Technological Revolutions, Fast and Slow

This article is the second installment in our four-part series examining artificial intelligence through a wider economic lens. In the first essay, we explored the history of artificial intelligence and the recurring cycles of enthusiasm and disappointment that have shaped the field’s development. If you did not read the first piece, we encourage you to start there.

Across this series, we examine: (1) the history of AI and machine learning, (2) the economics of innovation and how automation spreads through the economy, (3) the implications for labor markets and the possibility of worker displacement, and (4) the potential impact of generative AI on financial markets and portfolio positioning.

Executive Summary

Technological revolutions rarely translate into immediate productivity gains. Economists have long observed that transformative technologies often appear across the economy before their benefits show up in the data. This lag reflects the time required for complementary investments, organizational changes, and new skills to develop around technology.

Generative Artificial Intelligence (AI) shows many characteristics of a general-purpose technology like electricity, the internal combustion engine, or digital computing. Such technologies reshape entire economies, but their impact is not truly felt until supporting infrastructure, new institutions, and novel workflows evolve to put them to commercial use.

Early evidence suggests that generative AI is meaningfully improving productivity at the task level. Just a few years after this technology exploded into public consciousness, we are already seeing faster output in knowledge work in activities such as writing, coding, and customer service. However, these gains do not automatically translate into either a firm-level or economy-wide productivity pickup. Bottlenecks in energy and power requirements and regulatory uncertainty conspire to slow the pace of adoption, at the same time as human users and organizations must adapt their activities to take fuller advantage of AI’s potential.

Generative AI, however, shows signs of diffusing more quickly than past platform technologies. Its natural-language interface and integration with existing business software look to significantly compress the learning curve. Major complementary infrastructure (chips and datacenters) is being built at breakneck speed. Even so, widespread economic transformation will unfold gradually rather than immediately, as some bulls forecast.

For investors and business leaders, the challenge is less determining whether AI will revolutionize the economy, but understanding who will capture the value it creates, and how that value ultimately shows up in corporate earnings and markets.

About the Author

Josh Rowe, Managing Director of Research at HB Wealth, wrote a PhD thesis in the history and economics of technology, focusing on computer automation of office work in the 20th century. He has studied the history of AI, venture capital’s funding of technological innovation, and the impact of technological change on financial markets—both as a resident of the ivory tower and as an investor. This surprising moment in history is the first time that he can say with any confidence that the years he spent in libraries and databases working on a doctoral dissertation might be of any practical use. He used AI in organizing and editing these essays, but the ideas (right and wrong) here are his own.

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