Hong-Yi TuYe

Ph.D. Candidate, MIT Sloan

👋 Hi, my name is Hong-Yi and welcome to my page!

I’m a final year PhD Candidate at MIT Sloan (Information Systems/Technologies Group). I am also a graduate student affiliate at the MIT Initiative on the Digital Economy and the Stanford Digital Economy Lab. My research is focused on the economics of artificial intelligence with an emphasis on labor market impacts, entrepreneurship, and digital experimentation.

Previously I was a pre-doctoral fellow at Stanford studying the impact of Medicare quantity regulation (local coverage determination) on physician behavior.

Outside of research, you can find me training with the MIT Triathlon team, chasing after Strava kudos, or nerding out over a good history podcast/Youtube channel (e.g. Anita Anand and William Dalrymple’s Empire Pod).

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Research Interests

Economics of Artificial Intelligence Entrepreneurship and Innovation Digital Experimentation

Research

Rethinking Blitzscaling: Entrepreneurial Labor Impacts of Transformative AI

Work in progress (JMP)

Hong-Yi TuYe, Daniel Rock, Danny Kim

Sep 2025

Abstract

By combining funding event data from Pitchbook and resume data from Revelio, we construct panel dataset of headcount and funding data involving 2,500 YC companies from 2015-2025. Using a triple differences approach, we compare YC startups that received a series A round before the advent of ChatGPT in 2022Q4 against YC startups that received a series A round after 2022Q4. We find that companies who received series A funding in the post-ChatGPT era have a 5% slower overall headcount growth when compared to those who received series A funding in the pre-GPT era. Among these series A companies, those with pre-funding treatment workforce in the top quartile of exposure to LLM use (based on Elondou et al. 2024 task-based measure) experience greater downward pressure on headcount growth than the less exposed firms. Across role types and seniority levels, we find an inverse U relationship where both junior and executive level workers see slower headcount growth than their senior counterparts in the post-GPT era, and this pattern is strongest across engineering roles and weakest across external and commercial facing roles. Finally, we construct a novel measure of product exposure to AI by comparing company descriptions against recent themes found in YC’s Request for Startups documents. After controlling for product exposure to AI, we find that firms with high product exposure to AI experienced a 5% slower total headcount growth than firms with low product exposure to AI in the post-GPT era.

Augmenting Meta-Experimentation with Artificial Intelligence

Work in progress

Hong-Yi TuYe and Dean Eckles

Sep 2025

Prompt Adaptation as a Dynamic Complement in Generative AI Systems

Preprint, R&R at Information Systems Research

Eaman Jahani, Benjamin S. Manning, Joe Zhang, Hong-Yi TuYe, Mohammed Alsobay, Christos Nicolaides, Siddharth Suri, David Holtz

Jul 2024

Abstract

In a preregistered online experiment with 1,893 participants who submitted more than 18,000 prompts and generated over 300,000 images, users attempted to replicate a target image in 10 tries using one of three randomly assigned models: DALL·E 2, DALL·E 3, or DALL·E 3 with automated prompt rewriting. Users with access to DALL·E 3 achieved higher image similarity than those with DALL·E 2—but only about half of this gain (51%) came from the model itself; the other half (49%) resulted from users adapting their prompts. Prompt adaptation emerged across the skill distribution, was driven by trial-and-error, and could not be replicated by automated prompt rewriting, which erased 58% of the performance improvement associated with DALL·E 3. The findings position prompt adaptation as a dynamic complement to generative AI and suggest that without it, a substantial share of the economic value created when models advance may go unrealized.

Link

How Many Americans Work Remotely? A Survey of Surveys and Their Measurement Issues

Preprint, R&R at Review of Income and Wealth

Erik Brynjolfsson, John J. Horton, Christos Makridis, Alexandre Mas, Adam Ozimek, Daniel Rock, Hong-Yi TuYe

Apr 2023

Abstract

Remote work surged during the Covid pandemic, but published estimates disagree about the magnitude of the shift. Using the nationally representative Remote Life Survey fielded in October 2020, we document that 31.6% of continuously employed workers always worked from home, 21.9% worked from home sometimes or rarely, and 53.5% worked from home at least one day per week.

We benchmark these results against government surveys and quantify how four measurement choices drive discrepancies: (a) mode of survey administration, (b) whether self-employed workers are included, (c) differences in industry mix, and (d) whether surveys exclude respondents who were already remote before the pandemic. The last factor explains most of the gap between the Current Population Survey and other measures. Under our preferred adjustments, about half of the U.S. workforce was working remotely at least one day per week in December 2020.

Link

COVID-19 and Remote Work: An Early Look at US Data

NBER Working paper, w27344

Erik Brynjolfsson, John J. Horton, Adam Ozimek, Daniel Rock, Garima Sharma, Hong-Yi TuYe

Jun 2020

Abstract

We report the results of a nationally representative survey conducted in two waves (April 1–5 and May 2–8, 2020) to understand how COVID-19 reshaped US employment. Among individuals employed before the pandemic, about half were working from home by early May, including 35.2% who had recently shifted from commuting. Another 10.1% reported being laid off or furloughed.

States with higher COVID-19 incidence saw more workers switch to remote work, while states with greater shares of information-intensive occupations experienced larger shifts to working from home and fewer layoffs. Younger workers were more likely to transition to remote arrangements. We observe little change between the survey waves, indicating that most remote-work adjustments occurred by early April 2020.

Link