs? for example, will they really be paying retail prices to infrastructure providers forever? is that a valid forecast? many reasonable ways to look at this. even if i take your cynical stuff at 100% face value, the thing about bailouts is that they're more complicated than what you are saying, but your instinct is to say they're not complicated, "grooming" this and "cold war" that, because your goal is to concern troll, not advance this site's goal of curiosity...
Correction: OpenAI <i>investors</i> do take that risk. Some of the investors (e.g. Microsoft, Nvidia) dampen that risk by making such investment conditioned on boosting the investor's own revenue, a stock buyback of sorts.
Apparently we all have enough money to put it into OpenAI.<p>Some players have to play, like google, some players want to play like USA vs. China.<p>Besides that, chatting with an LLM is very very convincing. Normal non technical people can see what 'this thing' can already do and as long as the progress is continuing as fast as it currently is, its still a very easy to sell future.
> Some players have to play, like google<p>I don't think you have the faintest clue of what you're talking about right now. Google authored the transformer architecture, the basis of every GPT model OpenAI has shipped. They aren't obligated to play any more than OpenAI is, they do it because they get results. The same cannot be said of OpenAI.
The fact is nobody has any idea what OpenAI's cash burn is. Measuring how much they're raising is not an adequate proxy.<p>For all we know, they could be accumulating capital to weather an AI winter.<p>It's also worth noting that OpenAI has not trained a new model since gpt4o (all subsequent models are routing systems and prompt chains built on top of 4), so the idea of OpenAI being stuck in some kind of runaway training expense is not real.
Didn't they create Sora and other models and literally burned so much money with their AI video app which they wanted to make a social media but what ended up happening was that they burned billions of dollars.
Why do you think they have not trained a new model since 4o? You think the GPT-5 release is /just/ routing to differently sized 4o models?
I think you are messing up things here, and I think your comment is based on the article from semi analysis. [1]<p>It said:
OpenAI’s leading researchers have not completed a successful full-scale pre-training run that was broadly deployed for a new frontier model since GPT-4o in May 2024, highlighting the significant technical hurdle that Google’s TPU fleet has managed to overcome.<p>However, pre-training run is the initial, from-scratch training of the base model. You say they only added routing and prompts, but that's not what the original article says. They most likely still have done a lot of fine tuning, RLHF, alignment and tool calling improvements. All that stuff is training too. And it is totally fine, just look at the great results they got with Codex-high.<p>If you got actually got what you said from a different source, please link it. I would like to read it. If you just messed things up, that's fine too.<p>[1] <a href="https://newsletter.semianalysis.com/p/tpuv7-google-takes-a-swing-at-the" rel="nofollow">https://newsletter.semianalysis.com/p/tpuv7-google-takes-a-s...</a>
The GPT-5 series is a new model, based on the o1/o3 series. It's very much inaccurate to say that it's a routing system and prompt chain built on top of 4o. 4o was not a reasoning model and reasoning prompts are <i>very</i> weak compared to actual RLVR training.<p>No one knows whether the base model has changed, but 4o was not a base model, and neither is 5.x. Although I would be kind of surprised if the base model hadn't also changed, FWIW: they've significantly advanced their synthetic data generation pipeline (as made obvious via their gpt-oss-120b release, which allegedly was entirely generated from their synthetic data pipelines), which is a little silly if they're not using it to augment pretraining/midtraining for the models they actually make money from. But either way, 5.x isn't just a prompt chain and routing on top of 4o.
<i>>It's also worth noting that OpenAI has not trained a new model since gpt4o (all subsequent models are routing systems and prompt chains built on top of 4)</i><p>At the very least they made GPT 4.5, which was pretty clearly trained from scratch. It was possibly what they wanted GPT-5 to be but they made a wrong scaling prediction, people simply weren't ready to pay that much money.
They have not <i>successfully</i> trained a new model since 4o. That doesn’t mean they haven’t burned a pile of cash trying.<p>I know sama says they aren’t trying to train new models, but he’s also a known liar and would definitely try to spin systemic failure.
> The fact is nobody has any idea what OpenAI's cash burn is.<p>Their investors surely do (absent outrageous fraud).<p>> For all we know, they could be accumulating capital to weather an AI winter.<p>If they were, their investors would be freaking out (or complicit in the resulting fraud). This seems unlikely. In point of fact it seems like they're playing commodities market-cornering games[1] with their excess cash, which implies strongly that they know how to spend it even if they don't have anything useful to spend it on.<p>[1] Again c.f. fraud
How are they updating the data then? Wouldn’t the cutoff date be getting further away from today?
RAG? Even for a "fresh" model, there is no way to keep it up to date, so there has to be a mechanism by which to reference eg last night's football game.
They’re just feeding a little bit of slop in every so often. Fine tuning rather than training a new one.
they're paying million dollar salaries to engineers and building data centers, it's not a huge mystery where their expenses are
> they could be accumulating capital to weather an AI winter<p>Doubtful. This would be the very antithesis of the Silicon Valley way.
wasn't 4.5 new
Yes it was, op didn't read the reporting closely enough. It said something to the effect of "Didn't pretrain a new broadly released, generally available model"
Wasn't 4.5 before 4o?
OpenAI has #5 traffic levels globally. Their product-market fit is undeniable. The question is monetization.<p>Their cost to serve each request is roughly 3 orders of magnitude higher than conventional web sites.<p>While it is clear people see value in the product, we only know they see value at today’s subsidized prices. It is possible that inference prices will continue their rapid decline. Or it is possible that OAI will need to raise prices and consumers will be willing to pay more for the value.
It's easy to get product-market fit when you give away dollars for the price of pennies.
Does that cost to serve multiple stay the same when conventional sites are forced to shovel ai into each request? e.g. the new google search
it's a simple problem really. what is actually scarce?<p>a spot on the iOS home screen? yes.<p>infrastructure to serve LLM requests? no.<p>good LLM answers? no.<p>the economist can't tell the difference between scarcity and real scarcity.<p>it is extremely rare to buy a spot on the iOS home screen, and the price for that is only going up - think of the trend of values of tiktok, whatsapp and instagram. that's actually scarce.<p>that is what openai "owns." you're right, #5 app. you look at someone's home screen, and the things on it are owned by 8 companies, 7 of which are the 7 biggest public companies in the world, and the 8th is openai.<p>whereas infrastructure does in fact get cheaper. so does energy. they make numerous mistakes - you can't forecast retail prices Azure is "charging" openai for inference. but also, NVIDIA participates in a cartel. GPUs aren't actually scarce, you don't actually need the highest process nodes at TSMC, etc. etc. the law can break up cartels, and people can steal semiconductor process knowledge.<p>but nobody can just go and "create" more spots on the iOS home screen. do you see?
depends if they can monetize that spot. So either ads or subscription. It is as yet unclear whether ads/subscription can generate sufficient revenue to cover costs and return a profit. Perhaps 'enough ads' will be too much for users to bear, perhaps 'enough subscription' will be too much for users to afford.
Archive/Paywall: <<a href="https://archive.is/rHPk3" rel="nofollow">https://archive.is/rHPk3</a>>
In a parallel universe, governments invest in the compute/datacenters (read: infra), and let model makers compete on the same playing field.
I’d rather stay far away from this parallel universe.<p>Why would you want my money to be used to build datacenter that won’t benefit me ? I might use a LLM once a month, many people never use it.<p>Let the one who use it pay for it.
You are already paying for several national lab HPC centers. These are used for government/university research - no idea if commercial interests can rent time on them. The big ones are running weather, astronomy simulations, nuclear explosions, biological sequencing, and so on.
>The big ones are running weather, astronomy simulations, nuclear explosions, biological sequencing, and so on.<p>these things constitute public goods that benefit the individual regardless of participation.
The biggest run classified nuclear stockpile loads, at least in the US. They cost about half a billion apiece. And are 30 (carefully cooled and cabled) megawatts. <a href="https://en.wikipedia.org/wiki/El_Capitan_(supercomputer)" rel="nofollow">https://en.wikipedia.org/wiki/El_Capitan_(supercomputer)</a><p>No chance they're going to take risks to share that hardware with anyone given what it does.<p>The scaled down version of El Capitan is used for non-classified workloads, some of which are proprietary, like drug simulation. It is called Tuolumne. Not long ago, it was nevertheless still a top ten supercomputer.<p>Like OP, I also don't see why a government supercomputer does it better than hyperscalers, coreweave, neoclouds, et al, who have put in a ton of capital as even compared to government. For loads where institutional continuity is extremely important, like weather -- and maybe one day, a public LLM model or three -- maybe. But we're not there yet, and there's so much competition in LLM infrastructure that it's quite likely some of these entrants will be bag holders, not a world of juicy margins at all...rather, playing chicken with negative gross margins.
Many more people materially benefit from e.g. good weather forecasts than form video slop generation.
if datacenters are built by the government, then i think it's fair to assume there will be some level of democratic control of what those datacenters will be used for.
What's the democratic control of existing resources? I would make the opposite assumption, it would be captured by the wealthiest interests.
Certainly! Your congress representatives would be voting on how to allocate its computing power. (Do you remember who did you vote for last time?)
Sure. Same for healthcare and education right? If you don't have a child or need medical attention, why should you pay for them?
That's like every government initiative. Same as healthcare? School? I mean if you don't have children why do you pay taxes... and roads if you don't drive? I mean the examples are so many... why do you bring this argument that if it doesn't benefit you directly right now today, it shouldn't be done?
There are arguments aplenty that schooling and a minimum amount of healthcare are public goods, as are roads built on public land (the government owns most roads after all).<p>What is the justification for considering data centers capable of running LLMs to be a public good?<p>There are many counter examples of things many people use but are still private. Clothing stores, restaurants and grocery stores, farms, home appliance factories, cell phone factories, laundromats and more.
Libraries with books are likely considered public goods right?<p>Why not an LLM datacenter if it also offers information? You could say it's the public library of the future maybe.
a distinction: the data centers have become the means of production, unlike clothing from a store
Healthcare, schools, roads, generative AI. One of these things is not like the others.
I have no idea why you're being downvoted because you're right. The entire point of taxation is to spread the cost among everyone, and since everyone doesn't utilise every government service every tax payer ends up paying for stuff they don't use. That like, the whole point...
If that did happen, how would the government then issue those resources?<p>OpenAI ask for 1m GPUs for a month, Anthropic ask for 2m, the government data center only has 500,000, and a new startup wants 750,000 as well.<p>Do you hand them out to the most convincing pitch? Hopefully not to the biggest donor to your campaign.<p>Now the most successful AI lab is the one that's best at pitching the government for additional resources.<p>UPDATE: See comment below for the answer to this question: <a href="https://news.ycombinator.com/item?id=46438390#46439067">https://news.ycombinator.com/item?id=46438390#46439067</a>
National HPC labs have been over subscribed for decades with extensive queueing/time sharing allocation systems.<p>It would still likely devolve into most-money-wins, but it is not an insurmountable political obstacle to arrange some sort of sharing.<p>Edit: I meant to say over <i>subscribed</i>, not over provisioned. There are far more jobs in the queue than can be handled at once
Huh, TIL - thanks for the correction.<p><a href="https://www.ornl.gov/news/doe-incite-program-seeks-2026-proposals-computing-innovation" rel="nofollow">https://www.ornl.gov/news/doe-incite-program-seeks-2026-prop...</a><p>> The Innovative and Novel Computational Impact on Theory and Experiment, or INCITE, program has announced the 2026 Call for Proposals, inviting researchers to apply for access to some of the world’s most powerful high-performance computing systems.<p>> The proposal submission window runs from April 11 to June 16, 2025, offering an opportunity for scientific teams to secure substantial computational resources for large-scale research projects in fields such as scientific modeling, simulation, data analytics and artificial intelligence. [...]<p>> Individual awards typically range from 500,000 to 1,000,000 node-hours on Aurora and Frontier and 100,000 to 250,000 node-hours on Polaris, with the possibility of larger allocations for exceptional proposals. [...]<p>> The selection process involves a rigorous peer review, assessing both scientific merit and computational readiness. Awards will be announced in November 2025, with access to resources beginning in 2026.<p>Not sure OpenAI/Anthropic etc would be OK with a six month gap between application and getting access to the resources, but this does indeed demonstrate that government issued super-computing resources is a previously solved problem.
Well, people bid for USA government resources all the time. It's why the Washington DC suburbs have some of the country's most affluent neighborhoods among their ranks.<p>In theory it makes the process more transparent and fair, although slower. That calculus has been changing as of late, perhaps for both good and bad. See for example the Pentagon's latest support of drone startups run by twenty-year-olds.<p>The question of public and private distinctions in these various schemes are very interesting and imo, underexplored. Especially when you consider how these private LLMs are trained on public data.
In a completely alternate dimension, a quarter of the capital being invested in AI literally just goes towards making sure everyone has quality food and water.
As we all know, throwing money at a problem solves it completely. Remember how Live Aid saved Ethiopia from starvation and it never had any problems again?
I'd rather live in a universe where that money is taken out of the military budget.
you'll never win that argument, but I absolutely agree.<p>people have no idea about how big the military and defense budgets worldwide are next to <i>any</i> other example of a public budget.<p>throw as many pie charts out as you want; people just can't see the astronomical difference in budgets.<p>I think it's based on how the thing works; a good defense works until it doesn't -- the other systems/budgets in place have a bit more of a graceful failure. This concept produces an irrationality in people that produces windfalls of cash availability.
Without capital invested in the past we wouldn’t have almost anything of modern technology. That has done a lot more for everyone, including food affordability, than actually simply buying food for people to eat once.
Datacenters are not a natural monopoly, you can always build more. Beyond what the public sector itself might need for its own use, there's not much of a case for governments to invest in them.
> <i>governments invest in the compute/datacenters (read: infra), and let model makers compete on the same playing field</i><p>Hmm, what about member-owned coöperatives? Like what we have for stock exchanges.
That could make sense in some steady state regime where there were stable requirements and mature tech (I wouldn’t vote for it but I can see an argument).<p>I see no argument why the government would jump into a hype cycle and start building infra that speculative startups are interested in. Why would they take on that risk compared to private investors, and how would they decide to back that over mammoth cloning infra or whatever other startups are doing?
In a better parallel universe, we found a different innovation without using brute-force computation to train systems that unreliably and inefficiently compute things and still leaves us able to understand what we're building.
why would they do that? not to mention governments are already doing that indirectly by taking equity stakes in some of the companies.
Same reason they should own access lines: everyone needs rackspace/access, it should be treated like a public service to avoid rent seeing. Having a data center in every city where all of the local lines terminate into could open the doors to a lot of interesting use cases, really help with local resiliency/decentralization efforts, and provide a great alternative to cloud providers that doesn't break the bank.
That sounds like a nightmare.
Do you like this idea?
That seems like a terrible idea. Data centers aren’t a natural monopoly. Regulate the externalities and let it flourish.
Prediction: on this thread you'll get a lot of talk about how government would slow things down. But when the AI bubble starts to look shaky, see how fast all the tech bros line up for a "public private partnership."
That's malinvestment. Too much overhead, disconnected from long term demand. The government doesn't have expertise, isn't lean and nimble. What if it all just blows over? (It won't? But who knows?)<p>Everything is happening exactly as it should. If the "bubble" "pops", that's just the economic laws doing what they naturally do.<p>The government has better things to do. Geopolitics, trade, transportation, resources, public health, consumer safety, jobs, economy, defense, regulatory activities, etc.
Burn rate often gets treated as a hard signal, but it is mostly about expectations. Once people get used to the idea of cheap intelligence, any slowdown feels like failure, even if the technology is still moving forward. That gap is usually where bubbles begin.
why does the article used words like burn and incinerate, implying that OpenAI is somehow making money disappear or something? They’re spending it; someone is profiting here, even if it’s not OpenAI. Is it all Nvidia?
Because that's normal language idioms in financial analysis reporting?
Because typically one expect a return on investment with that level of spending. Not only have they run at a loss for years, their spending is expected to increase, with no path to profitability in sight.
I suspect most of it is going to utilities for power, water and racking.<p>That being said, if I was Sam Altman I'd also be stocking up on yachts, mansions and gold plated toilets while the books are still private. If there's $10bn a year in outgoings no one's going to notice a million here and there.
Your burn is the money you spend that exceeds the money you earn, see also "burn rate".
“Burn rate” is a standard financial term for how much money a startup is losing. If you have $1 cash on hand and a burn rate of $2 a year, then you have six months before you either need to get profitable, raise more money, or shut down.
> They’re spending it<p>That's what the words mean in this context.
On the radio they mentioned that the total global chocolate market is ~100B, I googled it when I was home and it seems to be about ~135B. Apparently that is ... all chocolate, everywhere.. OpenAI's valuation is about 500B. Maybe going up to like 835B.<p>I'd love to see the rationale that OpenAI (not "AI" everywhere) is more valuable than chocolate globally.<p>... so crash early 2026?
Ignoring that those numbers aren't directly comparable, it did make me wonder, if I had to give up either "AI" or chocolate tomorrow, which would I pick?<p>Even as an enormous chocolate lover (in all three senses) who eats chocolate several times a week, I'd probably choose AI instead.<p>OpenAI has alternatives, but also I do spend more money on OpenAI than I do on chocolate currently.
I am just trying to help you write better. Your writing says "if I had to give up either AI or chocolate [...] I would probably choose AI". However, your language and intent seems to be that you would give up chocolate.
If you really wanted to know you could stop eating chocolate or stop using ai and see if you break. Or do both at different times and see how long you last without one or the other.
Wait, aren't you comparing revenue and market cap?<p>People take old things for granted often. Explains the Coolidge effect, and why plenty of people cheat.
I spend a lot more time using AI for work than I do eating chocolate
I love AI, and ChatGPT has been transformative for me. But would I give it up for chocolate? I honestly don't think I could.
2008: US Banks pump stocks -> market correction -> taxpayer bailout<p>2026: US AI companies pump stocks -> market correction -> taxpayer bailout<p>Mark my words. OpenAI will be bailed out by US taxpayers.
I'll take that bet.<p>Banks get bailed out because if confidence in the banking system disappears and everyone tries to withdraw their money at once, the whole economy seizes up. And whoever is Treasury Secretary (usually an ex Wall Street person) is happy to do it.<p>I don't see OpenAI having the same argument about systemic risk or the same deep ties into government.
Even in a bank bailout, the equity holders typically get wiped out. It's really not that different from a bankruptcy proceeding, there's just a whole lot more focus on keeping the business itself going smoothly. I doubt OpenAI want to be in that kind of situation.
In 2008 though the US government ended up making more money then they spent though (at least with the tarp), because they invested a ton of money when everything collapsed, and thus was extremely cheap. Once the markets recovered, they made a hefty sum selling all the derivatives they got at the lowest point. Seems like the epitome of buy when low and sell when high tbh.
Not really. It was not about stocks. It was the collapse of insurance companies at the core of 2008 crisis.<p>The same can happen now on the side of private credit that gradually offloads its junk to insurance companies (again):<p><i>As a result, private credit is on the rise as an investment option to compensate for this slowdown in traditional LBO (Figure 2, panel 2), and PE companies are actively growing the private credit side of their business by influencing the companies they control to help finance these operations. Life insurers are among these companies. For instance, KKR’s acquisition of 60 percent of Global Atlantic (a US life insurer) in 2020 cost KKR approximately $3billion.</i><p><a href="https://www.imf.org/en/Publications/global-financial-stability-notes/Issues/2023/12/13/Private-Equity-and-Life-Insurers-541437" rel="nofollow">https://www.imf.org/en/Publications/global-financial-stabili...</a>
For what I use them for, the LLM market has become a two player game, and the players are Anthropic and Google. So I find it quite interesting that OpenAI is still the default assumption of the leader.
Interestingly Claude is so far down in traffic it's below things like CharacterAI, it's the best model but it's something like 70% ChatGPT, 10% Gemini and Claude is only 1% or so
Only in HN and some reddit subs I even see the name claude. In many countries AI=ChatGPT.
And at one point in the 90s, Internet=Netscape Navigator.<p>I see Google doing to OpenAI today what Microsoft did to Netscape back then, using their dominant position across multiple channels (browser, search, Android) to leverage their way ahead of the first mover.
codex cli with gpt-5.2-codex is so reliably good, it earns the default position in my book. I had cancelled my subscription in early 2024 but started back up recently and have been blown away at how terse, smart, and effective it is. Their CLI harness is top-notch and it manages to be extremely efficient with token usage, so the little plan can go for much of the day. I don’t miss Claude’s rambling or Gemini’s random refactorings.
ChatGPT dominates the consumer market (though Nano Banana is singlehandedly breathing some life into consumer Gemini).<p>A small anecdote: when ChatGPT went down a few months ago, a lot of young people (especially students) just waited for it to come back up. They didn't even think about using an alternative.
When ChatGPT starts injecting ads or forcing payment or doing anything else that annoys its userbase then the young people won't have a problem looking for alternatives<p>This "moat" that OpenAI has is really weak
That's pretty nuts. With the models changing so much and so often, you have to switch it up sometimes just to see what the other company is offering.
paywall, no upvote