A technology blogger has revived an argument grounded in academic theory to suggest that the current AI investment surge may not represent a new technology cycle, but rather signals the maturity phase of the digital revolution that began in the 1970s.
The framework comes from Carlota Perez, an academic who built on Christopher Freeman's work to develop a model of how technology and finance interact to create long surges of investment. According to Perez's model, these surges run for 50-60 years and follow an S-curve pattern. The two most recent surges identified are the cars-and-oil surge beginning in 1908 and the Information and Communications Technology surge that started in 1971.
The model's structure matters: the first half of each surge involves slow infrastructure build-out, often below public attention. The internet, for example, was a closed academic network for most of its early S-curve phase. Halfway through, after substantial infrastructure has been built and often following a financial crash, 'deployment' companies take over—businesses with actual customers and business models that experience accelerated growth before hitting market limits and becoming ordinary businesses.
Applying this lens to current conditions, strategy and innovation blogger Nicolas Colin has identified what he calls 'late-cycle investment theory.' Under this framework, three indicators suggest the computing and networks era has entered its maturity phase.
First, the 2022 startup funding collapse may not be a temporary correction but a structural shift. When good ideas become obvious to everyone, including well-funded incumbents, the startup model faces real strain as an engine of innovation. The second indicator involves how AI itself is being deployed: not from garage startups but from established tech companies. ChatGPT's breakthrough came from OpenAI backed by Microsoft's vast computing power. Google, Meta, and Amazon responded with billions in capital. This pattern—big tech deploying massive resources against well-understood problems—fits late-cycle theory precisely.
Third, platform saturation now appears nearly complete. Digital transformation has reached most sectors where computing and networks can plausibly function. The sectors that remain—healthcare delivery, education, construction, government services—may reflect the paradigm's natural limits rather than untapped markets waiting for disruption.
Crucially, the deployment pattern of AI itself challenges the idea that a new surge is beginning. At the start of a new technology surge, the moment of transformation is typically understood only in retrospect. The Spinning Jenny, Watt's condensing engine, the Ford production line, and the microprocessor all became obvious inflection points only after the fact. With AI, the moment was highly visible and choreographed. Capital investment for AI is also off the scale for an early-stage surge. Early surges tend to be patchy and not fully understood, with investment scattered across emerging sectors. Current AI investment shows no such pattern of diffuse, tentative exploration.
The theory further suggests that AI functions as an efficiency breakthrough of the computing and networks era, similar to how lean production extended mass production's dominance for decades without replacing it fundamentally. Under this reading, AI optimises the existing paradigm rather than creating a new one.
If this analysis holds, it reframes the current technology narrative. Rather than the beginning of a new era of innovation and investment opportunity, AI would represent the final optimization of a technology cycle now entering its mature phase, with capital seeking the next genuinely transformative paradigm.