Slightly amusing story: the year is 1990-something and I'm in grad school working a lab where we do electromagnetic simulation. That code has to run fast, obviously, but some of us in the lab were looking for some glue language to wrap around the very utilitarian C that the simulation code was written in.
One of us started playing with Perl. He ended up dropping out and going to work as a SysAdmin.
One of us started playing with Tcl/Tk. He ended up changing majors and going into UI/UX testing.
I started playing with Python, which at that point was, I think, at 0.9.2 or something. Eventually, after some twists and turns, I went on to work on ML stuff.
Is there a lesson in that? Probably not, but somehow I find it amusing all the same.
I don't see how this works though. OpenAI doesn't exist in a vacuum, it has competitors, and the first company to stop improving their model will get obliterated by the others. It seems like they are doomed to keep retraining right up until the VC funding runs out, at which point they go bankrupt.
Some other company, that doesn't have a giant pile of debt will then pick up the pieces and make some money though. Once we dig out of the resulting market crash.
This only works if all the AI companies collude to stop training at the same time, since the company that trains the last model will have a massive market advantage. That not only seems extremely unlikely but is almost certainly illegal.
One of us started playing with Perl. He ended up dropping out and going to work as a SysAdmin.
One of us started playing with Tcl/Tk. He ended up changing majors and going into UI/UX testing.
I started playing with Python, which at that point was, I think, at 0.9.2 or something. Eventually, after some twists and turns, I went on to work on ML stuff.
Is there a lesson in that? Probably not, but somehow I find it amusing all the same.