I'm a PhD candidate in the Management of Organizations (Macro) group at UC Berkeley Haas School of Business.
I study how new technologies change work and organizations, especially when they move from being exciting tools to becoming part of how work actually gets done.
Right now, I am focused on generative AI. I am interested in how AI changes productivity, workflows, evaluation, expertise, and organizational design. I care about the practical questions organizations are facing: when AI helps people do better work, when it creates new kinds of work, how firms should structure knowledge so AI systems can use it, and how managers should govern AI-assisted or AI-executed work.
At the same time, I do not think technology adoption is ever just a technical problem. The same tool can have very different effects depending on how it is introduced, who uses it, what work it is applied to, and how organizations evaluate its outputs. That is what makes this moment so interesting: generative AI is not just changing individual productivity; it is pushing organizations to rethink roles, workflows, accountability, and the infrastructure of knowledge work.
Before starting the PhD, I spent six years working as a data scientist.
Some of the questions that motivate my research are:
- How is AI changing collaboration and coordination at work?
- How do organizations build the infrastructure needed to use AI at scale?
- What should be transparent when AI is involved in work?
- Who benefits from new technologies, and who gets left out?
Across these questions, my research examines how organizations adopt, adapt to, and are changed by new technologies.
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01
Job Market Paper Effort Opacity: Generative AI and Reciprocal Effort in Collaborative Work
Can the individual productivity gains from generative artificial intelligence (GAI) travel upstream to make collaborative work more productive? I argue that they might not because GAI makes it difficult to infer how much effort went into producing work, which in turn creates friction in the reciprocal exchange expectations that underlie collaborative work. I term this friction effort opacity. I combine observational data on over 13,000 collaborative exchanges on GitHub with a pre-registered experiment. Using a fine-tuned AI detection classifier on code contributions on GitHub, I find that GAI-predicted code receives less detailed feedback and is 30 percentage points more likely to be eventually rejected, compared to code that is not GAI-predicted. To isolate reciprocal exchange as a mechanism driving this discounting of GAI-predicted work, I ran a 2x2 within-subjects pre-registered experiment on a custom code review platform on which I held code constant and only varied GAI disclosure and effort cues. I asked subjects' to rank code contributions based on which ones were most worth their review effort. Results of the pre-registered experiment show that GAI-disclosed work is seen as less worth of review effort compared to non-GAI-disclosed work, but this penalty is driven by low-effort GAI-disclosed work. This effect operates through perceived contributor effort, not perceived code quality or perceived contributor competence. These findings demonstrate a collaboration paradox. GAI can make it easier for individuals to produce work while creating effort opacity which counterproductively makes others less willing to invest the reciprocal effort required to turn that work into organizational value.
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02
Which Ideas Attract Workers? Gender and Labor Supply in Startups Revise & Resubmit · Management Science
Which startups can attract the talent they need to grow? We argue that worker preferences over which problems to solve shape labor supply to early-stage firms. In male-dominated labor markets, startups focused on women may struggle to recruit, especially for critical, male-dominated roles like engineering and senior leadership. We combine observational data on over 52,000 U.S. high-potential startups with a pre-registered field experiment. Linking startups to over 2.5 million worker histories, we find that female-focused startups employ fewer workers: a one standard deviation increase in a startup's female-focus predicts 11% fewer male employees. To isolate worker preferences, we run a field experiment where we randomize the startup idea itself, thereby developing a new experimental paradigm to causally identify how the nature of an idea matters. We built a job-search platform recruiting real tech job seekers in a within-subject design, where each participant evaluates 15 startup ideas, each randomized to be more or less female-focused. Crucially, our experiment is incentive-compatible, because participants' evaluations affect which real job postings our platform subsequently recommends to them. Male job seekers are 6.8 percentage points less likely to apply to female-focused startups than other startups, a 10% decrease from baseline. Offsetting the male penalty would require approximately $47,000 in additional yearly compensation. Our findings demonstrate that who benefits from an idea is an important nonpecuniary job attribute shaping talent acquisition, potentially helping explain why ventures serving women grow more slowly.
Nominated for the Best Conference Paper Award and the Research Methods Paper Prize, Strategic Management Society Annual Conference 2023.
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03
The Fabricated Front: Generative AI and the Opacity of Workplace Performance Under review The Paris Journal on AI & Digital Ethics
Generative AI (GenAI) has become a fixture of workplace life. Current research asks chiefly what this implies for jobs and outputs, measured in productivity, displacement, or bias. What remains underexamined are the interactional reconfigurations that GenAI produces at work. Chopra's (2026) theory of effort opacity has begun to fill this gap by noting the systematic decoupling of observable output from human engagement. When GenAI makes interactional cues less diagnostic, it weakens the reciprocal exchange that sustains collaborative trust. Extending this account of effort opacity, we examine the interactional mechanics that produce opacity in everyday workplace encounters. Drawing on Erving Goffman's dramaturgical framework and 1,250 interview transcripts from Anthropic's AI Interviewer dataset, we identify five opacity mechanisms through which workplace fronts are reorganized: voice (whose stance the words index), provenance (who can stand behind the artifact), vulnerability (whether the worker is uncertain), attention (whether the worker is engaged), and investment (how much labor the output reflects). We show that professionals defend the identity mechanisms while freely producing opacity around the labor mechanisms, and trace this asymmetry to the output-centered organization of contemporary work, where deliverables already stand in for the labor process that produced them. The governance task, accordingly, is one of involvement management: specifying which forms of human involvement (attention, effort, judgment) must remain inspectable, and to whom. Workplace AI policies built on universal disclosure will systematically misrecognize a social field in which inspectability is already audience-relative.
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04
Whose Transparency Is It Anyway? Working paper
Transparency has become a central principle in generative AI governance, yet there is little agreement about what transparency should actually mean, for whom, and in what contexts. Drawing on the Social Construction of Technology and Value Sensitive Design, this paper examines how different stakeholders understand and experience transparency in generative AI. We use a two-phase mixed-methods study: a survey of 400 end users, AI builders, and policymakers, followed by participatory design sessions with end users. We find that stakeholders broadly agree that transparency matters, but differ in what kinds of transparency they need and whether those needs are currently being met. In particular, existing transparency tools appear to serve builders more effectively than end users, revealing how power shapes which interpretations of transparency become embedded in practice. We argue that meaningful AI transparency requires moving beyond one-size-fits-all disclosures toward participatory, pluralistic transparency infrastructures that reflect the needs of diverse stakeholders.
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05
The Technical Debt Tradeoff: Generative AI, Shipping Speed, and the Future Cost of Work Work in progress
Generative AI promises to help organizations build and ship products faster, but it may also change the amount and kind of technical debt they accumulate. Technical debt is often treated as a software problem, but it is also an organizational tradeoff: teams sometimes take shortcuts to move quickly, release products, and respond to market pressure, while creating future costs for maintenance, coordination, and reliability. This project examines whether GAI changes that tradeoff. In collaboration with a B2B SaaS company that works with organizations on software delivery and productivity data, we use de-identified workplace data to study whether GAI use is associated with changes in technical debt and whether those changes help or hinder the shipping of products over time. The project asks whether GAI enables organizations to ship more by reducing development bottlenecks, or whether it accelerates the creation of work that is harder to review, maintain, and build on. By treating technical debt as an organizational outcome rather than only a code-quality issue, this study examines how GAI may reshape the relationship between speed, productivity, and long-term organizational capacity.
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06
From AI Tools to AI Systems: How Organizations Redesign Work Around Generative AI Work in progress
Generative AI is often introduced into organizations as a tool for individual productivity, but its organizational effects depend on whether firms can turn scattered experimentation into reliable systems of work. This project examines how organizations move from employees using AI on their own to building structured, governed, GAI-enabled workflows. In collaboration with a technology company undergoing an AI-first transformation, we study how GAI adoption changes the way organizational knowledge is documented, shared, governed, and used. The project focuses on a shift from individual GAI use toward systems in which humans increasingly design, manage, and oversee GAI agents, workflows, and knowledge infrastructures. We ask how organizations decide what knowledge becomes usable by GAI, how roles change when employees become builders and orchestrators of GAI-enabled work, and what forms of governance are needed when GAI systems begin carrying out organizational tasks. By examining GAI adoption as a process of organizational redesign rather than simple tool use, this study contributes to research on technology and work by showing how firms build the infrastructure needed to use GAI at scale.
I learn the sitar under the guidance of my guruji, Rajib Karmakar. Here's a short clip of one of the events I performed at.
An old habit of reading widely and writing to think. For a while I ran a newsletter rounding up recent management research, and wrote data-driven essays on cities, culture, and everyday life. I haven't posted in a while, but I like keeping the archive around.
I'd love to chat: about AI and work, research, or anything adjacent. The best way to reach me is email.