OPINION: Robotics are essential for Europe’s energy transition

Europe’s energy transition story is usually told through assets: more renewable generation, smarter grids, electric vehicles and battery storage. Yet while much of the debate focuses on financing and building infrastructure, a different challenge is emerging beneath the surface: operating and maintaining a rapidly expanding asset base over the next several decades.

But the high-margin subsidised programmes that kick-started the industry are being phased out, and operators are facing a new reality of low-margin operations at scale. Combined with the demographic squeeze on the European workforce, the industry looks ripe for automation. Maybe even for robots.

The problem is well-known and systemic. Transmission networks are aging – some 40% of Europe’s distribution grids are over 40 years old – and cross-border capacity is set to double. Offshore wind is moving further from shore and into deeper water. Solar is multiplying faster than construction and maintenance teams can keep up with.

The jobs required to operate all of this are highly skilled or physically demanding and impossible to scale quickly: rope-access technicians, electrical engineers, subsea specialists, offshore crews. You cannot train a generation of them in a hiring cycle, and Europe’s demographics make the recruitment harder every year.

On the other side sits the newest trend in AI. “Physical AI” – large models and computer vision wrapped around a machine that moves – feeds hope for something close to a science-fiction solution. Capital has followed: foundation models for robotics, task-specific platforms, middleware to run fleets. What was research five years ago is now in early production across several industries, also by European energy operators. Among European energy operators, though, the pilot is still the standard unit of deployment.

Reality moves slower, but the underlying idea – raise output and de-risk demographics at the same time – is understood, and it does not require full autonomy to pay off. For now it is about making the people already on the payroll more productive: engineers delivering higher inspecting output, system designs that remove the need to send a crew into a hostile environment, machines that facilitate maintenance work with little manual additions. These advancements create capacity without waiting years for new workers. The infrastructure can keep scaling even when headcount cannot.

The build is financed but the operating cost is the open question

Europe is entering a period of extraordinary expansion. Solar capacity needs to grow 107% by 2030, wind 84%, electricity demand 60%, and transmission must roughly triple – with nearly €600 billion of grid investment needed between now and the end of the decade. In practical terms that means around 150 million solar panels and 150,000 wind turbines to install and then keep running, alongside an existing asset base that is getting older.

For most of the last two decades this was very lucrative, derisked through subsidised feed-in tariffs over up to 20 years. As feed-in tariffs phase out and CfDs roll in and new builds compete at market prices, operating on thin margins becomes normality.

The hardware got cheap, the margin moved

For years robotics was simply too expensive for this work. That has inverted, though not quite in the way the headlines suggest.

LiDAR is projected to fall from around €4,297 ($5,000) to €1,203 ($1,400) by 2040, actuators have roughly halved, battery packs sit near €92.81/kWh ($108/kWh), and inference at the edge costs cents per hour.

Autonomy has matured alongside it: transformer-based approaches have cut the demonstrations needed to teach a new task from tens of thousands to a few dozen, and most of the training now happens in simulation before anything is flown or driven near a live substation.

None of this favours European hardware manufacturers directly. Chinese robot makers undercut European prices by 20 to 30%, and in some cases by up to 50%. For investors, the profits need to be made on the intelligence. Two software layers decide whether a fleet of cheap sensors and commodity actuators actually becomes useful to an energy operator, rather than an expensive way to generate footage nobody watches.

The first is physical observability: the ability to reliably capture, attribute and continuously refresh real-world asset conditions. A thermal camera, microphone or LiDAR scanner only creates value when the observation can be tied to a specific transformer, switchgear compartment or span, stored in context, and compared against its operating history.

The challenge is therefore not simply collecting more data, but capturing the right signals at the right frequency and linking them to the correct asset. In critical infrastructure, where access is difficult, hazards are high and failure modes are rare, this observability layer becomes the prerequisite for any meaningful intelligence.

The second is spatial interpretation: turning what a camera sees into something an energy operator can act on. A generic vision model can flag discolouration on a turbine blade; a system built for energy assets can place that defect on the span, compare its progression against the rest of the fleet, check it against the threshold that triggers a mandatory inspection, and decide whether it justifies pulling a technician forward or waiting for the next weather window.

That judgement comes from fusing thermal, visual and historical inspection data against a specific asset class, and from having seen enough of those assets to know what normal looks like. Together, they form a loop that gets more valuable with every site added, because the data behind it cannot be bought off a shelf.

Why now is the time to act

Two regulatory dates give this some urgency. The EU Machinery Regulation applies from 20 January 2027, and it treats AI safety components as high-risk, requiring certification through a notified body. The AI Act’s obligations for robots operating in critical infrastructure follow on 2nd August 2028, covering transparency, technical documentation and post-market monitoring. Companies that can show compliance evidence alongside their product will move faster through procurement than those retrofitting it under deadline pressure.

Components will likely keep coming from Asia, and much of the underlying AI research from the US. Both enable value creation within Europe. The layer that captures the profits orchestrates customer needs, European and national regulation, and earns the operator’s trust through tangible results. That is where Europe is genuinely positioned to win, provided founders and investors resist the temptation to compete where the economics do not favour them.

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