Sure.. and neural networks came out a very long time ago, but are now arguably approaching usefulness in LLMs.

Perhaps thats because it takes a while for the ideas to get polished/weeded and diffuse into the engineer zeitgeist .. or it could be that compute / GPUs are now powerful enough to run at the scale needed.

re : "RL cannot be used to solve real world problems" .. well, I would argue that these are useful real-world problems :

  - predict protein folding structure from DNA sequence
  - stabilizing high temperature fusion plasma
  - improving weather forecasting efficiency
  - improve DeepSeek's recent LLM model

Im currently using RL techniques to find 3D geometry - pipes, beams, walls - in pointclouds. It is of practical benefit, as a lot of this is done manually, ballpark $5Bn/yr

But I concede I cannot point to a plethora of small startups using RL for these real-world problems .. yet.

This is a prediction, and I could be wrong in many ways - not least that LLMs digest RLs in full and learn to express their logical reasoning, approaching AGI, and use RLs internally, and so subsume and automate the use of RL.

Are VCs better at predicting the future.. I guess that is their job, and they have money on the line... but I think even they would admit they need a large portfolio to capture the unicorns.

VCs probably get a less detailed tech view than founders, but the large number of pitches they review should give them a noisy but wider overview of the whole bleeding edge of innovation.

I think startup founders are in the same future prediction business .. and arguably have more skin in the game.

Predictions would be pretty useless if they weren't somewhat controversial - a prediction we all agree on doesn't say much. Come back and chastize me if we dont see more RL startups in 12 months time !