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.)
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:
This was about AlphaFold, by the way, not AlphaFold2. (!!!)
I was once chatting with someone being business development at Sanofi. According to him they took 1 1⁄2 years to exchange a button on their website because their internal processes are filled with bureaucracy.
From that perspective there’s the justified belief that big pharma has no capacity to develop new technology of this kind inhouse. They could hire a bunch of AI Phds but they likely would drown them in bureaucracy so that they wouldn’t get the kind of results that AlphaFold got. It’s much easier to let someone else do the work and then license it.
Exactly. Machine learning is not pharma’s comparative advantage.
I submit this is usually a result of rational behavior. The reason is straightforward: the job of business executives is nominally to increase the share price, but what this actually entails is exploiting their capital as efficiently as possible. This matches what we would expect rational people to do on a couple of levels:
This is what formal training consists of during an MBA; there are formal processes for conducting the analysis; capital efficiency is included directly in financial analysis by investors on Wall Street. It would be very weird for successful corporate titans to go screw process and Wall Street all the time.
Even in the basic person trying to do their best case, what do I have and what can I do with it is as fundamental an approach as possible.
These two examples carry in them an implicit assumption, which I want to point to as a good predictor of the phenomenon: the new investment will decrease the value of investments they have already made. In other words, it will cannibalize value.
This is the logic behind Blockbuster/Netflix; if they had bought them, all the gains Netflix made at the expense of Blockbuster stores would have looked like shooting themselves in the foot. Let us consider the counterfactual case of Blockbuster buying Netflix for a song: their stores continue to get hammered yielding definite losses; the titanic success of Netflix is uncertain and in the future (deeply uncertain; would they have made the further transition to digital from mail-order? Could they have managed the state-of-the-art IT infrastructure to make it work if they had? Would they have had the foresight to invest in original content?). Would the investors have spared the rod after setting their capital on fire for such uncertain gains?
You can also consider another interesting case: Kodak and the digital camera. Now as it transpires Kodak didn’t miss the boat so much as miss the shots it took, but I posit a causal mechanism at work: Kodak’s primary investments were in chemicals and paper, so their leadership was not positioned to implement decisions well, even when they made strategically good ones.
So I say it is rational because they are doing what a method that a lot of smart people have worked very hard on refining says to do (usually successfully). I say the predictor for when it will happen is that it makes what they are already doing less valuable, and therefore they are ill-positioned to execute and even if they do they will be punished.
I have some data on this on the top of my head from having read the history of 50 mostly random technologies (database.csv in the post):
People not believing that heavier than air flight was a thing, and Zeppelins eventually becoming obsolete
Various camera film producing firms, notably Kodak, failing to realize that digital was going to be a thing
(Nazi Germany not realizing that the nuclear bomb was going to be a thing)
London not investing in better sanitation until the Great Stink; this applies to mostly every major city.
People not investing in condoms for various reasons
People not coming up with the bicycle as an idea
Navies repeatedly not taking the idea of submarines seriously
Philip LeBon failing to raise interest in his “thermolamp”
So that’s 8⁄50 of the top of my head (9/50 including Blockbuster, mentioned by another commenter)
I also have some examples of technology timelines here and some technology anecdotes from my sample of 50 technologies here, which might serve as inspiration.
Car companies have done too little too late to switch to making EVs.
See also: The Innovator’s Dilemma.
The Moneyball story would be a good example of this. Essentially all of sports dismissed the quantitative approach until the A’s started winning with it in 2002. Now quantitative management has spread to other sports like basketball, soccer, etc.
You could make a similar case for quantitative asset management. Pairs trading, one of the most basic kinds of quantitative trading, was discovered in the early 1980s (claims differ whether it was Paul Wilmott, Bamberger & Tartaglia at Morgan Stanley, or someone else). While the computation power to make this kind of trading easy was certainly more widely available starting in the 80s, nothing would have prevented someone from investing sooner in the research required for this style of trading. (Instead of, for instance, sending their analysts to become registered pseudoscientists)
The Xerox Palo Alto Research Center produced many innovations in information technology—including the graphical user interface and mouse—that Xerox as a corporation completely failed to capitalize on. (There were PARC innovations that Xerox did successfully exploit, but the ones it missed the boat on are the ones that get all the attention.)
Blockbuster failed to invest in internet tech for their movie rental business and was outcompeted by smaller, more savvy startups.
How do you know they aren’t investing in developing AI? Corporate research goals are proprietary.
There is just as much evidence of corps developing quantum computers, astrological calculators, geocentric ocean navigation, especially, and a whole host of other unproven technologies, some of which don’t actually work. So the prior on “people with money aren’t doing it” needs to include a healthy dose of “there’s no there there”