AI and green technology are the new engines of creative destruction, reshaping productivity and competitive hierarchies; how much they raise broad prosperity depends on long-wave cycles, supply-chain fragilities and political‑economic forces that determine diffusion, market structure and distributional outcomes.
Economic Waves, Crises and Profitability Dynamics of Enterprises: A Review in the Context of Kondratieff, Schumpeter and Mandel Approaches
Combining Kondratieff, Schumpeter, and Mandel perspectives, the paper argues that AI and the green transition act as contemporary Schumpeterian waves whose productivity and market effects are mediated by long-wave cyclical patterns and political-economic constraints, producing heterogeneous impacts on firm profitability and distribution.
In the historical development of the capitalist system, long waves have played a fundamental role in sustaining its dynamic structure. Among the factors influencing the formation of these waves, various socio-economic and political events such as financial crises, wars, famines, and pandemics stand out. Periodically occurring crises in capitalist economies have led economists to investigate their causes and effects; in particular, the repercussions of these crises on the socio-economic balances of the social structure and their reflections on business profitability have constituted a broad area of research. In this context, long waves and crisis cycles hold significant importance in the analysis of economic growth and stagnation processes. This study examines economic waves within the theoretical frameworks of Mandel, Schumpeter, and Kondratieff, and compares these approaches in terms of the main factors influencing the waves. While Kondratieff's long wave theory posits the regular recurrence of periods of economic stagnation and revival, inferences are provided on how contemporary crises such as the COVID-19 pandemic, global inflation, and geopolitical tensions affect corporate cost and revenue structures. Schumpeter, on the other hand, explained these cycles through technological innovations and entrepreneurial activities, with attention drawn to the effects of new technological waves like artificial intelligence and green transformation on companies' operational efficiency and competitiveness. Mandel focused on the decisive role of capitalist production relations and class struggles in this process, emphasizing the pressures of global supply chain fragilities and trade wars on corporate profitability. As a result, this study thoroughly examined the effects of long waves on economic and social dynamics through these approaches, particularly how they shape the profitability and sustainability of businesses, and revealed how the capitalist system is re-shaped by crises. This study interprets current developments and firm profitability for economic waves, highlighting the effectiveness of these theories in explaining contemporary events.
Summary
Main Finding
Long-wave (Kondratieff/Schumpeter/Mandel) frameworks remain useful for understanding how recurring techno-economic paradigms and crises reshape firm profitability. Contemporary shocks (COVID-19, inflation, geopolitical tensions) and the diffusion of Industry 4.0 technologies — notably artificial intelligence (AI) and advanced automation — can be interpreted as a prospective “Sixth Long Wave” that alters cost/revenue structures, generates concentrated surplus profits for winners, and interacts with structural factors (capital composition, class relations, global supply chains) to determine whether gains are broad-based or destabilizing.
Key Points
- Paper type: theoretical review comparing three long-wave approaches (Kondratieff, Schumpeter, Mandel) and applying them to contemporary developments.
- Kondratieff: long waves as relatively regular cycles of expansion and contraction observable in prices, production, and investment; crises recur and reshape paths of revival and stagnation.
- Schumpeter: long waves driven by clusters of radical technological innovations and entrepreneurial “creative destruction”; new waves (digital/AI) can produce renewed growth and firm-level competitive shifts.
- Mandel: emphasizes internal capitalist relations — profitability dynamics, class struggle, unequal development — as central; surplus profit arises via technological advantage, external exploitation, and shifts in the organic composition of capital.
- Marxian insights highlighted: mechanization/automation raises the organic composition of capital, creating a tendency for the general rate of profit to fall, while temporary “surplus profit” periods motivate capital re-allocation, imperialist expansion, and financialization.
- Contemporary context: COVID-19, global inflation, trade tensions and supply-chain fragilities interact with technological diffusion (AI, green transition) to affect firms’ costs, revenues, and investment incentives.
- Heterogeneous firm outcomes: early/mass adopters of new technologies and large monopolies are likely to capture disproportionate surplus profits, while SMEs and labor can be disadvantaged, exacerbating inequality.
- Policy and structural responses (financialization, credit expansion, relocation of capital) can delay but not prevent deeper profit-rate crises; crises function as moments of structural reorganization.
Data & Methods
- Methodological approach: literature review and theoretical synthesis (comparative, historical interpretation).
- Sources and evidence: secondary literature on Kondratieff, Schumpeter, Mandel, Marxian theory, historical episodes (steam engine, electrification, post-1940 electronic/automation wave), and contemporary events (pandemic, inflation, Industry 4.0).
- No original empirical dataset or econometric analysis; arguments built from theoretical constructs, historical cases, and interpretive application to present technologies.
- Limitations: conceptual/theoretical focus means empirical generalizability is not tested; selective use of historical examples and Marxian constructs.
Implications for AI Economics
- AI as a techno-economic paradigm: AI/automation can plausibly function as a core element of a Sixth Long Wave — altering productivity, firm organization, market structure, and the geographic/sectoral locus of surplus profits.
- Profitability dynamics:
- Short-run: AI adoption can generate surplus profits for frontrunners via productivity advantages, data/network effects, and market power.
- Medium/long-run: widespread AI-driven capital intensity raises the organic composition of capital; per Marxian reasoning, this can exert downward pressure on the general rate of profit unless countervailing dynamics (new markets, increased exploitation, falling capital costs, faster turnover) intervene.
- Distributional effects: AI may intensify unequal gains between large incumbents and smaller firms/workers, amplifying concentration, rent extraction, and bargaining asymmetries — core concerns for AI economics.
- Financialization and crisis risk: if AI investment is financed through credit and speculative flows, the resulting asset bubbles could mimic past patterns (e.g., 2008), increasing systemic fragility.
- Supply chains and geopolitical constraints: supply-chain fragility and trade conflicts mediate firms’ ability to capture AI gains (hardware bottlenecks, data localization, export controls), implying that macro and geopolitical context matters as much as technology per se.
- Policy and governance implications for AI economics:
- Need for industrial and competition policy to prevent excessive concentration and to diffuse AI benefits (support SMEs, open standards, data-sharing frameworks).
- Labor and social policy (retraining, income supports, collective bargaining) to manage displacement and bargaining-power shifts.
- Regulation of finance and targeted public investment to avoid destabilizing speculation and to finance complementary assets/infrastructure (digital public goods, data governance).
- International coordination around trade, data flows, and technology controls to reduce negative spillovers and fragmentation.
- Research agenda for AI economics inspired by the review:
- Firm-level empirical studies linking AI adoption to changes in profit margins, rates of return, and capital composition across sectors and countries.
- Measurement efforts: operationalize “organic composition of capital” in modern AI-intensive production (capital embodied in data/algorithms/hardware vs. labor).
- Causal identification of AI’s effects on productivity vs. market power (distinguish innovation-driven gains from rent extraction).
- Macro-financial modeling of AI-driven investment cycles and crisis propagation.
- Distributional and labor-market studies assessing how AI alters surplus value capture, wage bargaining, and employment composition.
- Cross-country comparisons to study how institutional settings mediate AI’s economic impacts.
Note on scope: this article is a conceptual/theoretical review (2026) rather than an empirical paper; implications above are interpretive extensions to connect the review to questions central to AI economics.
Assessment
Paper Typetheoretical
Evidence Strengthlow — The paper is a qualitative, comparative theoretical synthesis without micro-econometric or experimental identification; claims are plausible and grounded in historical interpretation but not tested with causal empirical evidence.
Methods Rigormedium — The work conducts a careful intellectual-historical review and conceptual mapping across three literatures (Kondratieff, Schumpeter, Mandel) and applies them to contemporary episodes, which is appropriate for theory-building; however it lacks formal models, pre-registered hypotheses, or empirical validation that would raise methodological rigor.
SampleNo original microdata; a qualitative literature synthesis and interpretive analysis of historical long-wave episodes and recent macro/firm-level trends (e.g., COVID-19, inflation, geopolitics, AI and green-technology adoption) based on secondary sources.
Themesinnovation productivity inequality adoption governance labor_markets
GeneralizabilityNo empirical validation limits external/internal validity — mechanisms and magnitudes are not estimated., Broad, stylized framework may mask heterogeneity across sectors, firm sizes, and countries., Historical long-wave interpretation may not predict timing or magnitude of contemporary outcomes., Institutional and policy contexts (regulation, labor relations, trade regimes) can materially alter implications., Assumes AI and green tech operate as general-purpose waves; applicability varies with technological specifics and diffusion patterns.
Claims (20)
| Claim | Direction | Confidence | Outcome | Details |
|---|---|---|---|---|
| Kondratieff, Schumpeter, and Mandel each highlight different drivers of capitalist long waves: Kondratieff emphasizes regular technological-driven renewal, Schumpeter emphasizes entrepreneurship and innovation-led creative destruction, and Mandel emphasizes class relations and production structures. Fiscal And Macroeconomic | mixed | high | theoretical drivers of capitalist cycles |
0.06
|
| Contemporary shocks (COVID-19, global inflation, geopolitical tensions) interact with long-wave mechanisms to reshape firms' cost and revenue structures. Firm Revenue | mixed | medium | firm cost structures and revenue prospects |
0.04
|
| New technological waves—most notably artificial intelligence (AI) and the green transformation—act as Schumpeterian forces that can alter productivity, competition, and profitability. Firm Productivity | mixed | medium | productivity, competitive dynamics, firm profitability |
0.04
|
| Supply-chain fragilities and trade conflicts (emphasized by Mandel) mediate distributional and macroeconomic outcomes during long waves and crises. Fiscal And Macroeconomic | mixed | medium | distributional outcomes and macroeconomic indicators (e.g., income distribution, output volatility) |
0.04
|
| Kondratieff's framework is useful for identifying broad periodicities (recurring phases of expansion and stagnation) in capitalist development but is less specific about microeconomic mechanisms. Other | null_result | high | ability to identify periodicities versus micro-mechanisms |
0.06
|
| Schumpeterian cycles are driven by clusters of technological innovations and entrepreneurial activity; AI and green technologies represent contemporary innovation clusters with strong potential for productive disruption. Innovation Output | positive | medium | innovation-driven economic disruption and cycle dynamics |
0.04
|
| Mandel's account—that capitalist production relations, class struggle, and global imbalances shape the course and consequences of waves—implies that crises expose and amplify supply-chain fragilities and bargaining conflicts that affect profitability. Firm Revenue | negative | medium | firm profitability and bargaining outcomes |
0.04
|
| Contemporary crises change firms' cost structures (logistics, inputs, financing) and revenue prospects (demand shifts, market access). Firm Revenue | mixed | medium | firm costs (logistics, inputs, financing) and revenues (demand, market access) |
0.04
|
| AI and the green transformation function as modern long-wave drivers by improving operational efficiency, enabling new products and services, and reorganizing competitive hierarchies. Firm Productivity | positive | medium | operational efficiency, product/service innovation, competitive hierarchy changes |
0.04
|
| The three frameworks (Kondratieff, Schumpeter, Mandel) are complementary: Kondratieff frames periodicity, Schumpeter provides micro-mechanisms of innovation-driven change, and Mandel foregrounds socio-political constraints and distributional outcomes. Other | null_result | high | comprehensiveness of explanatory framework for long waves |
0.06
|
| Firms' profitability and sustainability are shaped both by technological adoption (which can raise productivity and market power) and by structural pressures (trade wars, labor relations, supply constraints) that can erode margins. Firm Revenue | mixed | medium | firm profitability and sustainability (margins) |
0.04
|
| AI is a Schumpeterian general-purpose technology that can increase aggregate productivity potential but will do so unevenly across firms and sectors, producing heterogeneous effects on profitability. Firm Productivity | mixed | medium | aggregate productivity potential and cross-firm profitability heterogeneity |
0.04
|
| AI adoption exerts downward pressure on routine labor costs while raising capital and recurrent costs (R&D, computing infrastructure, data, cybersecurity); higher fixed and lower marginal costs favor scale and incumbents with access to data and capital. Market Structure | mixed | medium | labor costs, capital/recurrent costs, market concentration/scale advantages |
0.04
|
| AI can enable new revenue streams (platforms, personalized pricing, automation-as-a-service) and increase market concentration, producing 'winner-takes-most' dynamics that raise profit rates for leading adopters and compress margins for laggards. Market Structure | mixed | medium | profit rates (leaders vs laggards), market concentration, firm margins |
0.04
|
| Crises (pandemics, supply shocks) tend to accelerate digital and AI adoption, potentially shortening adjustment time to new technological regimes. Adoption Rate | positive | medium | speed of digital/AI adoption |
0.04
|
| Inflation and geopolitical fragmentation can raise the cost of AI deployment (hardware shortages, supply constraints) and complicate cross-border data flows, slowing diffusion or creating regionalized AI ecosystems. Adoption Rate | negative | medium | cost of AI deployment, diffusion speed, regionalization of AI ecosystems |
0.04
|
| AI-driven productivity gains may not translate into broad-based demand if income is concentrated among capital owners, which could dampen aggregate profitability over time. Fiscal And Macroeconomic | negative | low | aggregate demand and aggregate profitability |
0.02
|
| Class and labor responses (bargaining, regulation, strikes, political backlash) can shape AI adoption patterns, increase the costs of labor substitution, and affect the redistribution of AI rents. Adoption Rate | mixed | low | adoption patterns, labor substitution costs, redistribution of rents |
0.02
|
| Policymakers should combine competition policy, data governance, retraining/redistribution measures, and targeted R&D/green-AI incentives to manage the transition and preserve broad-based demand. Governance And Regulation | positive | speculative | effectiveness of policy mix in managing technological transition and preserving demand |
0.01
|
| Empirical validation of the integrated Kondratieff–Schumpeter–Mandel framework requires firm-level adoption and profitability data, sectoral investment series, and cross-country comparisons using panel methods and identification strategies (e.g., diff-in-diff, IV). Other | null_result | high | data/methods needed for empirical validation of the theoretical framework |
0.06
|