NYU professor Aswath Damodaran on an AI bot that is trained on his equity valuation work that is in the public domain, why AI will empower generalists, and more
Whether the risks to jobs is real or not, it behoves us all to act like it is given how consequential the costs could be X: @tonytassell and @adsinuk X: Tony Tassell / @tonytassell : Strategies to beat the AI bots - fascinating column by valuation guru @AswathDamodaran on how people should prepare for AI (and how a bot has been created to replicate his work using everything he has ever written, said in interviews etc) https://www.ft.com/... Aarish Shah / @adsinuk : “... in a world of specialists operating in silos and exhibiting tunnel vision, AI will empower generalists, comfortable across disciplines, who can see the big picture.” Strategies to beat the AI bots https://www.ft.com/... via @ft
Context & Ripple Effects
The bot is a concrete example of generative AI turning an expert’s accumulated public output into a reusable interface. It extends earlier discussion of AI systems shifting users from search toward direct answers from information retrieval into professional judgment workflows.
The story also sharpens a labor question raised in the debate over AI making some skills more valuable: Damodaran’s emphasis is not simply on automating specialists, but on giving cross-disciplinary users more leverage.
First-order effects
- Damodaran’s publicly available valuation framework can be replicated in bot form, reducing the exclusivity of packaging and applying his published methods.
- People doing equity-analysis tasks gain a tool that can make specialist-style valuation guidance more accessible; the differentiator shifts toward framing the question and assessing the output.
Second-order effects
- Generalists who can connect valuation with broader business context may gain relative usefulness, while narrowly procedural analysis faces greater pressure to demonstrate judgment beyond repeatable methods.
- Educators, analysts and firms that publish extensive bodies of work may need to treat public expertise as both distribution and input material for AI-based replicas.
Third-order effects
- If expert methods are increasingly captured in AI interfaces, professional-services value may concentrate less in routine production and more in accountability, original insight and the ability to work across domains.
- The pattern could broaden access to expert techniques while intensifying questions over who captures value when publicly shared knowledge becomes a product feature.
The trend: Generative AI is moving from retrieving expert knowledge to operationalizing expert workflows, increasing the premium on synthesis and judgment around the model.