Developers Want More Sustainable AI  🔗 **Source:** [Developers Want More Sustainable AI](https://leaddev.com/ai/developers-want-more-sustainable-ai) > [!note]+ Document Notes > --- > tags: [sustainable-ai, ai-energy-consumption, developer-sentiment, climate-change, software-efficiency] > author: Bill Doerrfeld > source: LeadDev > reference: https://leaddev.com/ai/developers-want-more-sustainable-ai > date created: 2026-09-28 08:39 > --- > > [!info] Generated analysis — not the author's words. Sections below are machine-written > > from the captured source. Only text inside `> [!quote]` blocks is verbatim from the author. > > Do not quote or attribute any other line to the named author. > # 🏷️ Title: Developers want more sustainable AI > ## 🧭 Table of contents > - [📝 Summary](#-summary) > - [📖 Main content](#-main-content) > - [🗒️ Notes](#️-notes) > - [📚 References](#-references) > - [🗣️ Paraphrase](#️-paraphrase) > - [📰 Newsletter](#-newsletter) > - [✅ Action items](#-action-items) > - [🧪 Hypotheses](#-hypotheses) > - [📊 Methodology](#-methodology) > - [❓ Questions](#-questions) > - [🔮 Future research](#-future-research) > - [💡 Implications](#-implications) > - [🧠 Learning styles assessment](#-learning-styles-assessment) > - [📊 Accessibility assessment](#-accessibility-assessment) > - [🌍 DEIB assessment](#-deib-assessment) > - [✅ Logical validity](#-logical-validity) > - [🛡️ DARVO analysis](#️-darvo-analysis) > - [🎯 Conclusion](#-conclusion) > ### 🔗 References > - [GitHub x Yale YPCCC joint report](https://climatecommunication.yale.edu/publications/github-survey/) — the survey the whole article reports on > - [IEA: Energy demand from AI](https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai) — source for the 1.5%-of-global-electricity figure > - [MIT Sloan: Will AI derail global climate goals?](https://mitsloan.mit.edu/press/will-ai-derail-global-climate-goals?utm_source=chatgpt.com) — source for the 0.1 degree C warming estimate > ## 📝 Summary > - **What happened.** A joint GitHub / Yale Program on Climate Change Communication (YPCCC) report surveyed 1,039 US GitHub monthly active users on climate change, AI's environmental impact, and sustainable engineering practice. > - **The claim.** 63% think AI systems contribute to climate change "a great deal" or "a moderate amount"; 70% want tech companies and employers to do more; 80% want tools that help them write more energy-efficient code. > - **Concrete asks.** The report's qualitative section (¶20, list) names seven specific developer requests: renewable-powered data centers, tools to measure software's environmental impact, energy-efficiency scores for large open-source projects, metrics for the computational toll of GitHub Actions, delaying unnecessary compute-heavy CI/CD steps, more efficient open-source local AI models, and help writing efficient code. > - **Why it matters.** The survey lands as AI workloads drive new data-center construction, and as IEA names AI the top driver of future electricity demand growth. > - **Key stakeholders.** GitHub (Paull Young, head of sustainability), Yale YPCCC, Pascal Joly (IT Climate Ed), anonymous surveyed developers, and by extension employers, cloud providers, and regulators, whom respondents assign more responsibility than themselves. > - **Risks and tradeoffs.** The sample is US-only, self-selected GitHub users, and the article says so plainly. The MIT Sloan 0.1 degree C figure is one modeled scenario, not a settled forecast. Respondents rate their own individual impact as "small" (63%) while naming institutional-level fixes (data centers, OSS scoring, CI/CD metrics) rather than personal habit changes — consistent, not contradictory, though the article never tests whether GitHub or employers act on the list. > - **Bottom line.** Developers want the fix applied upstream of their own keyboard: efficient models, employer accountability, and better visibility into energy costs, more than they want to change their own habits. > ## 📖 Main content > - The report's spine is a belief gap: strong agreement that global warming is real and AI contributes to it, next to a belief that responsibility sits mostly with institutions rather than individual coding choices (¶2, ¶6, ¶18). > - Compute scale gives the belief its backdrop: data centers used about 1.5% of global electricity in 2024 per the IEA, and MIT Sloan models AI's direct emissions contributing roughly 0.1 degree C of extra warming by 2100 under current trends, rising in a high-adoption scenario (¶8-¶9). > - Pascal Joly (IT Climate Ed) widens the footprint past electricity: air pollution from backup generators, cooling-system noise, and water consumption (¶10), arguing operational emissions "may look small" today but are "growing rapidly" (¶11). > - The qualitative comments name two waste sources: model choice ("give me efficient AI, not frontier AI," ¶14) and general software bloat that stays invisible to the people writing it (¶15-¶16). The article links this to separate reporting on AI-era duplicate code, technical debt, and maintainability decline (¶17), though that's the reporter's extension, not a survey finding. > - Between the complaints and the experts' advice sits a direct developer wishlist (¶20, list): renewable-powered data centers, tools to measure software's environmental impact, energy-efficiency scores for large open-source projects, metrics for the computational toll of GitHub Actions, delaying unnecessary compute-heavy CI/CD steps, more efficient open-source local AI models, and help writing efficient code. These are the surveyed developers' own asks, distinct from the Young/Joly recommendations that follow. > - Recommendations from Young and Joly are concrete and sequential: start with a problem the team already wants fixed (¶22), get educated on the scale of the problem (¶24), then baseline and track AI energy/emissions alongside cost dashboards and consider smaller open-weight models sized to the task (¶25-¶26). > - Uncertain or contested: the survey's representativeness (self-flagged, ¶27); whether "small individual impact" is accurate self-assessment or diffusion of responsibility; and whether GitHub, as the entity commissioning the report, has an incentive to frame the fix as tooling it can sell rather than models it should shrink. > ## 🗒️ Notes > #### ¶0–¶13 Developers care deeply about future sustainability > - Frame (¶0-¶1): developers say they care about sustainability and want employers to act on AI's environmental impact. > - Headline numbers (¶2-¶4): the GitHub/Yale YPCCC survey of 1,039 US GitHub monthly active users finds 63% think AI contributes to climate change "a great deal or a moderate amount," and 70% want tech companies and employers to do more; Paull Young frames this as developers wanting help making software more efficient. > - Climate belief context (¶6-¶7): 86% say global warming is happening, higher than the general US-adult comparison in Yale's national survey; 79% are worried, and 82% expect it to harm future generations at least moderately. > - Compute-scale context (¶8-¶9): data centers used about 1.5% of global electricity in 2024 (IEA), which names AI the top driver of future growth; MIT Sloan estimates AI's direct emissions could add roughly 0.1 degree C of warming by 2100, or push a 25%-GWP-boost scenario from 3.3 to 3.6 degrees C above pre-industrial levels. > - Broader footprint and concern (¶10-¶13): Joly adds air pollution, generator noise, and water use to the footprint, warning operational emissions are "growing rapidly"; 71% are concerned about AI's environmental impact and three in four say it matters that their employer actively reduces its footprint. > #### ¶14–¶19 Developers: kill unnecessary resource drains > - Model inefficiency (¶14): one respondent wants "more efficient AI models," explicitly preferring local, efficient AI over frontier models. > - Process waste (¶15-¶16): another respondent says a "ton of wasted energy" comes from inefficient, unwanted software generally, and that energy and environmental impacts stay hidden from developers because they're abstracted away. > - AI-generated code risk (¶17): the article links this to separate reporting on rising duplicate code, technical debt, and declining maintainability in the AI coding era, as a further, unmeasured resource driver. > - Responsibility split (¶18): 63% rate their own individual coding practices as having only a "small" environmental [](https://leaddev.com/ai/developers-want-more-sustainable-ai)[article hero](https://leaddev.com/ai/developers-want-more-sustainable-ai) **Key takeaways:** * 63% of developers say **AI contributes to climate change** and 70% want **employers to do more about it**. * They blame inefficient models and unwanted software, **not their own habits**. * 80% want tools to write more energy-efficient code, starting with **baselining AI energy use**. Developers care deeply about sustainability and the environmental impact of [AI](https://leaddev.com/ai/best-ai-coding-assistants) and software systems. They also think employers should be doing more to respond. A clear majority of respondents (63%) believe AI systems contribute to [climate change](https://leaddev.com/culture/feeling-the-squeeze-greenops-offers-a-sustainable-solution-for-busy-engineering-teams) either a great deal or a moderate amount, according to a [joint report](https://climatecommunication.yale.edu/publications/github-survey/) from GitHub and the Yale Program on Climate Change Communication (YPCCC). As many as 70% say tech companies and employers should be doing more to address environmental impact. “Developers want help putting their concern about climate change into practice, primarily through making software more efficient,” says Paull Young, GitHub’s head of sustainability. The report surveyed 1,039 GitHub monthly active users in the US about climate change, AI’s environmental impact, and sustainable software engineering practices. It arrives as software engineering teams increasingly rely on compute-intensive AI systems, while new data centers are [being constructed](https://www.mckinsey.com/mgi/our-research/colocation-data-centers-the-infrastructure-race-behind-ai) to handle growing demand. #### Developers care deeply about future sustainability A hefty 86% of respondents say global warming is happening, with most linking it at least partly to human activities. Interestingly, that’s higher than among US adults in Yale’s national comparison survey. More than three quarters (79%) are at least somewhat worried about global warming, while 82% believe it will harm future generations at least a moderate amount. Data centers consumed about 1.5% of global electricity in 2024, [according to IEA](https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai), which identifies AI as the most important driver of future growth. [MIT Sloan researchers](https://mitsloan.mit.edu/press/will-ai-derail-global-climate-goals?utm_source=chatgpt.com) estimate that the direct emissions associated with AI could contribute roughly 0.1°C of additional global warming by 2100. In one MIT scenario where AI eventually boosts gross world product by 25%, expected warming rises from 3.3°C to 3.6°C above pre-industrial levels. Pascal Joly, founder of IT Climate Ed, a consultancy that analyzes the impacts of IT on climate change, adds that AI’s environmental footprint extends beyond data center emissions to air pollution from power generation and backup generators, noise from cooling systems, and a high level of water consumption. “If you look at it from a global perspective, these operational emissions may look small but they are growing rapidly,” he says. Against this backdrop, 71% of respondents to the survey expressed concern about the environmental impact of AI systems. #### Developers: kill unnecessary resource drains Three in four respondents say it is either “very” or “somewhat” important that their employer actively works to reduce its environmental impact. So, where do the biggest inefficiencies lie? The report’s anonymous qualitative responses point to several potential sources of waste. One respondent highlights model inefficiency: “Give me more efficient AI models. I don’t care about frontier models. I care about local AI. I care about efficient AI.” Inefficient processes and visibility gaps might extend well beyond AI, too. As another developer wrote: “There is a ton of wasted energy in the development and deployment of inefficient and unwanted software.” The energy and environmental impacts are completely obscured from the developers as they are abstracted away, they add. “Highlighting the energy and environmental impacts to the development community needs more attention.” Beyond the concerns raised in the report, inefficient AI-generated code could also contribute to unnecessary resource consumption. Other reports cite more duplicate code, [rising technical debt](https://leaddev.com/technical-direction/how-ai-generated-code-accelerates-technical-debt), and [declining code maintainability](https://leaddev.com/ai/code-maintainability-plummets-in-the-ai-coding-era) in the AI age. #### Ways software teams can improve efficiency Most respondents see their individual coding practices as having a relatively small environmental impact – 63% say its effect is “small.” Instead, they place more responsibility on governments and regulators, cloud providers, tech companies, and employers to act. Nonetheless, 80% are interested in tools that help them write more energy-efficient code. Selected qualitative responses suggest a number of ways GitHub could help developers reduce their software’s environmental impact: * Use data centers powered by renewable energy. * Provide tools to measure software’s environmental impact. * Include energy-efficiency scores for large open-source projects. * Add metrics for the computational toll of GitHub Actions. * Delay unnecessary compute-heavy CI/CD steps. * Promote more efficient, open-source local AI models. * Help developers write more efficient code. One respondent also emphasized training for the future: “Help train the current and future generations of developers in how to architect and design for sustainability.” Young recommends [engineering leadership](https://leaddev.com/the-engineering-leadership-report-2026/) start with something the team already wants to fix, whether that’s a slow build, an expensive workload, or code doing unnecessary work. “Give developers time to tackle it, and bring your sustainability team into the conversation,” he says. “Cost, performance, and sustainability teams often have more shared interests than they realize.” Joly says education is an important first step. “The first part is to get educated about the problem,” he says. “Software leaders need to clearly communicate to their team the magnitude of the problem, and why we should care at all.” He recommends baselining your AI tools’ energy consumption and estimated emissions, tracking them over time, and sharing those metrics alongside token-cost dashboards. Joly also advises considering smaller open-weight models, which can have a lower carbon footprint than frontier models. Matching model size to the task at hand can improve efficiency, too. NYC 27 Pre sale ticket block image #### Doing our bit for sustainable AI The survey isn’t representative of programmers at large, but as the world’s largest Git hosting platform, used by over 200 million developers, GitHub provides a useful window into developer attitudes. With most respondents saying it matters that their employer actively reduces its environmental impact, sustainability could also have implications for [morale](https://leaddev.com/management/how-maintain-team-morale-during-downturn) and [talent retention](https://leaddev.com/career-development/seven-things-you-need-know-about-retaining-engineering-talent). Another motivator is [the rising cost](https://leaddev.com/ai/why-your-ai-bill-is-bigger-than-it-should-be) of AI platforms. While many of the recommendations above are aimed at GitHub, most could apply to software organizations more broadly as they look to build more efficient coding and AI practices., Developers Want More Sustainable AI, Bill Doerrfeld, 01m3ksfy2ff9sb4z60xnp1zwkn.