NYU's Vasant Dhar on AI's Growing Role and Human Agency
January 6, 2026
Business News

NYU's Vasant Dhar on AI's Growing Role and Human Agency

An In-depth Conversation on AI's Influence, Challenges, and the Balance Between Machine and Human Decision-making

Summary

Professor Vasant Dhar of NYU’s Stern School of Business shares insights into artificial intelligence's evolving role in society, raising concerns about human reliance on AI as gatekeepers in various life domains. Dhar discusses the concept of bounded rationality, parallels between sports and markets, and his collaborations to replicate expert decision-making through AI, while emphasizing the importance of human engagement to prevent cognitive decline.

Key Points

Vasant Dhar introduces the concept of bounded rationality, highlighting humans’ limited cognitive capacity and reliance on heuristics for decision-making, a principle foundational to early AI expert systems.
Small advantages, such as slight probabilistic edges, compound over time in fields like sports and finance, illustrating how incremental gains can lead to substantial success.
Recent AI developments enable replication of expert valuation reasoning at scale, allowing for systematic processing of long-term investment scenarios, exemplified by collaboration with valuation expert Aswath Damodaran.

In a recent dialogue with Motley Fool Senior Analyst Asit Sharma, Professor Vasant Dhar of NYU Stern elaborated on the transformative and sometimes unsettling impact of artificial intelligence on contemporary life. Having pioneered the integration of machine learning in financial trading and authored the book, Thinking With Machines, The Brave New World of AI, Dhar offers a seasoned perspective on the opportunities and challenges AI presents.

Born in Kashmir in the 1950s and having experienced diverse educational environments, including a challenging incident in Ethiopia where a grade placement error led him to study with significantly older peers, Dhar credits his unusual upbringing with fostering resilience that shaped his academic path. His early exposure to AI began during his doctoral studies in Pittsburgh, where he encountered Herbert Simon, a Nobel laureate known for the concept of bounded rationality — the idea that human decision-making is limited by cognitive capacity and thus relies on heuristics to arrive at satisfactory conclusions rather than exhaustive optimization.

Dhar highlights that while economic theories often favored idealized models of rationality, AI research embraced Simon's concepts to develop expert systems. He recounts being particularly impressed by an early medical diagnosis AI system producing accurate assessments by mimicking expert heuristics, an experience that influenced his lifelong engagement with the field.

Expanding on patterns of success, Dhar references tennis champion Roger Federer's demonstration that winning a slight majority of points – around 54% – compounded over many matches yields a dominant overall record, underscoring how small advantages can create outsized results over time. He compares this phenomenon to financial markets and discusses how small probabilistic edges are crucial both in short-term systematic trading and, increasingly, in long-term investment strategies.

He recalls skepticism in 2015 about applying machine learning to long-term investing due to data constraints but describes recent advances, notably collaborations with NYU colleagues such as Aswath Damodaran, a valuation expert. Together, they sought to build an AI model replicating Damodaran’s analytical approach, integrating his extensive valuation frameworks and narrative reasoning capabilities into a system that evaluates companies at scale, potentially assisting with scenario analyses and investment decisions.

Dhar details the complexity of modeling Damodaran’s thought process, which goes beyond numbers to incorporate framing questions, such as assessing whether AI represents an incremental or disruptive technology, influencing valuation significantly. He observes that such systematic approaches align with concepts from forecasting research, where effective decision-makers ask the right questions, maintain curiosity, and avoid cognitive biases.

Despite advances, Dhar expresses concern about society’s gradual ceding of agency to AI systems, pointing out that machines increasingly serve as gatekeepers—whether in job application screenings or interviews—shifting power dynamics and potentially eroding human skills if over-relied upon. He warns that without conscious stewardship, this shift could lead to a dystopian scenario reminiscent of Huxley’s works, where humans unintentionally diminish their capabilities.

He calls for broad stakeholder involvement, emphasizing individual responsibility in how AI is consumed and warning against passive reliance on AI tools, which may result in cognitive decline analogous to diminished navigation skills seen with over-dependence on GPS. Dhar stresses that AI can amplify human abilities if used judiciously but cautions against using it as a crutch. Moreover, he highlights social media's demonstrated harm to youth as a cautionary parallel for potential AI impacts.

On a personal note, Dhar reveals his choice to write his book without AI assistance to preserve individual expression and creativity, underscoring the human fulfillment derived from such endeavors. He frames his work and advocacy as an invitation for all to think critically about AI's role and maintain active human participation in this brave new world.

This conversation offers a nuanced exploration of artificial intelligence's dual capacity to empower and disempower, urging a balanced approach to integration that safeguards human judgment and cultural values amidst rapid technological evolution.

Risks
  • Increasing dependence on AI as gatekeepers across domains may lead to unintentional disempowerment and erosion of human cognitive skills.
  • Unfettered or inappropriate use of AI, particularly without user awareness, carries risks of cognitive decline and diminished critical thinking abilities.
  • Lack of effective stakeholder engagement, including individuals, governments, and industry, poses challenges to managing AI’s societal impact and mitigating potential harms.
Disclosure
This article is for informational purposes and does not constitute investment advice. Readers should conduct independent research before making financial decisions.
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