AI at the Technology Frontier
For the last few months I have been stockpiling articles on AI: What it is; Where it’s going; Its likely effects on economic growth and scientific research; And its implications for employment and inequality.
There’s so much to pass along that I’ve divided it into manageable pieces and will be doing a number of posts over the next two weeks as a result. The first includes links to articles on developments at the frontier of AI, including some philosophical reflections, the impact of AI in the crucial area of math and computational science, and possible consequences for future economic growth.
1) In an introductory overview, historian of capitalism Jerry Muller reflects on the significance of AI as a transformative technology that may well introduce a radically new period of global history:
What is happening in AI, then, is not one thing but several at once — a labor-market reorganization, a productivity puzzle, a macroeconomic shift toward polarization, and a regulatory and geopolitical contest still in its opening moves. The technology is moving faster than any of the institutions designed to absorb it.
2) In a striking approach to the significance of AI, Sebastian Galiani has recourse to the work of Nobel-winning economist Thomas Sargent. Best known to economists for his work on monetary macroeconomics, Sargent also speculated broadly on the stages of human intellectual development and how AI fits in:
In this essay, I discuss a remarkable paper by Thomas Sargent, the Nobel Prize-winning economist whose work reshaped modern macroeconomics, on the intellectual origins of artificial intelligence. What makes the paper so interesting is that Sargent does not approach AI primarily as an engineering achievement or a set of recent computational breakthroughs. He approaches it instead as part of a much longer intellectual history. His central claim is that AI is not something alien to human intelligence. It is, rather, an extension of a deeply human project: the effort to compensate for the limits of our own minds through disciplined reasoning, formalization, and science.
Sargent’s argument can be read as a sequence of simple but powerful claims.
First, human beings are cognitively limited in domains that matter enormously in modern life. Here Sargent draws on the work of Steven Pinker, who has long emphasized that our intuitions are poorly suited for reasoning in areas such as probability, statistics, markets, and evolutionary processes. We are not natural Bayesians. We misread randomness. We struggle with nonlinear systems. These are not peripheral weaknesses. They sit at the core of economics, science, and policy. Education, then, becomes more than the transmission of knowledge. It becomes a way of correcting the mind’s natural blind spots.
Second, AI emerges as a human-made response to those limits. Not as a replacement for human intelligence, but as its extension. If our minds are not built to process high-dimensional data, to update beliefs consistently under uncertainty, or to solve complex optimization problems, we construct tools that can. In that sense, AI is continuous with earlier intellectual technologies: the calculator, the statistical model, the ledger, the algorithm. It is what happens when disciplined reasoning is pushed further, and partially automated.
This will take you to Sargent’s paper (Highly Recommended):
http://www.tomsargent.com/research/AI_Sargent.pdf
3) A number of recent articles and books have focused on the way in which AI is advancing the process of scientific research itself, so that progress in AI becomes recursive and self-generating, fueling speculation on the possible achievement of Artificial General Intelligence (AGI). One important area of work is the application of AI to generating and checking mathematical proofs:
The tipping point came in the summer of 2025. That July, several artificial intelligence models solved five out of six problems at the International Mathematical Olympiad, an annual challenge for some of the world’s best high school students. But while mathematicians were shocked — few had expected the programs to get that good that quickly — the impressive results didn’t necessarily mean that AI would make important strides in research math. After all, Olympiad problems are challenging puzzles with known answers, not open questions.
Nevertheless, the results made people pay attention. Mathematicians who had dismissed AI models as too error-prone to be useful started playing around with them. Those early adopters found, to their surprise, not only that the models were good at puzzles, but that they could help break genuinely new ground. Soon, mathematicians were using AI to discover and prove new results, accomplishing in a day what would have once taken them weeks or months. “2025 was the year when AI really started being useful for many different tasks,” said Terence Tao, a prominent mathematician at the University of California, Los Angeles.
https://www.quantamagazine.org/the-ai-revolution-in-math-has-arrived-20260413/
4) Kevin Hartnett, author of the just-published book, The Truth in the Code: How a Truth Machine Is Transforming Math and AI, describes how Terence Tao and other leading mathematicians are using the proof-checking program, Lean:
With automated proof-checkers, a problem can be broken up into small chunks, solved bit-by-bit, then reassembled with confidence that every piece is correct.
This is only one example out of many demonstrating that AI will not only revolutionize the world of work but is already transforming the process of scientific research itself.
https://www.quantamagazine.org/how-terry-tao-became-an-evangelist-for-ai-in-math-20260608/
5) Debate swirls among growth economists over the question of the extent to which AI will or won’t accelerate the rate of growth. Timothy Taylor reviews a comprehensive report from the OECD Economics Department examining the range of predictions of future growth in the G7 countries, as it depends on the rate of adoption of AI techniques and applications. Research reports from Filippucci et al and Bick et al explain why predicted growth acceleration from AI is stronger in the U.S. than elsewhere:
But why are some economies, like the United States, getting a bigger boost from AI than others? Some answer are presented in “Mind the Gap: AI Adoption in Europe and the US,” by Alexander Bick, Adam Blandin, David J. Deming, Nicola Fuchs-Schündeln, and Jonas Jessen (Brookings Papers on Economic Activity, Spring 2026). As a big-picture overview, they point out that US investments in information and communications technologies have been substantially higher since at least the 1990s, and perhaps unsurprisingly, the US edge in output per hour worked has been expanding since the mid-1990s, too.
https://conversableeconomist.com/2026/04/17/ai-and-future-growth/