Byrne Hobart - Optionality, Stagnation, and Secret Societies

The interview opens with a line from Byrne Hobart's own essay on foxes and hedgehogs: if someone seems to lack a single big idea, they probably have one, and it is a good one. Hobart writes about finance, technology and much else every weekday, so the host guesses his organizing idea: most human decisions, by individuals or institutions, reduce to simple financial concepts such as expected value, optionality and volatility, and other people miss this and therefore misreport important trends or overlook their long-term effects.

Hobart accepts this as a useful mental model that he has used a great deal, but he adds a caution. Any model should be used together with awareness of its deficiencies. To make a prediction you must list the assumptions the model requires, and if those assumptions cannot all be met, the model does not really apply, or at least you should not be surprised when the result surprises you.

He then offers a different candidate for his big idea, which is less a thesis than a question: how do people coordinate when they solve complicated problems? Many interesting problems cannot be solved by one lone genius. Institutions try to solve them, but the institution's mandate is often something other than the problem itself, and there may be both a real mandate and a fictional one.

His example is the mission-driven public company. SpaceX can say it is going to Mars while also trying to maximize shareholder value, and it is never clear which is the external story told so the company can reach its internal goals. Even inside the company, Hobart suggests, some people may see the Mars mission as a way to recruit engineers and raise money at a high stock price while the real aim is earnings per share over ten or twenty years, whereas others may believe the money-making is incidental to making humans an interplanetary species.

Institutions also carry unstated internal goals, and often these are simply that the people there want to stay, get raises and feel important. If an organization fails at its goal but the problem it works on keeps growing, it can stay important for a very long time. Because coordination between groups with different goals requires a certain amount of double talk, Hobart calls it a very hard problem to study, one that recurs in finance, in tech companies, and in politics, both in the narrow sense of who gets elected and which bills pass and in the broader sense of how humans with intractable desires reach an accommodation.

When the host asks whether the big idea is uncovering institutions' secret goals, Hobart ties coordination to a concrete problem: productivity stagnation. He calls it important, dire and affecting everyone, and says it tends to compound because some technologies cause other technologies to accelerate.

He distinguishes technological stagnation, which is quite visible in the productivity statistics, from stagnation in social technology, which is much harder to measure. Social technologies are invisible until they go away. You may not know you worked at an effective company until it stops being effective, or until you move elsewhere and find you cannot trust that an email with a question will get a good answer. As a rough indicator he points to how the US government functioned in the 1930s through the 1960s compared with today, and says some decline there is clear. Both kinds of stagnation involve coordination problems, so studying how people collaborate toward goals is a way to learn how to bring productivity growth back to where it used to be.

The host recalls an article in which Hobart was optimistic, on anecdotal evidence, that the great stagnation was ending, while noting that individual advances such as mRNA vaccines matter less if the regulatory apparatus cannot approve and scale them quickly. He asks Hobart to quantify how much higher total factor productivity would be since 1971 under perfectly enabling regulation.

Hobart first objects to the framing: it is a mistake to say that keeping things the same would have produced more growth. The better counterfactual is that society would have adapted in better ways. He then gives a rough number. The gap in total factor productivity growth is around one percent a year, and compounded over fifty years that yields something like 60 or 70 percent higher GDP per capita in the US. He says this is hard to picture concretely, because it would not mean houses and cars that are 70 percent bigger or more numerous; it would more likely mean additional products and longer lifespans, or, he hopes, longer health spans, which he considers more solvable and more pressing than the lifespan problem in terms of total human disutility.

He is clear about the limits of such aggregate estimates. The data can show the gap between a hypothetical continuation of the mid-twentieth-century productivity boom and reality, but it cannot show what amazing things we would have. For that you need specific examples, because productivity growth was not a dial that someone turned up and then back down. It was a set of specific inventions that were developed, deployed and kept getting more efficient over many decades.

Hobart recommends Where's My Flying Car as a place to begin, describing it as an extended, detailed, thoughtful and well-footnoted rant by someone who wants a flying car, has flown things that are essentially flying cars, and has done the math on how they could work. The author examines safety concerns and the effects on city layout and the nature of travel. His case is that flying cars were not specifically made illegal; rather, regulation tightened until it became much harder to fly anything, and far harder still to build something totally new and fly it without serious trouble.

Hobart acknowledges the trade-off. There are few flying-car accidents because there are no flying cars, and early innovators tend to have more accidents. He cites the early history of the steam engine, which involved many explosions and taught people the physical limits of materials by building higher-capacity boilers that then exploded. The downsides of regulation are therefore real but easy to see, since they cluster at the beginning when a technology works poorly and can kill people. The upside is harder to see: we cannot observe what things would look like in twenty years, when the technology works well, is extremely safe, and takes real effort to misuse fatally.