Efficient social learning is the human advantage
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Efficient social learning is the human advantage over AI<br>Evolution has hardwired us for an ASI world
David Deming<br>Jul 15, 2026
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A few months ago, Alex Imas wrote an essay about the future of the labor market called “What will be scarce?” He argued that material abundance from transformative AI will create a post-commodity economy and increase the importance of products in which the human touch has intrinsic value, what he calls the “relational” sector. In other words, consumers want a person in the loop, not because people are better or more efficient than machines. The essay went viral, mainly because it was excellent, but also because it was an optimistic picture of a world with artificial superintelligence (ASI).<br>He talks about how Starbucks learned that customers want to get to know the baristas well enough to feel ok about their absurdly baroque drink orders. Adam Ozimek has a similar piece with a great example about the rise and fall of the player piano. The higher your airline status, the more likely you are to get a human on the other end of the phone when you need to change your flight, even though AI agents are much better than people at complex rerouting algorithms. Humans are more expensive, and that’s precisely the point. The company is sending a costly signal to their best customers that they are worthy of scarce attention.<br>Thanks for reading Forked Lightning! Subscribe for free to receive new posts and support my work.
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The demand for human relationships shows up everywhere once you look for it. Jasmin Sun argues that relational jobs will pay low wages and provide a niche product for the superrich. Jason Abaluck thinks that future generations will not care as much about having humans in the loop. Noah Smith thinks the human touch is just an AI company sales pitch.<br>These are all arguments about whether we will continue to pay for the privilege of interacting with other humans. These arguments are difficult to resolve, because nobody knows how the future of labor demand will unfold.<br>I want to make a different point about future labor supply. Because humans learn more efficiently than AI, we (currently) have a comparative advantage in sparse, high-context tasks. And nothing is higher-context than social interactions and relationships.<br>Comparative advantage and learning efficiency<br>The famous mathematician Stanislaw Ulam once challenged Paul Samuelson to name one proposition in the social sciences that was both true and non-trivial. After some thought, he chose Ricardo’s theory of comparative advantage. Comparative advantage is the non-trivial insight that two countries can trade goods for mutual benefit, even if Country is better than Country B at producing every single good.<br>The same logic also applies to teamwork – rather than countries trading goods, it’s workers “trading tasks”. I wrote a paper a few years ago called the Growing Importance of Social Skills in the Labor Market that works through the task trade logic more formally and treats social skills as reducing coordination frictions in team production.<br>The principle of comparative advantage tells us that people – even when they are less skilled - are almost always additive to a production process if you structure it right. And that’s true for human-AI teams too. Even if the AIs are way better than us at everything, they won’t DO everything.1 And the human tasks may end up being especially valuable because they are scarce. Everyone will have access to AI, so it will be a commodity. If you have a skill that no one else has, and you can inject it into a mostly AI-driven process in the right way, a lot of the value could accrue to you. Noah Smith has a very thoughtful piece about how comparative advantage could preserve good jobs in a world of superintelligent AI.<br>So, what is the human comparative advantage over machines? What are our highest leverage tasks, meaning in which job tasks are humans relatively – not absolutely - more efficient?<br>Humans’ comparative advantage is our learning efficiency. We are very good at learning patterns from small amounts of data, and we “compress” information very well so that we can store it efficiently into memory. Dwarkesh Patel estimates that human learning is somewhere between several thousand to a million times more sample efficient than AI models. The comparative advantage of AI, on the other hand, is knowledge storage and distribution. Once AI learns a task it can replicate forever at nearly zero cost, so you can sometimes solve the inefficiency issue with brute force and extreme scale. The AI will burn a million times more tokens than a human to learn a task for the first time, but it can eventually pay back the investment by performing it millions of times at lower marginal cost.<br>Because LLM learning is so costly, humans have a comparative advantage in tasks that require continual...