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Here is a charitable perspective on what's happening: - Nvidia has too much cash because of massive profits and has nowhere to reinvest them internally. - Nvi
by vmg12 1y ago
Here is a charitable perspective on what's happening:
- Nvidia has too much cash because of massive profits and has nowhere to reinvest them internally.
- Nvidia instead invests in other companies that use their gpus by providing them deals that must be spent on nvidia products.
- This accelerates the growth of these companies, drives further lock in to nvidia's platform, and gives nvidia an equity stake in these companies.
- Since growth for these companies is accelerated, future revenue will be brought forward for nvidia and since these investments must be spent on nvidia gpus it drives further lock in to their platform.
- Nvidia also benefits from growth due to the equity they own.
This is all dependent on token economics being or becoming profitable. Everything seems to indicate that once the models are trained, they are extremely profitable and that training is the big money drain. If these models become massively profitable (or at least break even) then I don't see how this doesn't benefit Nvidia massively.
- deleted 1y ago[deleted]
- belter 1y ago> Everything seems to indicate that once the models are trained, they are extremely profitable Some data would reinforce your case. Do you have it? Here is my data point: "You Have No Idea How Screwed OpenAI Actually Is" - https://wlockett.medium.com/you-have-no-idea-how-screwed-openai-actually-is-8358dccfca1c https://wlockett.medium.com/you-have-no-idea-how-screwed-ope...
- trollbridge 1y agoRight. As far as I can tell, OpenAI, Grok, etc sell me tokens at a loss. But I am having a hard time figuring out how to turn tokens into money (i.e. increased productivity). I can justify $40-$200 per developer per month on tokens but not more than that.
- koolba 1y agoThere’s about 5M software devs in the US so even at $1000/year/person spend, that’s only $5B of revenue to go around. Theres plenty of other uses cases but focusing on pure tech usage, it’s hard to see how the net present value of that equates to multiple trillions of dollars across the ecosystem.
- treis 1y agoIt's the first new way of interacting with computers since the iPhone. It's going to be massively valuable and OpenAI is essentially guaranteed to be one of the players.
- _aavaa_ 1y agoWhy is their product not palm? Or windows mobile?
- treis 1y agoIt's not windows mobile because OpenAI was first and is the clear leader in the market. Windows mobile was late to the party and missed their window. Palm is closer but it's a different world. It's established that Internet advertising companies are worth trillions. It's only in retrospect that what Palm could have been is obvious. Barring something very unexpected OpenAI is coming out on top. They're prepaying for a good 5-10 years of compute. That means their inference and training for that time are "free" because they've been paid for. They're going to be able to bury their competition in money or buy them out.
- _aavaa_ 1y agoWindows mobile by the time it looked like the iPhone was late to the party. But windows had been releasing a mobile os for a long time before that. Microsoft was first, they just didn’t make as good of a product as Apple despite their money. OpenAI is also first, but it is absolutely not a given that they are the Apple in this situation. Microsoft too had money to bury the competition, they even staged a fake funeral when they shipped windows phone 7. > Barring something very unexpected Like the release of an iPhone?
- simianwords 1y agowhy would they sell you at a loss when they have been decreasing prices by 2x every year or so for the last 3 years? people wanted to purchase the product at price "X" in 2023 and now the same product costs X costs 10 times less over the years.. do you think they were always selling at a loss?
- schwarzrules 1y agoI'm not trying to be annoying, but surely if you'd justify spending $200/developer/month, you could afford $250/month... The reason I wonder about that is because that also seems to be the dynamic with all these deals and valuations. Surely if OpenAI would pay $30 billion on data centers, they could pay $40 billion, right? I'm not exactly sure where the price escalations actually top out.
- h2zizzle 1y agoNo? That's a 25% expense increase. You just ate the margins on my product/service, and then some.
- simianwords 1y agoInference cost has been going down for a while now. At what point do you think it will be profitable? When cost goes down by 2x? 5x?
- bwfan123 1y agoThis is behind a paywall. Is there a free link you can share ?
- logicprog 1y agoI can't read your hyperbolically titled paywalled medium post, so idk if it has data I'm not aware of or is just rehashing the same stats about OpenAI & co currently losing money (mostly due to training and free users) but here's a non paywalled blog post that I personally found convincing: https://www.snellman.net/blog/archive/2025-06-02-llms-are-cheap/ https://www.snellman.net/blog/archive/2025-06-02-llms-are-ch...
- ragingregard 1y agoThe above article is not convincing at all. Nothing on infra costs, hardware throughput + capacity (accounting for hidden tokens) & depreciation, just a blind faith that pricing by providers "covers all costs and more". Naive estimate of 1000 tokens per search using some simplistic queries, exactly the kind of usage you don't need or want an LLM for. LLMs excel in complex queries with complex and long output. Doesn't account at all for chain-of-thought (hidden tokens) that count as output tokens by the providers but are not present in the output (surprise). Completely skips the fact the vast majority of paid LLM users use fixed subscription pricing precisely because the API pay-per-use version would be multiples more expensive and therefore not economical. Moving on.
- logicprog 11mo ago> Nothing on infra costs, hardware throughput + capacity (accounting for hidden tokens) & depreciation That's because it's coming at things from the other end: since we can't be sure exactly what companies are doing, we're just going to look at the actual market incentives and pricing available and try to work backwards from there. And to be fair, it also cites, for instance, deepseek's paper where they talk about what their power foot margins are on inference. > just a blind faith that pricing by providers "covers all costs and more". It's not blind faith. I think they make a really good argument for why the pricing by providers almost certainly does cover all the costs and more. Again, including citing white papers by some of those providers. > Naive estimate of 1000 tokens per search using some simplistic queries, exactly the kind of usage you don't need or want an LLM for. Those token estimates were for comparing to search pricing to establish whether — relative to other things on the market — LLMs were expensive, so obviously they wanted to choose something where the domain is similar to search. That wasn't for determining whether inference was profitable or not in itself, and has absolutely no bearing on that. > Doesn't account at all for chain-of-thought (hidden tokens) that count as output tokens by the providers but are not present in the output (surprise). Most open-source providers provide thinking tokens in the output. Just separated by some tokens so that UI and agent software can separate it out if they want to. I believe the number of thinking tokens that Claude and GPT-5 use can be known as well: https://www.augmentcode.com/blog/developers-are-choosing-older-ai-models-and-16b-tokens-of-data-explain-why https://www.augmentcode.com/blog/developers-are-choosing-old...; typically, chain of thought tokens are also factored into API pricing in terms of what tokens you're charged for. So I have no idea what this point is supposed to mean. > Completely skips the fact the vast majority of paid LLM users use fixed subscription pricing precisely because the API pay-per-use version would be multiples more expensive and therefore not economical. That doesn't mean that selling inference by subscription isn't profitable either! This is a common misunderstanding of how subscriptions work. With these AI inference subscriptions, your usage is capped to ensure that the company doesn't lose too much money on you. And then the goal is with the subscriptions that most people who have a subscription will end up on average using less inference than they paid for in order to pay for those who use more so that it will equal out. And that's assuming that the upper limit on the subscription usage is actually more costly than the subscription being paid itself, and that's a pretty big assumption. If you want something that factors in subscriptions and also does the sort of first principles analysis you want, this is a good article: https://martinalderson.com/posts/are-openai-and-anthropic-really-losing-money-on-inference/ https://martinalderson.com/posts/are-openai-and-anthropic-re... And in my opinion it seems pretty clear that basically everyone who does any kind of analysis whether black box or first principles on this comes to the conclusion that you can very easily make money on inference. The only people coming to any other conclusion are those that just look at the finances of U.S. AI companies and draw conclusions on that without doing any kind of more detailed breakdown — exactly like the article you linked me, which now I have finally been able to read, thanks to someone posting the archive link, which isn't actually making any kind of case about the subscription or unit economics of token inference whatsoever, but is instead just basing its case on the massive overinvestment of specifically open AI into gigantic hyperscale data centers, which is unrelated to the specific economics of AI itself.
- Eisenstein 1y agoYour conclusion about training being the cost factor that will eventually align with profitability in the inference phases relies on training new models not being an endless arms race.
- vmg12 1y agoIf the inference is profitable and training new models is actually an endless arms race that's actually the best outcome for nvidia specifically.
- Eisenstein 1y agoOnly in the short term.
- pols45 1y agoYup. Not just Nvidia. Just look at the quarterly results reported by Amazon, Google, Meta, Microsoft and Apple. Each one is reporting revenues never before seen in history. If you make 100 Billion a quarter you have to spend it on something. These guys are running hyper optimized cash extraction mega machines. There is no comparison to previous bubbles, cause so no such companies ever existed in the past.
- trollbridge 1y agoOdd how they are simultaneously having large layoffs even as reporting record revenues. The question is where the profits are.
- wmeredith 1y agoAmazon - 14,000 layoffs; significant Microsoft - 14,000 (multiple rounds); significant Meta - 600 layoffs; insignificant for company size Google - "Several hundred layoffs"; insignificant for a company size Apple - No layoffs Source: https://techcrunch.com/2025/10/24/tech-layoffs-2025-list/ https://techcrunch.com/2025/10/24/tech-layoffs-2025-list/
- _DeadFred_ 1y agoGoogle's Youtube unit is doing soft layoffs. https://news.ycombinator.com/item?id=45766368 https://news.ycombinator.com/item?id=45766368
- ponector 1y agoAlso every one of them has hundreds of thousands of external contractors which are not reported anywhere. And offshoring is also a huge cost-cutting effort everywhere.
- jcheng 1y agoThe layoffs at Amazon and Microsoft are not due to lack of profits. They’re massively profitable right now. https://www.macrotrends.net/stocks/charts/MSFT/microsoft/ebitda https://www.macrotrends.net/stocks/charts/MSFT/microsoft/ebi... https://www.macrotrends.net/stocks/charts/AMZN/amazon/ebitda https://www.macrotrends.net/stocks/charts/AMZN/amazon/ebitda
- moogly 1y ago> Nvidia has too much cash because of massive profits and has nowhere to reinvest them internally. Here's an idea: they could make actual GPUs used for games affordable again, and not have Jensen Huang lie on stage about their performance to justify their astronomical prices. Sure, companies might want to buy them for ML/AI and crash the market again but I'm sure a company of their caliber could solve that if they _really_ wanted to.
- HDThoreaun 1y agoWhy would they want to do that? The only sector that matter to nvidia is datacenter, its where 90%+ of their profits are. Making their consumer sector even less profitable just seems like a waste of time
- moogly 1y agoHow about positive mindshare? Regular people not growing up absolutely hating nvidia's guts and only begrudgingly buying their products. Also ensuring that a pretty big industry won't die from becoming too expensive. Plus, diversification is good for when the bubble inevitably bursts. But that's long-term thinking and we can't have that. People give Huang credit for having had a long-term vision on AI, but it feels like he definitely has blinders on right now.
- senordevnyc 1y agoThe consumer gaming card market is minuscule in comparison to their primary market now, to the point where worrying about diversifying there probably doesn’t make sense. Nor does it really matter whether consumer gamers hate them. That is likely to have zero effect on their core customer now.
- Eisenstein 1y agoUnderestimating compounding and secondary effects, especially while rationalizing the abandonment of their core market and capability is one of the most famous ways that big companies provide evidence of their downward spiral. I can feel the MBA energy from here.
- MangoToupe 1y agoI'm just confused why people think token-based computing is going to be in such demand in the future. It's such a tiny slice of problems worth solving.
- thorncorona 1y agoIt's like how every big co these days is ML. It will transition to LLMs as well. Just give it a few years.
- brookst 1y agoYep. Same vibes as “ha ha who needs internet connected appliances” (pretty much all appliances are internet connected now). And the apocryphal “there is a worldwide market for maybe 5 computers”.
- baobabKoodaa 1y agoNo-one "needs" or even wants appliances to be connected to the internet. You claim that "pretty much all" appliances are internet connected, while almost none of the appliances in my house are.
- MangoToupe 1y agoI mean, who DOES need internet connected appliances? Are you implying these token-based techs will be shoved down our throats regardless of demand?
- PeterStuer 1y agoIt is'nt just nvidia though.
- tqian 1y agoIf models are profitable once trained, isn't it weird that chatgpt and Claude have $200 tiers that still have usage limits?
- mikewarot 1y ago>This is all dependent on token economics being or becoming profitable What is the actual value of a token? If it were generated by a human expert in a given field? This should set an upper limit for now.
- creer 1y ago> - Nvidia also benefits from growth due to the equity they own. aka this might be nvidia's next pivot. Contrary to gaming cards, the AI GPUs are productive assets. If nvidia feels that they will be very productive, then it makes sense that they invest broadly in the companies that are likely to make these profits. To share in the rewards while selling more GPUs.