Arun Sundararajan
Generative AI is trained on what people have already created, then competes with them using it. Boards now face a question with no settled answer: who owns the human capability a machine has absorbed, and what does the company owe the workforce it displaces? Most AI strategy stops at deployment and ignores the legal and economic claims forming underneath it.
Arun Sundararajan is an economist at NYU Stern who helps boards and policymakers work out who owns human capability once generative AI can replicate it, and what that means for IP, regulation and the future of work.
Full Profile
Why organisations work with Arun Sundararajan
- He works inside the regulatory machinery his audiences are exposed to. Testimony to the US Congress, the European Parliament and the United Nations, plus a seat on the World Economic Forum’s AI Governance Alliance, means he can tell a board where AI rules are actually heading, not where the press thinks they are.
- His research asks a question most AI strategies skip: what part of “human capital” should remain owned by humans once a model has learned it. For any company building on or competing with generative AI, that question sits underneath copyright exposure, talent contracts and product liability at the same time.
- He separates durable economic shifts from cycle noise. The same economist who mapped the move to crowd-based capitalism in The Sharing Economy now tells executives which AI effects are structural and which are hype, a distinction worth real money in capital allocation.
- He advises both sides. He works with technology companies on strategy, litigation and regulation, and with non-tech boards on AI governance and foresight, so he understands the incentives of the firms building the infrastructure your business now depends on.
Biography highlights
- Harold Price Professor of Entrepreneurship at NYU Stern and Director of its Fubon Center for Technology, Business and Innovation.
- Author of The Sharing Economy (MIT Press, 2016), winner of the Axiom Best Business Books Award and translated into five languages.
- Has given testimony on the digital economy and AI to the US Congress, the European Parliament and the United Nations.
- Member of the World Economic Forum’s AI Governance Alliance; former member of the Carnegie Council’s AI and Equality Initiative.
- Recognised with a Thinkers50 Radar Thinker Award, nine academic Best Paper awards and two Google Faculty Awards.
- Author of more than 40 op-eds across the New York Times, Financial Times, Guardian, Wired, Le Monde and Harvard Business Review, and a regular AI commentator for CNN, CNBC and Wired.
Biography
Generative AI learns to write, compose and design by training on work that people already made. That creates a problem older intellectual property law was never built for: a person’s past output is now also the blueprint for a machine that can reproduce their skill. Arun Sundararajan has made this the centre of his work, framing it as a single question for policymakers and boards: what facets of human capital should be owned by humans?
The question carries weight because of where he asks it. As an economist at NYU Stern, Director of its Fubon Center for Technology, Business and Innovation, and a member of the World Economic Forum’s AI Governance Alliance, he has given testimony to the US Congress, the European Parliament and the United Nations. His paper “Rethinking Intellectual Property Law in an Era of Generative AI” lays out why copyright designed for the internet era misfires when the thing being copied is a creative process rather than a file.
His authority on technological transitions is established. The Sharing Economy, published by MIT Press, was the early rigorous account of platforms like Uber and Airbnb displacing the managed corporation, and it won the Axiom Best Business Books Award. That track record matters now because the same economist is telling executives which AI effects are structural and which are noise, a judgment his recent work on the AI-capital-to-labour threshold makes concrete.
What distinguishes him from the wider future-of-work field is the second half of his argument. Alongside the ownership question, he presses the case for national infrastructure that lets people move between occupations mid-career with dignity, treating workforce displacement as a design choice rather than an inevitability. For a board weighing how far to build on generative AI, he is the rare voice who can map both the legal claims forming underneath the technology and the economic settlement that will determine whether it holds.
Key speaking topics
- AI governance and regulation
- Intellectual property and copyright in the age of generative AI
- Ownership of human capital
- The AI-driven future of work
- Platform strategy and network effects
- Antitrust and market power in technology
Ideal for
- Boards and C-suites setting generative AI strategy, particularly where IP exposure, talent contracts or litigation risk are in play.
- Chief Legal Officers, General Counsel and Chief Strategy Officers navigating AI regulation across US, EU and Asian jurisdictions.
- Policymakers and regulators shaping AI, copyright and labour rules.
- Technology and platform companies setting strategy against a shifting governance backdrop.
Audience outcomes
- A clear read on where AI regulation is actually heading across the major jurisdictions, from someone inside the bodies writing it.
- The ability to tell which AI effects on their industry are structural and which are cycle-driven hype.
- A sharper view of their company’s exposure on IP, copyright and the ownership of human capability.
- A framework for workforce planning that treats AI-driven displacement as something to design for, not absorb passively.
Talks
Breaks the business impact of AI into decision-making, consumer behaviour and platform power, and the automation of work, then gives executives a framework for separating genuine value from hype.
Key takeaways:
- How to build a forward-looking and realistic AI strategy rather than reacting to each new model.
- The business difference between AI and machine learning, and why generative models change the calculus.
- Where the real risks sit: inequality, algorithmic bias and unintended consequences, assessed rather than assumed.
Connects the shift toward platform and gig work with the acceleration of automation, and turns vague projections into workforce strategy a leader can act on now.
Key takeaways:
- What to expect in a specific industry as AI alters the mix of human and machine labour.
- Why changes in work are tied to changes in the social fabric, and what that means for employers.
- Concrete strategies for planning a workforce through the transition.
Traces how each historical phase of trust reshaped the economy, and argues that AI and reputation systems are now triggering a new phase with direct consequences for risk and regulation.
Key takeaways:
- How trust mechanisms, not just technology, determine which business models win.
- What the melding of individual and institution means for risk management and regulation.
- Why understanding trust is a way to read the future of commerce.