The study explores the uncertainty surrounding the AI economy's effect on economic cycles and the potential of Industry 4.0 to stabilize the global economy amidst various crises. Existing theories on long waves, primarily focusing on technological innovation, are reviewed, highlighting the contributions of Kondratieff, Schumpeter, and Kuznets. The accelerating pace of scientific and technological advancement, particularly within the context of MANBRIC technologies (medical-additive-nano-bio-robo-info-cognitive) and the rise of the AI economy, necessitates a re-evaluation of Kondratieff's theory. The central research question is whether the AI economy contributes to economic growth or exacerbates crisis risks. The authors' hypothesis is that the traditional 'growth-reduction' cycle transforms into linear growth due to sufficient competency reserves, creating new market growth drivers and competencies. The paper's originality lies in its application of the theory of economic cycles to describe the humanization of the AI economy within the context of Industry 4.0, suggesting a gradual, socially adaptive approach to technological progress.

The literature review analyzes contrasting viewpoints on the AI economy's impact on economic cycles. Some scholars emphasize the risks of increased unemployment and social inequality due to automation and the digital divide. Others highlight the AI economy's contribution to accelerated economic growth. The authors note a lack of consensus on the AI economy's role in cyclical economic systems and aim to address this gap by situating the AI economy within their 'competence-innovation-markets' model. The review also covers the social implications of the AI economy, with studies examining artificial empathy in marketing, human-AI communication, and the 'man vs. machine' dynamic. The paper bridges the gap between technocratic and social market economy approaches by advocating a humanistic approach to AI development.

The authors develop a mathematical model using dynamic parameters to describe the relationship between technological competence, innovative technologies, consumer potential, and market development. The model incorporates feedback loops to illustrate the spiral co-development of competencies and new consumer markets. A key component is the non-linear transition coefficient, K(t, M(t)) = k₀(t)(1 + M(t))⁻ᵃ, where α represents market saturation. This coefficient demonstrates how market expansion has limitations and the need for continuous competency development and innovation to avoid market stagnation. The model is simulated using balance equations to illustrate the spiral growth pattern. The authors show how this spiral effect neutralizes crisis phenomena typically associated with the decline phase of Kondratieff waves, transforming it into a near-linear growth process. The model highlights the need for continuous competency development and the creation of technology platforms to generate unique products and avoid economic downturns. The simulation results are supported by empirical evidence from sources including the World Bank, World Intellectual Property Organization, and the US National Bureau of Economic Research, focusing on global economic development indicators, patent data, and business cycles.

Simulation modeling demonstrates a spiral growth of consumer markets for high-tech products, driven by the continuous development of competencies. The model shows a conical growth pattern, indicating limits to growth within specific competency areas, highlighting the need for diversification and the development of new competencies to avoid crises. Empirical data from Southeast Asian countries (China, Japan, South Korea) and the USA are presented, illustrating how effective competency development and innovation can mitigate the downward trend of Kondratieff waves. Analysis of US economic data from 1980 onwards shows a trend toward a less cyclical pattern, with longer periods of growth and shorter periods of recession. The analysis of US R&D expenditure, state vs. private funding, basic vs. applied research, and R&D expenditure as a percentage of GDP, reveals distinct convergent and divergent stages within Kondratieff waves. The findings also show a significant increase in the average time period from economic decline to the next peak and a decrease in the time between peaks and the subsequent decline, indicating a smoothing of the wave dynamics towards a more linear growth trend. The authors present the results of theoretical experiments that show a reduction in the amplitude and frequency of Kondratieff waves due to increased competency development and accelerated introduction of innovations.

The findings support the authors' hypothesis that the traditional cyclical economic model is being transformed by the accelerated development of competencies and innovations, particularly within the AI economy. The study contributes to the literature by expanding Kondratieff's theory, incorporating the AI economy's role, and emphasizing the importance of a humanized approach to AI development. The results contradict perspectives that solely focus on the negative social consequences of AI-driven automation and highlight the positive potential of AI for sustainable economic growth, if managed effectively. The humanized AI economy approach is presented as a strategy for mitigating social risks associated with rapid technological change, promoting social adaptation, and balancing the supply and demand of labor. The study challenges the assumption that Kondratieff's long waves are an inevitable feature of capitalist economies, demonstrating that strategic investments in competencies and innovations can smooth out the cyclical nature of economic development.

The paper provides evidence that the traditional Kondratieff wave model is evolving, with a shift towards a more linear growth trajectory driven by continuous competency development and innovation. The humanization of the AI economy, through a gradual and socially adaptive approach to Industry 4.0, is presented as a key factor in this transformation. The study's main contribution is the development of a model that explains this shift and highlights the importance of proactive competency management in sustaining long-term economic growth. Future research should explore the national-specific aspects of AI economy humanization and adapt the proposed approach to various socio-economic contexts.

The study's limitations include its generalized approach and primarily theoretical elaboration of the humanistic approach to AI economy development. The authors acknowledge the significant variations in socio-economic development and levels of engagement with Industry 4.0 across different countries, and suggest that future research should explore the national specificities of AI economy humanization.