The economy has always known how to cope with change. The steam engine, electricity, the personal computer: each shook the foundations, created new markets, closed old ones and gave rise to a wave of uncertainty. But behind every revolution stood one principle that had held until now: pace.
Change did come, but it unfolded at a pace that allowed adaptation, regulatory, institutional and psychological. Now it seems that the principle of pace has broken. The technology of the 21st century is not only innovative; it is fast, perhaps too fast for the economy.
We live in an era of continuous revolutions: artificial intelligence, automation, synthetic biology, distributed networks. Each carries transformative potential, but what determines the force of its impact is not only its content, but its timing. And today, timing is compressing to the point of collapsing the mechanisms of adaptation.
The economic system, especially in its macro-institutional dimensions (regulation, monetary policy, risk measures, conceptions of the labor market), functions like a body adapted to the pace of a slow train, now being dragged behind a supersonic jet. The indicators that guide central banks respond with a lag of quarters, while the crypto market flips within hours.
International legislation moves like a UNESCO committee, while algorithms change the rules of the game in real time. A new situation has thus emerged, in which there is a gap in pace between innovation and the economy. And this is not merely a technical gap.
It is an epistemic gap: a gap in the ability to understand, respond, forecast and control. Companies built on machine-learning models, for example, generate value at a pace that traditional financial analysis cannot capture. Investors pour capital into them on the basis of unmeasured potential, and then pull it out on the basis of a rumor on X.
The result is markets that lose the connection between value and price, between product and capital, between outcome and indicator. At the same time, models of employment and welfare policy rest on 20th-century ideas such as job stability, tenure and slow training. Meanwhile, industry is changing too fast for a person to complete relevant training before the field itself disappears.
This is not a recession; it is an inability to catch up with change.
The problem is not that technology harms the economy, but that the economy was built on a conception of control, according to which processes develop gradually, measurably, and can be managed with tools such as interest rates, taxes or regulations. But when technology escapes measurement itself, what is left of the economy? Instead of forecasting crises, institutions find themselves responding after the fact; instead of managing risks, they track outdated curves; and instead of leading policy, they are swept along by trends.
Thus a reactive economy is born, not by choice, but for lack of one.
The great challenge of the coming decade is neither only technological nor only economic, but strategic and systemic. The real challenge is how to build economic mechanisms that can operate at the pace of rapid innovation while preserving principles of stability, accountability and human understanding of processes. Perhaps the answer is not to try to "rein in" the pace, but to develop new tools: distributed regulation, indicators adapted to dynamics, real-time computational economics, and even moral models suited to periods of overspeed.
Because if we do not understand that pace is the problem, we will not understand either that pace may also be the key to the solution.