We Asked the Man Who Mapped the AI Economy If the Boom Is Real — And Who Keeps the Money

Azeem Azhar joins Kai Wu to break down the real economics of the AI boom, including the $110 billion demand base, where profits may accrue across chips, hosting, foundation models and applications, and whether spending can translate into enterprise productivity. They discuss AI infrastructure bottlenecks, open-source competition, vertical integration, organizational redesign, software moats, human judgment and the signals investors can use to identify companies turning AI adoption into durable competitive advantage.The State of the AI Economyhttps://intelligence.exponentialview.co/assets/ev-state-of-ai-economy-2026.pdfWhy AI Isn't Showing Up on Your Bottom Linehttps://www.exponentialview.co/p/why-ai-isnt-showing-up-on-your-bottom-lineAzeem Azhar on Xhttps://x.com/azeemExponential Viewhttps://www.exponentialview.co/Topics CoveredThe size and growth rate of real generative AI demandHow the AI stack divides between chips, hosting, foundation models and applicationsWhy memory and energized data centers may be the key AI infrastructure bottlenecksOpen-source models, proprietary pricing and enterprise assuranceVertical integration and foundation model labs moving into applicationsHow AI value could flow to consumers rather than infrastructure providersWhy AI productivity requires workflow and organizational redesignWhat investors can learn from earnings calls, hiring and enterprise spendingForward-deployed engineers, consulting firms and vendor lock-inWhich intangible business moats strengthen or weaken as intelligence becomes abundantTimestamps00:00 The economics and sustainability of the AI boom06:34 Mapping the four layers of the AI stack10:43 Vertical integration and cross-stack competition15:31 Why memory is becoming an AI infrastructure bottleneck20:01 Open-source models versus proprietary AI24:36 Why foundation model labs are moving up and down the stack28:51 Could AI profits become consumer surplus?33:00 Why more copilots cannot create an AI-native company37:17 Job postings and the intangible investments behind AI adoption44:16 Can forward-deployed engineers transform legacy companies?49:15 Which business moats strengthen or weaken in the AI economy?54:20 Do foundation models really have network effects?59:00 Why judgment, verification and human provenance become more valuable01:04:56 The exponential gap in data centers and education01:10:06 How Azeem uses AI to deepen research and generate ideasLearn more about the Excess Returns podcast network:https://excessreturns.coNo information discussed in this podcast should be construed as investment advice. Securities discussed may be held by the hosts and guests, their firms or their clients.