Curriculum vitae

Curriculum vitae

• Ph.D., Massachusetts Institute of Technology 2018–2024
• Economics
• Thesis: Essays in Monetary Policy and Growth
• Committee: George-Marios Angeletos, Iván Werning, Christian Wolf
• B.S., University of Chicago 2011–2015
• Mathematics, Economics, Chinese

• University of Virginia
• Assistant Professor, Department of Economics 2025 –
• Associate Director, Economics of Transformative AI Initiative 2025 –
• Stanford Digital Economy Lab
• Digital Fellow 2025 –
• Stripe Economics of AI Fellowship
• Director 2025 –

• Stanford Digital Economy Lab
• Postdoctoral Fellow 2024–2025
• Uber
• Data Scientist, Ubernomics 2017–2018
• AQR Capital Management
• Quantitative Research Analyst 2015–2016

• 1. Toward an understanding of the economics of apologies: evidence from a large-scale natural field experiment (with Ben Ho, John A. List, and Ian Muir), The Economic Journal, 132(641): 273–298, 2022.

  ▸ Abstract
  We use a theory of apologies to analyze a nationwide field experiment involving 1.5 million Uber ridesharing consumers who experienced late rides. Several insights emerge. First, apologies are not a panacea: the efficacy of an apology and whether it may backfire depend on how the apology is made. Second, across treatments, money speaks louder than words - the best form of apology is to include a coupon for a future trip. Third, in some cases sending an apology is worse than sending nothing at all, particularly for repeated apologies. For firms, caveat venditor should be the rule when considering apologies.

• 1. Optimal monetary policy under menu costs (with Daniele Caratelli)

  ▸ Abstract
  We analytically characterize optimal monetary policy in a multisector economy with menu costs and contrast it with the textbook Calvo model. Following a sectoral productivity shock, the textbook model prescribes stable inflation, providing a formal justification for inflation targeting. In contrast, under menu costs, policy should “look through” such shocks and allow inflation to move inversely with output. We provide sharp intuition: stabilizing inflation causes shocks to spill over across sectors, forcing firms to pay menu costs unnecessarily. In a quantitative model, a simple look-through policy – stabilizing nominal wages rather than inflation – improves welfare via lower menu costs.



• 2. Transformative AI, existential risk, and real interest rates (with Trevor Chow and J. Zachary Mazlish)

  ▸ Abstract
  We study how financial market prices can be used to forecast the likelihood of transformative artificial intelligence. Transformative AI is a double-edged sword: while advanced AI could lead to rapid economic growth, some researchers argue that superintelligence misaligned with human values could pose an existential risk to humanity. Theoretically, we show that either possibility would predict a large increase in long-term real interest rates, due to consumption smoothing. We then use rich cross-country data on real rates and growth expectations to show that, contrary to other recent findings, higher long-term growth expectations do indeed cause higher long-term real interest rates.



• 3. Reexamining optimal policy in the New Keynesian “liquidity trap”

  ▸ Abstract
  I make five conceptual points about optimal monetary and fiscal policy at the zero lower bound (ZLB) in representative agent New Keynesian models, using the simplest possible version of such a model.
  1. Monetary policy is typically described as facing a time consistency problem at the zero lower bound; but if ZLB episodes are a repeated game rather than a one-shot game – as is empirically realistic – then the time consistency problem can be easily overcome by reputational effects.
  2. The ZLB is not special, in terms of the constraint it creates for monetary policy: an intratemporal rigidity, such as the minimum wage or rent control, creates exactly the same kind of constraint on monetary policy as the intertemporal rigidity of the ZLB.
  3. Austerity is stimulus: in the representative agent New Keynesian model, fiscal stimulus works through the change in government spending. Promising to cut future spending – committing to austerity – has precisely the same effect on inflation and the output gap as a decision to raise spending today.
  4. Fiscal stimulus can be contractionary, when targeted heterogeneously: if fiscal spending is targeted at certain sectors, this can in fact lower inflation and deepen the output gap.
  5. Fiscal policy faces a time consistency problem at the ZLB, just as monetary policy does.
  Overall, I suggest that – in this class of models – the power of monetary policy at the ZLB has been underrated, and the power of fiscal policy has been overrated.



• 4. Overreaction and forecast horizon (with J. Zachary Mazlish)

  ▸ Abstract
  We use survey data on macroeconomic expectations, across 89 countries and going back to 1989, to establish four facts about how forecast biases depend on the time horizon of the forecast. The data cover average expectations and horizons from 0 to 10 years. (1) Expectations underreact at a horizon of one year or less. (2) Expectations overreact at horizons of two years or more. (3) Expectations are “too extreme” at all horizons. (4) Overreaction and over-extremity increase with forecast horizon. These four patterns hold across advanced and emerging economies, and across multiple macroeconomic variables. They are inconsistent with several popular models of overreaction, where the degree of overreaction is independent of forecast horizon. However, we show that a model featuring costly recall, uncertainty about the long-run mean, and sticky-information can match all four of our facts. Finally, although long-term expectations exhibit stronger overreaction, it is short-term expectations that are most strongly associated with fluctuations in GDP, investment, and the stock market.



• 5. When Does Automating AI Research Produce Explosive Growth? (with Tom Davidson, Thomas Houlden, and Anton Korinek)

  ▸ Abstract
  AI labs are increasingly using AI itself to accelerate AI research, creating a feedback loop that could lead to an intelligence explosion. We develop a general semi-endogenous growth model with an innovation network, where research and automation in one sector increase the productivity of research in other sectors, and derive a clean analytical condition under which growth becomes superexponential (“explosive”). We find that automating research can offset diminishing returns to ideas by activating two reinforcing channels: a technological feedback loop across research sectors, and an economic feedback loop in which higher output finances further research. Growth becomes explosive if the combined strength of technological and economic feedback loops overcomes diminishing returns. In a simple simulation calibrated to trends in AI progress, fully automating software research and modest (5%) automation in other sectors generates a singularity within six years. Bottlenecks do not overturn the result if task automation advances sufficiently fast.



• 6. The Economics of Recursive Self-Improvement (with Tom Cunningham, Lukas Althoff, Brian Jabarian, Andrew Koh, Arjun Ramani, Phil Trammell, Parker Whitfill, and Cheryl Wu)

  ▸ Abstract
  We model the economics of recursive self-improvement (RSI) and assess its plausibility and impacts. First, we build a sequence of increasingly rich models of AI progress to highlight the feedback loops behind RSI. We represent our models as directed graphs and show that net acceleration in AI capabilities depends on the product of elasticities across each feedback loop. Second, we distinguish between “narrow” and “broad” AI capabilities, capturing the possibility that AI systems improve narrowly at optimizing AI R&D benchmarks without improving at broader economically valuable tasks. Third, we document existing estimates of key parameters, and provide a wish list of empirical objects that AI companies can measure and feasibly share publicly. Finally, we calibrate the model with existing data. A back-of-the-envelope calculation suggests that feedback loops are not currently strong enough to generate a self-sustaining acceleration, though they appear to be strengthening. We conclude by assessing the plausibility and implications of such an acceleration.



• 7. Forecasting the Economic Effects of AI (with Ezra Karger, Otto Kuusela, et al.)

  ▸ Abstract
  We elicit forecasts of how AI will affect the U.S. economy, comparing the beliefs of five groups: academic economists, employees at AI companies, policy researchers focused on AI, highly accurate forecasters, and the general public. The median respondent in each group expects substantial advances in AI capabilities by 2030, small declines in labor force participation consistent with demographic shifts, and an annual GDP growth rate of 2.5%, which exceeds both the typical medium-run (2.0%) and long-run (1.7%) baseline forecasts from government agencies and private-sector forecasters. Conditional on a “rapid” AI progress scenario, in which AI systems surpass human performance on many cognitive and physical tasks, experts forecast substantial, though not historically unprecedented, economic shifts: annualized GDP growth rising to around 4% and the labor force participation rate falling from its current level of 62% to 55% by 2050, with roughly half of that decline—equivalent to around 10 million lost jobs—attributable to AI. A variance decomposition suggests that expert disagreement about these effects is driven primarily by different beliefs about the economic effects of highly capable AI systems rather than by disagreement about the pace of AI progress. These forecasts map onto notably different policy preferences across groups: experts strongly favor targeted measures such as worker retraining, whereas the general public supports both targeted programs and broader interventions, including a job guarantee and universal basic income.

• AI Worldviews Contest, first prize (Open Philanthropy) 2023
• Global Priorities Fellowship (Global Priorities Institute) 2022
• Emergent Ventures Fellow (Mercatus Center) 2021
• Doctoral Grant (Washington Center for Equitable Growth) 2021
• Humane Studies Fellow (Institute for Humane Studies) 2021
• Oskar Morgenstern Fellow (Mercatus Center) 2021
• Avanessians Fellowship (MIT) 2018–2024
• Phi Beta Kappa (UChicago) 2015
• Becker-Friedman Institute Award for Outstanding Undergraduate Service (UChicago) 2015

• 2026: Barcelona School of Economics, IESE Business School, NBER Summer Institute (EFEG), MIT FutureTech


• 2025: San Francisco Fed; Touro Law; Hong Kong Institute for Monetary and Financial Research; Hong Kong University Business School; UC Berkeley; NBER Summer Institute; Richmond Fed; MIT FutureTech; International Monetary Fund


• 2024: University of Virginia; Einaudi Institute for Economics and Finance; Office of Financial Research (US Treasury); Global Priorities Institute (Oxford); Carnegie Mellon University – Tepper; Midwest Macro Spring (Richmond Fed); Euro Area Business Cycle Network Conference (Mannheim); Southern Economic Association


• Pre-2024: Global Priorities Institute (Oxford); Equitable Growth conference 2022; Economics Graduate Student Conference 2021 (WashU); Advances with Field Experiments 2019 (UChicago); AEAs 2018; Université Paris-Sud; Advances with Field Experiments 2017 (UChicago)

Referee: Journal of Political Economy, Journal of Political Economy Microeconomics, Journal of Monetary Economics, National Science Foundation, The Yale Law Journal, Macroeconomic Dynamics, American Economic Journal: Macroeconomics, Journal of Human Capital, The Economic Journal, Review of Economic Dynamics, Journal of Economic Dynamics and Control, American Economic Review

• University of Virginia
• Undergraduate intermediate macro 3020 (fall 2025)
• Graduate second year macro 8360 (spring 2026)
• Massachusetts Institute of Technology (Teaching Assistant)
• Undergraduate intro macro 14.02 (fall 2020, fall 2021, fall 2022)
• Undergraduate intermediate micro 14.03 (spring 2021, spring 2024)
• Undergraduate micro theory 14.04 (fall 2022)
• Graduate monetary economics 14.453 (spring 2021)

• Last updated: August 2026