It may be rational and it may be irrational. All I'm saying is I want to see an argument. My impression of the AI space generally is that there are many, many obvious ideas which are simply waiting to be picked up off the ground and tested, and that it's not clear to anyone how much better (what are we even quantifying this with -- log loss?) the base LLM capabilities have to be before they're suitable for making tools that automate large quantities of work. Even if you assume that 240B is the limit for how many parameters number of engineers who have the ability to fine-tune GPT-4 or whatever Google is about to come out with is vanishingly small compared to the number of engineers who are participating in the open-source ML community, and the number of engineers in the open-source ML community is vanishingly small compared to the number of engineers in the long-tail of app developers who will ultimately adopt LLMs to their use-cases. Even assuming that GPT-4 is the best an LLM can possibly be, (which, again, I've seen no argument for), the widening of LLM availability and the building of practical tooling is a strong reason to believe that the utility of LLMs to concrete products will dramatically increase in the next 4-5 years.