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Demis Hassabis, Google DeepMind’s chair and co-founder, argues that artificial intelligence could ultimately help create the technologies needed to address climate change. Speaking at the International AI Summit in Dublin, he pointed to climate and weather modelling, disaster prediction, fusion, superconductors, batteries and solar materials. His optimism comes with an important qualification: the data-centre infrastructure required to develop and run advanced AI is expanding rapidly and consumes substantial energy and other resources.

What Hassabis said in Dublin

At the summit, Hassabis framed AI as a potential accelerator for scientific discovery rather than only a source of digital products. “I actually think with the help of AI, that’s what’s going to end up giving us the technology to tackle the climate question,” he said.

He added that, if developers build the right kinds of systems, they should be able to support “climate modeling and weather modeling.” The remarks, reported by NewsATW while attributing coverage to CNET on October 14, 2026, describe future possibilities rather than measured climate results from a specific AI system.

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Where AI might help climate work

Climate, weather and disaster prediction

More capable models could process large volumes of atmospheric, oceanic and land data to improve forecasts. Better weather modelling could help governments and communities prepare for floods, storms, heatwaves and other hazards. Hassabis also listed disaster prediction as an area in which AI may contribute. The practical value would depend on reliable data, scientific validation and deployment in places where warnings can reach people in time.

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Energy technologies and materials

Hassabis identified fusion and superconductors as potential targets for AI-assisted research. He also mentioned batteries and solar materials, where discovering or screening promising compounds can require testing many combinations. AI could help researchers prioritize candidates, but laboratory work, manufacturing constraints and long-term performance still determine whether a discovery becomes usable technology.

Scientific discovery beyond prediction

Hassabis drew a distinction between tasks at which AI has progressed quickly—mathematics, coding and games—and the physical and natural sciences. Scientific discovery requires interaction with the real world, experiments and human judgement. “I think that’s the hardest thing in science is actually asking and articulating the right question,” he said. “That’s even harder than finding the answer.”

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That distinction matters for climate claims. A model can suggest a pattern, material or experiment, but scientists must decide which question is worth pursuing, design tests, check whether results hold outside the training data and weigh safety and environmental trade-offs.

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Why the optimism is paired with a warning

Training and operating frontier AI requires data centres filled with specialised computing equipment. Expanding those facilities increases demand for electricity, cooling and network infrastructure. Where additional power comes from fossil-fuel generation, the associated emissions can increase; even lower-carbon electricity has land, equipment and supply-chain requirements. Water use for cooling and the strain placed on local grids are also part of the environmental discussion.

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The Dublin report does not provide a measured figure for AI’s energy use, water consumption, emissions or full lifecycle impact. It therefore cannot establish whether AI is currently a net environmental benefit or burden. Hassabis’s comments are a qualitative, forward-looking case for potential benefits, while the infrastructure concerns describe real categories of cost that need to be measured system by system.

Potential benefits versus established evidence

  • Climate and weather modelling: Hassabis presents improved modelling and disaster prediction as possible applications; the report does not document a quantified improvement from a named system.
  • Fusion, superconductors, batteries and solar materials: These are research areas where AI might help identify designs or materials; no commercial breakthrough or emissions reduction is attributed to the remarks.
  • Data-centre expansion: The report highlights rising energy and resource requirements, including fossil-fuel demand, but supplies no numerical footprint.
  • Scientific reliability: Real-world experiments and human judgement remain necessary, especially when systems propose novel scientific questions or designs.

This distinction prevents two common overstatements. AI’s possible contribution to climate technology is not proof that today’s AI systems reduce emissions, and concern about data centres is not proof that every AI application has the same footprint. Results depend on the model, hardware, electricity mix, cooling system, usage pattern and whether the application produces a meaningful benefit.

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What the Nobel Prize reference adds

The report places Hassabis’s comments in the context of the 2024 Nobel Prize in Chemistry, awarded to Hassabis, John Jumper and David Baker for work involving AI and protein-structure research. That recognition illustrates how computational methods can contribute to difficult scientific problems. It does not, by itself, demonstrate that AI has solved a climate problem; it is an example of the kind of scientific progress Hassabis believes could be extended to energy and environmental research.

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What would make the climate case convincing?

A credible assessment would need more than ambitious applications. Researchers and policymakers would need to compare the energy, water and material costs of developing and operating an AI system with the measurable benefit it delivers. Useful evidence could include forecast improvements that reduce disaster losses, a validated material that increases renewable-energy efficiency, or a battery design that performs better over its full lifecycle.

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Those comparisons should include the computing used during training, repeated model runs, data-centre construction, electricity generation, cooling and hardware replacement. They should also account for whether an AI-enabled discovery reaches deployment at a scale large enough to outweigh those costs.

The practical takeaway

Hassabis’s position is hopeful but conditional. He sees AI as a tool that could help scientists ask better questions, model climate and weather systems, predict disasters and discover technologies such as improved batteries or solar materials. At the same time, the computing infrastructure behind frontier AI can increase electricity, fossil-fuel, water and grid pressures. The Dublin remarks support treating climate benefits as a possibility to test—not as an established environmental outcome.

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