AI Versus Cleantech - Collusion or Collision?

Artificial intelligence and cleantech innovation are both in the news a lot and both are the subject of significant and potentially world-changing innovation, with a predictable knock-on effect on the patent application landscape. Cambridge can boast its share of cleantech luminaries, as previous articles in this series have highlighted. It also bears its fair share of successes in AI and machine learning; in this regard, companies such as Darktrace, Healx, Monumo and Quantinuum come to mind. 

One might argue that a key difference between the two fields is that, whereas cleantech innovation is intended to have a world-changing impact which it is hoped will address (or at least mitigate) a global concern, AI’s world-changing impact is, conversely, provoking a global concern in some quarters for reasons that have been more than adequately highlighted elsewhere. However, the two fields also share much in common. For example, both can, in technical and market sector terms, crop up in a variety of areas. AI can fuel product design, information processing and analysis, the generation of creative content and medical treatment and diagnosis. In the same way, disparate products such as an EV charger or battery, photovoltaic material, a more efficient propeller and biofuels, may have little in common in terms of technology, application and customer base, but all share the same underlying purpose of promoting sustainability.

Furthermore, AI is already showing the potential to enhance and accelerate cleantech applications. Cleantech’s key sectors are already undergoing a digital transformation and the exponential growth of AI has lit a fire under that. In general, the application of AI has resulted in great strides in efficiency, which has obvious environmental benefits, as more efficiency means less expenditure of energy. More specific examples include the use of AI to discover pesticide or drought-resistant crop strains, AI-driven fusion reactor simulation and AI-enhanced climate change modelling. 

Ironically, at the same time as the benefits detailed above have come on the scene, concern has been expressed that the exponential increase in AI’s adoption and capabilities is actually bad for the environment and worsens the energy crisis. It guzzles a huge amount of energy, particularly in the training and use of LLMs, the construction and operation of data centres and the production of e-waste. Consequently, the UK – and several other countries –, in boasting of their aspirations to become both a “green energy superpower” and an “AI superpower”, may have to square the circle at some point. On a more mundane level, companies adopting AI in their businesses will have to give thought to how to reconcile this with their ESG policies and values.

Perhaps the next high impact bout of innovation (and attendant patent filings) will be in the field of making AI more efficient. Technological leaps in energy efficiency are happening anyway and AI stands to benefit from them, so we can be hopeful that the “AI or the planet” dilemma will fade in time – in part, at least.