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.
Why Everybody Is Losing Money On AI
41–50 of 117 posts
Re: Why Everybody Is Losing Money On AI
#42I 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?
The obvious reason is that they think it will pay off in the future. Google didn't start profitable, it is now one of the most profitable companies in the world.
Those who invest in money-losing AI believe that it will be the next Google and that profits will come.
Or alternatively, they hope to sell to the greater fool.
Re: Why Everybody Is Losing Money On AI
#43Does 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
Re: Why Everybody Is Losing Money On AI
#44I 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.
Re: Why Everybody Is Losing Money On AI
#45> 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’m confused. If 50% of the revenue goes to inference, that means the other 50% goes into research?
My understanding is it means half of what a subscriber pays is spent just on the compute required as you chat with the models. Which leaves the other have to be divided up among salaries, marketing, R&D, etc.
Re: Why Everybody Is Losing Money On AI
#46Earlier quoted context omitted.
Meanwhile, I stopped using AI months ago. Have no subscriptions to any of the AI services anymore. Life goes on, quality of life is pretty good, haven't suffered in any way nor do I feel like I'm missing out.
Yeah, but I have also dreamed of living in the woods, being completely self sustainable blissfully. It doesn't mean there aren't capitalists out there looking to produce and sell more, and people out there looking to buy.
The difference is that choice to live out in the woods costs you. Choice to not have a phone costs you. A choice to not pay ai at this point ... does not cost you unless you live in special situation.
Re: Why Everybody Is Losing Money On AI
#47I 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 t…
> 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. LLMs have been around for two years. it took decades before the PC really took hold.
Segways existed for a long time and they never took off. Zeppelins too. Not every technology automatically gets good just because time passes.
Re: Why Everybody Is Losing Money On AI
#48The 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.
Current frontier models are not good enough because they still suffer from major hallucinations, sycophancy, and context drift. So there has to be at least (and I have no reason to believe it will be the last, GPT-5 demonstrates that the transformer architectures are hitting diminishing returns) one more training cycle.
Re: Why Everybody Is Losing Money On AI
#49I'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 th…
The issue is we're moving past the "initially" phase and people are starting to suspect that the $10T golden goose is mythical. GPT-5??
Than why don't they raise prices? If AI developers were only worth 200k a year, nobody would pay X times those salaries and development would be cheaper. Similar, if none of the AI coding companies had free offerings, they would have less inference cost or more revenue. Yet they have the feeling that they need to offer those, likely because of competition. The article paints it as if the big companies are a factor of 2 away from profitability. Would absolutely nobody use AI if their tokens were double the price? I highly doubt that.
Re: Why Everybody Is Losing Money On AI
#50> Please don't waste your breath saying "costs will come down." They haven't been, and they're not going to. 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 but due to reasoning models the same query uses VASTLY more tokens today then a couple years ago
Sure, if you enable reasoning for your prompt. A lot of prompts don't need that.