AI Innovation Wave with Adaptive Control Policy: A Schumpeter-Minsky Perspective

Willi Semmler () and Samuel Owusu ()
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Willi Semmler: Department of Economics, New School for Social Research, USA and Bielefeld University, Germany
Samuel Owusu: Department of Economics, New School for Social Research, USA

No 2613, Working Papers from New School for Social Research, Department of Economics

Abstract: Since the 19th century, we have seen long-run waves of innovation: first with the introduction of machinery, then with the railroad boom, the creation and diffusion of electricity, the automobile boom, the nuclear power wave, and the internet and digitization boom. AI, as a new innovation wave, is best understood, at least for now, as a rapidly developing cluster of technologies within the wider information revolution, such as numerical, algorithmic, and software innovations, rather than as driven by a single-purpose technology. The scale and wide range of buildup, as well as the possible perils arising from them, make the AI investment boom an urgent case for examining a question that is both technological and financial: How does such a major wave of innovation influence capital and innovation investments? Does it drive excessive borrowing and an asset valuation boom, thereby increasing the economy’s and financial market’s vulnerability, possibly triggering deterioration in credit conditions, declining investments and cash flows, and widening credit spreads and deteriorating refinancing conditions, with sudden disruptions and corrections? Those joint structural changes on the real and financial sides create new governance and adaptive policy challenges. Fixed rules or fixed behavioral features can be costly and ill-suited to economic dynamics that move from new inventions and innovations to rapid diffusion, then to severe balancesheet stress, deleveraging, and possibly financial crashes. We propose a model-guided understanding of the interaction of a Schumpeterian innovation wave with Minskyan financial fragility dynamics. Model Predictive Control (MPC) is a useful numerical method to explore the dynamics in this setting because it solves finite-horizon adaptive policy problems starting from the current state and helps implement governance rules by considering the interactions of the various aspects.

Pages: 32 pages
Date: 2026-10
New Economics Papers: this item is included in nep-pke
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