Most importantly, in these fields results can be easily verified which means that if you have enough compute, that you can generate correct training data. OpenAI had an early story - which could be a lie of course - about how they tried to train a robot but the actuators would be toast before the robot would have learned. Math and programming doesn't have such problems.
Could be hallucinating by I just asked Gemini to estimate global average compensation of developers and multiply by estimate of total number of developers in the world, and it came up with $1 trillion to $2 trillion.
So the total addressable market is the right order of magnitude, let alone all the other automatable high wage occupations..
No it isn't. Doing work more efficiently through the use of LLMs is not RSI. RSI requires a closed loop automated LLM iteration process which we are not at all close to right now.
Sure, if you redefine what that means.
Before this year, "recursive self-improvement" in the context of AI research meant the holy grail: an artificial intelligence that was capable of improving itself, unattended, in ways that humans can't even imagine, so that within a relatively short period of time, it reaches superintelligence and ignites the Singularity.
What's happening now is emphatically not that. And if you think it is, you are buying straight-up propaganda. "I gave an LLM a tool that lets it make largely-random decisions about how to train its next iteration when I tell it to do so" bears little to no resemblance to RSI. Because it is, beyond a rounding error, completely missing out on the "improvement" part.