Economy

Scott Galloway is the kind of commentator the AI conversation rarely produces: not a researcher, not a founder, not a doomer, not a booster. He is a marketing professor and a serial entrepreneur with a record of correctly reading the corporate stories of the last two decades, and he has spent the last two years pointing at the AI story with increasing concern. The headline of his pitch - that AI was not built for ordinary people and that the rich no longer need them - is provocative on purpose. The argument underneath is more careful, and worth pulling apart on its own terms. ...

TL;DR The marginal cost of AI intelligence is halving roughly every two months and heading toward a level where rationing stops making sense - similar to how bandwidth and storage became effectively unconstrained This will break pricing models built on scarce cognition: anything billed per word, per hour, or per consult faces a hard ceiling set by what machines charge for the same work The Jevons paradox means total cognitive work in the economy likely goes up, not down - cheaper thinking means we apply thinking to far more problems, not the same problems more cheaply Three categories of human work survive: accountability (being the named responsible party), taste (choosing well from infinite AI-generated options), and real-world coupling (a body in a place, a relationship that took years to build) The political question of who captures the surplus and who absorbs the transition cost is still open - it will be decided by institutions and policy, not by the technology itself This is a personal reflection, not a forecast dressed up as one. I am writing about a trend I think is real, but the second-order consequences are guesses, and I am sure some of them are wrong. ...

TL;DR Scoring four future scenarios against real-world signals: winner-take-most has the clearest corporate and capital logic behind it as of April 2026, driven by vertical integration across chips, data centres, models, and distribution Broad abundance gets partial credit - inference costs have fallen two orders of magnitude and open-weight models are competitive, but institutional-level gains in healthcare and education haven’t materialized Techno-feudalism is quietly accumulating through agentic platform lock-in (Claude Code, Cursor, Devin) and payment rail consolidation, with competition enforcement as the main counterweight Managed transition is the weakest scenario - UBI pilots haven’t scaled nationally, compute taxation remains a proposal, and institutional response cycles are mismatched with AI deployment speed The three signals that will determine where this goes: whether the open-weight frontier gap widens or closes, whether agentic memory becomes portable or platform-owned, and whether any serious economy moves past pilot-scale on redistribution I recently mapped four plausible futures for the machine-speed economy and listed the signals to watch for each. The obvious next question is the one I deliberately held back from answering: which signals are actually firing right now, and what does the mix say about where we’re heading? ...

TL;DR AI is collapsing build times across the entire software stack, meaning small teams can now ship in weeks what once required 50-person organisations working for a year Four plausible futures are mapped: Broad Abundance (gains widely distributed), Winner-Take-Most (rents accrue to infrastructure owners), Techno-Feudalism (intelligence rented from platform landlords), and Managed Transition (governments respond with UBI and regulation) Signals to watch include open-source model performance, vertical integration of chips and data centres, platform lock-in of agentic workflows, and serious UBI pilots at national scale Leading AI researchers including Geoffrey Hinton and Yoshua Bengio argue the critical variable is no longer how capable models become, but how gains are distributed and how fast institutions adapt Across most scenarios, the things that hold their value are consistent: trust, relationships, physical presence, and creativity rooted in specific human experience The pace of AI development over the past three years is genuinely unlike anything in recent economic history. The Stanford AI Index has tracked frontier model capability roughly doubling on a yearly cadence, and private AI investment has reached levels that dwarf the dot-com peak in inflation-adjusted terms. What’s less widely understood is what that pace actually means for competition, investment, and the structure of the economy. ...