🚀 Faster, Please! Week in Review+ #23
The true legacy of 'The Jetsons'; 'Friending bias' vs upward mobility; can the global nuclear renaissance survive an accident?; AI and economic growth; deep geothermal energy
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In This Issue
Best of 5QQ
Best of the Pod
A happy belated birthday to George Jetson, born July 31, 2022. The Jetsons, set in the year 2062, is perhaps the most important work of postwar futurism. The cartoon premiered on September 23 in 1962, a pretty future-optimistic Up Wing year in an extraordinarily Up Wing decade, from John Glenn becoming the first American to orbit the Earth to John F. Kennedy’s “We choose to go to the Moon” speech. One other Up Wing thing about 1962: The US economy absolutely boomed, surging at an inflation-adjusted 6.1 percent. And over 1962–1969, real GDP growth averaged 5.0 percent annually. The Jetsons, with its jetpacks, flying cars, and robot maids, suggested the outline of things to come — perhaps sooner rather than later. You can credit the show with creating a durable tech-positive image of the future against which subsequent dystopian vision would be measured. We still have forty years to build a future that matches the scale and scope of what The Jetsons’ suggested back in 1962. Let’s get moving.
A new study done by Harvard’s Raj Chetty and his team of colleagues finds that absolute upward mobility for lower-income kids is helped by having friendships with kids who are better off. As Chetty told the Times: “Growing up in a community connected across class lines improves kids’ outcome and gives them a better shot at rising out of poverty.” There’s a striking difference in outcomes between poorer kids who grow up in a community where most of their friends are in the lower half of the socioeconomic distribution vs. those who grow up in a community where their friends mostly come from the upper half. What are the policy implications? Policymakers might aim for more diverse K-12 schools created by drawing school districts so that they include both poorer and richer neighborhoods. But probably the most significant approach involves housing, just as it seems with every big problem facing America. We need land-use reform to boost supply, especially in some of the most productive and highest earning regions. You can’t make friends if you’re not even physically present.
Netflix’s new, four-part documentary series, Meltdown: Three Mile Island, tells the story of the March 1979 incident to the backdrop of ominous music, throwing in lots of in media res speculation from panicked residents of Middletown, Pennsylvania, where Three Mile Island was located. The series concludes with a video montage of the 2011 Fukushima nuclear disaster and then gives TMI clean-up supervisor turned whistleblower Rick Parks the final word: “I still believe in the promise of nuclear energy. But we’ll never have a viable nuclear energy industry in this country until we take the profit motive out of it. You can’t have a profit motive overriding nuclear safety.” It’s a remarkable way to end this documentary series when you consider that by far the worst nuclear disaster was Chernobyl in Ukraine, where there was no profit motive involved with the reactors built and operated by the Soviet Union. Moreover, Japan’s nuclear backlash following the Fukushima accident led to higher energy prices and a reduction in energy consumption, contributing to more deaths than the incident. So would another nuclear incident stop the current momentum for nuclear? Geopolitical risk and climate change are pro-nuclear, action-forcing mechanisms that are here to stay. Maybe get ready for a new documentary: Meltdown: The collapse of global resistance to nuclear power.
Best of 5QQ
▶ Tamay Besiroglu is a visiting research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory, where his work focuses on the economics of computing and big-picture trends in machine learning.
How optimistic are you that AI will deliver significant productivity gains in the 2020s?
I think that there is only a modest chance—say, around 25 percent—that by the end of this decade, AI will significantly boost aggregate US productivity growth (by “significantly,” I have in mind something like reverting to the 2 percent productivity growth rate that we observed before the productivity slowdown that occurred in the early 2000s).
I’m not more optimistic because boosting aggregate productivity is a tall order. In the past, few technologies—even powerful, general-purpose, and widely adopted ones like the computer—have had much of an effect. Deep learning has been applied with some success to a few problems faced by large tech companies (such as facial recognition, image detection, recommendations, and translation, among others). However, this has benefited only a small sliver of the overall economy (IT produces around 3 percent of US GDP). It also does not seem likely that AI has enhanced the productivity of technology companies by a large enough margin to produce economy-level productivity effects.
Over longer timescales—say, 15 or 30 years—I think there are good reasons to expect that conservative extensions of current deep learning techniques will be generally useful and reliable enough to automate a range of tasks in sectors beyond IT; notably in manufacturing, energy, and science and engineering. Concretely, I think it is more likely than not that over such a time frame AI productivity effects will dominate the productivity effects that computers had in the late 20th century.
Given the importance of technological progress for driving economic growth among frontier economies, I pay particular attention to the use of AI tools for automating key tasks in science and engineering, such as drug discovery, software engineering, the designing of chips, and so on. The widespread augmentation of R&D with AI could enable us to improve the productivity of scientists and engineers. Automating relevant tasks will also enable us to scale up aggregate R&D efforts (as computer hardware and software for AI are much easier to scale up than it is to increase the number of human scientists and engineers). I think it’s possible that by the middle of this century, the widespread augmentation of R&D with AI could increase productivity growth rates by 5-fold or more
Best of the Pod
From the Club of Rome to the degrowth movement, environmental concerns often lead to calls for scaling back our energy use. But a future of abundant, clean energy is possible. One promising technology is advanced geothermal energy, whereby power plants convert the heat beneath the Earth's crust into electricity. And how do we get to that heat? The folks at Quaise Energy want to use microwaves to vaporize rock, boring superdeep shafts into the surface of the planet.
In this episode of Faster, Please! — The Podcast, I'm joined by Kevin Bonebrake, chief financial officer and head of corporate development at Quaise Energy. Bonebrake joined the company in May, coming over from Lazard, where he was a managing director in the financial advisory business focused on the energy industry.
So what problem will this technology solve?
It'll solve a whole host of problems. But the big problem it’s going to solve is what the whole world is focused on right now, which is the emission of carbon into our atmosphere. It will provide us with clean, baseload energy that is dispatchable, which means that you can control the volume going into the grid. You don't have the intermittency issues that some other renewable technologies have. And it's also accessible, as you indicated, to anybody, anywhere on the planet. So from a geopolitical perspective, it's going to be extremely transformational. And our technology, in particular, because we're going to the depths and the temperatures we're talking about — you said that magic word, “supercritical” — is going to have a power density such that (a) we're going to be able to achieve a step change in terms of the economics associated with geothermal such that we're going to be competitive with solar and wind and other renewable technologies; and (b) we're going to be able to produce steam that can be plugged into the existing power infrastructure around the planet. And what that means is that you can roll out this technology between, say, 2030 and 2050. Because when you think about the scale that's involved [in] repowering the entire planet — by some estimates, it’s one-eighth of the global domestic product each year, just to give you a sense — so the scale of repowering that entire infrastructure, in a timely fashion in order to eliminate carbon emissions by 2050, is something that people don't talk about a lot. But just the time required to do that is immense. And this technology will be a shortcut, essentially.
Thanks for reading this far! Just a quick note for first-time visitors and free subscribers. In my twice-weekly issues for paid subscribers, I typically also include a short, sharp Q&A with an interesting thinker, in addition to a long-read essay. Here are some recent examples of those interviewees: