And I wouldn’t know where to start using it. My problems are often of the “integrate two badly documented company-internal APIs” variety. LLMs can’t do shit about that; they weren’t trained for it.
They’re nice for basic rote work but that’s often not what you deal with in a mature codebase.
Like every time there’s an AI bubble. And like every time changes are that in a few years public interest will wane and current generative AI will fade into the background as a technology that everyone uses but nobody cares about, just like machine translation, speech recognition, fuzzy logic, expert systems…
Even when these technologies get better with time (and machine translation certainly got a lot better since the sixties) they fail to recapture their previous levels of excitement and funding.
We currently overcome what popped the last AI bubbles by throwing an absurd amount of resources at the problem. But at some point we’ll have to admit that doubling the USA’s energy consumption for a year to train the next generation of LLMs in hopes of actually turning a profit this time isn’t sustainable.
They did PR campaigns against Linux and OpenOffice for quite some time – until cloud computing took off and it turned out they could earn more money by supporting Linux than by fighting it.
In fact, Microsoft weren’t happy about FOSS in general. I can still remember when they tried to make “shared source” a thing: They made their own ersatz OSI with its own set of licenses, some of which didn’t grant proper reuse rights – like only allowing you to use the source code to write Windows applications.