But like Google and Facebook have a lot of hardware. They aren’t just pure in the cloud (though Google is running other peoples clouds). But yes their hardware demands per dollar of revenue is much lower
The big foundational labs are not like this. Every model they release takes a ton of data infrastructure and training (pre, train, post) compute. Then inference itself requires compute. Even if the labs sell tokens, each token embodies a lot of training and inference compute. This is a much more capital heavy business, so I can see revenue multiples being smaller than pure SaaS businesses. A model release bust (like Opus 5) ends up leading to big losses because of the necessary training compute spent on the model and the labor spent on training. A bust SaaS product ends up being wound down quickly, and only costs the labor spent on building it and the compute spent running it.
A good analogy is chip companies before all of the AI related compute pressure. These were largely low margin businesses because of the fixed capital cost associated with each new chip gen, architecture, process, etc
So I think the reason for the value gap is purely in the proprietary IP. The recipe for making a good LLM is more or less public, unless you buy into the "everyone just distils claude/gpt" angle. The recipe for making a good search engine is private.
Google is also a lot more than just search. Services like Gmail or Gdocs scale much more similarly to other SaaS.
Note the huge difference in strategy and outcome!
Google published essentially nothing on how to build a search engine. Huge R&D spend, zero publications, intense culture of secrecy, extremely low attrition to startups or competitors. They did publish some papers on big data systems but only on tech that was many years old - a typical Google "research" paper pre-2015 would announce that the system it described had been running at scale in production for years already. Google also never faced any kind of split or mutiny in which lots of people with the core secrets left the firm to set up a competitor.
With AI however this changed and they published all their research immediately, also their best employees constantly ran away to join startups. The result was predictable: smaller competitors deployed their ideas faster and better than they did, and now Google has no moat anymore.
OpenAI took the classic Google approach and stopped publishing R&D after developing GPT but this didn't help them build margins because:
1. Apparently the core transformer idea is powerful enough that breakthroughs after that didn't matter much. Nobody has been able to develop some killer edge that leaves competitors unable to catch up. Most improvements to transformers have been about cost reduction, not capability improvement.
2. They had a huge split when the Anthropic guys walked out over safety, taking the knowledge of how to build a frontier LLM with them. Result was a peer competitor from day one, whereas Google had at least six years to build edge before anyone realized web search mattered.
Result: Google-style hardware costs but without any secret IP that could generate revenue commensurate with those costs.