Can we finally put a stake in the heart of the climate benefits of the AI bubble argument?

For as long as people have been noticing the environmental issues associated with the AI bubble, commentators have been supplying articles like this despite the massive fallacy that's obvious as soon as you take a serious look at the problem.

 Andrew Beebe writing for Heatmap.

 The Future Climate Benefits of AI Are Worth the Risks 

For three years the entire conversation about artificial intelligence in the climate tech and clean energy communities has been about demand. This, of course, is reasonable. The scale of what the world is building right now has no precedent. Amazon, Google, Meta, and Microsoft together spent more than $420 billion on data center infrastructure in 2025, a number dwarfed by the $745 billion they’re expected to spend in 2026. The McKinsey Global Institute puts the global data center buildout through 2030 at $7 trillion — more than the New Deal, the Marshall Plan, and the Apollo program combined.

About 15% to 20% of that unfathomable spending is going exclusively to power the data center scale-up. By 2030, data centers will consume between 3% and 5% of all electricity generated on Earth.

The carbon cost is worse than the financial cost. Google's total greenhouse gas emissions rose by more than 50% in 2024 compared to five years earlier, even as the company worked harder than any of its peers to source clean power. In Armstrong County, Texas, the company is working with developer Crusoe Energy on a nearly gigawatt-scale natural gas plant to power its Goodnight data center campus.

But here is something else to consider: In 2024, Google ran a 17-week trial on 2,400 transatlantic American Airlines flights using a system designed to predict and avoid the formation of contrails. Contrails are the ice crystal trails left by jet engines that account for roughly a third of aviation's total warming impact — more than the impact of the fuel burning. The AI model rerouted flights slightly to avoid the atmospheric conditions that produce persistent contrails, and in doing so, cut contrail formation by 62% without any meaningful increase in fuel burn.

Around the same time, Microsoft used its Azure Quantum Elements platform to sift through 32 million possible chemical candidates for new battery chemistries, and in 80 hours narrowed the field to a handful of promising compounds that could reduce the amount of lithium required by as much as 70%. Meta, working with Georgia Tech, built one of the largest open-source datasets for discovering better sorbent materials for direct air capture, the process of pulling carbon dioxide directly from the atmosphere. Researchers ran nearly 40 million quantum mechanics calculations across 8,400 candidate materials, looking for those that could grab CO2 efficiently without also absorbing water from the air.

All of these things happened in the past two years. They were largely invisible to consumers. Yet they produced meaningful, even transformative climate benefits. Crucially, they cost almost nothing compared to the AI infrastructure buildout. They were side projects, pursued by teams whose quarterly numbers did not depend on their product’s success.

I argued two years ago in an interview with Heatmap that the steep financial and carbon costs of the AI buildout are worth it, and that if we stick with it, the power of AI will quickly yield innovative solutions to address climate change. But the opposition to data centers and AI deployment has created a frustrating paradox. A sector that has spent years describing a technology primarily as a threat — to the grid, to society, to humanity itself — will not, at the end of that time, be in a strong position to invest in what that technology can build. The sector wrote itself into the role of the regulator and critic at precisely the moment it should have been adopting the role of the main customer.

 It is true that the AI bubble is creating a huge spike in fossil fuel usage and greenhouse gas emissions while also producing non-trivial demands on water and creating huge urban heat sources. It is also true that AI has been used to reduce emissions of greenhouse gases and address climate change. What is not true is that one can be used to justify the other.

As with the cigarettes and cocaine arguments we've discussed in the past, the problem here is one of inappropriate aggregation. AI is a big and notoriously badly defined term, including a wide array of tools and techniques.

All but a sliver of the AI boom is built around internet-trained large language models designed for natural language processing and other mass market generative AI (image,video, music). That includes the trillions of dollars' worth of existing and proposed data centers and the endless lines of gas turbines used to power them. These frontier LLMs are genuinely impressive technology that are very good at a number of things and have wide potential applications, but their role compared to other AI tools in areas like weather forecasting or materials science (see also drug discovery) are small, often bordering on trivial.

You'll note that none of the examples in this article of AI helping to address climate change involve LLMs. They instead involve specialized tools which use only a vanishingly small fraction of the energy required to train a major trillion-parameter frontier model from Anthropic or OpenAI.

Not only can we have one type of AI tool without the other, numerous researchers, most notably  Yann LeCun , have argued that the obsessive focus on large language models has actually hurt the development of other tools.