A very interesting article by my Hoover Institution colleague Andy Hall (who is also at the Stanford Graduate School of Business); here’s the Introduction, though the whole thing is much worth reading:
There has never been an economic shock in modern American history like the one the leaders of the AI industry are telling us is coming. Dario Amodei has warned of “unusually painful” labor impacts “bigger than any before,” predicting that AI could eliminate half of all entry-level white-collar jobs and push unemployment to 10–20 percent within five years. He is hardly alone. Both OpenAI and Anthropic have begun laying out, in expansive policy memos, the kind of social contract they say the post-AGI economy will demand, with proposals for shorter working weeks, public wealth funds, and a completely modernized taxation system. The abundance is coming, they tell us, and they would like to help us figure out how to share it.
Can the tech industry successfully pre-empt American populism, sketching the post-AGI social contract before the public has even decided it wants one, and before we even know if speculator growth and job displacement is actually coming? My answer, after months working with my coding agents to pore over polling data, policy proposals, and historical parallels, is that it cannot.
In the scenario the labs are sketching, the politics of AGI will be the politics of jobless prosperity. And this makes it hard to forecast well. The economy will be growing rapidly even as jobs disappear, more like the Industrial Revolution or the China Shock than a normal recession, with mass disruption alongside the explosive enrichment of a small class of elites at the top.
Voters in this world will not be anxious about a shrinking economy but furious about being shut out of a booming one, and they may well stop the boom from arriving at all. Jasmine Sun has documented how this anxiety is already curdling into nascent political anger, observing that “the anti-elite and nihilistic attitudes that have dominated US political culture in the last few years are transmuting into anger at AI billionaires.” Alex Imas, in “What will be scarce?“, has made the most careful economic case for taking the underlying disruption seriously, even while laying out why both the short and long-term doomers may be wrong about mass unemployment.
The labs see all of this coming, which is why their policy memos have grown so ambitious. It would be easy to read this as good news, since the parties who would have to pay for redistribution are pre-emptively volunteering to do it.
But it cannot work. First, social contracts tend to get extracted from the powerful by the affected, not handed down from above to a public that has not yet decided what it wants. And second, we don’t even know yet what the economic contours of AGI will look like—we don’t even really know that it’s going to lead to job loss, let alone to massive job loss.
As we fluctuate between promises of catastrophe and abundance, I’ve come to three conclusions:
- The backlash to AI isn’t here yet. There is anxiety among American voters, but there is no populist backlash yet, because the structural conditions for it have not arrived. Hence, we have a potentially narrow window in which to plan out our response to job loss before it becomes a populist issue.
- Real backlash will happen if and when job losses pick up steam. The backlash will properly arrive if and when unemployment climbs by two percentage points—I hypothesize—alongside a clear public narrative that AI is to blame. At that point, if we do not have a good inventory of smart policy ideas, we will be overwhelmed with bad populist ones.
- The labs should focus on measurement, not redistribution. Their best contribution in the window before backlash is the infrastructure that lets society see this transition clearly—usage data, displacement indicators, self-activating triggers—not pre-emptive social contracts that lack credibility and a coalition to enforce them. The eventual bargain is something that affected people should play a direct role in negotiating; the data and tools that can help them negotiate from a position of clear information are what the labs can build now.
