Basically- the same math as modern automated manufacturing. Super expensive and complex build-out - then a money printer once running and optimized.
I know there is lots of bearish sentiments here. Lots of people correctly point out that this is not the same math as FAANG products - then they make the jump that it must be bad.
But - my guess is these companies end up with margins better than Tesla (modern manufacturer), but less than 80%-90% of "pure" software. Somewhere in the middle, which is still pretty good.
Also - once the Nvidia monopoly gets broken, the initial build out becomes a lot cheaper as well.
The difference is the money printer right now only prints for ~6 months before it needs to be replaced with an even more expensive printer.
And if you ever stop/step off the treadmill and jack up prices to reach profitability, a new upstart without your sunk costs will immediately create a 99% solution and start competing with you. Or more like hundreds of competitors. Like we've seen with Karpathy & Murati, any engineer with pedigree working on the frontline models can easily raise billions to compete with them.
Expect the trend to pick up as the pool of engineers who can create usable LLMs from scratch increases through knowledge/talent diffusion.
The LLM scene is an insane economic bloodbath right now. The tech aside, the financial moves here are historical. It's the ultimate wet dream for consumers - many competitors, face-ripping cap-ex, any missteps being quickly punished, and a total inability to hold back anything from the market. Companies are spending hundreds of billions to put the best tech in your hands as fast and as cheaply as possible.
If OpenAI didn't come along with ChatGPT, we would probably just now be getting Google Bard 1.0 with an ability level of GPT-3.5 and censorship so heavy it would make it useless for anything beyond "Tell me who the first president was".
the difference is you can train on outputs deepseek style, there are not gates in this field profit margins will go to 0