641–650 of 668 posts
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Demis Hassabis seems to think this and not only does he not focus only on LLMs, he got a nobel prize for a non-LLM system ;)
As far as I know, that Nobel prize was for being the project manager...
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They account internally for each model separately; Dario said they even think of each model as a separate company on Dwarkesh some time ago. Inference services are wildly profitable. Currently companies believe it’s economically sensible to plow that money into R&D / Investment in new models through training. For reference, oAI’s monthly revs are reportedly between $1b and $2b right now. Monthly. I think if you do a…
Again with the "this is very profitable if you don't account for the cost of creating it?" Then my selling 2 dollars for 1 dollar is a wildly profitable business as well! Can't sell them fast enough! Why does it seem like so many people have ceased to think critically?
Say the first model cost $2 to make. On metered sales, they’ve made $10 on it.
They then decide to make a $20 model, raising more money. It turns out, that model made $100.
They then decide to make a $1,000 model. That model made $5,000.
There are two possible paths for their shiny new $10,000 model: either it will be a better market fit than the 1k model, or it will not.
If it is a better market fit than the 1k model, then it seems very likely that at some point it will make more than $10,000 (2x the prior model’s utility).
If it does not provide better value, then you can scrub that model, and keep selling the $1k model. Eventually it will likely provide the additional $5k of investor capital back through profits.
What we have seen is this above scenario, with a couple twists: first, the training (capital investment) decisions overlay the useful life of the prior model, so you have to tease out the profitability when you think strategy. Second, it turns out there’s quite a lot of money to be made distilling models the market likes into models that give like 90% better profit.
So, these businesses paying billions of dollars to train frontier models are absolutely rational actors. They are aggressive actors, engaged in an arms race, and not all of them will survive. But right now, with current inference demand, if all the global training capital dried up, (and therefore we are stuck with current models for some time), they would become highly, highly profitable companies during the period where fast followers tried to come in and compete on price.
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Almost every model trained by the majors has paid for itself with inference fees. I’m not saying there isn’t a bubble, but I am saying if the researchers and strategists absolutely closest to the “metal” of realtime frontier models are correct that AGI is in reach, then this isn’t a bubble, it’s a highly rational race. One that large players seem to be winning right now.
The idea that it’s a bubble on the frontier model side is insane. AI assisted coding alone makes it the most valuable thing we’ve ever created.
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The idea that it’s a bubble on the frontier model side is insane. AI assisted coding alone makes it the most valuable thing we’ve ever created.
Get your head out of the proverbial, a bullshitting machine that lets some developers do things faster if they modify how they develop isn't even close to the most valuable thing we've ever created.
You can choose to have a Claude API portal to the future where you pay 2025 prices for token inference, or you can skip it, and use 1995 devs to build your competitor.
Which do you do?
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Almost every model trained by the majors has paid for itself with inference fees. I’m not saying there isn’t a bubble, but I am saying if the researchers and strategists absolutely closest to the “metal” of realtime frontier models are correct that AGI is in reach, then this isn’t a bubble, it’s a highly rational race. One that large players seem to be winning right now.
> Almost every model trained by the majors has paid for itself with inference fees. Even if we assume this is true, the downstream customers paying for that inference also need it to pay for itself on average in order for the upstream model training to be sustainable, otherwise the demand for inference will dry up when the music stops. There won't always be a parade of over-funded AI startups burning $10 worth of tok…
I propose software creation, and therefore demand for software creation are subject to Jevon’s Paradox.
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> OpenAI is profitable if they stop training their next generation models. Their unit economics are extremely favorable. But OpenAI can't stop training their next generation models. OpenAI already spends over 50% of their revenue on inference cost [1] with some vendors spending over 100% of their revenue on inference. The real cash cow for them is in the business segment. The problem here is models are rapidly cloned…
I agree the market dynamics are weird now, I disagree that says much about the existence of other equilibria. For example, inference on older GPUs is actually more profitable than bleeding-edge right now; the shops that are selling hosted inference have options to broaden their portfolio the advancement of the frontier slows. Cloud providers are currently “un-differentiated”, but there are three huge ones making prof…
Anybody who has worked in a compliance heavy segment (PCI-DSS, HIPAA, etc.) will tell you the big 3 clouds have very significant differences from the smaller players. The differentiation is not on compute itself, but on the product. It's partially why products like AWS Bedrock exist and are actively placing model providers both in competition with eachother and AWS itself which is exactly the market dynamic they should seek to avoid.
> The idea that OAI is un-differentiated is just weird. They have a massively popular consumer offering, a huge bankroll, and can continue to innovate on features. Their consumer offering has remained sticky even though Claude and Gemini have both had periods of being the best model to those in the know.
This is exactly where this line of reasoning goes off the rails. The consumer market is problematic (see the recent post about the segment its growing in; basically young women of limited spend in low income countries); a huge bankroll is also a huge liability, model providers are on a clock to get huge or die, and the innovation we are seeing is effectively attempting to "scale-up" models, not provide novel features.
> Their consumer offering has remained sticky even though Claude and Gemini have both had periods of being the best model to those in the know.
This isn't a good thing with current market mix.
> And generally speaking there are huge opportunities to do enterprise integrations and build out the retooling of $10T of economic activities, just with the models we have now; a Salesforce play would be a natural pivot for them.
Do you have any indication these are achieving buy in or profitable? Most significantly, we have seen a recent study by MIT that 95% of generative AI pilots fail. The honeymoon period is rapidly coming to a close. Tangible results are necessary.
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It isn't even a political bias but rather we can't deny that the economy feels like kissing the ring whether its us buying intel stocks or sort of forcing nvidia to buy some intel stocks and etc. And I feel like its in a bull market because of AI Hype which was the main comment of the original parent to which you responded I think... If this AI hype fails to deliver. Literally the magnificient 7 will have a huge loss…
You can find excuses not to invest at any time. Easiest thing in the world has always been finding an excuse not to invest. Mag7 are some of the most profitable and well run companies in history investing their insane profits. No other country has public markets as developed, regulated and liquid as the US. Likely you are just investing into the unknown with a ton of risk factors you are not aware of. In places outsi…
I can be wrong, I usually am.
That being said, My question to you is:
Do you believe that it is an excuse if I don't invest in mag7 while they are most profitable and well run because I believe that their stock price is highly overflated and past performances aren't indicative of future performances unless we are talking over an aggregate time which the general markets do have.
Now the question is, Do you think its an excuse if I don't want to invest in mag7 because I am worried that its an AI bubble and that worry is backed up by the fact of this AI craze.
If AI doesn't deliver on its prices, can you wager that MAG7 would actually do good? Of course it wouldn't.
What do you mean to think that AI would deliver to its prices as it seems to be either only hyper applicable in tech and all other AI tech is seemingly run at a loss and I can see no way how they might force normal users when there is so much foss ai to actually pay for ai...
What is the monetization plan? Is it to churn the money that you get from stocks into AI to get a higher evaluation of stocks and do some passing around the circle from one company to other and repeat?
Well run is another questionable term given how Magnificient7 includes tesla but maybe we can talk about it later.
I believe that time in the markets beats timing in the markets, so your experiences shouldn't be with a market that feels bubbly y'know? Otherwise, you might just stop it alltogether and I feel like that things might fall down quicker than we think as AI is kinda scrambling through, A lot of people felt disappointed in gpt-5. Reality is settling in, but is reality settling in those magnificient 7 stocks?
I consider myself to be an average investor in the sense that being a superior investor is insanely insanely difficult and its much easier to think you are a superior investor because you might get lucky and then lose more money than you could've made over a long term of time and you try to recoup previous money and previous money.... I definitely don't want to experience it in first place to keep my experiences somewhat moderate y'know?
This is HN so I presume you don't get bored with this response as I love this talk & trying to understand your point in good faith!
In accounting terms, this is a shady business practice known as "round tripping" where you invest in a company for the sole purpose of them buying your product. It allows you to count your revenue multiple times.
This is being done out in the open (we’re reading the press announcement) and will be factored into valuations. Also, investing in OpenAI means they get equity in return, which is not a worthless asset. There is actual mutually beneficial trade occurring.
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Customer A pays you $100 for goods that cost you $10. You invest $100-$10=$90 in customer B so that they'll pay you $90 for goods that cost you $9. Your reported revenue is now $100+$90=$190, but the only money that entered the system is the original $100.
Yes, but you’ve also incurred a $90 expense in purchasing the stock of Company B and that stock is on the balance sheet. In the actual shady version of this, Company B isn’t the hottest AI investment around, it’s a shell company created by your brother’s cousin that isn’t actually worth what you’re claiming on the balance sheet because it was only created for the round tripping shell game.
There are venture capital firms. Nvidia is transforming into a venture GPU company. X GPUs for Y percent.