[KyeRussell]

There are similarities, sure. But there are also stark differences. Due to the existence of ChatGPT, the GPT-3 API, and the general viability of natural language prompting, LLMs are now essentially commoditised. They are now in the hands of orders of magnitude more people. Barring sector-specific regulations, people are free to iterate (with varying degrees of care, ethical consideration, and success) at a much faster pace compared with the field of medicine, or even academia in general, where there’s non-zero involvement of ethics committees. At DAYJOB we already have immense domain expertise to tune GPT-3 and prove its reliability in our sector. For giggles I also implemented an incredibly naive approach to a problem we set out to solve, and still ended up with a result that’s considered very impressive, and is usually the sort of thing many companies have spent countless hours working toward. My sector certainly won’t be an edge case. And we all know that everyone and their dog is trying to see how GPT-3 can deliver value. It’s all happening at the same time, and very quickly. As someone that’s generally quite jaded and skeptical of new technologies, my experience in my day job has completely changed my perspective. At this stage I’m willing to go out on a limb and say that this is going to be quite disruptive to labour markets at the very least. And this itself could very well be at the level where it raises serious ethical and societal questions. I’ll happily eat humble pie if I’m wrong.

[tjopies]

The point about this being more generally available for tinkering is fair, and from my experiments and usage, I can state it is impressive as absolute hell. First, however, we need a discussion focused on how we manage work as industry groups on at least trying to manage the proliferation of the technologies.