Bytes: Week in Review – Are we in an AI bubble? - marketplace-tech Recap
Podcast: marketplace-tech
Published: 2026-01-30
Duration: 13 minutes
Guests: David A. Kirsch
Summary
The episode investigates whether the current boom in AI technology exhibits characteristics of a speculative bubble, drawing parallels with historical technological innovations.
What Happened
David A. Kirsch, a historian and management professor at the University of Maryland, discusses the concept of technological bubbles and how they relate to the current AI landscape. He identifies four factors that often lead to technological bubbles: uncertainty, novice investors, investment opportunities, and compelling narratives. Kirsch notes that the presence of uncertainty is a significant factor, as new technologies naturally create uncertainty by disrupting expertise. He explains that technological innovations, especially AI, generate uncertainty because they are fundamentally new and untested in the existing capitalist economy.
Kirsch also highlights the role of infrastructure in tech booms and busts, noting that the build-out of infrastructure acts as a 'timekeeper' for technological bubbles. He suggests that, although the development of AI infrastructure is rapid, it will take time to assess AI's real value as it becomes more integrated into organizations, markets, and businesses. Kirsch evaluates the current AI boom against his bubble criteria, rating it seven out of eight, indicating that while there is significant speculative activity, the lack of many IPOs suggests it is not at a full-blown bubble yet.
The episode delves into historical examples, such as aviation, to illustrate how new business models evolve over time before creating lasting value. Kirsch compares the current stage of AI development to early aviation phases, suggesting that the current AI applications might just be the beginning of finding the right business model that delivers value. He raises the notion of general artificial intelligence (AGI) as a powerful narrative that influences investor behavior, though it challenges traditional bubble models due to its potentially transformative impact.
Kirsch argues that while there might be less novice public investment compared to past bubbles like the dot-com boom, private credit markets remain speculative. He suggests that even sophisticated investors in these markets may lack experience with AI's unique challenges and opportunities. The conversation touches on the idea of AI becoming 'too big to fail,' noting that while AI technologies are increasingly embedded in various systems, the failure of companies might not hinder AI's overall success.
The episode concludes with Kirsch emphasizing that while the AI boom shares many characteristics of past technological bubbles, the outcome remains uncertain. He suggests that the success of AI will depend on whether its infrastructure and business models can catch up with current enthusiasm and investment. Overall, the discussion underscores the complexity of evaluating AI as a potential bubble, given its unique qualities and the narratives surrounding it.
Key Insights
- Technological bubbles often arise from four factors: uncertainty, novice investors, investment opportunities, and compelling narratives. Current AI developments are marked by significant uncertainty due to their new and untested nature in the capitalist economy.
- Infrastructure development serves as a 'timekeeper' for tech bubbles, and while AI infrastructure is rapidly advancing, its true value will only be clear as it integrates into organizations and markets over time.
- The current AI boom scores seven out of eight on a bubble criteria scale, indicating significant speculative activity, but the lack of many IPOs suggests it is not yet a full-blown bubble.
- Despite less novice public investment compared to past bubbles, private credit markets remain speculative, with even sophisticated investors lacking experience in navigating AI's unique challenges and opportunities.