[1] https://blogs.nvidia.com/blog/open-models-data-tools-acceler...
Nvidia has always had its own family of models, it's nothing new and not something you should read too much into IMHO. They use those as template other people can leverage and they are of course optimized for Nvidia hardware.
Nvidia has been training models in the Megatron family as well as many others since at least 2019 which was used as blueprint by many players. [1]
It doesn't get a ton of attention on /r/LocalLLaMA but it is worth trying out, even if you have a relatively modest machine.
[0] https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B...
[1] https://huggingface.co/unsloth/Nemotron-3-Nano-30B-A3B-GGUF
It isn't as though GLM-4.7 Flash is significantly better, and honestly, I have had poor experiences with it (and yes, always the latest llama.cpp and the updated GGUFs).
Megatron was a research project.
NVidia has professional services selling companies on using Nemo for user facing applications.
Point me to these? Would like to have a look.
I didn't find the original lawsuit documents, but there's a screenshot in this video: https://youtu.be/csybdOY_CQM?si=otx3yn4N26iZoN7L&t=182 (timestamp is 3:02 if you don't see it)
There's more details about the behind-the-scenes and greg brockman's diary leaks in this article: https://www.techbuzz.ai/articles/open-ai-lawsuit-exposed-the... Some documents are made public thanks to the Musk-OpenAI trial.
I'll let you read a few articles about this lawsuit, but basically they said to Musk (and frankly, to everyone else) that they were committed to the non-profit model, while behind the scenes thinking about "making the billion" and turning for-profit.
Edit: Ah, so the fake investment announcements started from the very beginning. Incredible.
Commodity businesses are price chasers. That's the only thing to compete on when product offerings are similar enough. AI valuations are not setup for this. AI Valuations are for 'winner takes all' implications. These are clearly now falling apart.
As problematic as SWE-Bench is as a benchmark, the top commercial models are far better than anything else and it seems tough to see this as anything but a 3 horse race atm.
I'm not saying this is what will happen, but people obviously bet a lot of money on that.
1. OpenAI bet largely on consumer. Consumers have mostly rejected AI. And in a lot of cases even hate it (can't go on TikTok or Reddit without people calling something slop, or hating on AI generated content). Anthropic on the other hand went all in on B2B and coding. That seems to be the much better market to be in.
2. Sam Altman is profoundly unlikable.
People like to complain about things, but consumers are heavily using AI.
ChatGPT.com is now up to the 4th most visited website in the world: https://explodingtopics.com/blog/chatgpt-users
The difference there is it became hated after it was established and financially successful. If you need to turn free visitors in to paying customers, that general mood of “AI is bad and going to make me lose my job/fuck up society” is yet another hurdle OpenAI will have to overcome.
My point was more that it seems this wave of AI is more profitable if you're in B2B vs. B2C.
The strategy here is more valid in my opinion. The value in AI is much more legible when the consumer uses it directly from their chat UI than whatever enterprises can come up with.
I can suggest many ways that consumers can use it directly from chat window. Value from enterprise use is actually not that clear. I can see coding but that’s about it. Can you tell me ways in which enterprises can use AI in ways that is not just providing their employees with chaggpt access?
https://www.davidrevoy.com/article1090/the-amphora-of-great-...
https://www.davidrevoy.com/article1092/the-amphora-of-great-...
it's that he has no ethics to speak of at all. it's not that he's out of touch, it's that he simply does not care.
He is clearly disliked by a lot of tech community, I don't see his AGI belief as a big part of that.
And when two people want different things from him, he "resolves" the conflict by agreeing with each of them separately, and then each assumes they got what they wanted, until they talk to the other person and find out that nothing was resolved.
Really not a person who is qualified to run a company, except the constant lying is good for fundraising and PR.
Interesting that he's got as far as he has with this issue. I don't think you can run a company effectively if you don't deal in truth.
Some of his videos have seemed quite bizarre as well, quite sarcastic about concerns people have about AI in general.
And today it seems everyone will at YC hate him but pretend not
The second one is a bunch of SJW hand-wringing about things that are only tangentially related, like indigenous Bolivians being oppressed by Spanish Conquistadors centuries ago. That part I don't care for as much.
Example, Sam Altman and OpenAI hoarding 40% of the RAM supply as unprocessed wafers stored in warehouses bought with magical bubble investors money in GPUs that don't exist yet and that they will not be able to install because there's not enough electricity to feed such botched tech, in data centers that are still to be built, with intention to punch the competence supply, and all the people of the planet in the process along two years (at least).
For a brief moment I thought you were talking about Elon there
You can see it when he talks, he's clearly trying (very unconvincingly) to emulate normal human emotions like concern and empathy. He doesn't feel them.
People like that are capable of great evil and there's a part of our lizard brains that can sense it
I don't and I see Sam Altman as a greater fraud than that (loathsome) individual. And I don't think Sam gets through the coming bubble pop without being widely exposed (and likely prosecuted) as a fraudster.
Besides OpenAI was never going to recoup the billions of dollars based on advertising or $20/month subscriptions
I don't think it is at all
The CEO just has to have followership: the people who work there have to think that this is a good person to follow. Even they don't have to "like" him
Source on that?
Lots of organizations offer ChatGPT subscriptions, and Microsoft pushes Copilot as hard as it can which uses GPT models.
How do you figure?
He and his personality caused people like Ilya to leave. At that point the failure risk of OAI jumped tremendously. The reality he will have to face is, he has caused OAIs demise.
Perhaps hes ok with that as long as OAI goes down with him. Would expect nothing less from him.
The demise of OpenAI is rooted in the bad product market fit, since many people like using ChatGPT for free, but fewer are ready to pay for it. And that’s pretty much all there is to it. OpenAI bet on consumers, made a slopstagram that unsurprisingly didn’t revolutionise content, and doesn’t sell as many licenses as they would like.
He says a lot of fluff, doesn’t try to be very extreme, and focuses on selling. I don’t know him personally but he comes across like an average person if that makes sense (in this environment that is).
I think I personally prefer that over Elon’s self induced mental illnesses and Dario being a doomer promoting the “end” of (insert a profession here) in 12 months every 6 months. It’s hard for me to trust a megalomaniac or a total nerd. So Sam is kinda in the middle there.
I hope OpenAI continues to dominate even if the margins of winning tighten.
Now I have him muted on X.
Personally I don't think Elon is the worst billionaire, he's just the one dumb enough to not have any PR (since 2020). They're all pretty reprehensible creatures.
There will always be enough people willing to suck up to money that they'll have all the yes-men they need to rationalize it as "it's EVERYONE ELSE who's wrong!"
Props to him for letting people mute him on his own platform. The issue with Sam and OpenAI is they their bias on any controversional topic can't be switched off.
If you nail the bar to the floor, then sure, you can pass over it.
> He says a lot of fluff, doesn’t try to be very extreme, and focuses on selling.
I don't now what your definition of extreme is but by mine he's pretty extreme.
> I think I personally prefer that over Elon’s self induced mental illnesses and Dario being a doomer promoting the “end” of (insert a profession here) in 12 months every 6 months.
All of them suffer from thinking their money makes them somehow better.
> I hope OpenAI continues to dominate even if the margins of winning tighten.
I couldn't care less. I'm on the whole impressed with AI, less than happy about all of the slop and the societal problems it brings and wished it had been a more robust world that this had been brought in to because I'm not convinced the current one needed another issue of that magnitude to deal with.
Let's assume they think they're better than others.
What makes you think that they think it's because of their money, as opposed to, say, because of their success at growing their products and businesses to the top of their field?
I think over time it (LLM based) will become like an augmenter, not something like what they’re selling as some doomsday thing. It can help people be more efficient at their jobs by quickly learning something new or helping do some tasks.
I find it makes me a lot more productive because I can have it follow my architecture and other docs to pump out changes across 10 files that I can then review. In the old way, it would have taken me quite a while longer to just draft those 10 files (I work on a fairly complex system), and I had some crazy code gen scripts and shit I’d built over the years. So I’d say it gives me about 50% more efficiency which I think is good.
Of course, everyone’s mileage may vary. Kinda reminds me of when everyone was shitting on GUIs, or scripting languages or opinionated frameworks. Except over time those things made productivity increase and led to a lot more solutions. We can nitpick but I think the broader positive implication remains.
Gemini will own everything normie and professional services, and Anthropic will own engineering (at least software)
Honestly as of the last few months anyone still hyping ChatGPT is outing themselves.
https://news.ycombinator.com/newsguidelines.html
https://hn.algolia.com/?sort=byDate&dateRange=all&type=comme...
I meant thinking patterns that go beyond our understanding. High functioning autism that is beyond jealousy/envy, and beyond the need to hold or be on a leash and beyond the enigma of emotions that come with the influence to dump and pump stock market prices of precious, precious metals.
Or, in other terms, the kind of intelligence that is built for abstract, distant, symbiotic humanity. From the POV of Earth as a system, we're quite the dumb nuisance. "Just get it, man". :D
> Anthropic relies heavily on a combination of chips designed by Amazon Web Services known as Trainium, as well as Google’s in-house designed TPU processors, to train its AI models. Google largely uses its TPUs to train Gemini. Both chips represent major competitive threats to Nvidia’s best-selling products, known as graphics processing units, or GPUs.
So which leading AI company is going to build on Nvidia, if not OpenAI?
For these massive, and expensive to train, AI models the differences hit harder since at the kernel level, where the pedal hits the metal, they are going to be wringing every last dollar of performance out of the chips by writing hand optimized kernels for them, highly customized to the chip's architecture and performance characteristics. It may go deeper than that too, with the detailed architecture of the models themselves tweaked to best perform on a specific chip.
So, bottom line is that you can't just take a model "compiled to run on TPUs", and train it on NVidia chips just because you have spare capacity there.
>But is Google buying those GPU chips for their own use
>google buys nvidia GPUs for cloud, I don't think they use them much or at all internally.
We're not talking about GPUs.
My point is, does Apple have any useful foundation models? Last I checked they made a deal with OpenAI, no wait, now with Google.
I see no evidence of this happening.
But seriously, would one be for newer phone/tablet models, and one for older?
Seems like it's more a ramp-up than two completely separate Siri replacements.
1. Sit out and buy the tech you need from competitors.
2. Spend to the tune of ~$100B+ in infra and talent, with no guarantee that the effort will be successful.
Meta picked option 2, but Apple has always had great success with 1 (search partnership with Google, hardware partnerships with Samsung etc.) so they are applying the same philosophy to AI as well. Their core competency is building consumer devices, and they are happy to outsource everything else.
It's definitely rational to decide to pay wholesale for LLMs given:
- consumer adoption is unclear. The "killer app" for OS integration has yet to ship by any vendor.
- owning SOTA foundation models can put you into a situation where you need to spend $100B with no clear return. This money gets spent up front regardless of how much value consumers derive from the product, or if they even use it at all. This is a lot of money!
- as apple has "missed" the last couple of years of the AI craze, there has been no meaningful ill effects to their business. Beyond the tech press, nobody cares yet.
They tried to do something that probably would have looked like Copilot integration into Windows, and they chose not to do that, because they discovered that it sucked.
So, they failed in an internal sense, which is better than the externalized kind of failure that Microsoft experienced.
I think that the nut that hasn't been cracked is: how do you get LLMs to replace the OS shell and core set of apps that folks use. I think Microsoft is trying by shipping stuff that sucks and pissing off customers, while Apple tried internally declined to ship it. OpenClaw might be the most interesting stab in that direction, but even that doesn't feel like the last word on the subject.
I’m not sure it matters though. They just had a stonking quarter. iPhone sales are surging ahead. Their customers clearly don’t care about AI or Siri’s lacklustre performance.
I would rather say their products didn’t just loose in value for not getting an improvement there. Everyone agrees that Siri sucks, but I’m pretty sure they tried to replace it with a natural language version built from the ground up, and realised it just didn’t work out yet: yes, they have a bad, but at least kinda-working voice assistant with lots of integrations into other apps. But replacing that with something that promises to do stuff and then does nothing, takes long to respond, and has less integrations due to the lack of keywords would have been a bad idea if the technology wasn’t there yet.
That doesn't sound like a financial decision to me.
[1] https://www.apple.com/uk/newsroom/2024/06/wwdc24-highlights/
[2] https://www.bloomberg.com/news/features/2025-05-18/how-apple...
[3] https://nypost.com/2025/12/02/business/apple-shakes-up-ai-te...
If I were Nvidia I would be hedging my bets a little. OpenAI looks like it's on shaky ground, it might not be around in a few years.
https://blogs.nvidia.com/blog/open-models-data-tools-acceler...
Interesting times.
Interesting times indeed!
> So which leading AI company is going to build on Nvidia, if not OpenAI?
It's xAI.
But what matters is that there is more competition for Nvidia and they bought Groq to reduce that. OpenAI is building their own chips as well as Meta.
The real question is this: What happens when the competition catches up with Nvidia and takes a significant slice out of their data center revenues?
https://techcrunch.com/2026/01/26/nvidia-invests-2b-to-help-...
For example, Amazon isn’t able to train its own models so it hedges by investing in Anthropic and OpenAI. Oracle, same with OpenAI deal. Nvidia wants to stay in OpenAI and Anthropic’s tech stack.
It’s all jockeying for position.
I guarrantee you that in 10 years time, you will get claims of unethical conduct by those companies only after the mania has ended (and by then the claimants have sold all their RSUs.)
https://github.com/openai/codex/issues/9253
OTOH, if Anthropic did that to Claude Code, there wasn’t a moderately straightforward workaround, and Anthropic didn’t revert it quickly, it might actually be a risk-the-whole-business issue. Nothing makes people jump ship quite like the ship refusing to go anywhere for weeks while the skipper fumbles around and keeps claiming to have fixed the engines.
Also, the fact that it’s not major news that most business users cannot log in to the agent CLI for two weeks running is not major news suggests that OpenAI has rather less developer traction than they would like. (Personal users are fine. Users who are running locally on an X11-compatible distro and thus have DISPLAY set are okay because the new behavior doesn’t trigger. It kind of seems like everyone else gets nonsense errors out of the login flow with precise failures that change every couple days while OpenAI fixes yet another bug.)
"Root Cause
The backend enforces an Enterprise-only entitlement for codex_device_code_auth on POST /backend-api/accounts/{account_id}/beta_features. Your account is on the Team plan, so the server rejects the toggle with {"detail":"Enterprise plan required."} "
and so on and so forth. At any given day i have several such long-term tickets that get ultimately escalated to me (i'm in dev and usually the guy who would pull the page with ssh tunnel or credentials copying :)
The backstory here is that codex-rs (OpenAI’s CLI agent harness) launched an actual headless login mechanism, just like Claude Code has had forever. And it didn’t work, from day one. And they can’t be bothered to revert it for some reason.
Sure, big enterprises are inept. But this tool is fundamentally a command line tool. It runs in a terminal. It’s their answer to one of their top two competitors’ flagship product. For a company that is in some kind of code red, the fact that they cannot get their ducks in a row to fix it is not a good sign.
Keep in mind that OpenAI is a young company. They should have have a thicket of ancient garbage to wade through to fix this — it’s not as if this is some complex Active Directory issue that no one knows how to fix because the design is 30-40 years old and supports layers and layers of legacy garbage.
It’s also possible that the majority of people hitting it are using the actual website support (which is utterly and completely useless), since the bug is only a bug in codex-rs to the extent that codex-rs should have either reverted or deployed a workaround already.
The tools on top of the models are the path and people building things faster is the value.
Those without models are hugely vulnerable to sudden rug pulls.
They’re never gonna recover their investment and eventually their partners will run away.
The GPT models are not a moat.
- Nvidia is the most valuable company. Why? It makes GPUs. Why does that matter? Because AI is faster on them than CPUs, ASICs are too narrowly useful, and because first-mover advantage. AMD makes GPUs that work great for AI, but they're a fraction of the value of Nvidia, despite the fact that they make more useful products than Nvidia. Why? Nvidia just got there first, people started building on them, and haven't stopped, because it's the path of least resistance. But if Nvidia went away tomorrow, investors would just pour money into AMD. So Nvidia doesn't have any significant value compared to AMD other than people are lazy and are just buying the hot thing. Nvidia was less valuable than AMD before, they'll return there eventually; all AMD needs is more adoption and investment.
- Every frontier model provider out there has invested billions to get models to the advanced state they're in today. But every single time they advance the state of the art, open weights soon match them. Very soon, there won't be any significant improvement, and open weights will be the same as frontier, meaning there's no advantage to paying for frontier models. So within a few years, there will be no point to paying OpenAI, Anthropic, etc. Again, these were just first-movers in a commodity market. The value just isn't there. They can still provide unique services, tailored polished apps, etc (Anthropic is already doing this by banning users who have the audacity to use their fixed-price plans with non-Anthropic tools). But with AI code tools, anyone can do this. They are making themselves obsolete.
- The final form of AI coding is orchestrated agent-driven vibe-coding with safeguards. Think an insane asylum with a bowling league: you still want 100 people to autonomously (and in parallel) knock the pins knocked over, but you have to prevent the inmates from killing anyone. That's where the future of coding is. It's just too productive to avoid. But with open models and open source interfaces, anyone can do this, whether with hosted models (on any of 50 different providers), or a Beowulf cluster of cobbled together cheap hardware in a garage.
- Eventually, in like 5-10 years (a lifetime away), after AI Beowulfs have been a fad for a while, people will tire of it and move back to the cloud, where they can run any model they want on a K8s cluster full of GPUs, basically the same as today. Difference between now and then is, right now everyone is chasing Anthropic because their tools and models are slightly better. But by then, they won't be. Maybe people will use their tools anyway? But they won't be paying for their models. And it's not just price: one of the things you learn quickly by running models, is they're all good for different things. Not only that, you can tweak them, fine-tune them, and make them faster, cheaper, better than what's served up by frontier models. So if you don't care about the results or cost, you could use frontier, but otherwise you'll be digging deep into them, the same way some companies invest in writing their own software vs paying for it.
- Finally, there's the icing on the cake: LLMs will be cooked in 10 years. I keep reading from AI research experts that "LLMs are a dead end" - and it turns out it's true. LLMs are basically only good because we invest an unsustainable amount of money in the brute-forcing of a relatively dumb form of iteration: download all knowledge, do some mind-bogglingly expensive computational math on it, tweak the reasults, repeat. There's only so many of that loop you can do, because fundamentally, all you're doing is trying to guess your way to an answer from a picture of the past. It doesn't actually learn, the way a living organism learns, from experience, in real-time, going forward; LLMs only look backward. Like taking a snapshot of all the books a 6 year old has read, then doing tweaks to try to optimize the knowledge from those books, then doing it again. There's only so much knowledge, only so many tweaks. The sensory data of the lived experience of a single year of life of a 6 year old is many times more information than everything ever recorded by man. Reinforcement Learning actually gives you progressive, continuously improved knowledge. But it's slow, which is why we aren't doing it much. We do LLMs instead because we can speed-run them. But the game has an end, and it's the total sum of our recorded knowledge and our tweaks.
So LLMs will plateau, frontier models will make no sense, all lines of code will be hands-off, and Nvidia will return to making hardware for video games. All within about 10 years. With the caveat that there might be a shift in global power and economic stability that interrupts the whole game.... but that's where we stand if things keep on course. Personally, I am happy to keep using AI and reap the benefits of all these moronic companies dumping their money into it, because the open weights continue being useful after those companies are dead. But I'm not gonna be buying Nvidia stock anytime soon, and I'm definitely not gonna use just one frontier model company.
The closed LLMs with the biggest amount of users will eventually outperform the open ones too, I believe. They have a lot of closed data that they can train their next generation on. Especially the LLMs that the scientific community uses will be a lot more valuable (for everyone). So in terms of quality, the closed LLMs should eventually outperform the open ones, I believe, which is indeed worrisome.
I also felt anxious early december about the valuations, but, one thing remains certain. Compute is in heavy demand, regardless of which LLM people use. I can't go back to pre-AI. I want more and more and faster and faster AI. The whole world is moving that way it seems like. I'm invested into phsyical AI atm (chips, ram, ...) whose evaluations look decently cheap.
- LLMs have fixed limitations. The first one is training, the dataset you use. There's only so much information in the world and we've largely downloaded it all, so it can't get better there. Next you can do training on specific things to make it better at specific things, but that is by definition niche; and you can actually do that for free today with Google's Tensors in free Cloud products. Later people will pay for this, but the point is, it's ridiculously easy for anyone to fine-tune training, we don't need frontier companies for that. And finally, LLM improvements come by small tweaks to models that already come to open weights within a matter of months, often surpassing the frontier! All you have to do is sit on your ass for a couple months and you have a better open model. Why would anyone do this? Because once all models are extremely good (about 1 year from now) you won't need them to be better, they'll already do everything you need in 1-shot, so you can afford to sit and wait for open models. Then the only reason left to use frontier cloud is that they host a model; but other people do cloud-hosted models! Because it's a commodity! (And by the way, people like me are already pissed off at Anthropic because we're not allowed to use OAuth with 3rd party tools, which is complete bullshit. I won't use them on general principle now, they're a lock-in moat, and I don't need them) There will also be better, faster, more optimized open models, which everyone is going to use. For doing math you'll use one model, for intelligence you'll use a different model, for coding a different model, for health a different model, etc, and the reason is simple: it's faster, lower memory, and more accurate. Why do things 2x slower if you don't have to? Frontier model providers just don't provide this kind of flexibility, but the community does. Smart users will do more with less, and that means open.
On the hardware:
- Def it will continue to be investment-worthy, but be cautious. The growth simply isn't going to continue at pace, and the simple reason is we've already got enough hardware. They want more hardware so they can continue trying to "scale LLMs" the way they have with brute force. But soon the LLMs will plateau and the brute force method isn't going to net the kind of improvements that justify the cost. Demand for hardware is going to drop like a stone in 1-2 years; if they don't cease building/buying then, they risk devaluing it (supply/demand), but either way Nvidia won't be selling as much product so there goes their valuation. And RAM is eventually going to get cheaper, so even if demand goes up, spending is less. The other reason demand won't continue at pace is investors are already scared, so the taps are being tightened (I'm sure the "Megadeal" being put on-hold is the secret investment groups tightening their belts or trying to secure more favorable terms). I honestly can't say what the economic picture is going to look like, but I guarantee you Nvidia will fall from its storied heights back to normal earth, and other providers will fill the gap. I don't know who for certain, but AMD just makes sense, because they're already supported by most AI software the way Nvidia is (try to run open-source inference today, it's one of those two). Frontier and cloud providers have Tensors and other exotic hardware, which is great for them, but everyone else is gonna buy commodity chips. Watch for architectures with lower price and higher parts availability.
What about all the input data into LLMs and the conversations we're having? That must be able to produce a better next gen model, no?
> it's ridiculously easy for anyone to fine-tune training, we don't need frontier companies for that.
Not for me. It'll take me days, and then I'm pretty sure it won't be better than Gemini 3 pro for my coding needs, especially in reasoning.
> For doing math you'll use one model, for intelligence you'll use a different model, for coding a different model, for health a different model, etc, and the reason is simple: it's faster, lower memory, and more accurate.
Why wouldn't e.g. Gemini just add a triage step? And are you sure it's that much easier to get a better model for math than the big ones?
I think you underestimate the friction this causes regular users by handpicking and/or training specific models, whilst the big vendors are good enough for their needs.
Better models are largely coming from training, tuning, and specific "techniques" discovered to do things like eliminate loops and hallucinations. Human inputs are a small portion of that; you'll notice that all models are getting better despite the fact that all these companies have different human inputs! A decent amount of the models' abilities come from properties like temperature/p-settings, which is basically introducing variable randomness. (these are now called "low" and "high" in frontier models) This can cause problems, but also increased capability, so the challenge isn't getting better input, it's better controlling randomness (sort of). Even coding models benefit from a small amount of this. But there is a lot more, so overall model improvements are not one thing, they are many things that are not novel. In fact, open models get novel techniques before the frontier does, it's been like that for a while.
> Not for me. It'll take me days, and then I'm pretty sure it won't be better than Gemini 3 pro for my coding needs, especially in reasoning.
If you don't want the improvements, that's up to you; I'm just saying the frontier has no advantage here, and if people want better than frontier, it's there for free.
> Why wouldn't e.g. Gemini just add a triage step? And are you sure it's that much easier to get a better model for math than the big ones?
They already do have triage steps, but despite that, they still create specific models for specific use-cases. Most people already choose Thinking by default for general queries, and coding models for coding. That will continue, but there will be more providers of more specific models that will outperform frontier models, for the simple fact that there's a million use-cases out there and lots of opportunity for startups/community to create a better tailored model for cheaper. And soon all our computers will be decent at doing AI locally, so why pay for frontier anyway? I can already AI-code locally on a 4 year old machine. Two years from now, there likley won't be a need for you to use a cloud service at all, because your local machine and a local model will be equivalent, private, and free.
Microsoft has GitHub - the world’s biggest pile of code training data, plus infinite cash.
OpenAI has …… none of these advantages.
Google has data, TPUs, and a shitload of cash to burn
but in this case it is, ChatGPT name is really, really strong, it's like "just google it" instead of "just search the web"
First mover advantage matters only if it has long-lasting network effects. American schools are run on Chromebooks and Google Docs/Slides, but these have no penetration in enterprise, as college students have been discovering when they enter their first jobs.
https://www.macrobusiness.com.au/2021/05/the-great-semicondu...
Here is a long article from last year about Sam Altman.
https://www.nytimes.com/2024/09/25/business/openai-plan-elec...
https://finance.yahoo.com/news/tsmc-rejects-podcasting-bro-s...
> TSMC’s leadership dismissed Altman as a “podcasting bro” and scoffed at his proposed $7 trillion plan to build 36 new chip manufacturing plants and AI data centers.
I thought it was ridiculous when I read it. I'm glad the fabs think he's crazy too. If he wants this then he can give them the money up front. But of course he doesn't have it.
After the dot com collapse my company's fabs were running at 50% capacity for a few years and losing money. In 2014 IBM paid Global Foundries $1.5 billion to take the fabs away. They didn't sell the fabs, they paid someone to take them away. The people who run TSMC are smart and don't want to invest $20-100 billion in new fabs that come online in 3-5 years just as the AI bubble bursts and demand collapses.
https://gf.com/gf-press-release/globalfoundries-acquire-ibms...
I don't think demand will collapse though, since the Mag7 has the cash flow to spend, and they can monetize if the time's ripe.
What do you think?
I know a lot of people in the 45+ age range including many working on AI accelerators. We all think this is a bubble. The AI companies are not profitable right now for the prices they charge. There are a bunch of articles on this. If they raise prices too quickly to become profitable then demand will collapse. Eventually investors will want a return on their investment. I made a joke that we haven't reached the Pets.com phase of the bubble yet.
Companies like Google produce and operate AI models largely using their own TPUs rather than NVidia's GPUs. We've seen the Chinese produce pretty competitive open models with either older NVidia GPUs or alternative GPUs because they are not allowed to buy the newer ones. And AMD, Intel and other chip makers are also eager to get in on the action. Companies like Microsoft, Amazon, etc. have their own chips as well (similar to Google). All the hyperscalers are moving away from NVidia.
And then Apple runs a non Intel and non NVidia based range of workstations and laptops that are pretty popular with AI researchers because the M series CPU/GPU/NPU is pretty decent value for running AI models. You see similar movement with ARM chips from Qualcomm and others. They all want to run AI models on phones, tablets, laptops. But without NVidia.
NVidia's bubble is about vastly overcharging for a thing that only they can provide. Their GPU chips have enormous margins relative to CPU chips coming out of the same/similar machines. That's a bubble. As soon as you introduce competition, the companies with the best price performance wins. NVidia is still pretty good at what they do. But not enough to justify an order of magnitude price/cost difference.
NVidia's success has been predicated on its proprietary software and instruction set (CUDA). That's a moat that won't last. The reason Google can use its own TPUs rather than CUDA is that it worked hard to get rid of their CUDA dependence. Same for the other hyperscalars. At this point they can do training and inference without CUDA/NVidia and its more cost effective.
The reason that this 100B deal is apparently being reconsidered is that it is a bad deal for OpenAI. It was going to overpay for a solution that they can get cheaper elsewhere. It's bad news for NVidia, good news for OpenAI. This deal started out with just NVidia. But at this point there are also deals with AMD, MS, and others. OpenAI like the other hyperscalers is not betting the company on NVidia/CUDA. Good for them.
Yes it is. I think even for multiple reasons. Competition in that space not sleeping is one but it's also a huge overestimation of demand combined with the questionable believe those GPUs and the Datacenters housing them can actually be built and put into operation as fast as envisioned.
> The reason that this 100B deal is apparently being reconsidered is that it is a bad deal for OpenAI. It was going to overpay for a solution that they can get cheaper elsewhere. It's bad news for NVidia, good news for OpenAI. This deal started out with just NVidia. But at this point there are also deals with AMD, MS, and others. OpenAI like the other hyperscalers is not betting the company on NVidia/CUDA. Good for them.
I think in case of OpenAI both may be true. While what you are saying makes sense, NVs first mover advantage obviously can't last forever, OpenAI currently does have little to no competitive advantage over other players. Combine this with the fact that some (esp. Google) sit on a huge pile of cash. In contrast for OpenAI the party is pretty much over as soon as investors stop throwing money into the oven so they might need to cut back a bit.
https://www.theregister.com/2026/01/29/oracle_td_cowen_note/
Edit: Another src https://www.cio.com/article/4125103/oracle-may-slash-up-to-3...
And yes, Sam is incredibly unlikable. Every time I see him give an interview, I am shocked how poorly prepared he is. Not to mention his “ads are distasteful, but I love my supercar and ridiculous sunglasses.”
https://preview.redd.it/sam-altman-on-the-model-v0-7u2a2o7lr...
We all know this is a speculative run-up. We all know it'll end somehow. Crashes always start with something like this. Is this the tipping point? Damned if I know. But it'll come.
[1] Though recognize that by engaging in that frame you're painting yourself as an unserious amateur being influenced by partisan media. Real governments do not "print money" in any real sense, and attempts to conflate things like bond debt with it run afoul, yet again, of the money/value mistake.
