Motley Fool Money — Interview with Vasant Dhar: "Thinking With Machines"

Air Date: December 28, 2025
Host: Asit Sharma (The Motley Fool) | Guest: Professor Vasant Dhar (NYU Stern)

Overview

This episode features an in-depth interview with NYU Professor Vasant Dhar, a pioneering researcher in AI and author of the new book Thinking with the Brave New World of AI. The discussion explores Dhar’s formative experiences, the evolution of artificial intelligence, the parallels between AI and investing, the compounding power of “small edges,” building an AI modeled after valuation expert Aswath Damodaran, and the broader societal stakes of how humans interact with powerful new technologies. The tone is thoughtful, conversational, and sometimes humorous, with a clear emphasis on both the promise and the risks of the AI-driven future.

Key Discussion Points and Insights

1. Formative Experiences and Early Exposure to AI (01:32–05:35)

• Dhar’s Unusual Upbringing
  • Born in 1950s Kashmir; moved frequently, including a formative childhood stint in Ethiopia where he was accidentally placed in 7th grade instead of 4th.
  • “That was a hell of an experience growing up...It made me resilient, I guess, in some way.” — Dhar (02:21)
• Encounter with Herbert Simon & Bounded Rationality
  • Studied at the University of Pittsburgh, learning from Nobel Laureate Herbert Simon, who challenged the then-mainstream notion of perfect human rationality with the concept of “bounded rationality.”
  • "Humans have limited cognitive resources...we tend to focus on the most plausible things to pursue, and we do this through heuristics that are learned through experience." — Dhar (03:42)
  • Simon’s ideas didn't mesh easily with economists but “took root in artificial intelligence”—especially the use of heuristics and expert systems.
  • Dhar’s “mind was blown” by observing early AI systems in medicine.
    • “That's when I decided this is what I'd like to do with my life.” — Dhar (05:27)

2. Compounding Small Edges: Investment and Sports Parallels (05:35–08:53)

• Dhar discusses Roger Federer’s 2023 Dartmouth commencement address, illustrating success as the result of small, consistent edges compounding over time.
  • Federer: Won 80% of matches, but only 54% of points.
  • “If the match was just one point long, then Federer would win 54% of his matches, right? But that little edge just keeps multiplying over time...You don’t need to be perfect. You need to be just slightly better than the average...that edge will just continue to compound." — Dhar (06:31)

3. Systematic Investing: Is AI Just for Short-Term Trades? (08:53–12:16)

• Short-Term vs. Long-Term Investing:
  • Historically, Dhar believed AI and machine learning best fit short-term, data-rich trading scenarios, but not long-term investing where training data is sparse.
  • Debates with colleagues like Aswath Damodaran (valuation expert) and Scott Galloway about the promise and limits of "bots" for long-term investing.
    • “Should you trust your money to a robot?...for high frequency trading and short term, but when it came to long-term investing...I said ‘no way’.” — Dhar (09:52)
  • Post-ChatGPT, Dhar and Damodaran reconsidered, leading to an effort to build a "Damodaran bot."
  • A more intriguing application: using AI to enable deeper scenario analysis and valuation thought experiments (e.g. "What if Trump escalates tariffs—how does that affect Nvidia?").
    • “It’s very laborious for people...I find it interesting that we can apply AI now systematically to long-term investing as well.” — Dhar (11:53)

4. Building the Damodaran Bot / AI That “Reasons” (12:16–15:46)

• Challenges and Insights
  • Initial attempts (stuffing all Damodaran’s data into an LLM) fell flat: the AI lacked depth and failed to replicate his reasoning.
    • “It didn’t sound like him. There was nothing deep about it, there was nothing profound about it.” — Dhar (13:13)
  • The breakthrough: Not just numbers, but mimicking the way Damodaran frames problems and analyzes broadly (e.g. asking, “Is AI incremental or disruptive?” for Nvidia).
    • Framing is crucial: distinguishing between incremental and disruptive trends shapes everything that follows.
    • Superforecasting: Damodaran as example of “superforecaster” (research from Phil Tetlock), anchoring analysis in the right place with insatiable curiosity.

5. Societal Stakes: Who Governs AI—Us or the Machines? (16:52–20:21)

• A Huxleyan Future?
  • Dhar warns of a society “disempowering ourselves,” where “the machine has become a gatekeeper of human activity.”
    • "My fear is that we are slipping into a Huxleyan kind of world, perhaps even without our realization, right, that we are gradually disempowering ourselves in many areas of our life." — Dhar (17:01)
  • AI now screens job candidates and is increasingly handling critical human interactions.
    • “It's not a warm fuzzy feeling, right, when the machine has become a gatekeeper to human activity.” — Dhar (17:19)
• Who are the Stakeholders?
  • Everyone is responsible, but Dhar stresses individual awareness and discipline as paramount.
    • “It's easy to use this technology as a crutch...but that, in the long run, will be debilitating...the burden really is on the consumer to be aware of how you're consuming AI.” — Dhar (18:29)
  • Laments overdependence (“I don’t navigate as well spatially as I used to...I think I've lost that facility by relying more and more on maps”) and draws parallels to broader cognitive decline if humans outsource too much to AI.
    • “If you become dependent on [AI], it'll lead to cognitive decline, and that's no good.” — Dhar (19:58)
  • Opportunities: AI can help us become “superhuman”—if we use it to amplify skills, not replace them.

6. On Writing, Creativity, and Fun (20:21–22:03)

• Dhar is repeatedly asked why he didn’t use ChatGPT to write his book.
  • Cites the quality of personal expression, unique style, and—importantly—the joy and satisfaction of human creativity.
    • “It's also so much more fun to do that. And at the end of the day, what's life about if not for having fun?...There's so much of a sense of accomplishment and satisfaction from producing something good by yourself. And that's what we should strive for.” — Dhar (21:24)

Notable Quotes & Memorable Moments

• On Bounded Rationality and AI:
  “Heuristics actually sort of focus our attention, you know, to the right parts of the problem...and we move on. Right. So that was his theory, which was called bounded rationality.” — Dhar (03:42)

• On Compounding Edges in Life:
  “You don't need to be perfect. You don't even need to be really good. You need to be just slightly better than the average or some benchmark in order to be successful, you know? And that applies to like almost everything in life.” — Dhar (07:14)

• On Data and Investment AI:
  “We built this bot that's designed to think like him...But I've actually become intrigued with a different type of application...run scenarios and say, ‘what if Trump escalates tariffs?’” — Dhar (10:52)

• On Machine Gatekeepers:
  “The machine has become a gatekeeper of human activity in many ways. You apply for a job, you're screened by the AI. You might even be interviewed by the AI...It's not a warm fuzzy feeling.” — Dhar (17:07)

• On the Human Stake in AI:
  “Among all these people, the burden really is on the consumer...You can consume [AI] to become superhuman, right? It can really serve to amplify your skills if you use it in the right way. But if you become dependent on it, it'll lead to cognitive decline, and that's no good.” — Dhar (19:25)

• On Writing and Fun:
  “And at the end of the day, what's life about if not for having fun? I mean, life is about having fun and this should be fun. And I had so much fun writing it and there's so much of a sense of accomplishment and satisfaction from producing something good by yourself. And that's what we should strive for.” — Dhar (21:24)

Timestamps of Important Segments

• [02:05] — Dhar’s formative years and global upbringing
• [03:42] — Bounded rationality and heuristics in AI
• [05:35] — Federer, compounding edges, and lessons for markets and life
• [08:53] — Applying machine learning to short-term and long-term investing
• [13:00] — Building and troubleshooting the Damodaran bot
• [16:52] — The social question: Who is really governing whom, AI or us?
• [18:11] — How individuals can avoid cognitive decline by responsible AI usage
• [21:17] — The case for human creativity, fun, and satisfaction in the AI era

Conclusion

Professor Vasant Dhar’s thoughtful, sometimes cautionary, but ultimately optimistic perspective encourages listeners to embrace AI as a tool to amplify human capabilities, not to replace them. He urges both risk awareness and responsible engagement from all stakeholders—especially individuals—and closes with a stirring reflection on the joy that comes from genuine, personal accomplishment.

This episode is especially relevant for investors, technologists, and anyone reflecting on the role of AI in both their daily lives and the future of society.