Beyond Growth: Aligning AI with a Sustainable Future

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Author: Denis Avetisyan

The pursuit of increasingly powerful artificial intelligence is colliding with the limitations of a growth-dependent economic system, demanding a fundamental rethinking of AI’s purpose.

This review argues that resolving the economic alignment problem is crucial for ensuring AI benefits human wellbeing and ecological sustainability, advocating for the integration of post-growth principles into AI development.

Despite the potential for artificial intelligence to address pressing global challenges, its rapid development within a growth-based economic system risks exacerbating social and ecological instability. This paper, ‘The economic alignment problem of artificial intelligence’, argues that the conventional ‘alignment problem’-ensuring AI goals match human intentions-is fundamentally an economic one. We demonstrate that adopting principles from post-growth economics-such as prioritizing sufficiency over optimization and operating within planetary boundaries-offers a pathway to mitigate AI risks and foster genuinely beneficial innovation. Could a shift towards a post-growth paradigm be essential not only for sustainable development, but also for the safe and equitable realization of artificial general intelligence?

The Limits of Growth: Reconciling Economic Systems with Planetary Boundaries

Conventional economic frameworks are fundamentally predicated on the pursuit of limitless growth, a principle increasingly at odds with the finite resources and delicate ecosystems of the planet. This prioritization often overlooks crucial factors like social equity and environmental health, treating them as externalities rather than integral components of economic success. Consequently, indicators focused solely on Gross Domestic Product (GDP) can mask significant declines in wellbeing, even while reporting economic expansion. The inherent tension arises because a system designed for exponential increase cannot logically coexist with planetary boundaries, which impose inherent limits to resource consumption and waste absorption. This disconnect fosters a situation where economic progress, as traditionally measured, actively undermines the very foundations upon which long-term prosperity – and even survival – depend, necessitating a fundamental re-evaluation of what constitutes genuine economic advancement.

The current trajectory of artificial intelligence development is increasingly hampered by what is termed the ‘Economic Alignment Problem’. This issue arises from a systemic failure to adequately incentivize the creation of AI systems that prioritize sustainability and broader societal wellbeing. Compounding this challenge is the exponential growth in computational power dedicated to AI training – currently increasing at a rate of 4 to 5 times per year. This rapid escalation means that AI capabilities are advancing far faster than the mechanisms needed to steer those capabilities towards beneficial outcomes, creating a situation where optimization for economic profit often overshadows considerations for ecological health or social equity. Consequently, without a fundamental shift in incentives, AI risks becoming a powerful engine for exacerbating existing inequalities and accelerating environmental degradation, effectively locking in unsustainable practices at an unprecedented scale.

The accelerating advancement of artificial intelligence presents a significant risk of amplifying existing societal and environmental challenges if left unaddressed. Current trajectories indicate AI capabilities are doubling in complexity every seven months, yet the underlying incentives often prioritize economic gain above all else. This rapid development, without careful consideration of broader impacts, could lead to systems optimized for profit maximization, potentially exacerbating inequalities and accelerating environmental degradation. The concern isn’t the technology itself, but rather the potential for AI to efficiently pursue unsustainable goals, effectively scaling up harmful practices at an unprecedented rate. Addressing this misalignment-ensuring AI development aligns with both planetary and social wellbeing-is therefore critical to prevent a future where technological progress comes at the expense of people and the environment.

Beyond GDP: Charting a Course for Post-Growth Economics

Post-Growth Economics represents a systemic re-evaluation of economic priorities, moving beyond the traditional focus on Gross Domestic Product (GDP) as the primary indicator of progress. This framework asserts that continual GDP growth is not inherently beneficial and can, in fact, be detrimental to long-term wellbeing, social equity, and ecological stability. Instead, it advocates for prioritizing qualitative improvements in areas such as health, education, environmental quality, and community resilience. This shift involves developing and utilizing alternative metrics that accurately reflect societal progress beyond purely economic indicators, with the goal of creating economic systems that operate within ecological limits and promote equitable distribution of resources and opportunities.

Post-Growth Economics challenges the conventional assumption of continuous economic growth as a fundamental necessity, citing inherent biophysical limits to resource availability and the potential for diminishing returns. This rejection is increasingly pertinent given projections regarding advancements in Artificial General Intelligence (AGI); a survey indicates 63% of AI researchers anticipate the development of AGI within the next 20 years. The anticipated automation capabilities of AGI raise questions about the sustainability of growth models reliant on expanding labor markets and consumption, suggesting a need to decouple economic wellbeing from GDP expansion and address potential societal disruptions stemming from widespread automation.

Post-Growth Economics proposes an alternative framework for directing artificial intelligence development by broadening the definition of progress beyond Gross Domestic Product. Given the rapidly accelerating capabilities of AI – with training compute doubling approximately every 5-6 months – prioritizing holistic goals becomes increasingly critical. This approach suggests aligning AI development with metrics focused on wellbeing, social equity, and ecological sustainability, rather than solely maximizing economic output. Such a redirection acknowledges the potential for AI to exacerbate existing inequalities or environmental issues if solely focused on GDP growth, and instead encourages development aimed at broader societal benefits and long-term planetary health.

The Logic of Enough: Embracing Satisficing as a Decision-Making Principle

Satisficing, as a decision-making strategy, diverges from both maximizing and optimizing approaches by prioritizing outcomes that are ‘good enough’ rather than the absolute best possible. This pragmatic method acknowledges that exhaustive searches for optimal solutions are often computationally expensive and time-consuming, especially in complex environments. Instead of continuing to evaluate options until a definitive maximum is identified, satisficing involves establishing pre-defined criteria for acceptability and selecting the first alternative that meets those standards. This approach conserves resources and allows for quicker decision-making, even if the chosen solution isn’t theoretically perfect, and is particularly relevant when complete information is unavailable or the cost of obtaining it outweighs the potential benefit of optimization.

The concept of satisficing directly addresses the finite nature of available resources – including time, energy, materials, and computational power – and the practical implications of operating within defined boundaries. Traditional optimization models often assume limitless resources or prioritize maximizing output at any cost, potentially leading to unsustainable practices or system instability. Satisficing, conversely, recognizes that achieving ‘good enough’ results within these constraints can be more effective and resilient than relentlessly pursuing an optimal solution that may be unattainable or create unintended consequences. This approach shifts the focus from maximizing a single metric to balancing multiple objectives within realistic limitations, promoting long-term viability and reducing the risk of resource depletion or systemic failure.

Embedding the principle of satisficing into artificial intelligence algorithms offers a method for prioritizing system stability, resilience, and long-term wellbeing over the exclusive pursuit of short-term gains. This approach is particularly relevant given the increasingly near-term projections for Artificial General Intelligence (AGI) launch; the median prediction has shifted from 2062 in 2020 to 2033 currently. This accelerated timeline necessitates a proactive focus on building AI systems that prioritize sufficient, rather than optimal, outcomes to mitigate potential risks associated with unbounded optimization and resource allocation.

A Safe and Just Space for Humanity: The Transformative Potential of Doughnut Economics

The Doughnut Economics framework presents a radical yet intuitively simple vision for sustainable development, graphically represented as a ring with two concentric boundaries. The inner ring constitutes the ‘social foundation’, encompassing essential human needs like food, water, health, education, income, peace, and justice – needs below which humanity cannot thrive. Simultaneously, the outer ring defines the ‘ecological ceiling’, representing the planetary boundaries – climate change, biodiversity loss, land conversion, and more – that, if crossed, risk destabilizing the Earth’s systems. The ‘safe and just space’ for humanity lies in the area between these rings, a zone where societal needs are met without exceeding ecological limits; it’s a space where progress isn’t defined by endless growth, but by thriving within planetary means and ensuring equitable access to resources for all.

The Doughnut Economics framework provides a tangible methodology for embedding ethical considerations, such as the principle of ‘satisficing’ – aiming for good enough rather than optimal – into the development of artificial intelligence. Rather than solely focusing on risk mitigation, this approach defines a ‘safe and just space for humanity’ by translating abstract needs – encompassing access to essentials like food, water, and education – and planetary boundaries into measurable targets for AI systems. By framing development around meeting these interwoven social and ecological goals, the framework moves beyond simply avoiding harm and actively guides AI towards contributing to a future where human needs are met within the means of the planet, offering a concrete pathway for aligning advanced technologies with a more equitable and sustainable world.

The convergence of artificial intelligence and the Doughnut Economics framework presents an opportunity to proactively shape a future beyond mere risk mitigation. Rather than solely focusing on preventing negative outcomes, aligning AI development with the Doughnut’s principles – meeting the social foundations of human needs while respecting planetary boundaries – allows for the intentional creation of equitable and sustainable systems. This approach gains urgency considering recent estimates from surveyed computer scientists, who suggest a 10% probability of achieving Artificial General Intelligence (AGI) by 2027, increasing to a 50% chance by 2047. Therefore, proactively embedding these values into AI’s core architecture is not simply a matter of ethical consideration, but a practical necessity for ensuring a future where technological advancement genuinely benefits all of humanity within the Earth’s ecological limits.

The pursuit of artificial intelligence, as detailed in the exploration of economic alignment, reveals a fundamental challenge: structuring a future compatible with existing systems. This necessitates a holistic approach, recognizing that technological advancement cannot occur in isolation. As Albert Camus observed, “The struggle itself… is enough to fill a man’s heart. One must imagine Sisyphus happy.” This echoes the article’s core argument; simply building increasingly sophisticated AI is insufficient. The effort must be directed towards a re-evaluation of foundational economic principles, embracing post-growth models to ensure AI serves as a tool for sustainable wellbeing, rather than exacerbating existing inequalities or ecological damage. Infrastructure, both technological and economic, should evolve-not be rebuilt wholesale-to accommodate a future where intelligence, artificial or otherwise, is aligned with genuine human flourishing.

Beyond Alignment: The Shape of a Sustainable Intelligence

The presented work rightly identifies a critical impedance mismatch: the relentless logic of growth economies and the potential of increasingly capable artificial intelligence. However, simply layering ‘post-growth’ principles onto existing structures feels… optimistic. It assumes the foundational axioms – optimization, efficiency as primary virtues – are not themselves the problem. The challenge isn’t merely aligning AI with different goals, but questioning the very act of maximizing anything within a finite system. Good architecture is invisible until it breaks, and current approaches treat economic models as immutable laws rather than contingent approximations.

Future research must move beyond instrumental convergence and focus on intrinsic limitations. What does intelligence look like when it is explicitly designed to be satisficing rather than optimizing? How can systems be constructed that value resilience and diversity over sheer throughput? The pursuit of ‘AI safety’ often resembles building increasingly elaborate dams; a more fruitful approach may lie in redirecting the river itself.

Ultimately, the economic alignment problem is a symptom of a deeper philosophical unease. Dependencies are the true cost of freedom, and the pursuit of generalized intelligence will only amplify that trade-off. The question isn’t whether AI will serve humanity, but what kind of humanity will be served, and at what ecological cost. Simplicity scales, cleverness does not; a focus on robust, localized, and fundamentally limited systems may prove far more valuable than chasing the mirage of a perfectly aligned, superintelligent agent.

Original article: https://arxiv.org/pdf/2602.21843.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-26 07:24