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AI holds the key to faster battery tech development<br>Opportunity to transform materials discovery could outweigh risks of high energy consumption<br>Andreas Hoepner<br>Add to myFTGet instant alerts for this topic<br>Manage your delivery channels hereRemove from myFT
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Andreas Hoepner
PublishedJune 15 2026
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Andreas Hoepner co-leads the GreenWatch team at University College Dublin. He also heads the Data Science Hub of the EU Platform on Sustainable Finance<br>While most commentators see the current surge in AI use as an obstacle to achieving the goals of the Paris agreement due to the increased energy consumption, my view is that the opportunities probably outweigh the risks.<br>I do not base my view, however, on large language models (LLMs), whose gazillions of instant autocomplete decisions lack any humanlike understanding of knowledge, but on the potential for AI to substantially speed up battery technology development, which is the prime requirement for the green energy transition.<br>The Chinese philosopher Confucius differentiated between three ways of learning wisdom: learning from imitation (the easiest way), from experience (often the bitterest) and from reflection (the noblest). This third approach requires an in-depth humanlike understanding of what knowledge is — a so-called epistemology.<br>The machine “learning” underlying the current LLM-driven AI rally is nearly exclusively based on neural networks — binary computing systems whose nomenclature is inspired by the human brain. These networks are trained to classify information into a binary, probabilistic world view at lightning pace. That is very useful for imitating behaviour (“let’s do it” vs “let’s not do it”) or to try enormous possibilities in a short space of time such as in protein folding or materials discovery for batteries.<br>But learning by reflection, and having an epistemology, requires something more complex than what these binary results provide. For example, a human learner can reason about appropriate behaviour based on moral principles or heuristics, whereas neural networks can only imitate this behaviour once observed from a large number of humans.<br>In fact, when asking half a dozen major LLMs if they possess an epistemology, all decline except Claude and ChatGPT. One LLM, Meta AI, is particularly candid, stating that it does not experience doubt, justification or truth in the way humans do.<br>This may change with the introduction of quantum computing. Until then, the computational power that is required to layer more complex outcome functions over each other in high frequencies is virtually impossible to access.<br>The neural networks that are used in most LLMs are incredibly fast in imitating language, usually scraped from (nearly) the entire internet. They can determine the most likely letter succeeding and preceding a set of text.<br>An LLM completes trillions of text auto-completes in seconds to provide its user with the most probable plausible response to a prompt. Essentially, LLMs are hugely powered auto-complete machines.<br>This is fantastic when asking a general question on a subject where the LLM has millions of letters of text to imitate, such as corporate net zero targets. But for a more specific question on a subject where it has a much smaller corpus of letters available for imitation, actual CO₂ emissions for example, the results will be limited.<br>For instance, when asking six LLMs if a British energy company has a net zero target, they all...