The question is too broad. What's an AI company? It could be anything. The particular sub class here that is implied is companies that are spending many billion

The question is too broad. What's an AI company? It could be anything. The particular sub class here that is implied is companies that are spending many billions to develop LLMs. The business model for those is to produce amazing LLM models that are hard to re-create unless you have similar resources and then make money providing access, licensing, etc. What are those resources? Compute, data, and time. And money. You can compensate for lack of time by throwing compute at the problem or less/more data. Which is a different way of saying: spend more money. So, it's no surprise that this space is dominated by trillion dollar companies with near infinite budgets and a small set of silicon valley VC backed companies that are getting multi billion dollar investments. So the real question is whether these companies have enough of a moat to defend their multi billion dollar investments. The answer seems to be no. For three reasons: hardware keeps getting cheaper, software keeps getting better, and using the models is a lot cheaper than creating them. Creating GPT-3 was astronomically expensive a few years ago and now it is a lot cheaper by a few orders of magnitude. GPT-3 is of course obsolete now. But I'm running Llama 3.2 on my laptop and it's not that bad in comparison. That only took 2 years. Large scale language model creation is becoming a race to the bottom. The software is mostly open source and shared by the community. There is a lot of experimentation happening but mostly the successful algorithms, strategies, and designs are quickly copied by others. To the point where most of these companies don't even try to keep this a secret anymore. So that means new, really expensive LLMs have a short shelf life where competitors struggle to replicate the success and then the hardware gets cheaper and others run better algorithms against whatever data they have. Combine that with freely distributed models and the ability to run them on cheap infrastructure and you end up with a moat that isn't that hard to cross. IMHO all of the value is in what people do with these models. Not necessarily in the models. They are enablers. Very expensive ones. Perhaps a good analogy is the value of Intel vs. that of Microsoft. Microsoft made software that ran on Intel chips. Intel just made the chips. And then other chip manufacturers came along. Chips are a commodity now. Intel is worth a lot less than MS. And MS is but a tiny portion of the software economy. All the value is in software. And a lot of that software is OSS. Even MS uses Linux now.