This is a really well written article and contains references to back up the claims made. This part was mind blowing though:
> Cursor sends 100% of their revenue to Anthropic, who then takes that money and puts it into building out Claude Code, a competitor to Cursor. Cursor is Anthropic's largest customer. Cursor is deeply unprofitable, and was that way even before Anthropic chose to add "Service Tiers," jacking up the prices for enterprise apps like Cursor.
> total revenue: $4B
> compute for training models: -$3B
> compute for running models: -$2B
> employee salaries: -$700M
Though not really representative of what users of said models may experience financially, at this point the question should be raised: if AI compute is 7x more expensive than developer salaries, what's the point? I thought the whole idea was to save money on human resources...
I mean, we know everyone is losing money on AI. I thought from the title it was going to explain why. As in, why are they choosing to lose all that money?
I'm not making any claims as to whether AI will become profitable or when, but if there's a new tech that has high potential or is highly desirable, I think it's expected that initially money will be lost.
Simply because strategically if there's high long term potential, it initially makes sense to put more money in than you get out of.
Not saying that AI is this, but if you determined that you have a golden goose that laid out 10 trillion USD worth of eggs when it got 10 years old, how much would you pay for it in the auction, and what would you have to show for it for the initial 9 years?
Now what if the golden goose scaled to 10 trillion each year linearly? First years people sound of mind would overpay for what it makes.
The cost can be significantly reduced immediately and drastically if OpenAI or Anthropic were to choose to do so.
By simply stopping the training of new models, profitability can be achieved on the same day.
With the existing models, we have already substantial use cases, and there are numerous unexplored improvements beyond the LLM, tailored specifically to the use case.
> At this point, it's becoming obvious that it is not profitable to provide model inference, despite Sam Altman recently saying that OpenAI was.
Except the authors own provided data says it cost them $2B in inference costs to generate $4B in revenue. Yes training costs push it negative, but this is like tech growth 101, debt now to grow faster leads to larger potential upsides in the future.
> OpenAI spent 50% of its revenue on inference compute costs alone
This means that they operate existing models with very healthy 50% profit margin, that’s excellent unit economics actually. Losing money by investing more into R&D that you make is not the same as burning it by selling a dollar for 90 cents.
I think the most interesting part is that, in AI, software does not have zero marginal cost anymore. You can’t build once and scale to billions just investing in infrastructure.
Still, companies like OpenAI and Twitter are doing just that. Thus losing money.
Will AI evolve to be again as regular software or will the business model of tech AI become closer to what traditional non-tech companies are?
How the WalMart of AI will look like?
Does SaaS with very high prices and very thin margins even work as a scalable business model?
I think Ed hits on an interesting point about the new user who spends $4 on a TODO file. Current LLM users are very enthusiastic about finding different models for different use cases and evaluating the cost-benefit of those models. But the average end user doesn't give a shit. If LLMs are going to "eat the world" they need to either be a lot better in the median case (bad prompts, bad model selection) or they need to be so cost-effective that you can farm out your query to an ensemble and choose the result dialogue-tree-style.
Another day, another person not getting discounted cash flow.
Models trained in 2025 don’t ship until 2026/7. That means the $3bn in 2025 training costs show up as expense now, while the revenue comes later. Treating that as a straight loss is just confused.
OAI’s projected $5bn 2025 loss is mostly training spend. If you don’t separate that out with future revenues, you’re misreading the business.
And yes, inference gross margins are positive. No idea why the author pretends they aren’t.
Is this really surprising given how VC funded capitalism works? Spend money to build amazing technology and gain market share, then eventually flip into extraction mode.
Yes, a pullback will kill some weaker companies, but not the ones with sufficient true fans. Plus, we’re talking about a wide-ranging technological revolution with unknown long term limits and economics, you don’t just give up because you’re afraid to spend some money.
I don’t want to pay Anthropic, because I don’t trust them, but I will absolutely pay cursor, because I trust them, and I doubt I’m alone. My cursor usage goes to GPT-5, too, so it’s definitely not 100% Anthropic, even if I’m the only idiot using GPT5 on Cursor
It’s fun to innovate. Making money is a happy byproduct of value creation. Isn’t the price of success always paid in advance, anyway? Why would winning AI tech companies pack it up and stop crushing it over the long term just because they’re afraid to lose someone else’s money in the short term? Wouldn’t capitulation guarantee losses moreso than continued effort?
What about Google? Anyone has any insights on their unit economics since they own the models and the infrastructure (which is also custom TPUs)? Are they doing better or are they in the same money losing business?
Nice read, but I'd add an objection here: even if models don't improve any more, and they raise the standard subscription to 100$/month, I'd still buy it (and a lot of other people, I guess) because I'd extract far more value from it.
Does controversy cause articles to slide on HN? I noticed that this had more points in less time than several articles ranked above it, which surprises me a bit
e.g. at time of writing a post about MentraOS has 11 points in 1 hour compared to this article's 51 in 53 minutes, but this is ranked 58th to Mentra's 6
I don't understand these posts. Do people not understand how venture capital works?
The majority of these companies know they are burning money, but more than that knew they would be losing money at this point and beyond. That is the play, the thesis is: AI will dominate nearly everything in the near future, the play is to own a piece of that. Investors are willing to risk their investment for a chance of getting a piece of the pie.
Posts that flail around yelling companies 'losing money', without addressing the central premise are just wasting words.
In short, do you think AI is not going to dominate nearly everything? Great, talk about that. If you do believe is, then talk about something other than the completely reasonable and expected state of investors and companies fighting for a piece of the pie.
As a somewhat related tangent, people seem to not understand the likely cost trajectory of model training/inference costs:
* Models will reach a 'good enough' point where further training will be mostly focused on adding recent data. (For specific market segments, not saying that we'll have a universal model anytime soon, but we'll soon have one that is 'good enough' at c++, might already be there).
* Model architecture and infrastructure will improve and adapt. I work for a company that was among the first use deep learning to control real-time kinetic processes in production scenarios, our first production hardware was a nvidia Jetson, we had a 200ms time budget for inference, and our first model took over 2000! We released our product, running under 200ms, *using the same hardware* the only difference was improvements in the cuDNN library and some other drive updates and some domain specific improves on our YOLO implementation. Long story short, yes inference costs are huge, but they are also massively disruptable.
* Hardware will adapt. Nvidia cash machine will continue, right now nvidia hardware is optimized for balance between training and inference, where TPUs, the newer ones are more tilted towards inference. I would be surprized if other hardware companies don't force Nvidia to give the more inference based solution and 2-3x cost savings at time point in the next 5 years. And for all I know, perhaps a hardware startup will disrupt Nvidia, it would be one of the most lucrative hardware plays on the planet.
Focusing inference cost is a deadend to understanding the trajectory of AI, understanding the *capability* of AI is the answer to understanding it's place in the future.
The big labs have 50+% margins on serving the models, the training is where they lose money. But every new model boosts OpenAI's revenue growth which is unheard of at their size (300+% YoY). Therefore it's completely reasonable to keep doubling down and making bigger bets.
Most people miss that they have almost a billion free users that are waiting to be monetized. Google makes 400B a year and it's crazy to think OpenAI can't achieve some percentage of that. Why would you slow down and let Google catch up for the sake of short term profitability.
Cursor burning cash to subsidize Anthropic's losses to subsidize Amazon's compute investments is their problem, not mine.
The people writing all of these "AI is unprofitable" pieces are doing financial journalism similar to analyzing the dot-com bubble by looking at pets.com's burn rate. The infra overspend was real as well as the bankruptcies, but it existentially foolish for a business to ignore the behavioral shift that was taking place.
I have to constant remind myself to stop arguing and evangelizing about AI. There is a growing crowd who insists that AI is a waste of money and that AI cannot do things I'm already doing on a daily basis.
Every minute spent explaining to AI skeptics is a minute not spent actually capitalizing on the asymmetry. They don't want to hear it anyway and I have little incentive to convince anyone otherwise.
The companies bleeding money to serve AI below cost prices won't last, but thats all more the reason use them now while they're cheap.
But back then, you was better off not depending on pet.com. If products of one of those vaporware companies became important in your process, your company went down along with.
Those were companies that had multiple expensive IT restructurings one after another, each making them more innefective and then either run out of cash or barely made it.
It worked well for companies that were choosing smart.
Amazon was unprofitable for years (like over a decade), famously. I don't see any difference with AI companies.
There is clearly some kind of market for this technology. It will eventually be profitable either through some technology breakthrough that allows creating/processing tokens cheaper or by finding a cost structure consumers can live with.
The cat is already out the bag. This technology isn't going away.
24 comments
[ 1.0 ms ] story [ 43.8 ms ] thread> Cursor sends 100% of their revenue to Anthropic, who then takes that money and puts it into building out Claude Code, a competitor to Cursor. Cursor is Anthropic's largest customer. Cursor is deeply unprofitable, and was that way even before Anthropic chose to add "Service Tiers," jacking up the prices for enterprise apps like Cursor.
Though not really representative of what users of said models may experience financially, at this point the question should be raised: if AI compute is 7x more expensive than developer salaries, what's the point? I thought the whole idea was to save money on human resources...
Yes, every new technology has always stayed exorbitantly priced in perpetuity.
Simply because strategically if there's high long term potential, it initially makes sense to put more money in than you get out of.
Not saying that AI is this, but if you determined that you have a golden goose that laid out 10 trillion USD worth of eggs when it got 10 years old, how much would you pay for it in the auction, and what would you have to show for it for the initial 9 years?
Now what if the golden goose scaled to 10 trillion each year linearly? First years people sound of mind would overpay for what it makes.
By simply stopping the training of new models, profitability can be achieved on the same day.
With the existing models, we have already substantial use cases, and there are numerous unexplored improvements beyond the LLM, tailored specifically to the use case.
Except the authors own provided data says it cost them $2B in inference costs to generate $4B in revenue. Yes training costs push it negative, but this is like tech growth 101, debt now to grow faster leads to larger potential upsides in the future.
This means that they operate existing models with very healthy 50% profit margin, that’s excellent unit economics actually. Losing money by investing more into R&D that you make is not the same as burning it by selling a dollar for 90 cents.
Still, companies like OpenAI and Twitter are doing just that. Thus losing money.
Will AI evolve to be again as regular software or will the business model of tech AI become closer to what traditional non-tech companies are?
How the WalMart of AI will look like?
Does SaaS with very high prices and very thin margins even work as a scalable business model?
Models trained in 2025 don’t ship until 2026/7. That means the $3bn in 2025 training costs show up as expense now, while the revenue comes later. Treating that as a straight loss is just confused.
OAI’s projected $5bn 2025 loss is mostly training spend. If you don’t separate that out with future revenues, you’re misreading the business.
And yes, inference gross margins are positive. No idea why the author pretends they aren’t.
Cost to run a million tokens through GPT-3 Da-Vinci in 2022: $60
Cost to run a million tokens through GPT-5 today: $1.25
Yes, a pullback will kill some weaker companies, but not the ones with sufficient true fans. Plus, we’re talking about a wide-ranging technological revolution with unknown long term limits and economics, you don’t just give up because you’re afraid to spend some money.
I don’t want to pay Anthropic, because I don’t trust them, but I will absolutely pay cursor, because I trust them, and I doubt I’m alone. My cursor usage goes to GPT-5, too, so it’s definitely not 100% Anthropic, even if I’m the only idiot using GPT5 on Cursor
It’s fun to innovate. Making money is a happy byproduct of value creation. Isn’t the price of success always paid in advance, anyway? Why would winning AI tech companies pack it up and stop crushing it over the long term just because they’re afraid to lose someone else’s money in the short term? Wouldn’t capitulation guarantee losses moreso than continued effort?
e.g. at time of writing a post about MentraOS has 11 points in 1 hour compared to this article's 51 in 53 minutes, but this is ranked 58th to Mentra's 6
The majority of these companies know they are burning money, but more than that knew they would be losing money at this point and beyond. That is the play, the thesis is: AI will dominate nearly everything in the near future, the play is to own a piece of that. Investors are willing to risk their investment for a chance of getting a piece of the pie.
Posts that flail around yelling companies 'losing money', without addressing the central premise are just wasting words.
In short, do you think AI is not going to dominate nearly everything? Great, talk about that. If you do believe is, then talk about something other than the completely reasonable and expected state of investors and companies fighting for a piece of the pie.
As a somewhat related tangent, people seem to not understand the likely cost trajectory of model training/inference costs:
* Models will reach a 'good enough' point where further training will be mostly focused on adding recent data. (For specific market segments, not saying that we'll have a universal model anytime soon, but we'll soon have one that is 'good enough' at c++, might already be there).
* Model architecture and infrastructure will improve and adapt. I work for a company that was among the first use deep learning to control real-time kinetic processes in production scenarios, our first production hardware was a nvidia Jetson, we had a 200ms time budget for inference, and our first model took over 2000! We released our product, running under 200ms, *using the same hardware* the only difference was improvements in the cuDNN library and some other drive updates and some domain specific improves on our YOLO implementation. Long story short, yes inference costs are huge, but they are also massively disruptable.
* Hardware will adapt. Nvidia cash machine will continue, right now nvidia hardware is optimized for balance between training and inference, where TPUs, the newer ones are more tilted towards inference. I would be surprized if other hardware companies don't force Nvidia to give the more inference based solution and 2-3x cost savings at time point in the next 5 years. And for all I know, perhaps a hardware startup will disrupt Nvidia, it would be one of the most lucrative hardware plays on the planet.
Focusing inference cost is a deadend to understanding the trajectory of AI, understanding the *capability* of AI is the answer to understanding it's place in the future.
Most people miss that they have almost a billion free users that are waiting to be monetized. Google makes 400B a year and it's crazy to think OpenAI can't achieve some percentage of that. Why would you slow down and let Google catch up for the sake of short term profitability.
The people writing all of these "AI is unprofitable" pieces are doing financial journalism similar to analyzing the dot-com bubble by looking at pets.com's burn rate. The infra overspend was real as well as the bankruptcies, but it existentially foolish for a business to ignore the behavioral shift that was taking place.
I have to constant remind myself to stop arguing and evangelizing about AI. There is a growing crowd who insists that AI is a waste of money and that AI cannot do things I'm already doing on a daily basis.
Every minute spent explaining to AI skeptics is a minute not spent actually capitalizing on the asymmetry. They don't want to hear it anyway and I have little incentive to convince anyone otherwise.
The companies bleeding money to serve AI below cost prices won't last, but thats all more the reason use them now while they're cheap.
Those were companies that had multiple expensive IT restructurings one after another, each making them more innefective and then either run out of cash or barely made it.
It worked well for companies that were choosing smart.
There is clearly some kind of market for this technology. It will eventually be profitable either through some technology breakthrough that allows creating/processing tokens cheaper or by finding a cost structure consumers can live with.
The cat is already out the bag. This technology isn't going away.
Standard market share logic.