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Alessio Terzi

April 16th, 2024

If AI uses a lot of energy and you care about the climate, should you be anti-AI?

0 comments | 11 shares

Estimated reading time: 5 minutes

Alessio Terzi

April 16th, 2024

If AI uses a lot of energy and you care about the climate, should you be anti-AI?

0 comments | 11 shares

Estimated reading time: 5 minutes

Artificial intelligence consumes an inordinate amount of energy, but voluntary restraint is hardly a solution to preserve the environment. AI can be used instead to find technological solutions. Alessio Terzi writes that rather than opting for outright bans on the construction of new data centres, clear incentive structures should be put in place to steer AI innovation towards solutions that are both sustainable and commercially viable.


The techno-optimism surrounding the surge of artificial intelligence (AI) is palpable. Enter, however, a classroom of sustainability master’s students, as I regularly do, and you will hear voices of complaint against what is perceived as a massively energy-intensive Silicon Valley toy with limited tangible upside for humanity’s pressing social and environmental challenges. Recent reports that data centres could consume as much electricity as Japan by 2026, and globally could require as much as half of the UK’s fresh water use by 2027, lend evidence to this concern. Consequently, several jurisdictions are considering curbs on new server farms, including Germany, China, Ireland and Singapore. A former French minister went as far as suggesting rationing access to the internet. If you care about climate action, should you be anti-AI?

Two considerations could help answer this question. First, it is important to recognise that the real promise of AI, as a general-purpose technology, lies not in offering an immediate fix to all our predicaments but in accelerating technological progress, including in the realm of environmental sustainability. In the context of climate mitigation, the early applications of AI have already begun to demonstrate its potential. For instance, making air travel more fuel-efficient, developing faster electric batteries, or helping reduce the reliance on critical raw materials, essential for the green transition but often associated with significant environmental and ethical concerns. Recently, AI helped solve a crucial hurdle related to plasma containment, representing an important step in the long road towards safe fusion energy use.

AI could help with climate adaptation too. Consider the fact that the spread of zoonotic diseases is a crucial risk stemming from climate change, and that AI is being deployed to accelerate the development of vaccines. Or that extreme weather events will become more frequent, with AI helping improve weather forecasting allowing life-saving early warnings.

The second fundamental consideration relates to energy use. Many national decarbonisation strategies include plans to reduce energy consumption. However, as I’ve argued in a recent paper with energy economist Roger Fouquet, energy sobriety was always going to play a junior role in decarbonisation strategies vis-à-vis the imperative to foster innovation. If history is of any guidance, it is worth noting that declines in energy consumption are very uncommon, short-lived, and rarely happen by design. In line with this reality, the International Energy Agency expects sobriety to contribute just eight per cent to CO2 reductions in its roadmap towards net zero by 2050. Innovation offers the only credible pathway to permanently decoupling economic activity from climate change and environmental degradation. When assessing the trade-offs between a short-term increase in energy use of AI and a medium-term boost in innovation, this element should be taken into account.

We are in the early stages of the AI revolution, implying that the technology is still being fine-tuned. If clear incentives are set right now, innovation will be designed also to maximise energy efficiency, prioritise more efficient models, or minimise cooling needs. Indeed, while a command on OpenAI’s ChatGPT requires ten times the electricity of a Google search, it has been shown that it is possible to build a large language model similar to ChatGPT-3 with much lower emissions.

Which brings us to the importance of regulation. Rather than opting for outright bans to the construction of data centres, clear incentive structures should be put in place to steer AI innovation towards solutions that are not only commercially viable but also aligned with sustainability. This could start with clear environmental reporting requirements for AI companies and could extend all the way to legislation requiring only renewable energy sources be used for new data centres. Aware of the problem, tech behemoths are turning to AI itself also to improve the efficiency of their data centres, or pledging to make them carbon neutral by 2030. To do so, tech companies are developing strategic partnerships with energy companies, be they nuclear, geothermal, solar and batteries. But voluntary measures should be complemented by regulation. Something both the US and the EU are considering in recent bills.

Electrification was always expected to be a crucial component of the green transition, and therefore demand for electricity was inevitably set to increase as fossil fuels get progressively phased out. But part of the current predicament originates from the fact that it can take as little as one year to build a data centre, and up to five years to build renewable energy facilities. This mismatch is aggravating the climate cost of AI, and in most cases is the result of poor regulation.

From a climate perspective, AI should neither be glorified nor vilified. Alone it cannot install the necessary solar and wind capacities, promote a circular economy, or restore degraded ecosystems and biodiversity. These challenges require action by consumers, businesses and governments navigating trade-offs between living standards, energy security and sustainability. But AI can augment human ingenuity in finding a solution to these wicked problems.

 


  • This blog post represents the views of the author(s), not the position of LSE Business Review or the London School of Economics and Political Science.
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About the author

Alessio Terzi

Alessio Terzi is a Lecturer in Public Policy at the University of Cambridge, an Adjunct Professor of Economics at Sciences Po and an economist at the European Commission. He holds a PhD in political economy from the Hertie School.

Posted In: Economics and Finance | Sustainability | Technology

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