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How to Think About the Future of Technology

For a clear view of broader market trends, one should always distinguish between the real "operating economy" and the financial "betting economy," where speculation often runs amok. Success in business and investing lies in identifying cases where these two domains will become one and the same.

SEBASTOPOL, CALIFORNIA – It is often hard to see the true trajectory of the technology industry because it is hidden by so much financial-industry smoke. For a clear view, one must start with the notion that there are two economies: the operating economy, where people make things or provide services for paying customers; and the betting economy, where people “invest” in things that people might be able to make, and that others might want to buy and sell.

In mature sectors, the two economies are relatively close together, with investors providing capital to maintain or expand business activities for which there are knowable expectations about financial outcomes. But at the cutting edge of technology, the betting economy can get way ahead of the operating economy, assigning enormous value to unproven technologies and encouraging wild speculation or even fraud.

The fate of cryptocurrencies and Web3 is a recent case in point. But as economist Carlota Perez has shown, speculative bubbles have accompanied every transformative technology since the start of the industrial revolution. Thus, the question we need to ask, notes William H. Janeway, is whether a bubble is productive.

A productive bubble is one in which the speculative mania, however wasteful, leaves behind the infrastructure for the next technological golden age. In the case of Web3, the jury is still out on whether there is anything worth salvaging. By contrast, in the case of Tesla’s frothy stock-market valuation, no one can deny that the company was at the forefront of a wave of productive investment – not just in electric vehicles but also in the cheap, abundant energy that will be provided by renewables at scale. Whether or not Tesla’s stock ever regains its previous valuation, the world has already changed as a result of the hype around it.

But how can we tell, in the moment, whether speculative activity is productive or unhinged from reality? I generally look for five signs. First, is usage large and growing rapidly? Second, are innovators applying the technology to the operating economy, even though it isn’t yet clear how the technology will be monetized? Third, are any of the relevant companies profitable, even if they are reinvesting those profits in growth? Fourth, is capital being used to create infrastructure and capabilities that will survive even if the companies receiving it are wildly overvalued? And fifth, is the technology aligned with long-term innovation trends and market needs?

Consider generative artificial intelligence. As of November 2022, more than three million people were using the AI art generator DALL-E 2, a full five months after its release, creating more than four million images per day. Similarly, MidJourney, another AI art generator, claims a million users and says it is already turning a profit.

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Unlike cryptocurrencies and non-fungible tokens (NFTs), whose primary use was financial speculation, inflating a bubble that has already burst, generative-AI engines are being used to produce everything from commercial graphic design to wildly experimental fine art. They are empowering ordinary people to do things that previously required a great deal of expertise, and helping the experts to push their craft even further. This is a sign of genuine disruption.

AI assistance for computer programming is also gaining momentum. According to the CEO of GitHub, the company’s AI assistant, Copilot, generated nearly 40% of the code created by the 1.2 million developers who signed up for a technical preview last year; and the developers who used it were 55% more productive. Meanwhile, OpenAI’s ChatGPT language-prediction tool, which reached a million users within five days of its release, has reset public expectations about what computer interfaces can look like, giving Microsoft (a major investor in OpenAI) hope that future integration into Bing will restart competition in the search-engine market. And for its part, Google is already exploring AI-based chat interfaces trained specifically on health-care knowledge. Law is sure to follow.

So, yes, AI innovation is clearly focused on the operating economy. The same is true of investments in biotech, renewable energy, and commercial space. The massive amounts of capital being deployed are not just being exchanged for phantom stakes in speculative assets; they are being used to build the infrastructure of the future. You have only to look at the role Starlink, satellite imagery, and drones have played in the Russia-Ukraine war to see that today’s space technology companies are on trend.

This notion of “on trend” has two faces: the logical inevitability of demand; and the contingency of supply. Political scientist Herbert Simon put his finger on the inevitability of demand for AI in 1971, noting that, “a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.” He predicted that machines would be needed to help us allocate our attention. But that still left open the contingent part – the matter of supply. Not until Google arrived on the scene did everyone come to understand what the nature of the game was going to be. Like Google, the latest generation of AI is a supply-side response to the problem of information overabundance.

Similarly, epidemiologists have been warning us for decades that outbreaks of novel pathogens were inevitable. When the COVID-19 pandemic arrived in 2020, the fortunate contingency was that mRNA vaccines were waiting in the wings, thanks to government-funded basic research. We also know that “demographics are destiny” and that populations in developed economies are aging. So, whatever booms and busts biotechnology and health care go through, we can already see that investments in these sectors are going toward something important. Likewise, given the specter of climate change, future demand for renewable energy is inevitable, and the contingency is confined to understanding what technologies and which companies will best meet that need.

The distinction between the operating economy and the betting economy also helps make sense of Meta (Facebook) and other Big Tech stocks’ recent fall from grace. The greater the integration of new technologies into the overall economy, the greater the deflation of the imagined future. What was once speculative is now known, and even outsize profits will begin to be valued at more reasonable multiples.

In short, don’t look for hot takes on which technologies and companies will rise or fall. Rather, learn how to distinguish between financial hype and operating-economy innovation. Then, build a mental model based on the long-term needs of the operating economy, not the speculative froth, and invest in companies that do the same.