I love the 1987 quote from Robert Solow that "You can see the computer age everywhere but in the productivity statistics" (from a New York Times column that you can read here). Arguably, you can recycle that quote to refer to generative AI now. Where are the economic impacts of AI? What should we be looking for? Are we looking in the wrong places?
Those are some of the questions that Kevin Bryan (University of Toronto) tries to address in this new paper, which reviews seven books from the last twelve years that cover some aspect of the economic impacts of AI. Why books, and not research articles? Bryan notes that:
...books play a unique role. Research articles construct a literature. Books summarize it; they situate research articles in a broader context; they draw out implications; they take stands. Not all books need to do all of this, but books are an important vector by which the aggregated knowledge of research journals reaches the public and non-subject-matter experts.
The seven books included in the review are as follows:
“The Second Machine Age” (Brynjolfsson and McAfee, 2014) offers an early argument that changes in computation and digitization were leading to an Industrial Revolution-sized economic shift; “Prediction Machines” and “Power and Prediction” (Agrawal et al. (2018) and Agrawal et al. (2022)) provide a particularly compelling framework for the basic economic feature of AI, its role in reducing the cost of prediction; “The Data Economy: Tools and Applications” (Baley and Veldkamp, 2025) covers the economic theory of data, an important input into that prediction; “The Skill Code” (Beane, 2024) and “Co-Intelligence” (Mollick, 2024) examine practical implementation challenges for AI, via sociology and management research, that are frequently misunderstood by industry practitioners; and “Situational Awareness” (Aschenbrenner, 2024), a book-length treatise self-published online for speed reasons, offers a view from Silicon Valley about the most transformative possibilities of AI.
Of interest to me, I have only read one of the books (Co-Intelligence, which I reviewed here back in July). I'm not going to summarise Bryan's reviews of the books, since it is really difficult to do so without repeating a lot of what he says in the review. If you're interested, you should read the paper. However, I do want to pick up one bit from the reviews, on Aschenbrenner's book. Of the reviewed authors, Aschenbrenner is the most bullish on the impacts of AI, and Bryan notes that:
...it is simply a fact that the view of the future expressed in “Situational Awareness” is closer to the modal view of AI researchers and the folks running the most prominent AI labs than less bold analyses of AI, even those that treat AI as a potential general purpose technology with substantial economic importance. If economists are to play a public role in the debate of AI, it is essential to at least understand the economic model in the heads of many of the people we are trying to communicate with.
Having read those seven books, Bryan is able to tease out a number of open questions on the economic impacts of AI. This might be the most interesting part, because it suggests where economists might have influence on the policy and practical conversations around AI and where economists might best help people, particularly policymakers but also businesspeople, to understand the implications and impacts of AI. Bryan presents the following questions:
How should monetary policy respond to simultaneous deflationary pressure from productivity gains and potential unemployment from labor demand shifts? What are the implications for interest rate policy when technological change accelerates dramatically? How is public debt affected? If AI adoption reduces employment while requiring large public investments in education, social protection, and infrastructure, how will governments finance these expenditures? What are the optimal tax policies for an economy where capital captures an increasing share of income?...
AI development exhibits strong network effects and scale economies that could create winner-take-all dynamics among countries. How should trade policies respond when AI capability determines comparative advantage? What are the implications for international capital flows when AI investment becomes central to national competitiveness? Is “sovereign AI” necessary? How should it be taxed, considering the inequality discussion? How can we, as with climate change, coordinate internationally on AI safety concerns? Game theorists have a role to play here.
If AI development requires trillion-dollar investments concentrated among a few companies, how will this affect financial system stability? How should banking regulations adapt as AI systems conduct increasing shares of financial transactions? What are the systemic risks when AI companies become too big to fail?
What should we measure? What early warning signs indicate negative effects or growth takeoff?...
How should science and innovation be structured? Are patents more or less important? How does information flow across firms? To what extent should we allow trade secrets in an AI-driven intelligence explosion? What should agencies like the NIH or NSF be doing differently? What should universities be teaching differently?
That's a lot of open questions. I don't have the answers to them, but hopefully there are already teams of economists working on answering them.
Obviously, the very last question hits especially close to home for me. However, Bryan presents a narrow view of the issues in university education in that single question. The open question extends beyond what universities should be teaching differently. We should also be considering how universities should teach differently, and how universities should assess differently. This requires us to understand and anticipate changes in the labour market for graduates, the changing role of internships and work-integrated learning, and whether an apprenticeship model of workplace learning still makes sense in an era where generative AI can replace much of the low-level work that new graduates previously did. We also need to consider how pervasive generative AI changes how students approach their learning, and whether generative AI democratises learning in a way that makes the mass-market model of university education obsolete (but where elite university education may still persist). Anyway, I'll be writing a little bit more on those topics in future posts.
[HT: Marginal Revolution]
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