William H. Janeway
THE LONG VIEW
By contrast, the focus of the tech bubble of the late 1990s was on building the internet’ s physical and logical infrastructure on a global scale, accompanied by the first wave of experiments in commercial applications. Speculation during this period was mainly concentrated in public equity markets, with some spillover into the market for tradable junk bonds, and overall leverage remained limited. When the bubble burst, the resulting economic damage was relatively modest and was easily contained through conventional monetary policy.
The history of modern capitalism has been defined by a succession of such“ productive bubbles.” From railroads to electrification to the internet, waves of financial speculation have repeatedly mobilized vast quantities of capital to fund potentially transformational technologies whose returns could not be known in advance.
In each of these cases, 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, dismantled the electricity grids, or dug up the underground fiber-optic cables. The infrastructure remained, ready to support the creation of the imagined“ new economy,” albeit only after a painful delay and largely with new players at the helm. The experimentation needed to discover the“ killer applications” enabled by these“ General Purpose Technologies” takes time. Those seeking instant gratification from LLMs are likely to be disappointed.
For example, while construction of the first railroad in the United States began in 1828, mail-order retail, the killer app in this instance, began with the founding of Montgomery Ward in 1872. Ten years later, Thomas Edison introduced the Age of Electricity by turning on the Pearl Street power station, but the productivity revolution in manufacturing caused by electrification only came in the 1930s. Similarly, it took a generation to get from the Otto internal combustion engine, invented in 1876, to Henry Ford’ s Model T in 1908, and from Jack Kilby’ s integrated circuit( 1958) to the IBM PC( 1981). The first demonstration of the proto-internet was in 1972: Amazon and Google were founded in 1994 and 1998, respectively.
Where does the AI bubble fit on this spectrum? While much of the investment so far has come from Big Tech’ s vast cash reserves and continued cash flow, signs of leverage are beginning to emerge. For example, Oracle, a late entrant to the race, is compensating for its relatively limited liquidity with a debt package of about $ 38 billion.
And that may be only the beginning. OpenAI has announced plans to invest at least $ 1 trillion over the next five years. Given that spending of this scale will inevitably require large-scale borrowing, LLMs have a narrow window to prove their economic value and justify such extraordinary levels of investment.
Early studies offered reason for optimism. Research by Stanford’ s Erik Brynjolfsson and MIT’ s Danielle Li and Lindsey Raymond, examining the introduction of generative AI in customer-service centers, found that AI assistance increased worker productivity by 15 %. The biggest gains were among less experienced employees, whose productivity rose by more than 30 %.
Brynjolfsson and his co-authors also observed that employees who followed AI recommendations became more efficient over time, and that exposure to AI tools led to lasting skill improvements. Moreover, customers treated AI-assisted agents more positively, showing higher satisfaction and making fewer requests to speak with a supervisor.
The broader picture, however, appears less encouraging. A recent survey by MIT’ s Project NANDA found that 95 % of private-sector generative AI pilot projects are failing. Although less rigorous than Brynjolfsson’ s peer-reviewed study, the survey suggests that most corporate experiments with generative AI have fallen short of expectations. The researchers attributed these failures to a“ learning gap” between the few firms that obtained expert help in tailoring applications to practical business needs— chiefly back-office administrative tasks— and those that tried to develop inhouse systems for outward-facing functions such as sales and marketing.
The Limits Of Generative AI
The main challenge facing generative AI users stems from the nature of the technology itself. By design, GenAI systems transform their training data— text, images, and speech— into numerical vectors which, in turn, are analyzed to predict the next token: syllable, pixel, or sound. Since they are essentially probabilistic prediction engines, they inevitably make random errors.
Earlier this year, the late Brian Cantwell Smith, former chief scientist at Xerox’ s legendary Palo Alto Research Center, succinctly described the problem. As quoted to me by University of Edinburgh Professor Henry Thompson, Smith observed:“ It’ s not good that [ ChatGPT ] says things that are wrong, but what is really, irremedia-
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