TiB 130: Why elections today seem so high stakes; evaluating extreme talent; the golden age of R&D; and more...

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How to reverse economic stagnation of the last few decades is a frequent TiB theme - and an important sub-question is who should fund and carry out R&D. Ben Southwood - whose paper with Tyler Cowen on scientific slowdown we discussed in TiB 92 - has an excellent new essay that argues for a key role for corporate labs (This is in the new and superb Works in Progress - pretty much every article is worth your time).

Southwood points to the golden era of corporate R&D in the decades after 1945, during which organisations like Xerox PARC and Bell Labs seem to have been extraordinarily productive (If you’re interested in this, Arnaud Schenk and I created a reading list on the great labs as part of our Organising Genius course). He cites the challenges of innovating in other settings like startups and universities, and suggests that there’s a strong link between concentrated market power and successful R&D labs.

The bullish interpretation is that this means we’re due another wave - perhaps Google X is the new Bell Labs. Southwood suggests this is a reason to hope for laxer anti-trust enforcement. I’m not so sure that’s a price worth paying. It seems that there is a deeper problem here: R&D is a public good, but the state often lacks the incentives to supply it well. One solution, perhaps, is more institutional innovation in funding public goods, as we discussed in TiB 129 last week. It would be interesting to see a country - or even a monopoly - give it a try. 

One of the essays that best explains the current political/economic moment globally is Nils Gilman’s 2017 piece, “The Official Future is Dead, Long Live the Official Future”. As we head into a US election and worldwide post-pandemic uncertainty, it’s worth revisiting. The core idea is that stability requires an “official future” - that is, a shared sense of the basic parameters that will govern the future (Nilman credits this book by Peter Schwartz with coining the term).

Nilman argues that the "official future” in most of the West was stable for around 25 years after the fall of Communism. But that version - for which The End of History is a decent shorthand - has collapsed. Today, “the range of potential ‘outcomes’ that one can imagine over some middle-term horizon—say, five to ten years—seems fantastically broad”. This is one reason the electoral stakes seem so high in so many countries today: what’s up for grabs is not the right merely to tinker with the controls, but to reimagine the whole system.

We’ve talked before about Peter Thiel’s idea of “definite optimism” (e.g. TiB 28 and TiB 71) - the idea that we can and should lay out vision for a better future and a plan to achieve it. The positive take is that a wide open “Schwartz window” creates a lot of space for definite optimist entrepreneurs, both political and commercial. But Gilman lays out a grimmer possibility in the closing line of his essay: “in revolutionary situations, it’s usually the Leninists who win”. 

This week I came across this interesting paper on the optimal way to experiment with new ideas when the outcomes are power law distributed (We talked about power laws in TiB 113 and TiB 127). Interestingly, the authors conclude that the right strategy looks a lot like Lean Startup methodology: try lots of things and double down on outlier results, even when there’s a lot of noise in the data.

It’s interesting to think about how this idea applies to talent discovery. It seems plausible that talent has a “fat tailed” distribution where, at least in some fields, even small differences among the most talented are associated with large changes in real-world outcomes (see TiB 125 for some suggestive evidence from the world of maths). The challenge is that in many domains, it’s difficult to identify exceptional talent by testing. Most of the time, you have actually to observe the activity you care about - but that tends to be time consuming and expensive.

Given this, one conclusion we might draw from the paper is that we should run many more experiments that allow talent to be revealed. Entrepreneur First is one such approach in the startup domain - and some smart people have suggested that similar models might be impactful in science, art and other fields (see, e.g., Sam Altman here). It’s an area where I’d love to see more work.  

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Until next week,

Matt Clifford