I don't think so.
The reality is that the adoption is incredibly fast and that revenues of the AI labs are like doubling by the quarter.
Further, there is the expectation that breakthroughs in applying AI to science, robotics, or medicine may unlock trillions in value.
That this will take 20 years, rather than happen before 2030, is just speculation. None of us can see the future.
The speculation that the AI investment sector depends on is not just that adoption is incredible and that the value it generates is high, it's ALSO that the companies will be able to capture a significant amount of that value generation for themselves via fees or some other mechanism.
Are the LLM vendors more comparable to Google/Apple in this scenario, or more comparable to AT&T and Bell?
In this comparison they are more like Apple and Google: they are ahead in research and are able to offer better technology than their competitors.
The six month gap between the open-weight models and the frontier has not closed yet.
The bubble exists because c-suite people are sheep. They follow the herd, and the frontier CEO wolf-wannabes have been trying to herd them in a profitable direction.
But herds are easily frightened, and there's always a danger they'll stampede in a different direction.
And the risk of a catastrophic AI-enhanced online blackout is increasing.
The cheaper open-weight ones, even if they were available with our enterprise contracts, would be a huge downgrade.
They’re still losing gigantic amounts of money. Whether that is because they’re “flooring it” as it were and growth will eventually catch up, or because the business idea is fundamentally flawed and just temporarily alive because of investor risk appetite, is the fundamental question underpinning whether it’s a bubble or not, and nobody knows the answer to that question for sure. That’s how bubbles work.
They are already profitable and are investing enormous amounts into datacenter buildout to meet demand over the next years.
I believe your $14B OpenAI number is from 2025 though.
"Boom and bust is not about revenue. To find that kind of risk, start by reading the balance sheet... [The 2008] bust came from off-balance-sheet instruments."
https://kennyfraser.substack.com/p/anthropic-ipo-part-one-wh...
Patrick Boyle goes into a deep dive that's well worth the watch:
How is that compared to the speed at which money is thrown in them? And how much of the profit downstream they might indeed potentially capture with high retention rate?
Bubble can’t be assessed on mere quarter profit expansion over short period (relative to large company group life expectancy).
Don't you think that it's less than reasonable to suggest that the technology will take on the order of 20 years to mature into widespread adoption and profitability?
Then you should invest heavily in their IPO, I suppose.
You'll do much better than those of us who believe there is no way their financials can ever work.
I came to my conclusion a different way, I ran different models locally and measured the amout of resources required for basic things. From that I extrapolated some napkin unit economics, and I couldn't find any way to build a profitable business from it.
I don't assume I'm the smartest guy in the room though, so for all of our sake, I hope you're right.
I'm not sure if that is relevant here, but I've often seen people dramatically overestimate inference costs.
If we look at eg. https://inferencex.semianalysis.com/run/kimi-k31-pro-on-b300, it suggests that SOTA models are likely served in the API with profit margins in the ballpark of 98%.
In which case, the people being profitable won't be the generic big LLM companies. It'll be the people who use the LLMs, or (maybe) specialized AI companies that still have a niche and moat.