There is a drive within certain Big Tech companies to express as many machine-learning problems (really, computational problems) as suitably-prompted LLMs, and

There is a drive within certain Big Tech companies to express as many machine-learning problems (really, computational problems) as suitably-prompted LLMs, and then deploy one uber-LLM model where all the other product tasks are specific prompts for the LLM. The goal is MBA-style synergy. If everything is an LLM, then you can optimize your TPU hardware for inferencing LLMs; you can put all your research dollars into LLM research; you have a common framework and common skillset for all engineering across the company; you have a common serving infrastructure when it comes to productionizing these models and deploying them on real traffic; and you have a centralized point where you can research & mitigate security attacks on LLMs. It kinda makes sense, but it's up to the market to say whether this is ultimately a winning strategy. When it works it looks like the Wintel monopoly of the 80s/90s, where standardizing the whole world on an instruction set and OS (even if they were a bad instruction set and bad OS) freed up lots of capital to invest in the physical aspects of making chips fast, and so Intel ate all the specialized hardware manufacturers like Symbolics/Tandem/IBM/Cray/Sun because their higher sales volumes let them invest more capital into pouring more transistors on the chip, and eventually even the crappy 8086 architecture started to have better price/performance than a Cray. When it doesn't work it looks like lambda calculus & Church numerals: just because you can express everything in two simple primitives doesn't mean that you should, or that it's economically viable. If it doesn't work the 2020s are going to be open-season for startups to start eating away at Big Tech monopolies.