Most modern tech companies are software companies. To them, the means of production are a commodity server in a rack. It might be an expensive server, but that's actually dependent on scale. It might even be a personal computer on a desk, or a smartphone in a pocket. Further, while creating software is highly technical, duplicating it is probably the most trivial computing operation that exists. Not that distribution is trivial (although it certainly can be) just that if you have one copy of software or data, you have enough software or data for 8 billion people.
Upthread used the term "tech" when the thread is very clearly talking about AI. AI is software, but because they used the term "tech" you cherry-picked non-software tech as a counter example. It doesn't fit because the type of tech that GPT-4 represents doesn't have the manufacturing cost like a chip fab does. It's totally different in kind regardless of the fact that they're both termed "tech".
Chip fabs are literally one of the most expensive facilities ever created. Saying that because they don't need a special moat so therefore nothing in tech ever needs a special moat is so willfully blind that it borders on disingenuity.
The first use of "moat" upthread:
> Curious why even companies at the very edge of innovation are unable to build moats?
About RISC-V: What does you think is different about RISC-V vs ARM? I can only think that ARM has been used in the wild for longer, so there is a meaningful feedback loop. Designers can incorporate this feedback into future designs. Don't give up hope on RISC-V too soon! It might have a place in IoT which needs more diverse compute.
academic performance is a bad predictor for real world performance
Compare this to the AI ecosystem and you get a huge difference. The architecture of these AI systems is pretty well-known despite not being "open," and there is a tremendous amount of competition.
How could I verify this information?
For a concrete example, the bitmanip extensions (which provide significant increases in MIPS/MHz) were used by SiFive in commercial cores before ratification and finalization. No other company could do that because SiFive employees could just change the spec if they did. They're doing the same thing with vector/SIMD instructions now to support their machine learning ambitions.