Not saying you're wrong or that your experience is invalid but this does not match my experience trying to do applied ML work or ML research. (More experienced people feel free to chime in and tell me how wrong I am...) Granted, I'm not that experienced, but my experience so far has been that most ideas I've had are either ideas that people had and tried or actually published on.

Further, my sense is that multiple experienced researchers often have all had similar ideas and the one who succeeds is the one who overcomes some key obstacle that others failed to overcome previously, so it's not just that I'm new/bad.

I do think this mostly applies to areas that ML researchers think are interesting and somewhat tractable. So, for example, I wouldnt make this claim about safety research and don't know how much it applies to gesture recognition in particular.

That said, since you're claiming your tools are better, I will note that the ML community does seem open to switching tools in general, as evidenced by the Theano to Tensorflow / PyTorch (somewhat) shifts of the past few years.

Would you be willing to describe at least at a high level what these tools let you do?

Edit: I'm more skeptical of the object-level claims than the title claim. Assuming you're using the classical definition of efficient market, I agree that "ML tooling" adoption doesn't follow efficient market dynamics in at least one respect.

This situation, if real, seems self-correcting. If you are sure you have a superior technology, and can show you do by monetizing it (otherwise, how is it superior?), then you will gain a lot of attention and change a lot of minds pretty quick.

Which tools are you talking about?