Artificial Intelligence and Energy Security: Plateaus, Bifurcations, Sinusoids, and Paradoxes of Development in the Context of Sustainability

The chosen topic—artificial intelligence and energy security—is of profound relevance, representing one of the key trajectories in contemporary technological and socio-economic development. A bibliometric search in the Scopus database highlights the expanding research landscape of artificial intelligence (AI) and its emerging intersection with energy security [1].

The query TITLE-ABS-KEY (“artificial intelligence” OR “AI” OR “machine learning” OR “deep learning” OR “neural network*”) retrieved 2,841,358 documents, reflecting the vast scope of AI-related scholarship. In contrast, the query TITLE-ABS-KEY (“energy security” OR “energy independence” OR “energy resilience” OR “energy sustainability”) yielded 27,860 records, signifying a more specialised yet well-established field. When these domains were combined—TITLE-ABS-KEY ((“artificial intelligence” OR “machine learning” OR “deep learning” OR “neural network*” OR “digital transformation”) AND (“energy security” OR “energy resilience” OR “energy independence”))—only 850 records were identified, revealing a nascent but rapidly developing area of interdisciplinary inquiry. Restricting the search to titles alone produced just 19 results, confirming that direct academic engagement at the intersection of AI and energy security remains limited, though it is expanding swiftly.

The rapid convergence of AI and energy security (ES) thus forms a dynamic research frontier characterised by both promise and complexity. The reviewed studies collectively indicate that AI is not merely a technological adjunct but a transformative force reshaping energy systems, governance mechanisms, and policy frameworks across scales and regions.

AI’s integration into urban energy systems has been shown to enhance resilience and adaptability [2]. Econometric and spatial analyses demonstrate that AI technologies—particularly predictive maintenance, smart grid optimisation, and energy forecasting—reduce energy intensity and strengthen real-time response capacity. These innovations help bridge the “resilience gap”, creating feedback loops that enable adaptive governance and resource efficiency. Within the conceptual framework of this Editorial, such developments represent an ascending plateau in AI-driven transformation, where incremental innovations begin to yield nonlinear benefits in governance and performance.

Further evidence suggests that AI-enhanced green finance plays a pivotal role in promoting regional energy resilience [3]. Machine learning models reveal that economies with higher digital financial inclusion exhibit greater renewable adoption and reduced vulnerability, marking a bifurcation point in the evolution of energy systems—where AI-augmented financial structures diverge from conventional policy mechanisms to form new equilibria of efficiency and sustainability.

At the macro level, AI adoption is positively correlated with national energy security indicators, including energy self-sufficiency, system stability, and import diversification [4]. However, this growing dependence introduces new paradoxes of vulnerability: algorithmic bias, cyber risk, and data dependency can undermine the very resilience AI seeks to strengthen. This duality aligns with the Editorial’s broader argument—that the digitalisation of energy governance simultaneously enhances and complicates security architectures.

A comprehensive triangulated review [5] identifies three primary operational functions of AI in energy security: predictive analytics for risk assessment, optimisation of resource allocation, and real-time situational control. Yet, the review also highlights that current research remains fragmented and predominantly technocentric, often overlooking socio-political and ethical dimensions. This oscillation between technological enthusiasm and critical reflection reflects a sinusoidal pattern of development—cycles of innovation and reassessment that shape the evolving discourse on AI’s role in sustainable governance.

Further modelling research reinforces this nonlinear dynamic [6]. Machine learning techniques reveal that AI can both stabilise and destabilise energy systems depending on the structure of feedback mechanisms. Minor algorithmic or regulatory changes may therefore lead to vastly divergent outcomes, from enhanced resilience to systemic fragility—a direct manifestation of the bifurcation metaphor central to this Editorial.

Complementary studies link digital finance to urban energy resilience [7], demonstrating that AI-enabled ecosystems promote investment diversification and more adaptive resource allocation. Similarly, inclusive green finance frameworks [8] show that AI-driven decision support aligns capital flows with energy vulnerability metrics, establishing self-correcting feedback loops fundamental to sustainable governance.

AI applications in energy transitions underscore its capacity to accelerate decarbonisation, optimise distributed generation, and enhance supply–demand coordination [9]. Yet, these benefits must be balanced against governance imperatives addressing ethical, cybersecurity, and labour-related risks. The evidence points to a cyclical evolution in AI–energy relations, marked by technological plateaus of maturity and paradoxical regressions driven by institutional or social constraints.

AI-driven innovation has also been shown to strengthen energy resilience through the mediating influence of green finance [10]. Using cross-country data and structural equation modelling, it has been demonstrated that AI significantly expands renewable energy capacity, reduces carbon dependency, and improves grid stability—effects that are amplified within economies possessing mature financial ecosystems. This synergy operates as a resilience multiplier, wherein digital intelligence and sustainable finance reinforce one another. Conceptually, this represents another plateau in the co-evolution of AI and energy security, as technological and financial capital converge to sustain progress.

Further extending this trajectory, machine learning approaches applied to rural sustainability reveal that local energy independence depends as much on social learning and participatory governance as on technological deployment [11]. AI tools enable decentralised decision-making, mapping optimal bioenergy configurations and forecasting demand fluctuations. This constitutes a bottom-up bifurcation within the AI–ES landscape, signifying a structural shift from centralised grids toward community-scale energy sovereignty.

The global dimension is explored through the integration of AI and macroeconomic modelling to analyse energy supply chains [12]. These findings indicate that AI enhances transparency and anticipatory risk management across international logistics networks, particularly for critical minerals and fuel transport. Yet, algorithmic dependencies can engender second-order vulnerabilities, rendering systems overly optimised and less adaptable to shocks. This dynamic expands the paradox dimension of AI–ES research: digital integration that fosters resilience may simultaneously erode systemic flexibility if redundancy and oversight are neglected.

A systems-oriented approach linking digital transformation, green practices, and energy security further illustrates AI’s role as a connective infrastructure harmonising economic, environmental, and governance subsystems [13]. AI adoption appears to catalyse “sustainable prosperity” when aligned with institutional reform and human capital development; absent such alignment, it risks deepening inequalities and ecological pressures. This cyclical interaction exemplifies the sinusoidal pattern of digital development—periods of progress alternating with phases of socio-political inertia.

At a micro level, advanced machine learning has demonstrated its capacity to optimise renewable generation in isolated regions, as evidenced by research from Mindanao, Philippines [14]. By enhancing solar and wind forecasting, AI systems reduce outages and fuel reliance, extending the benefits of energy security to peripheral and underserved communities. This stage represents a micro-level plateau in development—local stability achieved through algorithmic precision and contextual adaptation.

Machine learning has also been used to explore unconventional intersectoral linkages. Analysis of the relationship between tourism and energy security in the United States reveals that tourism intensity influences energy vulnerability through transportation, infrastructure strain, and policy prioritisation [15]. AI models facilitate early detection of instability patterns, allowing for more responsive policy design. Here, AI operates as an anticipatory governance instrument, demonstrating how data intelligence transcends traditional sectoral boundaries.

From an engineering perspective, the optimisation of hybrid microgrids illustrates AI’s technical maturity. Neural networks have been employed to balance competing objectives—cost minimisation, reliability, and renewable integration—in real time [16]. This represents a technical plateau of development, where advanced optimisation enables resilient microgrids aligned with both economic and environmental imperatives.

At the methodological frontier, quantitative hybrid models integrating entropy-weighted TOPSIS with machine learning offer new means of assessing national and regional energy security [17]. These models dynamically weigh indicators such as supply diversification, technological innovation, and geopolitical risk, capturing the adaptive and nonlinear behaviour of modern energy systems. The transition toward algorithmic governance—decisions guided by real-time analytics rather than static indicators—marks a plateau of methodological sophistication, redefining how energy security is measured and managed.

Further, deep learning and geospatial intelligence have been applied to map distributed solar systems using UAV imagery and convolutional neural networks [18]. This innovation transforms energy security from a macroeconomic construct into a fine-grained, spatially explicit concept, enabling the identification of underserved communities and the optimisation of renewable deployment. Methodologically, this represents a bifurcation, signalling a shift towards hyper-localised, data-intensive governance models that challenge traditional top-down approaches.

In high-risk sectors, such as nuclear energy, AI is being introduced to enhance operational safety and predictive maintenance [19]. The Belarusian experience demonstrates how algorithmic oversight improves anomaly detection and process reliability in environments historically resistant to automation. Yet, this technological advance also introduces paradoxical risks—cyber vulnerabilities, algorithmic opacity, and dependencies on foreign digital infrastructures. This embodies the paradox of intelligent centralisation: as AI enhances precision and control, it simultaneously deepens systemic dependency on opaque technological systems and external expertise.

Hassan and Megahed [20] advance an integrative urban resilience framework employing machine learning to optimise energy performance across socioeconomic, infrastructural, and environmental dimensions. Their model synthesises diverse datasets—ranging from building energy use and urban morphology to social vulnerability indices—to forecast resilience under varying climatic and policy scenarios. The results demonstrate that cities embedded with AI analytics achieve greater adaptability and resource efficiency. Yet, the authors emphasise the importance of human-centred design, cautioning that purely algorithmic optimisation may overlook social justice and community inclusion. Conceptually, this oscillation between efficiency and equity exemplifies the sinusoidal dynamic of technological progress in sustainability transitions: periods of rapid advancement followed by ethical recalibration.

At the microeconomic level, AI has begun to transform financial security assessment. Melnychenko [21] questions whether AI is sufficiently mature to evaluate enterprise financial stability, showing that algorithmic systems can model liquidity, solvency, and credit risk with enhanced precision. However, limitations in interpretability and transparency persist. Within the broader context of this Editorial, this introduces a foundational layer of the AI–security nexus: the capacity of AI to reinforce firm-level stability as a cornerstone of systemic energy and financial resilience.

At the macroeconomic scale, AI governance has emerged as a pillar of green economic growth within the European Union [22]. By improving energy efficiency, stimulating innovation diffusion, and supporting the circular economy, AI contributes meaningfully to sustainability—though only when guided by coherent and adaptive policy frameworks. This stage represents a plateau of institutional maturity, where digital intelligence becomes an integral component of strategic governance. Complementary analysis demonstrates that AI-driven digital business models significantly enhance energy efficiency across EU states through optimised logistics, production, and consumption patterns [23]. These findings reaffirm the cross-regional validity of the AI—energy efficiency—security mechanism previously observed in empirical work [2,8].

The role of AI within corporate sustainability extends further through its influence on Environmental, Social, and Governance (ESG) performance [24]. AI-enabled analytics enhance transparency, accountability, and value creation, aligning environmental objectives with market incentives. Yet, this relationship remains cyclical: AI strengthens ESG legitimacy when institutional trust is robust but risks eroding it under weak governance—a manifestation of the sinusoidal dynamic of legitimacy in digital transformation.

Nationally, the development of systemic frameworks for energy security underscores AI’s growing strategic function. A case study of Ukraine [25] integrates technological innovation, economic stability, and environmental safety within a unified model, implicitly positioning AI as a central tool for predictive management and monitoring. This marks a bifurcation point—the transition of AI from an auxiliary instrument to a structural pillar of national energy security policy.

AI’s interdisciplinary scope extends beyond systems optimisation to the shaping of social narratives. Sentiment analysis research on national green branding [26] demonstrates that AI-driven natural language processing can capture and influence public perception, thereby enhancing the legitimacy of sustainability policy. This highlights AI’s dual role: securing infrastructures while simultaneously constructing socio-political meaning within the sustainability discourse.

In the domain of sustainable finance, AI enhances the efficiency of capital allocation for energy infrastructure development [27]. Its application in risk assessment enables more precise evaluation of investment potential and resilience, positioning AI as both a financial instrument and a policy catalyst—a bridge between sustainability objectives and infrastructural stability.

However, digital inclusion remains an essential precondition for equitable green growth. Findings show that unequal access to digital infrastructure and AI technologies can exacerbate socio-economic disparities, creating paradoxes of inclusion wherein tools designed to promote sustainability inadvertently entrench inequality [28]. This mirrors earlier observations [13] concerning socio-technical asymmetries within the digital transition.

Kharazishvili et al. [29] contribute to this discourse by developing a system of indicators integrating environmental and energy metrics within the broader security dimension. Their methodological framework provides a basis for future AI-supported sustainability monitoring and represents a conceptual plateau bridging environmental science, systems theory, and data analytics.

The evolving relationship between artificial intelligence and energy security reflects deep systemic transformations at the nexus of digitalisation, sustainability, and global stability. Modern AI cannot exist independently of the energy systems that sustain its computational power, giving rise to an intrinsic energy–intelligence continuum. Within this continuum, AI functions simultaneously as a consumer, optimiser, and governor of energy processes—embodying the paradoxical interdependence between technological intelligence and energetic resilience.

Artificial intelligence (AI), on the one hand, stimulates the development of energy technologies by generating demand for new forms of energy production, storage, and distribution; on the other, it functions as an instrument of optimisation and efficiency through the creation of advanced algorithms, intelligent networks, and predictive control systems. Thus, AI simultaneously serves as a catalyst for increasing energy consumption and as a mechanism for its rationalisation [30].

This duality acquires particular significance given the exponential pace of technological evolution. The rate of AI advancement increasingly exceeds the adaptive capacity of existing energy infrastructures, producing resource constraints and emergent risks to energy security. Within this context, security may be conceptualised as an interval of stable system functioning—one within which performance remains predictable and quality is maintained despite external and internal disturbances. Yet, the boundaries of this stability are in constant flux, demanding continuous scientific reassessment of the variables that underpin the resilience and safety of energy systems.

The purpose of this Editorial is therefore twofold: to delineate potential research trajectories within the artificial intelligence–energy security (AI–ES) domain, and to stimulate academic reflection on the nonlinear processes that characterise its evolution. It seeks to articulate a conceptual framework grounded in the categories of plateaux, bifurcations, sinusoids, and paradoxes of development [31,32]. These categories are proposed as analytical models for interpreting the complex interactions between AI and energy security in the broader context of sustainability.

To proceed, it is first necessary to clarify the conception of energy adopted herein. In its broadest sense, energy is understood as a form of motion—physical, systemic, structural, and informational. It constitutes the foundational basis of existence and transformation for any system, as motion represents the actualisation of potential, while potential itself embodies motion in its latent state. Accordingly, energy should not be perceived merely as a physical quantity but as a universal principle of transformation—an ontological medium enabling the transition from possibility to actuality, from equilibrium to dynamism (Figure 1).

The history of human development is, at its core, a continuous process of optimisation. Every invention and technological revolution embodies humanity’s enduring pursuit of greater efficiency in its interaction with the world—a quest to minimise effort and maximise results. From the mastery of fire to the emergence of artificial intelligence, each breakthrough reflects an attempt to refine the transformation of energy, information, and matter into ever more effective forms.

The control of fire marked humanity’s first technology of optimisation: the management of an external energy source that ensured survival and accelerated civilisation’s advance. The invention of the wheel represented a pivotal moment in mechanics and, consequently, in every domain where mechanical motion served to amplify productivity and ease labour. The advent of writing enabled the optimisation of knowledge transmission, laying the foundation for science, education, and collective human memory.

The measurement of time brought structure to agricultural cycles, religious rituals, and systems of governance. The creation of money rationalised exchange and stimulated economic activity. The invention of the microscope and telescope expanded the boundaries of human perception, propelling the natural sciences forward. The printing press initiated the era of mass literacy, while the steam engine symbolised the first industrial revolution and the dawn of mechanisation.

Medical innovations such as vaccination, anaesthesia, and antibiotics optimised both the quality and longevity of human life. Electricity became the universal energy of modernity, while the telephone, telegraph, and radio bound humanity into a single informational continuum. The computer and subsequent digital technologies inaugurated the age of automation, and the Internet ushered in the epoch of global interconnectedness, in which information itself emerged as a novel form of energy. Artificial intelligence, in turn, constitutes a new evolutionary stage—an embodiment of what Hegel described as the self-development of reason within nature.

To systematise the trajectory of technological progress, scholars identify successive waves of industrial revolutions, each representing a further phase in the optimisation of energy and information transformation. The first industrial revolution, driven by the steam engine, marked the transition from manual labour to mechanisation. The second, associated with electricity and mass production, accelerated industrial expansion and efficiency. The third, enabled by automation and computing, ushered in the digital age—a stage characterised by the substitution of mechanical energy with informational logic.

The Fourth Industrial Revolution, characterised by the rise of artificial intelligence (AI), big data, and the Internet of Things, represents the integration of the physical and digital worlds into unified cyber–physical systems. The emerging Fifth Revolution envisions a deeper convergence between humans and technology through advances in cognitive and bioengineering disciplines, where AI functions not merely as an instrument but as a collaborator in cognition and creativity. The anticipated Sixth Wave is associated with the synthesis of biological, cognitive, and quantum intelligence, heralding the emergence of self-regulating systems of energy and information.

A comparable dynamic is evident in the long-wave theory proposed by Nikolai D. Kondratiev, which describes recurring cycles of innovation accumulation and diffusion. Each successive wave signifies a transition to a higher level of energetic and technological organisation—from mechanisation and industrialisation to digitalisation and, ultimately, intellectualisation. The current, eighth Kondratiev wave is linked to the ascent of cognitive technologies, with AI occupying a central role in modelling, extending, and augmenting human thought.

Accordingly, the present stage of technological evolution can be understood as a phase of synergy between reason and energy, in which AI emerges not solely as a mechanism of optimisation but as a structural element of sustainable development—a new mode of interaction between humanity, technology, and the energetic environment.

To conceptualise this relationship, the interaction between artificial intelligence and energy security may be represented as a coordinate system described by four interdependent categories: plateaux, bifurcations, sinusoids, and paradoxes of development. This framework interprets technological and social evolution not as a linear continuum of progress, but as a nonlinear sequence of oscillations and transitions. The central premise underlying this approach is that development must serve humanity, not the reverse. Sustainability thus becomes the pivotal criterion—a condition of equilibrium between the pace of technological change and the capacity of both humans and their environment to adapt to its consequences.

Any system aspiring to sustainability tends naturally toward homeostasis—a dynamic equilibrium attained when its principal parameters stabilise at a plateau ensuring predictability and structural coherence. When the qualitative or quantitative properties of this plateau shift, the system acquires new characteristics. The critical thresholds at which stability is lost and transformation begins are known as bifurcation points. These mark the thresholds of systemic reorganisation—moments when incremental quantitative change culminates in qualitative transformation, propelling the system to a higher level of complexity and order.

It is essential, however, to distinguish transformation from destruction. While bifurcation signifies the emergence of new forms and functions, destruction implies the loss of systemic integrity—the disintegration of both structure and purpose. In the broadest cosmological sense, even destruction may be understood as part of natural evolution—for example, planetary extinction or stellar collapse. Yet within the context of contemporary civilisation, such outcomes would constitute existential catastrophe rather than creative renewal.

As Hegel observed, this process reflects the law of the negation of the negation, whereby development unfolds through successive stages of sublation (Aufhebung)—the simultaneous negation, preservation, and transcendence of previous forms. Each new stage incorporates what preceded it, yet on a higher plane of organisation. Thus, destruction and creation are revealed as dialectically inseparable: two moments of a single process in which bifurcation embodies the transition from quantitative modification to qualitative transformation.

All systems, as previously noted, evolve through alternating progressive and regressive phases. The progressive phase represents the unfolding of accumulated potential, while the regressive phase embodies its reconfiguration and renewal. Together, these dynamics trace a sinusoidal trajectory of development, in which periods of expansion and contraction, and growth and consolidation, alternate to maintain a dynamic equilibrium between energy and stability.

From a philosophical standpoint, this process reflects the second fundamental law of dialectics—the transformation of quantity into quality and vice versa. Through quantitative accumulation, expressed in the growth of potential and structural complexity, systems generate the conditions for qualitative leaps that manifest at points of bifurcation. The sinusoid thus represents not merely a graphical metaphor, but an ontological model of systemic evolution—applicable alike to intellectual and energetic systems.

Within the context of artificial intelligence and energy security, a series of paradoxes inherent to self-developing systems becomes evident. On the one hand, the advancement of AI enhances the efficiency of energy management by optimising consumption and distribution; on the other, it simultaneously increases AI’s own dependence on energy, creating a positive feedback loop—the more powerful the intelligence, the greater its energy demand, and consequently, the higher the vulnerability of the system in the event of disruption.

The first paradox lies in humanity’s pursuit of sustainability through intelligent algorithms while becoming increasingly dependent on these very systems. Modern AI models have reached a level of complexity at which their internal logic is no longer fully comprehensible to the human mind within the span of a lifetime. AI, originally designed to extend human cognitive capacity, thus transcends human understanding, giving rise to a paradox of controllability.

The second paradox is that of complexification. To mitigate both cognitive and energetic risks, AI must generate new technologies of cognition—augmented intelligence based on neural interfaces and the integration of human consciousness with distributed computing systems. Yet, as systems grow in complexity, they also grow in instability, moving closer to bifurcation thresholds beyond which they may evolve into higher orders of organisation or collapse entirely.

These reciprocal transitions between quantity and quality exemplify Hegelian dialectics in practice: every stage of development contains within itself the seed of its own negation, which, in turn, becomes the precondition for progress. Applying the law of the unity and struggle of opposites, the AI–energy security system may be conceptualised as a self-developing pair of antagonistic yet interdependent elements. AI is created to ensure energy security—optimising distribution, forecasting, and stability—while energy security itself constitutes the precondition for AI’s existence, since without energy, intelligence cannot function.

This results in a paradox of mutual necessity: AI exists to sustain energy security, while energy security is preserved to sustain AI. Each serves simultaneously as both the condition and the limitation of the other. The system thus evolves through internal tension—not as a destructive conflict, but as a generative contradiction that propels it toward higher levels of complexity and stability.

In summing up the components of the artificial intelligence–energy security nexus, it must be emphasised that research in this field, despite its technological and engineering nature, should be conducted within the broader strategic framework of human civilisation’s development. At present, this framework is defined by the concept of sustainable development articulated by the United Nations, encompassing economic, social, environmental, and ethical dimensions [33].

Accordingly, the advancement of artificial intelligence and energy security should not be regarded as an end in itself, but as a means of realising this strategic vision. A principal task for contemporary science lies in achieving consensus on the boundaries of safety in the design and deployment of AI—both technologically and ethically. Without a clear understanding of these limits, the stability of AI and energy systems may prove illusory.

Looking forward, quantum technologies are expected to play a pivotal role in shaping future research and addressing emerging challenges. These technologies simultaneously broaden the horizons of AI development and introduce new risks to energy sustainability. The exponential growth of computational capacity and processing speed will demand novel approaches to energy management, especially in light of the classical economic dilemma of finite resources versus infinite human needs.

The advent of quantum computing marks a fundamentally new stage in scientific evolution. Based on the principles of superposition and simultaneity of states, quantum computation allows for the resolution of problems inaccessible to classical machines. This paradigm possesses the potential to transform not only the architecture of security systems but also the fundamental principles governing the interaction between artificial intelligence and the energy sector.

In the coming decades, these very principles—simultaneity and nonlinearity—may underpin breakthroughs across adjacent fields of scientific and technological knowledge. In additive manufacturing and 3D technologies, they will enable the convergence of energy and information in processes of materialisation, generating new models of production. In genetic research, aimed at decoding and editing DNA, quantum methods will accelerate the identification of patterns within biological systems. In materials science and nanotechnology, quantum effects are already forming the basis for innovative energy sources, information carriers, and memory systems, paving the way for the emergence of integrated cognitive–energetic technologies.

An open question remains: how will physics itself evolve once the principle of simultaneity, discovered at the quantum level, is fully applied to practical computation? Such a development could provoke a profound re-evaluation of the fundamental categories of time, causality, and energy—the very foundations upon which the modern scientific worldview rests.

The reflections presented in this Editorial are intended as an invitation—to scholars, scientists, and philosophers alike—to explore this evolving interface between artificial intelligence, energy security, and the philosophy of sustainable development. It is within this interdisciplinary frontier that a new understanding is beginning to emerge: not only of technology itself, but of the deeper meaning of human progress.