There was a time when technology seemed to have found a comfortable way to tell its climate future. The big companies talked about “clean energy”net zero emissions, increasingly efficient operations and commitments dated to 2030 or 2040. It was an attractive story because it coexisted with our daily use of the internet, services and applications. Generative AI, however, has complicated that picture: not only does it bring more smart services, it also requires more infrastructure, more electricity, and climate pressure that is much more difficult to square with the promises those same companies made just a few years ago.
The most recent movement comes from Microsoft. Bloomberg has published that the company would be considering delaying or even abandoning one of its most ambitious energy goals, at a time when the race for AI requires increasingly more computing capacity. Tell OpenAI or Anthropic. This case does not appear in a vacuum: other large technology companies are also facing increasingly visible challenges to fit their climate commitments with the expansion of their data centers. The question is no longer just what they promised, but what happens when those promises collide with the actual scale of AI.
The companies did not reach these commitments in a single way nor did they promise exactly the same thing. Some focused on the purchase of renewable energy, others on zero-carbon electricity, others on net-zero emissions, and others on eliminating more carbon than they generate. There were also different reasons for doing so: regulatory pressure, investor expectations, reputation and a fairly widespread conviction that digital infrastructure could grow. without triggering its climate impact. What interests us here is not to review all those promises, but to follow some of the most ambitious ones and see how they are holding up to the AI race that is unfolding before our eyes.
Climate promises in the face of expanding data centers
As we say, the fundamental change is that many of these commitments were formulated before generative AI became an absolute priority for the industry. Until then, the growth of data centers was already a challenge, but it could be projected with a more gradual logic. The new race has altered that pace: training models, deploying them in massive products, and answering large-scale queries requires computing power that grows very quickly. What once seemed like a difficult but manageable roadmap now faces a different dynamic.
Microsoft was one of the companies that formulated one of the most demanding goals. In July 2021 he announced his 100/100/0 commitment, a way of saying that by 2030 he wanted match 100% of your electricity consumption100% of the time, with zero-carbon energy purchases. The nuance matters: it was not just about offsetting annual consumption with renewables, but about getting closer to an hour-by-hour correspondence. Furthermore, the company proposed doing so in the same electrical networks from which it took that energy.
Now that commitment is under obvious pressure. The aforementioned economic media indicated that the Redmond company is studying delaying or even abandoning it, according to anonymous sources with knowledge of the matter, while seeking to clear obstacles to powering its data centers. Microsoft has not confirmed that change and its director of sustainability, Melanie Nakagawa, maintained that the company remains committed to its environmental goals. He also left an insight that sets the tone for the official response: any adjustment would be part of a review of approach, not a change in long-term ambition.
Google also set a powerful goal. In 2021, the Mountain View company set the goal to achieve net zero emissions across its operations and value chain by 2030, including its consumer hardware products. To achieve this, he proposed reduce 50% its absolute emissions compared to 2019, not only those generated directly by the company, but also those linked to its activity and its supply chain. What it could not reduce, according to its roadmap, it would compensate by removing carbon from the atmosphere through natural and technological solutions.
The current situation shows how difficult it is to put this roadmap into practice. In its 2025 environmental reportGoogle points out that in 2024 its emissions were 11.5 million tons of CO2 equivalent. That is 11% more than the previous year and 51% above its 2019 base. The nuance is important: they did not increase 51% in one year, but rather compared to the starting point chosen by the company. The report itself also recognizes that integrating more AI into its products can complicate the reduction of emissions due to the greater demand for computing and technical infrastructure.
Amazon also presented a high-ambition climate pledge. In September 2019the e-commerce giant announced together with Global Optimism The Climate Pledge, a commitment to achieve net zero carbon emissions by 2040ten years before the horizon set by the Paris Agreement. The company founded by Jeff Bezos became the first signatory of that initiative, which called for measuring and reporting emissions on a regular basis, applying decarbonization strategies and neutralizing remaining emissions with additional, quantifiable, real, permanent and socially beneficial compensations.
Amazon’s situation shows that these promises already had gray areas even before AI was at the center of the debate. In September 2023, Data Center Dynamics published that the Science Based Targets initiative had removed the Amazon commitment from its panel and placed it in the “expired commitment” category. The reason, according to the media, was that both parties were unable to agree on a sufficiently significant emissions target. Amazon responded that the requirements had changed and that it would continue to look for credible third-party validators.
In this sense, general photography goes in the same direction. The US Department of Energy estimates that the Data centers consumed around 4.4% of the country’s electricity in 2023 and could be between 6.7% and 12% in 2028. The International Energy Agency also projects a relevant leap on a global scale: from about 415 TWh in 2024 to about 945 TWh in 2030. Not all of this growth can be attributed solely to AI, but AI has become one of the great accelerators of that demand.
But the difficult thing is that the two logics do not move at the same speed. The AI race works with business urgency: launch models, expand capacity, close contracts and deploy data centers before rivals and, of course, compete with China. The climate agenda works with a different cadence: reduce real emissions, ensure carbon-free energy, validate objectives and sustain them for years. Between one and the other there is a gap that is now becoming visible. It is not enough for commitments to exist on paper; They have to survive a technological expansion that consumes more and more resources.
Images | Google | amazon
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