AI could be the end of the digital wave, not the next big thing

I have deliberately tried not to write too much about AI, because the signal gets swamped by the noise. But I think the picture is becoming clearer now. This week on The Next Wave, I’m going to re-publish versions of posts originally on my newsletter, Just Two: one from last summer, and one that goes live this week.

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Just by way of a thought experiment: what if the current surge in the bunch of technologies that goes under the label of ‘AI’ isn’t the beginning of a whole new technology surge, but is actually the final stage of the digital surge that started in the 1970s and accelerated at the turn of the century?

I’ve been wondering this for a while in a vague kind of a way because I haven’t been able to see the business model that supports the huge investment in AI in the USA. This is a long way in to couple of pieces by Nicolas Colin, the strategy and innovation blogger, who has been wondering the same thing, but a lot more coherently. He calls this ‘late cycle investment theory’.

The Perez model

Like me, he is a fan of the work of the academic Carlota Perez, who built on the work of Christopher Freeman to develop a model of how technology and finance interacted to create new long surges of investment, starting with canals and cotton, that run for 50-60 years. (She calls them ‘surges’ because unlike ‘waves’ each technology embeds itself in the society and its infrastructure.)

The two most recent surges are a cars/oil surge, which started in 1908, and the Information and Communications Technology, which started in 1971. The relevant point for the present discussion is that it follows an S-curve, and the first half is slow going, as new infrastructure is ‘installed’.

From infrastructure to ‘deployment’

Halfway through, after a lot of infrastructure has been built out, ‘deployment’ companies take over, with actual customers and business models, and have an accelerated period of growth, before they hit market limits and turn into ordinary businesses. And the investors who have made large returns from that period of growth start looking elsewhere—for the technologies that will make the next surge.

Three indicators

Colin points to three indicators from the tech sector that support this observation that we’re in the ‘late cycle’:

1. The startup funding collapse of 2022 wasn’t just a correction—it may be structural. Startups rely on uncertainty as a competitive edge. When good ideas become obvious to everyone—including well-funded incumbents—the startup model faces real strain.
2. ChatGPT’s breakthrough didn’t come from a garage startup but from OpenAI, backed by Microsoft’s vast computing power. This pattern—big tech deploying huge capital against well-understood problems—fits the late-cycle theory exactly.
3. Platform saturation now looks almost complete. Digital transformation has reached most sectors where computing and networks can plausibly work. What remains—healthcare delivery, education, construction, government services—may reflect the paradigm’s natural limits.

Optimising the existing system

Late-cycle investment theory suggests AI is the efficiency breakthrough of the computing and networks era, not the start of a new one. Just as lean production refined mass production in the 1970s without replacing it, AI optimises the existing paradigm rather than creating a new one.

Colin argues that AI allows computing to reach sectors that have in some ways resisted it, creating the illusion of radical novelty whilst actually representing computing and networks’ final conquest of the physical economy. There is also significant social pushback, as seen in the resistance to data centers and forced AI integration in consumer products, suggesting this technology is not being embraced as a transformational force of abundance.