Moat-like things going for them:

- access to the US government (for huge inflated government contracts as a source of free capital, having the competition banned, having their products legally mandated, getting other preferential treatment, having foreign governments bullied into granting similar advantages)

Too unsustainable, too fragile, too small:

- Strong brands: Brand loyalty seems like a pretty insignificant factor in AI right now

- Semi-exclusive hardware access, RAM buyouts: Unsustainable even in the short term

- Lock-in effects: Their models aren't very stable across versions, or very unique, and switching providers is about as easy as it can be in a highly competitive market.

- Exclusive technology that is important, advanced and hard to replicate to a degree that would materially set them apart and justify potentially much higher cost. Maybe I'm missing something here, but a lot of the claims they make to that end fall apart even on light scrutiny (due to things like test sets that are small, hand-picked and clearly overfitted on, or broad general claims that cannot be substantiated). I'm getting the feeling they don't have anything that materially sets them apart at all, but that's seems impossible to measure in any meaningful way, so who knows, they might have something here.

> yq is a portable command-line YAML, JSON, XML, CSV, TOML, HCL and properties processor

Though like as not you're still going to be right, after all, Stuxnet happened.

But giving up a local water table might even be seen as an acceptable sacrifice, it's done for agriculture all the time, if only it wasn't done by and for the immense profit of corporations that aim to destroy all entry level jobs and profess to be scared by what havoc their own creation will wreak, all while creating an unprecedented financing bubble.

Like, that's not very subtle at all. You can pick out some detail and try to make it about subtleties, but the larger context is very much not about subtleties.

But if you build on them you should have read them. I don't know about the specifics and I don't know if Schmidhuber is out of line or not, and citations and impact factors are a terrible mess, but generally speaking, you are responsible for finding and reading and citing any related work that needs to be cited, and if you work on neural networks in an academic context you probably have been forced to read that particular one at some point. Citation obligations don't just disappear because you don't want to do the research.