Not Once in 18,900 Tries | Michael Mauboussin on What History Says About AI Growth

This episode of The Intangible Economy explores how AI, intangible assets, and unprecedented capital investment are reshaping the future of markets. Michael Mauboussin joins Kai Wu to break down why today’s AI expectations may be historically unmatched—and what that means for investors trying to assess risk, returns, and who ultimately captures value. The conversation moves from base rates and AI growth expectations to competitive dynamics, capital cycles, and the fundamental shift toward intangible-driven business models that are changing how we think about valuation, moats, and market structure. Papers and Resources Discussed: Bayes and Base Rates: How History Can Guide Our Assessment of the Future https://www.morganstanley.com/im/en-u... The Impact of Intangibles on Base Rates – https://www.morganstanley.com/im/publ... Measuring the Moat: Assessing the Magnitude and Sustainability of Value Creation – https://www.morganstanley.com/im/publ... One Job: Expectations and the Role of Intangible Investments – https://www.morganstanley.com/im/publ... Capitalism Without Capital: The Rise of the Intangible Economy – https://books.google.com/books/about/... A Better Estimate of Internally Generated Intangible Capital – https://pubsonline.informs.org/doi/10... Underestimating the Red Queen: Measuring Growth and Maintenance Investments – https://www.morganstanley.com/im/publ... Explaining the Recent Failure of Value Investing – https://papers.ssrn.com/sol3/papers.c... Guest Links: Michael Mauboussin Twitter https://x.com/mjmauboussin Topics Covered: Why OpenAI’s projected growth would be unprecedented in market history How base rates provide a reality check on AI expectations The role of diffusion models and adoption curves in forecasting technology Why massive capital investment in AI may follow past boom-bust cycles Lessons from large-scale infrastructure projects and why timelines break How intangible assets change the distribution of business outcomes The rise of “fat tails” and why more companies now massively win or fail Who captures value in AI across the stack from chips to applications Why competition may drive AI profits toward consumers, not producers How accounting distorts intangible investment and misleads investors Timestamps: 00:00 Intro and OpenAI growth expectations vs historical base rates 04:32 Why no company has ever achieved 100%+ sustained growth at scale 08:47 Lessons from megaprojects and AI infrastructure buildouts 13:18 Intangible assets and why outcomes now have fatter tails 18:36 Why big tech is growing faster than historical precedents 23:52 Where value accrues in AI and why consumers may benefit most 28:21 Barriers to entry in AI including capital, talent, and scale 32:47 The risk of overinvestment and historical parallels to past bubbles 37:26 Game theory and competitive signaling in AI capital spending 41:58 Why investment returns—not “asset light” narratives—drive value 46:12 How accounting fails to capture intangible investment properly 50:44 Breaking down SG&A into maintenance vs investment spending 55:03 Why understanding reinvestment and ROI is the core investing skill 59:18 Final thoughts on uncertainty, expectations, and base rates in AI Full episode transcript: