I’m undertaking a 1000-day reinvention project, blogging here daily to track my progress. In Thursday Thinker, I share a smart idea or theory.
AI has already made many investors rich, especially those fortunate or smart enough to take positions in Nvidia and the hyperscalers a few years ago. But what do the possibilities look like from here? Are there even greater riches in the future, for those venture capitalists, angel investors, institutional investors, and retail investors who find the right AI businesses to put their money in?
Former venture capitalist Jerry Neumann writes that AI Will Not Make You Rich. He suggests that AI actually represents the last stage in the long technological wave of information technology development that started back in the early seventies, with the development of the personal computer.
In the framework of Carlotta Perez, technological waves can be broken down into four predictable phases: irruption, frenzy, synergy, and maturity, each with its own characteristic investment profile. Only frenzy and synergy are easy for investors, writes Neumann:
Frenzy is when everyone piles in and investors are rewarded for taking big risks on unproven ideas, culminating in the bubble, when paper profits disappear. When rationality returns, the synergy phase begins, as companies make their products usable and productive for a wide array of users. Synergy pays those who are patient, picky, and can bring more than just money to the table.
But the first and last phases, irruption and maturity, are more difficult to invest in.
In irruption as a new technological wave begins, it’s hard to see what’s going to happen and easy to waste your money investing in opportunities that will never pan out.
In the maturity phases, the companies who became successful in the synergy phase (think Google, Amazon, Meta, and Microsoft) control the returns for the technology that’s been developed, and they have the opportunity to create and take advantage of new innovations:
The lack of dynamism allows the successful synergy companies to remain entrenched (see: the Nifty 50 and FAANG), but growth becomes harder. They start to enter each other’s markets, conglomerate, raise prices, and cut costs. The era of products priced to entice new customers ends, and quality suffers. The big companies continue to embrace the idea of revolutionary innovation, but feel the need to control how their advances are used. R&D spending is redirected from product and process innovation toward increasingly fruitless attempts to find ways to extend the current paradigm. Companies frame this as a drive to win, but it’s really a fear of losing.
Innovation can happen during maturity, sometimes spectacularly. But because these innovations only find support if they fit into the current wave’s paradigm, they are easily captured in the dominant companies’ gravity wells. This means making money as an entrepreneur or investor in them is almost impossible. Generative AI is clearly being captured by the dominant ICT companies, which raises the question of whether this time will be different for inventors and investors—a different question from whether AI itself is a revolutionary technology.
We can see that for now, the only company making significant money on AI is Nvidia. The hyperscalers are instead spending money on AI infrastructure, including buying and stockpiling GPUs made by Nvidia, in preparation for the riches they see ahead.
Neumann uses shipper containerization in the 20th century as an example of how late-wave innovation can change the world but bring very little rewards to those investing in it. Few people became rich from containerization itself, because “competition and capex costs made it hard to grow fast or achieve high margins.”
Who benefited? Companies like IKEA, Costco, Target and Walmart and, notably, their customers who got access to cheaper goods provided in better, more efficient ways. Meanwhile companies that didn’t take advantage of containerization such as Sears and Woolworth saw their businesses deteriorate.
The way to invest in the containerization innovation space would have been to focus on the industries that benefited from containerization, not in the container shipping industry itself.
Similarly, for AI the way to invest is to find the companies who will serve customers more and better using artificial intelligence, rather than in the companies providing AI services (OpenAI, Microsoft, Meta, Google, Amazon).
So where to invest? How about in the infrastructure companies like IREN Limited, which owns and operates data centers? No, says Neumann, such companies are already valued with such an expectation — there’s no upside surprise possible. Instead, there may be a big downside.
In the containerization space, shipbuilding boomed from 1965 until about 1973, when demand collapsed. This could happen with AI infrastructure spending as well. If hoped-for returns don’t materialize and the industry pulls back in concert, looking to conserve cash, this “could turn into a serious, sudden, and long-lasting decline in infrastructure spending.”
Neumann suggests that investors “shouldn’t swim upstream, but fish downstream”:
companies whose products rely on achieving high-quality results from somewhat ambiguous information will see increased productivity and higher profits. These sectors include professional services, healthcare, education, financial services, and creative services, which together account for between a third and a half of global GDP and have not seen much increased productivity from automation. AI can help lower costs, but as with containerization, how individual businesses incorporate lower costs into their strategies—and what they decide to do with the savings—will determine success. To put it bluntly, using cost savings to increase profits rather than grow revenue is a loser’s game.
Meanwhile, consumers will be the biggest beneficiaries. Perhaps we could finally see deflation in knowledge-intensive service industries like healthcare and education.