Rob traces his dissertation on cable TV entry deterrence back to a stint at the Massachusetts economic development office, where he worked on broadband adoption. He then applies his earlier work on platforms and information asymmetries to AI. He argues that Meta’s free AI assistant, Muse, makes sense as a two-sided market strategy: consumers are subsidized to build an installed base, which raises advertisers’ willingness to pay. He sees AI as a general purpose technology, where investing in complementary assets takes time, but says two-sided market thinking and other existing frameworks are also useful. He sees that the “intelligence” aspect of AI may be genuinely new and says he tries to stay humble about predictions.

On policy, Seamans says public debate swings between “full steam ahead” and “shut it down,” and he sees a lot of middle ground in measures that build trust, such as AI safety standards, whether government-imposed or industry-led. He also favors labor policies like retraining that close the information gap between firms and workers about which skills will matter in 12 to 24 months. He describes survey research with Michael Impink and Jim Besson on AI startups, which found that firms taking costly ethical AI actions raise more follow-on funding, possibly because those actions signal quality to investors, though he thinks genuine belief in doing the right thing plays a role too. On AI alignment and safety, he defers to researchers who have worked on it for years. He closes by saying he is driven by his curiosity about why firms adopt new technologies and by a desire to inform policy that benefits both workers and firm owners.