The economics for some of these labs is also pretty different from what you'd normally expect. There are numerous semi-private organizations in the defense space ( such as Boston Dynamics ) and similarly in the pharmaceutical space. Which may only produce commercially viable technology after a decade or more. For many of these entities, the funding dynamics are dependent on the overall scope of the problem along with the quality of the team rather than any direct foreseeable economic outcome.

I understand, I don't mean commercial progress either.

Their 2017 paper: https://www.vicarious.com/img/icml2017-schemas.pdf

That's it? That tells me the same thing I saw when I attended a Vicarious researcher's talk at MIT CSAIL: they're basically reinventing stuff the Bayesian generative modelers in cognitive science have been doing for years, throwing some video-game playing at it, and calling it AI instead of cognitive science.

Give me $50mil and I could do a good deal more than that, just because I bother to look stuff up!

There are a few papers up on the site already and some more coming. As a business, publications aren't really the KPIs we prioritize (vs an academic-style lab like DM or OAI).

Out of curiosity, what's the KPI?

p.s. I don't think papers qualify as a "demo". I'm with you re: not running it like an academic lab and chasing a minimal publishable unit or fashions of the time. Demo = Demonstrably do something with your technology no one could do before, ie beat people at Go in DeepMinds case, in your case you could set up your own benchmark / pass a Turing test for 3 year olds or something.

KPI = amount of funds raised.

I'm honestly surprised how willing investors are to throw money at these ML startups that actually show barely any progress.

"Show barely any progress" and "show barely any progress to the public" are very different. I don't have any inside information here, but a lack of public announcements is not proof of anything.