Because their core product is not a long-term sustainable business strategy. Local hardware and models will continue to improve to the point of not needing the hosted solutions. And if you do need a hosted solution, remember that the big cloud providers already offer these solutions, so signing up for OpenAI/Anthropic _and_ AWS/GCP/Azure is not a sound business decision compared to just signing up with 1 of them that offers your cloud infra + GenAI infra. (Which is why the long-term benefits for cloud companies will probably be for the likes of AWS and not the likes of OpenAI).
They'll continue to burn money for marginal model improvements in the next few years all the while having no moat _and_ having Open-Weight / Local models eat their lunch.
The only way for them to stay relevant as a company is to expand beyond simply providing the models.
I'm old enough to remember the arrival of RDBMS, once IBM primed the space with DB2.
There was a pitched battle over features like row-level locking as competitors like Sybase, Ingress and Oracle scrapped it out. New features arrived on a monthly cadence, with immense engineering effort behind them. The winners (Oracle mostly) won a great moat which led to them to where they are today.
The fact that so many AI companies can produce amazing coding tools so quickly shows there is no moat, supporting your theory.
The AI companies moat, if any, is hoarding all the hardware so local solutions are no longer cost-effective.
That is impossible to fund. At some point someone will decide to stop throwing money on the firepit that's the current business model and then hardware prices crash back to earth as 60-70% of the global demand disappears overnight.
This is not really sustainable when there is a constant supply of increasingly powerful and efficient hardware.
The hardware miniaturization gains have finally dried up, though.
I am pretty certain that the current state of the art silicon feature size won't shrink again for at least another decade or two.
It normally takes about a decade to mature a tech which can create a smaller feature size into something commercially viable for mass production scale, and no further improvements have been in the pipeline for that long now.
So it's like the "next piece" indicator while playing Tetris is just blank.
Right: Capital markets know how to efficiently turn dollars into tokens, even if it means tens of billions invested in new DRAM fabs.
It is more of a regulatory capture byproduct to prop up an artificial token driven Ponzi scheme.
There is a serious alternative to NVIDIA "AI" hardware dropping out of China in February 2027. There is no moat, but a whole lot of unpaid debts in the near future.
Popcorn ready =3
Which Chinese alternative is that?
Huawei is upgrading its Ascend 950PR (2.8 times an Nvidia H20 performance.)
https://apnews.com/article/huawei-ai-chips-nvidia-superpod-t...
Take it lightly until the benchmarks drop. ymmv =3
I honestly have no expert knowledge about this stuff. What I say is based only on intuition.
>I don't buy the insurmountable moat of TSMC
Very wise, energy constraints are already feeding the hyper-scale gamblers their own hubris. =3
Reminiscent of tank warfare in WW2. The soviet T-34 was not remarkable in any particular way, but the sheer volumes it was produced in made it a very serious enemy to German tanks. As Stalin said "quantity has a quality all of its own".
I would phrase it slightly differently.
The companies who control the compute resources will ~always control the greatest "amount" of intelligence. They can lease that intelligence out, or they can use it themselves. Currently the "total amount of intelligence" or perhaps "total amount of ability-to-do-stuff" is split between humans and machines at a ratio that means it still makes sense to lease the machine intelligence to the human intelligence - plus there are things that humans are still better at. In maybe 2 more years that will stop being true, due to the availability of more physical compute resources, and far greater model intelligence per unit compute. At that point, the point at which the substantial majority of ability-to-do-stuff is controlled by machine intelligence, then the entities who control all the compute will control all the ability-to-do-stuff, i.e. "the economy."
So I agree that the core product is not long-term sustainable as a product but this is because the whole world will look so different in the near future that the framing of intelligence as a "product" breaks down.
Open-Weight models, of course, are fine and useful, but if you have one million times less compute than your competitor (the lab), then you're not really playing the same game. You can only tackle the problems that they have decided they're not interested in.
Their moat is the tens of GW of power and associated compute.