The death of corporate research labs

Why did it fade, though? AT&T was able to fund Bell Labs when it still had a monopoly over the telephone market. The article claims that antitrust laws were relaxed in the 80s, but AT&T was broken up in 1982 (https://en.wikipedia.org/wiki/Breakup_of_the_Bell_System). This break up lead to increased competition and innovation in telecom, but it did come at the cost of Bell Labs losing funding until eventually being spun off into Lucent.

Peter Thiel argued in the book Zero to One that only monopoly (or pseudo monopoly) businesses can afford to fund surplus salaries and corporate research labs that cost billions of dollars with potentially little or no gain for many years. I would argue that if a corporation exists in a competitive industry, their margins are driven too low to fund basic research.

Also note: even in the U.S. corporations fund a good chunk of basic research in academia: https://www.sciencemag.org/news/2017/03/data-check-us-govern...

Anecdata: My team of 12 or so at Google had (I think) 4 PhDs, and what we did was the usual "turn one proto into another" Google work that barely required a CS undergrad degree, to say nothing of a PhD. My wife has a PhD, works as a software engineer, and also does pretty routine data plumbing work.

But it’s simple, video is more versatile, and by removing youtube’s monolithic dominance, you remove the single point of failure that copyright cartels have been able to attack.

As an example, imagine a separate youtube-clone dedicated to education that was actually willing to fight for fair use! Or a youtube that didn’t automatically demonetized you for swearing.

Internet already gives these kids (and the rest of us) both access and audience, we have just been stupid enough to lock large parts of our cultural heritage into corporate silos, protected by “intellectual property”-laws. Competition wouldn’t solve this completely, but it would make it harder to distort the market.

The reason it appears rare is because funding research on the assumption it might be useful in more than 20 years from now is extremely wasteful. Companies don't work so far ahead because the risk of just going down a dead-end for half a lifetime is very, very high. In academia that doesn't matter because people are rewarded merely for researching novel things, in the real world people are rewarded for doing things that are useful.

It matters. For every DNA you can cite, others can cite dead end branches that despite decades of research have gone nowhere and probably never will. In CS, how many programmers are using Haskell every day? It's been in development for 35 years yet virtually nobody uses it - the new languages that gain traction (Rust, Swift, Go, Kotlin etc) invariably come from corporate R&D labs, and outside of a few bits of useful syntax borrow little from academic research languages. Even Haskellers have admitted now that lazyness was a dead end, new FP langs like Idris don't have it. Practically the entire field of PL research was swallowed up by FP and continues to be dominated by that paradigm (e.g. dependent types), despite the vast majority of PL users being disinterested in them.

Computational epidemiology. It's been researched for >20 years yet the models are always wrong. There are private sector epi models, but not surprisingly little work is done on them because there's obviously a missing piece, and developing huge and insanely complex simulations (e.g. the 15,000 LOC monster Ferguson produced) is obviously a dead end.

String theory. How much time has been sunk into that? Not a single testable prediction.

And in biology. You know, one biotech firm found 9 in 10 papers don't replicate. Papers professional labs can't replicate is not "useful in 20 years". That's "not useful today and never". People were selectively breeding for many centuries. Agritech firms would have eventually figured out the structure of DNA if Watson and Crick hadn't.

Meanwhile we tend to take for granted all the long term R&D projects the private sector does because it's much better at finding useful outcomes quicker. It doesn't need to wait 20 years to find products out of the research it does, and that's good!