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# Fireside Chat: AI and Sustainability - Thorsten Jonas

Thorsten Jonas argues that generative AI is accelerating digital waste, leaving 90% of internet data unused. Learn how intentional UX friction can break these addictive auto-consumption loops.

- **Speakers:** 
- **Event:** Perfomance & AI Day
- **Published:** November 27, 2025
- **Duration:** 50:07
- **URL:** https://www.wearedevelopers.com/videos/1769-fireside-chat-ai-and-sustainability-thorsten-jonas

## Summary

The conversation unpacks the intersection of artificial intelligence, digital sustainability, and user experience design, highlighting how rapid technological acceleration impacts both the environment and society. Thorsten Jonas from the Sustainable UX Network emphasizes that a staggering 90% of internet data remains unused, a problem systematically exacerbated by generative AI platforms repeatedly churning out disposable content. Rather than realizing efficiency gains that improve human well-being or reduce working hours, the current tech ecosystem often leverages AI to fuel addictive consumption loops and create excessive digital waste. Beyond ecological concerns, such as the massive energy consumption of modern data centers straining civil power grids, the discussion deeply examines the societal consequences of uncritically adopting AI outputs. Generative models frequently simulate human context and truth, risking the erosion of critical thinking when users passively accept AI-generated summaries or code without auditing for quality. True AI value lies in complex algorithmic problem-solving like predicting global climate models or analyzing medical imaging, rather than mass-producing automated marketing videos or purely synthetic social feeds. To counteract the addiction mechanics of platforms, the speakers advocate for bringing intentional friction back into UX design. Designing deliberate friction disrupts auto-consumption patterns and empowers informed user decisions, similar to hypothetically displaying the carbon footprint cost per interaction. Ultimately, preventing unchecked technological sprawl requires moving beyond blind efficiency to focus on stringent digital regulations, structural corporate transparency, and building sustainable codebases that actively respect environmental limits.

**Keywords:** digital sustainability, sustainable ux design, sustainable ux network, generative ai environmental impact, data center carbon emissions, data waste management, societal impact of ai, ux friction, large language models environmental cost, ai generated content overgeneration, energy grid strain, technology transparency, ai code generation sustainability, climate modeling with ai, blind technology consumption

## Chapters

1. **Sustainable UX and digital environmental impact** (00:11) — How the design of digital products and unused hosted data contribute to global greenhouse gas emissions.
1. **Questioning the enterprise data hoarding mentality** (04:27) — Reevaluating the legacy strategy of storing all data indefinitely versus prioritizing cold storage and deliberate deletion.
1. **Generative AI and social engagement loops** (07:12) — Combining artificial intelligence media generation with short-form video platforms creates harmful algorithmic consumption feedback cycles.
1. **Rethinking productivity metrics and automation efficiency** (17:26) — Assessing whether AI-driven productivity improvements actually create more time for workers or just increase output demands.
1. **Evaluating AI comprehension and output quality** (21:57) — Why large language models excel at mimicking human patterns but often produce inaccurate system summaries and flawed content.
1. **Externalized computing costs of AI scaling** (24:30) — The invisible strain that unchecked generative modeling queries and iterative content creation put on local power delivery infrastructures.
1. **Machine learning applications beyond generative AI** (29:58) — Exploring valuable neural network use cases like climate modeling and medical diagnostics that provide concrete societal improvements.
1. **Designing positive UX friction and transparency** (35:44) — Implementing architectural friction and environmental impact disclaimers to help users make informed software consumption choices.
1. **Preserving analog perspectives in system design** (41:05) — Maintaining historical context and platform transparency to prevent centralized ecosystem paradigms from becoming unquestionable defaults.

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- [Distinguishing between sustainability by software and in software](https://www.wearedevelopers.com/videos/988-times-of-climate-crisis-how-and-why-sustainable-software-is-a-must) (from "Times of (climate) crisis - How and why sustainable software is a must!")
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- [Addressing developer burnout and the perceived value of AI](https://www.wearedevelopers.com/videos/100093-the-integrated-ai-experience-a-new-paradigm-in-an-agentic-world) (from "The integrated AI experience: A New Paradigm in an Agentic World")
- [Balancing pragmatism and dogmatism regarding AI generated creative output](https://www.wearedevelopers.com/videos/100093-the-integrated-ai-experience-a-new-paradigm-in-an-agentic-world) (from "The integrated AI experience: A New Paradigm in an Agentic World")
- [Evaluating the environmental cost of AI and vibe coding](https://www.wearedevelopers.com/videos/1341-the-environmental-impact-of-software-development) (from "The Environmental Impact of Software Development")
- [Addressing developer burnout and the impact of artificial intelligence](https://www.wearedevelopers.com/videos/1504-cracking-the-code-to-tech-team-satisfaction) (from "Cracking the Code to Tech Team Satisfaction")

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