Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages
Technological revolutions follow a four-phase pattern: irruption, frenzy, turning point, deployment.
Each phase comes with its specific financial dynamic — bubbles in the frenzy, institutional consolidation in the deployment.
Pérez offers the best framework to argue that AI is not exceptional in its dynamic, only in its object. Gatopardismo with economic rigour: we have seen this before with the railway, electricity, the automobile, semiconductors.
If you are feeling too strongly that "this time is different", Pérez is the correction.
Central argument
Pérez argues that technological revolutions follow a recurrent two-period pattern: an Installation period (irruption and frenzy), where financial capital decouples from production capital and inflates speculative bubbles, and a Deployment period (synergy and maturity), where institutional frameworks catch up and productive capital drives broad societal adoption. The Turning Point between them is not accidental but structurally necessary — a collapse or regulatory reset that redirects capital from speculation to real-economy diffusion. Her central finding is that the 'golden ages' of economic prosperity are not the product of the technology itself but of the institutional and financial realignment that follows the crash.
Critique
Pérez's framework is compelling precisely because it is pattern-based, but that explanatory power comes at a cost: the model is retrospectively coherent in ways that make it difficult to falsify prospectively. The identification of which 'phase' we currently occupy is always contestable in real time — a limitation she largely sidesteps — meaning practitioners can invoke the framework to support almost any strategic position depending on where they locate the present moment. There is also a Euro-American bias in the historical cases, which raises questions about whether the phase dynamics hold in technological revolutions where production and adoption are geographically distributed differently from the outset, as with AI.
Why it matters for product
For a CPO, the most actionable insight is phase-dependent strategy: during frenzy, the right move is not to match speculative feature velocity but to build the institutional and data infrastructure that will compound during deployment — a direct argument against roadmap decisions driven by AI hype cycles. Pérez also provides structural justification for why product orgs should resist pressure to declare transformative impact prematurely: the genuine productivity gains from a technological revolution arrive late, after the consolidation phase, so success metrics and OKRs built around early adoption signals will systematically mislead.