If models become more efficient still, there are yet more uses to which they can be put. In recent months, several AI labs have launched “Deep Research” tools, combining reasoning models with the ability to search the web for information and set themselves follow-up tasks. The tools are one of the first mainstream examples of what the AI industry calls “agents”, quasi-autonomous AI systems that can carry out many tasks sequentially. And because it takes them between five and 30 minutes to give a response, running such an agent uses more energy than asking a simple query.
Such efficiency gains leave some wary of the Jevons paradox popping up in other industries. Lynn Kaack, who leads the AI and Climate Technology Policy Group at the Hertie School in Berlin, worries that, by increasing efficiency and reducing costs in areas like shipping, AI will incentivise companies to increase their activity.
Those concerned about the trajectory of AI’s environmental costs are looking for ways to alter it. Mr Gamazaychikov, for instance, hopes that his effort to rank various AI models will allow users and businesses to find the most efficient one for any given task, rather than always using the “best”.
But the closed nature of the biggest labs complicate things. OpenAI, for instance, gives away access to its top-tier models below cost, according to Sam Altman, its boss; Google and Amazon charge less for access to their own AI systems than the cost of the electricity alone, insiders claim. That means users have less motivation to hunt for the most efficient model than they would if they had to pay the true cost of their use. And greater transparency around efficiency and emissions may not result in meaningful behavioural change: after all, there is little evidence to show that growing awareness of the carbon cost of flying has stopped people taking flights.
Coming clean
Many observers think that the best way forward is through tighter regulation, both of AI itself and of the energy it consumes. The first has had limited success in Europe—from the summer of 2026, developers of “high risk” AI will need to tell regulators about the energy it consumes—and is struggling to get off the ground almost everywhere else. In America the Trump administration’s bonfire of red tape means voluntary efficiency drives are more likely than new regulations.
That said, trying to regulate the development of AI specifically is not the only option: broader policies meant to motivate emissions cuts, such as carbon pricing, can help too. Arguably the most important change will come from speeding up the transition to clean energy, and boosting the amount available so that demand for greener AI does not gobble up the low-carbon electricity also needed to decarbonise other sectors, from transportation to construction. Figuring out how to do that shouldn’t require Deep Research. ■
Such efficiency gains leave some wary of the Jevons paradox popping up in other industries. Lynn Kaack, who leads the AI and Climate Technology Policy Group at the Hertie School in Berlin, worries that, by increasing efficiency and reducing costs in areas like shipping, AI will incentivise companies to increase their activity.
Those concerned about the trajectory of AI’s environmental costs are looking for ways to alter it. Mr Gamazaychikov, for instance, hopes that his effort to rank various AI models will allow users and businesses to find the most efficient one for any given task, rather than always using the “best”.
But the closed nature of the biggest labs complicate things. OpenAI, for instance, gives away access to its top-tier models below cost, according to Sam Altman, its boss; Google and Amazon charge less for access to their own AI systems than the cost of the electricity alone, insiders claim. That means users have less motivation to hunt for the most efficient model than they would if they had to pay the true cost of their use. And greater transparency around efficiency and emissions may not result in meaningful behavioural change: after all, there is little evidence to show that growing awareness of the carbon cost of flying has stopped people taking flights.
Coming clean
Many observers think that the best way forward is through tighter regulation, both of AI itself and of the energy it consumes. The first has had limited success in Europe—from the summer of 2026, developers of “high risk” AI will need to tell regulators about the energy it consumes—and is struggling to get off the ground almost everywhere else. In America the Trump administration’s bonfire of red tape means voluntary efficiency drives are more likely than new regulations.
That said, trying to regulate the development of AI specifically is not the only option: broader policies meant to motivate emissions cuts, such as carbon pricing, can help too. Arguably the most important change will come from speeding up the transition to clean energy, and boosting the amount available so that demand for greener AI does not gobble up the low-carbon electricity also needed to decarbonise other sectors, from transportation to construction. Figuring out how to do that shouldn’t require Deep Research. ■
