I was prompted to write this question by reading this excellent blog post about AlphaFold I'll quote it at length because it serves as a candidate answer to my question:
What is worse than academic groups getting scooped by DeepMind? The fact that the collective powers of Novartis, Pfizer, etc, with their hundreds of thousands (~million?) of employees, let an industrial lab that is a complete outsider to the field, with virtually no prior molecular sciences experience, come in and thoroughly beat them on a problem that is, quite frankly, of far greater importance to pharmaceuticals than it is to Alphabet. It is an indictment of the laughable “basic research” groups of these companies, which pay lip service to fundamental science but focus myopically on target-driven research that they managed to so badly embarrass themselves in this episode.
If you think I’m being overly dramatic, consider this counterfactual scenario. Take a problem proximal to tech companies’ bottom line, e.g. image recognition or speech, and imagine that no tech company was investing research money into the problem. (IBM alone has been working on speech for decades.) Then imagine that a pharmaceutical company suddenly enters ImageNet and blows the competition out of the water, leaving the academics scratching their heads at what just happened and the tech companies almost unaware it even happened. Does this seem like a realistic scenario? Of course not. It would be absurd. That’s because tech companies have broad research agendas spanning the basic to the applied, while pharmas maintain anemic research groups on their seemingly ever continuing mission to downsize internal research labs while building up sales armies numbering in the tens of thousands of employees.
If you think that image recognition is closer to tech’s bottom line than protein structure is to pharma’s, consider the fact that some pharmaceuticals have internal crystallographic databases that rival or exceed the PDB in size for some protein families.
This was about AlphaFold, by the way, not AlphaFold2. (!!!)
The efficient market hypothesis applied to AI is an important variable for timelines. The idea is: If AGI (or TAI, or whatever) was close, the big corporations would be spending a lot more money trying to get to it first. Half of their budget, for example. Or at least half of their research budget! Since they aren't, either they are all incompetent at recognizing that AGI is close, or AGI isn't close. Since they probably aren't all incompetent, AGI probably isn't close.
I'd love to see some good historical examples of entire industries exhibiting the sort of incompetence at issue here. If none can be found, that's good evidence for this EMH-based argument.
--Submissions don't have to be about AI research; any industry failing to invest in some other up-and-coming technology highly relevant to their bottom line should work.
--Submissions don't need to be private corporations necessarily. Could be militaries around the world, for example.
(As an aside, I'd like to hear discussion of whether the supposed incompetence is actually rational behavior--even if AI might be close, perhaps it's not rational for big corporations to throw lots of money at mere maybes. Or maybe they think that if AGI is close they wouldn't be able to profit from racing towards it, perhaps because they'd be nationalized, or perhaps because the tech would be too easy to steal, reverse engineer, or discover independently. Kudos to Asya Bergal for this idea.)