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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.
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.
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…
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 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…
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 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?
Executive egos and market hype
> 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.
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 th…
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?