This essay makes three main points. First, even though US growth rates have been stable at roughly 2 percent per year for 150 years, it is distinctly possible that automating intelligence leads economic growth rates to accelerate. Second, this acceleration is likely to be slowed by the presence of "weak links." While we each have access to 100 million times more transistors on our desktop computer than people in the 1970s, we are not 100 million times more productive. Computers can invert matrices at lightning speed, but we humans must still decide what matrix to invert, what hypothesis to test, and so on. Accelerating economic growth requires the vast majority of the weak links to be automated away, which delays the large gains. Finally, even though weak links slow the benefits, they may actually speed up the risks. When a chain is only as strong as its weakest link, damaging one link in the chain can be very costly. A powerful AI that is superhuman at software engineering could be misused by a bad actor to do substantial harm by hacking the financial system or a virology lab.
This is a fairly standard economist’s take, I think. Jones hedges by writing
We begin by outlining two scenarios for the impact of artificial intelligence on the economy: one in which AI drastically accelerates economic growth and another in which AI is “business as usual.” Both scenarios are possible, and both scenarios have features that are informative about the future consequences of AI. . .This essay provides one perspective based on economic research, but the perspective is inherently speculative and will likely prove to be incorrect on many dimensions.
Colleges and universities are currently a brake on AI progress. As cultural institutions, responsible for forming the next generation of young people, the message is a lot of hand flapping about “needing to be workforce ready” and “don’t cheat” and “here are your required courses to graduate.” I’ve been focused for three years on the strangeness of higher ed’s preoccupation with “general education” and the sector’s commitment to staying the course while technology marches forward.
This is another difference between the 1990s and today. When I launched my web site in 1994, the academy was way ahead of the private sector with respect to using the Internet.
Schools teach algebra, history, biology, and standardized test preparation. Parents want AI literacy, mindset, and entrepreneurial thinking.
…I asked students, teachers, and professionals whether a college degree will become less important in an AI world. 79% said yes.
…The Socratic method is the pedagogy for the AI age.
…The ban-on-AI approach is the ban-on-calculator approach. It failed then and it will fail now. Write policy that mandates AI literacy, not AI prohibition.
Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen write,
We do not see widespread, economy-wide job displacement associated with AI.
However, young workers in AI-exposed occupations are increasingly falling behind their less-exposed peers. Employment among workers ages 22–25 in highly AI-exposed occupations now stands about 19% below where it would be if it had kept pace with employment among similarly aged workers in less-exposed occupations.
Pointer from Tyler Cowen
substacks referenced above: @
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