Bubbles and Breakthroughs: How Should Investors Respond?

This article is the fourth and final installment in our four-part series examining artificial intelligence (AI) through a wider economic lens. In the first three essays, we explored the history of AI and its recurring cycles of enthusiasm, the economics of technological adoption, and the implications for labor markets. If you have not yet read those pieces, we encourage you to start there for additional context.

In this final article, we turn to the question most relevant for investors: how to interpret current market movements, and how to position portfolios for a world of increasing rapid technological changes.

Executive Summary

Generative AI exhibits many of the characteristics of past technological revolutions—rapid capital deployment, rising valuations, stories about economic transformation. History is littered with examples of where revolutionary innovation was met with speculative excess and capital destruction. Where are there signs of a bubble? Which legacy businesses are at risk of disruption or obsolescence? Can investors still catch lightning in a bottle?

The question we face is not whether AI will change the ballgame – it is already doing so. The question is about who benefits and who pays the bill. Unlike railroads, electricity, or telecommunications, the AI’s transformation of the economy is happening in real time, not over decades. Like the Internet before it, it is absorbing enormous amounts of capital and loosing tremendous experimentation in business models that will increase the mortality rate not just of startups, but of incumbent businesses. The scale of current investment in AI infrastructure is unprecedented, raising legitimate questions about whether future demand will justify the capital being deployed. While chipmakers and cloud providers have frantically increased spending to keep pace, there remain doubts concerning how value will ultimately be distributed across the AI ecosystem. It is possible that, on the whole, consumers benefit to a greater extent than investors.

Financial market innovation is evolving almost as rapidly as the technology it funds. Private credit, new forms of leverage, complex multi-party non-cash contracted purchase arrangements: all of these shift the risk and opportunity calculus versus previous waves of technological change. We are, emphatically, in the early innings of the economic and commercial changes that AI and deep learning will wreak. Yet certain companies, both public and private, are valued as if the ultimate state of the world is guaranteed. Picking winners is a difficult game, even for venture capitalists and tech hedge funds. Investors should value flexibility and dry powder, even as they look to participate ratably in the early success stories.

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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