The development of Kondratieff's theory of long waves: the place of the AI economy humanization in the 'competencies-innovations-markets' model

The paper examines the long-debated phenomenon of long waves in economic, social, and political dynamics, focusing on the role of technological innovation as a driver of Kondratieff cycles. Building on the Kondratieff-Schumpeter tradition and responding to modern, accelerated innovation dynamics, the research asks whether the AI economy supports sustained growth or intensifies crisis risks. The goal is to refine long-wave theory by identifying relationships among economic laws and mechanisms that connect competencies, innovations, and markets, hypothesizing that the traditional cycle of market expansion and contraction transforms into a linear growth process in high-tech economies when sufficient competency reserves enable ongoing market and competency growth. The paper claims originality in applying the economic cycles methodology within Kondratieff’s framework to Industry 4.0, emphasizing social implications and proposing a humanistic approach to development (gradual, stepwise automation) to enable social adaptation and reduce crisis risks.

The paper situates itself within debates on the causes and structure of long waves, referencing Kondratieff, Schumpeter (innovation clusters), and empirical work by Mensch, Van Duyin, and Kleinknecht. It reviews conflicting interpretations of the AI economy’s impact on cyclicality: some argue AI amplifies crisis risks via unemployment and digital divide (Fleischer et al., Matilda Bez & Chesbrough, Prahl & Goh), while others highlight AI’s contribution to growth (Bobanović; De Nicola et al.; Merola; Pan & Yang). Studies on humanization stress ethical and social design of AI (DeFilippis et al.; Ivey; Morimoto; Seufert et al.). Additional literature explores social interaction with AI (Liu-Thompkins et al.; Lew & Walther), human responses to AI agents (Kim et al.), power dynamics (Hong), and autonomy (Formosa), as well as workplace implications and psychological contracts with social robots (Bankins & Formosa). The identified gap is the inconsistent interpretation of AI as a factor in cyclical dynamics; the paper aims to bridge technocratic and social market economy views via a humanistic AI approach.

The authors develop a dynamic systems model linking four time-varying parameters: (1) level of technological competence, (2) level of innovative technologies (intensity of innovation implementation), (3) consumer potential of products, and (4) level of market development. The core mechanism is a positive feedback loop between competency development and new market growth, yielding spiral co-development of competencies, radical innovations, and expanding markets. The model formalization includes balance equations: - Let IC(t) denote funding for increasing key technological competencies at time t; let M(t) represent a generalized consumer market indicator. - Market growth equation: M(t) = K(t)·IC(t−h), where K(t) is a transition coefficient reflecting how competency funding translates into market growth; h > 0 is a time lag. - Rewritten as a competency-management recursion: IC(t) = L(t)·K(t)·IC(t−h), where L(t) captures management intensity. - To model market saturation, K is made non-linear: K(t, M(t)) = k0(t)·(1 + M(t))^(−α), with 0 < α < 1, so that as M grows, marginal utility declines, constraining market expansion and signaling the need for product renewal or new markets. Simulation setup: initial conditions IC(0)=1, M(0)=1; parameters L(t)=2.5, k0=2, α=0.5. Simulations show rapid mutual growth of IC and M followed by saturation; with non-stationary balance coefficients, spiral dynamics emerge, consistent with the hypothesized co-development. The model implies continuous managerial actions are required to sustain competencies and innovation; without control, wave-like processes dampen. Conceptual extensions: The paper discusses technological platforms as drivers to overcome crises by enabling radical competencies and products; posits periodic 'intellectual explosions' leading to technological singularity-like accelerations that reduce pre-production costs and increase accessibility of high-consumer-value products, expanding markets and boosting growth. Empirical component and data sources: Theoretical experiments are supported by secondary data from the World Bank (global development indicators), WIPO (patent priorities by country), NBER (US business cycles), and the US Department of Commerce (economic indicators). Empirical analyses include: (1) GDP dynamics for Europe, Southeast Asia, and the USA since 1980; (2) patent leadership by technology domains (Southeast Asian countries leading, especially China, Japan, South Korea; USA typically 2nd/3rd); (3) US R&D dynamics 1960–2000 examining divergent vs convergent stages via indicators: total R&D expenditures, federal vs private R&D shares, basic vs applied research shares, and R&D as percentage of GDP; (4) historical US business cycle durations 1854–2020 (peak-to-peak and trough-to-trough).

- The competencies-innovations-markets model exhibits a positive feedback loop that can transform the recession phase of Kondratieff waves into a near-linear growth process, smoothing long-wave cyclicality when competencies are continuously developed and managed. - Non-linear saturation of consumer markets necessitates continual renewal (new products/markets); proactive competency development can avert deep downturns typical of classic long-wave declines. - Simulation results (IC(0)=1; M(0)=1; L=2.5; k0=2; α=0.5) show rapid initial growth of competencies and markets, moving to stationary values; with time-varying coefficients, spiral co-development appears, consistent with sustained growth. - Empirical observations: • Since ~1980 (turning point from IV to V Kondratieff wave), Southeast Asia has sustained super-high GDP growth, the USA shows steady growth, while Europe shows stagnation without a clear downturn—consistent with differing innovation/competency dynamics. • Patent data indicate technological leadership in key domains by China, Japan, and South Korea; the USA usually ranks 2nd/3rd. Such leadership correlates with avoiding downturns during long-wave downward phases. • In the US (1960–2000), during divergent stages, federal R&D exceeds private R&D (state-led breakthrough focus); during convergent stages, private R&D dominates (diffusion and incremental improvement). Basic research shares are higher in divergent stages; applied research predominates in convergent stages. R&D as a share of GDP declines at divergence onset (resource shifts) and rises during convergence (applied focus). • Business cycles (US, 1854–2020): average trough-to-peak duration increased ~2.4×, while peak-to-trough duration decreased ~1.9×, indicating smoother, elongated expansions and shorter contractions—consistent with a trend toward linearized growth influenced by accelerated innovation. - Humanization of the AI economy—gradual, step-by-step Industry 4.0 rollout—reduces social costs (unemployment, digital divide), mitigates crisis risks, and supports sustained growth compared to a purely technocratic, fast-automation approach.

The findings extend Kondratieff’s long-wave theory by positioning the AI-driven Industry 4.0 paradigm within a competencies-innovations-markets framework where positive feedback between competencies and market development can smooth cyclical downturns. The observed divergent (search, state-led, basic research) and convergent (diffusion, private-led, applied research) stages provide a quantitative lens for periodizing long waves and understanding transitions between techno-economic paradigms. Empirical patterns in patent leadership and business cycle durations support the model’s implications. Relative to literature emphasizing either growth acceleration or crisis amplification from AI, the paper argues the impact hinges on development strategy. A technocratic, rapid-automation approach raises social costs and crisis risks; in contrast, a humanistic approach—gradual implementation and balanced labor market adjustment—enhances social adaptation, reduces inequality (narrowing the digital divide), and stabilizes macro-dynamics. Thus, humanization becomes a policy lever that aligns innovation dynamics with social capacity, transforming wave dynamics toward sustained, near-linear growth and reconciling technocratic and social market economy perspectives.

The study proposes an evolution of Kondratieff’s theory: under accelerated competency development and continuous innovation, long waves can be transformed, smoothing downturns into a growth process approximating linearity. In the AI economy, the traditional cycle of expanding and contracting markets can be replaced by steady growth when sufficient competencies catalyze ongoing creation of new products and markets. Crucially, humanization of the AI economy—gradual Industry 4.0 adoption and stepwise automation—reduces cyclicality by lowering crisis risks and supporting stable growth. The work clarifies the nature of economic cycles under AI-driven transformation and offers practical guidance: prioritize humanization to stabilize cycles and harness Industry 4.0. Future research should adapt the approach to national contexts, accounting for socio-economic and institutional heterogeneity across countries.

The analysis is primarily theoretical and generalized; empirical illustrations rely on aggregate indicators and secondary data. Results may vary by national context due to differences in socio-economic development, institutional frameworks, and readiness for Industry 4.0. The humanization approach likely requires country-specific adaptation, which is beyond this study’s scope and is recommended for future research.