Bubbles, Productive & Unproductive; Builders; & Bots: Why the AI Boom Isn’t One Story, But Rather the Vector Driving the AI Economy Is at Least 12-Dimensional
Six dimensions are entrepreneurial-technological-industrial aspects of bubble dynamics that are at least somewhat familiar from history; six dimensions are wild and new. The AI boom isn’t a single narrative; it’s a tangle of grifters, overbuilders, positive externalities, and coordination plays colliding with platform power and techno‑millenarianism as natural‑language access to data becomes a general‑purpose upgrade like literacy, but with sustainable business models that look more like commoditized plumbing…
The AI surge looks to me half like a familiar “productive bubble” and half like something much more complicated and new and strange. The “productive bubble
terrain of grifters, wasteful overbuilding, socially valuable but privately unprofitable infrastructure construction, coordination cycles, a few rock‑solid business models, and financial-crisis risk is at least somewhat familiar. But then we also have
Platform near‑monopolists investing defensively at staggering scale;
Millenarian enthusiasts with their religious-cult agendas;
Natural‑language interfaces promise massive user surplus while commoditizing producers, as modes of human interaction with the infosphere are transformed utterly;
These transformations do not just produce new technologies of nature-manipulation and human coöperation, they also rewire the brain and restructure human thought in unpredictable ways;
Newer and stronger forms of attention extraction looming as the default monetization path.
The downstream consequences of what will be a revolution in the modes of human collective cognition
most durable value likely sits in small, task‑specific models tied to trusted data, and in moats built on workflow, reliability, and proprietary information. Even if many investors lose money, the infrastructure and capabilities will persist.
As an optimist, I see the likely equilibrium is user surplus rising fast—cheap, ubiquitous natural‑language access to data—while margins migrate to trusted data, integration, and uptime rather than model scarcity. I see policy choices around competition, energy, and data governance determining whether we get a broad productivity growth acceleration, or another round of attention enclosure.
But the future is one I cannot see.
The best cut at trying to set this out that I have seen this fall comes today, November 7, 2025, from Bill Janeway:
Bill Janeway: In Search of the AI Bubble’s Economic Fundamentals <https://www.project-syndicate.org/onpoint/will-ai-bubble-burst-trigger-financial-crisis-by-william-h-janeway-2025-11>: ‘A surge of investment in data centers and in the vast energy infrastructure… rising investment volumes fuel soaring valuations… new multibillion-dollar AI infrastructure projects. At the same time, a growing body of reports indicates that AI’s business applications are delivering disappointing returns, indicating that the hype may be running well ahead of reality…..
The history of modern capitalism has been defined by a succession of… “productive bubbles”… mobiliz[ing] vast quantities of capital to fund potentially transformational technologies whose returns could not be known in advance…. The companies that built the foundational infrastructure went bust. Speculative funding had enabled them to build years before trial-and-error experimentation yielded economically productive applications. Yet no one tore up the railroad tracks…. The infrastructure remained… to support the… “new economy’… after… delay and… with new players….
Brian Cantwell Smith…. “It’s not good that [ChatGPT] says things that are wrong, but what is really, irremediably bad is that it has no idea that there is a world about which it is mistaken.”… In business settings, tolerance for error is already low and approaches zero when the stakes are high…. What is the value-creating potential of LLMs? Their insatiable appetite for computing power and electricity, together with their dependence on costly oversight and error correction, makes profitability uncertain….
There are two distinct alternatives…. One lies in developing small language models—systems trained on carefully curated datasets for specific, well-defined tasks…. The other… is the consumer market, where AI providers compete for attention and advertising revenue… [and] value is often measured in entertainment and engagement, [so] anything goes…. given that Google’s and Apple’s browsers are free and already integrate AI assistants, it is unclear whether OpenAI can sustain a viable subscription or pay-per-token revenue model that justifies its massive investments…. [In] the Gartner Hype Cycle… a “trough of disillusionment” precedes the “plateau of productivity”…
I think that this is very good as it goes. But I think that it is greatly oversimplified. I see at least twelve different balls being juggled in the air here, only six of which are found in typical “productive bubbles”. And thus the situation is so complex that I find myself largely at sea.
In a “standard” “productive bubble”, there are, typically, five aspects to worry about: grifters, wasteful overbuilders, privately-unprofitable overbuilding made societally useful via positive externlities, coördinated implementation cycles à la Andrei Shleifer, and productivity increases driving the emergence of new rock-solid business models: