Where Disciplines Intersect
Free Will, Artificial Intelligence, Economics, and Prosperity
Many of the greatest advances of the past century have come through increasing specialization. As knowledge has expanded, researchers have focused on ever narrower fields, developing expertise that has transformed science, medicine, engineering, and economics.
Samir Varma brings together several of those disciplines into a different way of looking at the world.
“I’m basically interested in four subjects that are all interlinked in my head,” he told me. “Economics, physics, mathematics, and computer science.”
Four Subjects, One Lens
Whether discussing free will, artificial intelligence, capitalism, taxation, scientific research, or education, Varma approached each subject through the same interdisciplinary lens, asking whether an explanation remained consistent across disciplines and, ultimately, whether it survived contact with reality.
Varma traces that approach back to his childhood in India. He grew up wondering how a culture that celebrated wealth and entrepreneurship could also be one of the poorest countries in the world. The contradiction irritated him, and that irritation became a habit of mind.
“It’s all born out of irritation,” he said. “The irritation came from getting absurd answers to simple questions.” So he began asking why. That habit eventually became the organizing principle behind his writing as well as his book on free will, The Science of Free Will.
His standard for judging ideas is demanding. They should be tested against evidence, remain consistent with established knowledge, and explain more than the ideas they replace. As he put it, “The more radical the idea, the harder you really have to test it.”
That same standard leads him to distrust explanations that survive only within a single discipline. An economic theory should not contradict what we know about incentives. A theory of artificial intelligence should be consistent with physics and computer science. A philosophical claim should survive contact with mathematics. For Varma, understanding comes not from viewing a problem through one lens, but from seeing whether the same explanation continues to hold when examined from several directions at once.
Where Choice Comes From
His theory of free will illustrates the approach. Physics tells us that every particle in our bodies obeys the laws of nature. If those laws completely determine the future, where does choice come from?
His answer draws simultaneously from physics, computer science, and philosophy.
“The gap between the laws existing and making predictions from the laws,” he explained, “is the source of free will.”
The key idea is what computer scientist Stephen Wolfram calls computational irreducibility. Some systems are so complex that their future cannot be shortcut. Even if the underlying rules are completely deterministic, the only way to know what they will do is to let them unfold. Determinism, in other words, does not imply predictability.
Teaching Principles, Not Playing Whack-a-Mole
That conclusion extends naturally to artificial intelligence. If AI systems operate under the same physical laws and exhibit the same computational complexity, Varma argues there is no fundamental reason to believe they should be predictable either.
Importantly then, instead of trying to control increasingly capable AI by layering on more restrictions like whack-a-mole, he believes the better approach is to teach it guiding principles from the outset.
“You have to teach it principles,” he said. “The more you constrain a system, the less optimal it is.”
Incentives All the Way Down
The same framework shapes Varma’s views on economics and public policy. Rather than beginning with ideology, he begins with incentives.
“I think the biggest problem is that we don’t teach our students economics,” he said. “Do you really understand incentives? Incentives matter. All the rest is commentary.”
That perspective leads him to see a common thread running through many of today’s policy debates. Housing shortages, in his view, are less a failure of markets than of regulations that make building unnecessarily difficult. Slower economic growth reflects tax and regulatory systems that discourage investment and entrepreneurship. Political polarization stems from electoral rules that reward extremes instead of broad consensus. Even scientific progress depends on incentives: stable funding, intellectual freedom, and institutions that reward discovery over politics.
His views on taxation follow the same logic. Capital is not simply accumulated wealth. It is the stock of tools, equipment, and infrastructure that raises what each worker can produce. Taxing capital reduces the investment that builds this stock, and with it the living standards that follow. As he put it, “The more you tax capital, the less of it you have.”
The Long Game
Varma argues that many of the technologies that define modern life began as basic scientific research with no obvious commercial application. GPS, for example, depends on Einstein’s general theory of relativity, developed decades before anyone imagined satellite navigation. No private company could justify investing in research that might not pay off for seventy-five years. That, he believes, is one of government’s most important roles.
His concern is that the United States is beginning to weaken precisely the institutions that made it the world’s scientific leader. He points to funding uncertainty, political interference, and the loss of researchers to other countries as incentives that discourage discovery. Scientific progress, like economic growth, depends on institutions that reward long-term investment.
He sees this not simply as a question of research budgets but of national prosperity. Discoveries made today become the industries, companies, and technologies that raise living standards decades from now. Because those benefits often take decades to appear, they are easy for policymakers to undervalue.
That long-term perspective also shapes how he ranks America’s challenges. Rather than focusing primarily on more popular sources of pessimism and concern like healthcare or education, he believes artificial intelligence will improve both. The more important constraints, in his view, are housing and scientific research.
Many thinkers develop deep expertise within a single discipline. Varma connects disciplines that are usually treated separately and asks whether the same explanation survives each of them. As specialization continues to expand human knowledge, that kind of synthesis may become increasingly valuable. It is a perspective worth understanding.
Full interview.
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