AI in Energy Transition: Optimisation or Regeneration?
amsterdam, zondag, 5 oktober 2025.
Professor Marco Derksen discusses in his essay the dual role of AI in the energy transition. He introduces the ‘transformation matrix’ to show how AI can be used not only to optimise existing processes but also to create regenerative systems. Derksen warns against the ‘optimisation paradox’, where technology intended to solve problems may actually contribute to increased consumption and growth. The essay calls for reflection on the direction in which AI helps us progress in the energy transition.
The Dual Role of AI in the Energy Transition
In his essay ‘AI in the energy transition: beyond optimisation’, Professor Marco Derksen discusses the dual role of artificial intelligence (AI) in the energy transition. AI is often seen as the key technology to make grids smarter, predict maintenance, and better balance supply and demand [1]. However, Derksen raises the ‘optimisation paradox’: technology designed to solve problems can perpetuate them if it is only used for greater speed and lower costs [1].
The Transformation Matrix
To address this paradox, Derksen introduces the ‘transformation matrix’. This matrix has two axes: optimisation versus transformation, and extractive versus regenerative. By using this matrix, he shows where space opens up for a regenerative energy system [1]. The matrix positions AI on scales of optimisation, redesign, and regeneration, and operationally, tactically, and strategically [1].
Optimisation: Faster, Cheaper, More Efficient
One of the clearest applications of AI in the energy transition is the optimisation of existing processes. AI makes existing processes faster, cheaper, or more predictable. This can lead to efficient networks, better predictions, and more efficiency in energy production and distribution [1][2].
Redesign: New Structures and Collaboration Models
In addition to optimisation, AI can also serve as a lever to redesign processes, structures, and collaboration models. This means that technology is used to improve and modernise existing systems without completely replacing them. An example of this is the use of AI to manage microgrid systems, which can make local energy production and consumption more efficient [1][3].
Regeneration: Actively Restoring Nature and Society
The most ambitious application of AI is regeneration, where technology is used to actively restore nature and society. This goes beyond mere efficiency and focuses on creating systems that prioritise ecological and social values. Examples of this include the use of AI to monitor and restore ecosystems, or designing energy networks that better serve local communities [1][3].
Reflection on Direction and Impact
Derksen calls for reflection on the direction in which AI helps us progress in the energy transition. For leaders in the energy sector, this is not just a technical issue. It is a strategic and moral choice whether we primarily use AI to optimise, focusing on short-term profit and efficiency, or whether we dare to use AI for regeneration, where public values, ecosystem restoration, and resilient communities take precedence [2][3].
Conclusion of the Essay
The essay provides a methodology to shift thinking about AI: from optimisation to transformation, from extraction to regeneration. Derksen emphasises that AI is not neutral and gains meaning within the system in which it is used. Every technological revolution requires a new institutional framework to direct its potential [1][3].