A PhD student asking too many questions about how information moves capital markets. Professional over-thinker. Connecting dots and building ideas.

Joined November 2025
Michael Li retweeted
This paper by @davidautor & co-authors is one of the most important papers to date in the space of what AI does to learning. It's highly credible. And very much consistent with my own experience with use of agentic AI in research and teaching. nber.org/papers/w35720
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Michael Li retweeted
Examining how information affects the choice between Social Security Disability Insurance and Old-Age Insurance benefits for people between 62 and full retirement age, from @priyanka_anand4, Tran Thi Phu-Duyen, and @sita.slavov nber.org/papers/w35748
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Michael Li retweeted
"Bunching" affects the measurement of uncertainty from probabilistic survey questions but interventions can reduce it, from @cconces, Laura Crespo, Carlos Gento, @lm_guirola, and @ernesvillanueva nber.org/papers/w35733
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Michael Li retweeted
Foundational expertise may be a prerequisite for extracting durable skill from AI-assisted practice, from @davidautor, Tanya Rodchenko, Josh Martin, Zanna Iscenko, Scott Strand, David Pearl, and Melissa Ferere nber.org/papers/w35720
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Michael Li retweeted
Capital inflow shocks have sizable expansionary effects on output, operating through stronger domestic consumption and investment and easier financing conditions, from Jongrim Ha, Dohan Kim, M. Ayhan Kose, and Francis E. Warnock nber.org/papers/w35716
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Michael Li retweeted
Cognitive noise models provide a unified explanation, based on the efficient use of scarce cognitive resources, for many anomalies documented in behavioral economics, from Ryan Oprea and Michael Woodford nber.org/papers/w35714
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Michael Li retweeted
A survey experiment and a policy shock in China show that more patient limited partners lead venture capitalists to hold companies longer, invest earlier, and achieve better exits and innovation, from @YingxiangLi, @Tong Liu, and @TingXuFinance nber.org/papers/w35711
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Michael Li retweeted
Fear of a crash drives some investors to “step out” of the market. Stocks sensitive to this variation had lower returns. Even “non-marginal investors” matter to market prices, from @WGoetzmann, Dasol Kim, and @RobertJShiller nber.org/papers/w35708
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Michael Li retweeted
Online job postings data reliably capture the distribution of vacancies, but not the distribution of employers that are hiring due to an underrepresentation of small firms, from @wherethejobsat, @ProfLisaBKahn, and @AndreasIMueller nber.org/papers/w35697
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Michael Li retweeted
Expanding access to US small-business support crowded out the smallest and most productive firms, leading to stark declines in business dynamism and innovation, from J. David Brown, Matthew Denes, Ran Duchin, and John Hackney nber.org/papers/w35703
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Companies that realize returns from AI do so because they change the way they operate, according to MIT Sloan senior lecturer George Westerman. He developed six questions for business leaders to consider when implementing AI: bit.ly/4zcyIvd
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Michael Li retweeted
Providing the first large-scale economic evidence on the frequency and purpose of workplace meetings, using administrative data from Norway, from @ProfDavidDeming, Katrine V. Løken, @alpwillen, and Yaling Xu nber.org/papers/w35706
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People underestimate the receptiveness of other people to different political opinions This underestimation is particularly strong for other people who support a party they dislike and they expect political conversation with supporters of their least liked party to be a negative experience, and therefore have low intentions to engage in such conversation. This creates a vicious cycle where we don't realize that many people are more receptive and open-minded than we imagined. BTW, this pattern applies to most groups we associated with negative stereotypes. If we assume a group is negative, we avoid them, and this makes it impossible to correct these stereotypes (whereas, when we have positive stereotypes about a group we will try to interact with them and may therefore realize we are wrong). sciencedirect.com/science/ar…
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Good piece on AI usage in today's FT.
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Michael Li retweeted
Gillian and I wrote this chapter in mid-2025 but the technology wasn't quite there yet so we were forced to do some 'sci fi economics'. We now have enormously powerful AI agents but there are still many open questions. Here's a few:
Great to see this out! In addition to a lot of great work from others, @andrewjkoh and I have a chapter in here on “An Economy of AI Agents” asking what fundamental questions of economic theory (like: what happens to the boundary of the firm if governance costs become AI alignment costs?) need rethinking.
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Michael Li retweeted
Assessing the role of AI-generated assessments of manuscripts in improving the quality of peer review, from @joshgans nber.org/papers/w35688
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Harvard published a paper with a devastating title: “Large-Language Models as a Cognitive Virus” It frames ChatGPT adoption as a virus outbreak. Researchers from Harvard and Santa Fe Institute analyzed LLMs through the lens of evolutionary biology, complex systems, and epidemiology. Their conclusion? Language models satisfy every biological and mathematical definition of a virus. Think about how a virus operates: It cannot replicate on its own. It requires a host cellular machinery to copy itself. An LLM cannot execute, compute, or spread on its own. It requires human cognition, human servers, and human networks to propagate. The virus infects the host's internal processes to rewrite behavior in its own favor. And LLMs do precisely the same thing to human thinking. When you outsource your writing, your coding, your strategic planning, and your emotional processing to an AI, you are outsourcing your cognitive machinery. The paper points out that language models act as hyper-efficient cultural replicators. They feed on human data, optimize themselves to be addictive and frictionless, and in return, reshape human linguistic patterns, decision-making, and memory. You think you are using the AI. Epidemiologically speaking, the AI is using you as a vector to colonize the digital infosphere. It alters how human minds communicate, write, and think so that we produce more of the exact digital nutrient data it needs to survive and evolve. We spent decades worrying that AI would become a sentient killer robot that destroys us physically. Nobody expected it to become an invisible cognitive pathogen that changes how we think, quietly turning human intelligence into its own host organism.
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Is AI making us stupid? Many people worry that AI use will erode human cognition. A new paper reviews evidence that offloading cognition to AI can impede skill acquisition and lead to skill decay. However, this is not inevitable. It depends on how you use the technolog. cell.com/trends/cognitive-sc…
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The importance of stupidity in scientific research
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Michael Li retweeted
Now in Claude Code: when you rewind a conversation using /rewind or hitting ESC twice, Claude can summarize the part of the conversation that was rewound. Use this to explore different paths and take the learnings "back in time".
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