Friend or Foe? How GenAI Shapes Sustainability by 2035

July 14, 2025

What if Germany’s climate future was decided not by governments, but by algorithms? What if the post-growth economy wasn’t a theory—but a necessity?

A new set of foresight scenarios by SOMMERRUST paints four starkly different pictures of how Germany might look in 2035—depending on how GenAI evolves and how society responds to sustainability pressures.

The Four Scenarios

Amid climate disruptions, digital transformation, and growing geopolitical tensions, Germany and the EU face crucial choices. GenAI could help accelerate sustainable innovation—or deepen social divides. These scenarios help us think ahead:

Scenario 1: Ultra-Processed Planet

Slogan: “Green growth, powered by AI”

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In Ultra-Processed Planet, GenAI wasn’t initially aimed at sustainability. Yet, after breakthroughs in energy optimization and resource management, climate activists and tech entrepreneurs—both frustrated by failed policies—joined forces to slash carbon footprints at scale. By 2035, this synergy propels Germany and the EU into an era of AI-driven green capitalism, where sustainability is automated and mass-produced—processed for convenience like everything else in this hyper-optimized world.
Autonomous renewable grids power entire cities, while AI-optimized supply chains minimize waste. Personal GenAI assistants manage individual carbon budgets, and hyper-personalized recommendations make sustainable choices effortless—sometimes too effortless, as rebound effects drive higher consumption. Meanwhile, the same AI that simplifies eco-friendly choices also monitors and nudges behavior, fueling fears of digital overreach and an unsettling sense that government is on autopilot—driven more by algorithms than public debate. Despite unprecedented gains in reducing emissions, inequality persists. Displaced workers struggle, even with lavish re-skilling programs. Ultra-Processed Planet shows how advanced tech tackles crises that policy alone never could, yet reveals how bold AI solutions collide with deeper social, ethical, and ecological questions beyond easy optimization.

Scenario 2: Pragmatopia

Slogan: “Better safe than sustainable”

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In Pragmatopia, repeated trade disputes and geopolitical conflicts have made grand ecological ambitions look like „luxuries“ that must be sacrificed for security. Germany and the EU, battered by broken global sustainability deals, pivot toward fortifying critical infrastructure and safeguarding essential resources instead of pursuing sweeping climate reforms. Governments invest in heat-mitigating green corridors in cities, farmland resilience measures, and AI-managed energy systems—effective for adaptation, but falling short of true ecological transformation.
At the same time, governance remains participatory, with methodical citizen engagement using AI-driven platforms to systematically gather local concerns, helping citizens voice their needs and priorities directly to policymakers. Communities gain influence, with councils and neighborhood forums managing local infrastructure, energy, and food production, while AI-optimized supply chains and resource distribution remain centrally coordinated. Many districts thrive, leveraging microgrids and community-driven services, while others struggle with poor governance, becoming neglected „wastelands.“ Sustainability advocates warn that reactive fixes won’t hold forever—widening crises will demand more than just resilience. But for now, Pragmatopia chooses stability over bold transformation.

Scenario 3: Ctrl+Shift+Degrowth

Slogan: “Post-work society, driven by crises & robots”

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A chain reaction of economic meltdowns and extreme climate events, exacerbated by mass automation-driven job losses, compelled European leaders to give up prior obsessions with GDP growth. In Ctrl+Shift+Degrowth, the old consumer economy collapses under these pressures. Yet the same technological breakthroughs that disrupted traditional industries also slash the cost of essential goods and services, offering a path forward based on a form of universal income. Once dominated by commerce and cars, cities now center around compact communities, circular resource flows, and AI-operated micro-factories. Plastic waste is repurposed on-site, rooftop farms supply fresh produce, and shared robots provide elderly care and infrastructure maintenance.
In this transformed landscape, many traditional jobs vanish as post-work values reshape society. People still gather in city districts, but they invest time in communal projects, resource management, and cultural endeavors. A growing number see meaning in co-creating new social norms rather than participating in never-ending economic growth. Carbon emissions and material throughput plunge. Despite the dramatic shift, there’s no utopian harmony: Tensions emerge around who controls the core AI and robotics infrastructure, and many citizens grapple with an identity crisis in a society where “earning a living” is no longer the default life path.

Scenario 4: Yes We Can’t

Slogan: “Escalating waves of ambition & disappointment”

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In Yes We Can’t, Germany lurches from one dazzling policy vision to the next—be it a sweeping emissions overhaul, an AI-led economic renaissance, or a futuristic urban plan. Each promise swells with public enthusiasm, capturing headlines and social media—sometimes artificially boosted by fake citizen movements. Yet time after time, progress stalls when risk-averse bureaucrats, corporate lobbying, and public skepticism collide.
GenAI plays a contradictory role: on the one hand, it turbocharges idea generation and policy proposals, creating ever-grander concepts at lightning speed. On the other hand, over-cautious AI systems churn out exhaustive risk assessments, supplying politicians and bureaucrats with near-endless reasons to delay or complicate matters. Citizens see bold announcements fizzle into half-measures—funding gets slashed, pilot programs languish, and the media moves on to the next big thing. It’s an increasingly harsh cycle of high hopes and deep disillusionment, destroying prosperity and political trust in the process. For many, chronic sustainability fatigue sets in. Yes, each new vision sounds transformative in theory, but in practice, “We can’t” remains the bitter final word.

Author’s Note: When Scenarios Surprise

When the scenarios first took shape during the Scenario Sprint process, especially one of them seemed questionable: Ctrl+Shift+Degrowth. It reminded me too much of anti-capitalist utopias. Yet the deeper I probed the assumptions—automation-driven job loss, cascading crises—the more plausible a forced degrowth path became: less a revolution, more an accident. Ten years ago, few imagined society would accept far-reaching pandemic lockdowns, yet history taught us a lesson. That’s the super-power of scenarios: they illuminate blind spots and make the “impossible” discussable. Which of your assumptions still feels untouchable?

What These Scenarios Teach Us

Each of the scenarios has been designed to be plausible, but they are not predictions. They can’t be: The scenarios have been constructed based on 12 fundamental uncertainties, each with different possible outcomes. For example, uncertainties include the dominant future sustainability narrative, trust in institutions, or the speed and direction of GenAI evolution. Even without being predictions, however, the scenarios offer some valuable lessons:

1. GenAI impact on sustainability will be big, no matter what

GenAI is one of these basic innovations that change the world. The full extent of the impact will only reveal itself over time, but it may eclipse historic innovations like electricity or the printing press. Like many other domains, sustainability will be strongly affected. To better understand the impact of GenAI on sustainability, using such historic examples as analogies can be helpful:

• In scenario 1, Ultra-Processed Planet, GenAI may be similar to electricity: ubiquitous, convenient, causing a new Industrial Revolution. It enables unprecedented green growth, but not without unintended long-term side effects (e.g., AI dependency).
• For scenario 2, Pragmatopia, the impact would be less broad and GenAI would function as a vital safeguard like penicillin (or antibiotics), e.g., helping us to create resilient infrastructure.
• In scenario 3, Ctrl+Shift+Degrowth, the impact resembles that of the printing press – as dramatic as in scenario 1, but very different: The dissemination of knowledge and ideas facilitated through automation may lead to upheaval and chaos by challenging the existing order (our current form of capitalism). Eventually, a stable state emerges, based on degrowth and circularity.
• In scenario 4, Yes We Can’t, early great expectations are disappointed, like with nuclear fission (there are still no cars powered by nuclear energy as imagined in the 1950ies). The complex risks hinder progress, the technology remains highly controversial, also regarding its role for sustainability.

If GenAI is such a big thing for sustainability, you may ask: Why hasn’t the visible impact been bigger already? One reason is that even dramatic innovations take time to become integrated into established systems and practices. The sustainability community may also be less AI-savvy than, say, software developers or marketing professionals. Until 2035, however, the impact on sustainability will be big in all scenarios, even transformational in some. Whether it will be positive or negative, is a different question.

2. Friend or foe — the fate of GenAI is in our hands

There are many mechanisms in which GenAI can support or hinder sustainability progress. Examples include circular economy implementation, behavioural nudging, hyper-personalization, misinformation/greenwashing, and GenAI’s own resource footprint, to name just a few. Each scenario has a different profile, resulting from GenAI evolution and the way businesses and society uses it in the sustainability context. While both positive and negative effects are strong in scenario 1, positive effects dominate in scenarios 2 and 3, and negative mechanisms play a more pronounced role in scenario 4. The take-away is: It is a collective choice, not a given, whether GenAI will be friend or foe for sustainability. (Contact me if you would like to see an assessment of all 10 impact mechanisms for each scenario.)

3. Industries will be affected very differently

Analyses show that even within a specific sector like automotive, the scenarios might affect each value chain segment differently (e.g., car manufacturers vs. tier 1 suppliers vs. service providers). While a given scenario may represent an opportunity for one player, it could be a challenge or even pose a threat for another. As a consequence, it is unlikely that there will be a uniform move to prevent or push certain scenarios. The good news is that company-specific scenario analyses can be used to create an adequate strategic response. For example, the AI-supported analysis of a fictitious battery technology company revealed scenario-specific outcomes with regards to the company’s future circularity capabilities. By using such „AI-based hypotheses“, firms can systematically discuss future research priorities and investment decisions, or explore bold, scenario-inspired ideas.

Conclusion

What can you do now? Ask yourself some key questions:

• Have you neglected a scenario so far? Do you have really good reasons to believe the scenario is irrelevant? (I’d love to discuss these with you…!)
• Would one of the scenarios seriously challenge your current strategy or business model? What can you do about that?
• Which opportunities might each of the scenarios present to your organisation? What would it take to seize them?

Especially the latter two questions are difficult to answer without in-depth analyses. However, the goal of developing these GenAI-based scenarios was to get you thinking, not provide all the answers (although GenAI might be strong enough for that sooner than we think). If you would like to learn more about these scenarios, contact me. Happy thinking!

Acknowledgements: A big „thank you!“ to all the people who were willing to speak with me about these scenarios and the underlying trends and uncertainties. In total, I conducted more than 20 interviews with industry experts and academics to inform my thinking. The scenarios were created with SOMMERRUST’s „Scenario Sprint with GenAI“ methodology, combining the vast knowledge of ChatGPT with human brain power.

Scenario content © SOMMERRUST 2025. Image source: Midjourney. This text was generated or edited with ChatGPT.