AI boom thrusts Europe between power-hungry data centers and environmental goals
A large hallway with supercomputers inside a server room data center.
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The boom in artificial intelligence is ushering in an environmentally conscious shift in how data centers operate, as European developers face pressure to lower the water temperatures of their energy-hungry facilities to accommodate the higher-powered chips of firms such as tech giant Nvidia.
AI is estimated to drive a 160% growth in demand for data centers by 2030, research from Goldman Sachs shows — an increase that could come at a cost to Europe’s decarbonization goals, as the specialized chips used by AI firms are expected to hike the energy use of the data centers that deploy them.
High-powered chips — also known as graphics processing units, or GPUs — are essential for training and deploying large language models, which are a type of AI. These GPUs need high density computing power and produce more heat, which ultimately requires colder water to support reliable cooling of the chips.
AI can consume 120 kilowatts of energy in just one square meter of a data center, which is equivalent to the power consumption and heat dissipation of around 15 to 25 houses, according to Andrey Korolenko, chief product and infrastructure officer at Nebius, who referred specifically to the deployment of Nvidia’s Blackwell GB200 chip.
“This is extremely dense, and from the cooling standpoint of view you need different solutions,” he said.
The problem we’ve got with the chipmakers, is AI is now a space race run by the American market where land rights, energy access and sustainability are relatively low on the pecking order, and where market domination is key,” Winterson told CNBC
Michael Winterson
chair of the EUDCA
Michael Winterson, chair of the European Data Center Association (EUDCA), warned that lowering water temperatures will eventually “fundamentally drive us back to an unsustainable situation that we were in 25 years ago.”
“The problem we’ve got with the chipmakers is [that] AI is now a space race run by the American market where land rights, energy access and sustainability are relatively low on the pecking order, and where market domination is key,” Winterson told CNBC.
Major equipment suppliers in Europe say that U.S. chip designers are calling on them to lower their water temperatures to accommodate the hotter AI chips, according to Herbert Radlinger, managing director at NDC-GARBE.
“This is shocking news, because originally everybody from the engineering side expected to go for liquid cooling to run higher temperatures,” he told CNBC, referring to the technology of liquid cooling, which is said to be more efficient than the more traditional method of air cooling.
‘Evolution discussion’
Energy efficiency is high on the European Commission’s agenda, as it seeks to reach its goal of reducing energy consumption by 11.7% by 2030. The EU predicted in 2018 that energy consumption of data centers could rise 28% by 2030, but the advent of AI is expected to boost that number two or threefold in some countries.
Winterson said that lowering water temperatures is “fundamentally incompatible” with the EU’s recently launched Energy Efficiency Directive, which established a dedicated data base for data centers of a certain size to publicly report on their power consumption. The EUDCA has has been lobbying Brussels to consider these sustainability concerns.
Energy management firm Schneider Electric engages often with the EU on the topic. Many of the recent discussions have focused on different ways to source “prime power” for AI data centers and for the potential for more collaboration with utilities, said Steven Carlini, chief advocate of AI and data centers and vice president at Schneider Electric.
European Commission energy officials have also had exchanges with Nvidia to discuss energy consumption and the use of data centers with regard to the effectiveness of power use and that of chipsets.
CNBC has approached Nvidia and the Commission for comment.
“Cooling is the second-largest consumer of energy in the data center after the IT load,” Carlini told CNBC in emailed comments. “The energy use will rise but the PUE (Power Usage Effectiveness) may not rise with lower water temperatures despite the chillers having to work harder.”
Schneider Electric’s customers that are deploying Nvidia’s Blackwell GB200 super chip are asking for water temperatures of 20-24 degrees Celsius or between 68 and 75 degrees Fahrenheit, Carlini said.
He added that this compares to temperatures of around 32 degrees Celsius with liquid cooling, or of around 30 degrees Celsius that Meta has suggested for the water it supplies to the hardware.
Ferhan Gunen, vice president of data center operations for the U.K. at Equinix, told CNBC that there are a number of concerns about AI that Equinix has been discussing with its customers.
“They want to increase the density of their servers, which is, they want to have higher-power-using chips, or they want to have more servers,” she said, adding that the shift is not “clear cut.”
“It’s really an evolution discussion more than anything,” Gunen said.
Nvidia, which declined to comment on the cooling requirements of its chips, announced a new platform for its Blackwell GPUs earlier this year. It said that the architecture would enable organizations to run real-time generative AI on large language models at up to 25 times less cost and energy consumption compared to earlier technology.
Liquid cooling will require a “reconfiguration,” Gunen explained, adding that new data centers are already coming ready with this technology. “Yes, higher density will mean more power use, and will also mean more cooling requirement. But then the technology is changing, so you’re doing it differently. That’s why there is a balance in all of this,” she said.
Race for efficiency
Nebius, which has around $2 billion in cash on its balance sheet after splitting from Russia’s Yandex, has said it will be one of the first to bring Nvidia’s Blackwell platform to customers in 2025. The firm has also announced plans to invest more than $1 billion on AI infrastructure in Europe by the middle of next year.
Nebius’ Korolenko said liquid cooling is a “first step,” where cost of ownership will initially be worse before improving over time.
“There’s a big push to deliver, but at the same time, when you go to scale, you will want to have the ability to choose, to be economical and not sacrifice too much. Power efficiency is important for the running costs. It’s always a high priority,” Korolenko said.
Even before a boom in demand for AI applications hit the market, the data center industry in Europe was struggling to keep pace with the growing digital sector.
Sicco Boomsma, managing director of ING’s TMT team, said those involved in the market are “very sensitive to power” and that while Europe’s focus is on infrastructure, the U.S. has focused more on expanding assets in Europe where power is available.
“There’s a tremendous amount of data center operators also coming from the U.S. that are aligning in order to ensure that their data center infrastructure is in line with the various goals that the EU has as well, such as being carbon neutral, such as being efficient, on water utilization, maintaining biodiversity.”
“It is a sort of a race where they want to demonstrate that their knowledge is leading to super efficient infrastructure,” he said.
China’s EV automakers have surged ahead of the competition in global EV sales, and a new report shows just how far ahead they are.
The International Council on Clean Transportation (ICCT) just dropped its third annual Global Automaker Rating, showing that Chinese carmakers dominate the zero-emission vehicle (ZEV) space. China now accounts for over 11 million EVs sold annually – over half of global EV sales.
Its massive domestic market has helped Chinese automakers build serious momentum. They’ve scaled up, improved tech, and are now setting the pace globally. Companies like Geely and SAIC have already hit 50% EV sales share, meeting their 2025 targets a full year early. In fact, Chinese automakers took the top five spots for ZEV class coverage, and five out of the top six for EV sales share.
Meanwhile, automakers in the US and Europe are trying to catch up. But they’re facing a dual challenge of falling behind on tech while navigating shaky regulatory environments.
The report also confirmed a big milestone: In 2024, BYD officially surpassed Tesla in global battery electric vehicle (BEV) sales for the first time. BYD’s BEV sales jumped 25%, and its combined BEV and plug-in hybrid sales climbed an impressive 47% year-over-year. Still, both BYD and Tesla remain in the “Leaders” category.
Automakers boosted energy efficiency, charging speed, and driving range thanks to newer, high-performance models.
“Our assessment revealed widespread improvement in BEV technology performance across the industry,” said Zifei Yang, ICCT’s global passenger vehicle lead. “GM and Honda made significant advancements by introducing high-performance models to their previously limited offerings, while companies like Geely, Chang’an, and Chery improved substantially with new high-performance EV lines.”
India’s Tata Motors also hit a turning point. For the first time, it graduated from ICCT’s “laggard” group to “transitioner,” thanks to new EVs and big moves on battery recycling and repurposing. While Japanese and South Korean automakers are still lagging behind, Honda and Nissan are inching forward. Honda launched its first US BEV, and Nissan finally clarified its ZEV targets.
One newer addition to this year’s report: a green steel metric. Since steel is the second-largest source of emissions in vehicle manufacturing (after batteries), ICCT now tracks which automakers are cutting emissions in the supply chain. European brands like Mercedes-Benz, BMW, and VW earned high marks for sourcing renewable-powered green steel.
ICCT’s CEO, Drew Kodjak, summed it up: “The rapid evolution of the EV market in China has created technological and manufacturing advantages for companies there. For the wider global auto industry, this is no longer just about meeting future goals – it’s about remaining competitive today in a market that’s charging up.”
The full Global Automaker Rating, covering 21 major automakers, is now live on ICCT’s website.
Read more: EV prices dipped in May – and Tesla Model Y led the slide
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Environment
Bloomberg just released the most embarrassing report about Tesla, Waymo, and self-driving
Bloomberg has just released an embarrassingly bad report about the self-driving space, in which it claimed Tesla has an advantage over Waymo by misrepresenting data.
There are currently many eyes on Tesla’s imminent launch of its “robotaxi” service in Austin, Texas.
We have just published a new report today to highlight how the launch is a game of smoke and mirrors, meant to reframe the optics of Tesla’s self-driving effort following years of broken promises.
At the same time, Bloomberg Intelligence released its own report, claiming that Tesla is ahead in self-driving technology, but the firm misrepresented data to support its claim.
The report compares Tesla’s and Waymo’s self-driving efforts, going so far as to claim that “Tesla is closer to vehicle autonomy than peers.”
Here are the two main charts that Bloomberg circulated from the report:
The problem is that the report is misleading by comparing completely different data.
Steve Man, the Bloomberg Intelligence analyst behind the report, based his report on Tesla’s own quarterly misleading “Autopilot Safety Report.”
The report is widely considered to be unserious for several main reasons:
- Tesla bundles all miles from its vehicles using Autopilot and FSD technology, which are considered level 2 ADAS systems that require driver attention at all times. Drivers consistently correct the systems to avoid accidents.
- Tesla Autopilot, which is standard on all Tesla vehicles, is primarily used on highways, where accidents occur at a significantly lower rate per mile compared to city driving.
- Tesla only counts events that deploy an airbag or a seat-belt pretensioner. Fender-benders, curb strikes, and many ADAS incidents never appear, keeping crash counts artificially low.
- Finally, Tesla’s handpicked data is compared to NHTSA’s much broader statistics that include all collision events, including minor fender benders.
All these facts combined render the comparison between Tesla’s accident rate using “Autopilot technology” and NHTSA’s US average completely useless.
Yet, Bloomberg decided not only to use it but also to compare it to Waymo’s data to claim that “Tesla is 10 times safer”:
The problem with this is similar to the comparison with the US average, as the Waymo data includes all police-reported incidents, which is a much wider net than Tesla’s data, in addition to the previously mentioned issues.
To highlight how big a potential discrepancy there is in the data, NHTSA underscored in a report last year how Tesla is not aware of many crashes involving Autopilot and that only 18% of police-reported crashes involve airbag deployment:
Gaps in Tesla’s telematic data create uncertainty regarding the actual rate at which vehicles operating with Autopilot engaged are involved in crashes. Tesla is not aware of every crash involving Autopilot even for severe crashes because of gaps in telematic reporting. Tesla receives telematic data from its vehicles, when appropriate cellular connectivity exists and the antenna is not damaged during a crash, that support both crash notification and aggregation of fleet vehicle mileage. Tesla largely receives data for crashes only with pyrotechnic deployment, which are a minority of police reported crashes. A review of NHTSA’s 2021 FARS and Crash Report Sampling System (CRSS) finds that only 18 percent of police-reported crashes include airbag deployments.
Knowing full well the comparison is not fair and completely misrepresents the situation, the usual Tesla stock pumpers on X widely shared Bloomberg’s misleading report positively, and even CEO Elon Musk shared the misleading data:
Electrek’s Take
This is embarrassing for Bloomberg. It’s such a blatant error and misrepresentation that it is suspicious. They should issue a correction right away.
Tesla fanboys are now pushing this to try to prove that Tesla’s robotaxi is safe to launch amid Tesla doing everything it can to hide its self-driving crash data ahead of the launch. This is a dangerous report from Bloomberg.
Additionally, it’s not just the primary claim regarding the accident rate that is misleading. The report also contains several glaring errors.
In this chart, Bloomberg claims that Tesla is at “3 billion miles of data collected since launched”:
It looks like they simply use Tesla’s “cumulative miles driven with FSD (Supervised)”, which includes driver supervision, and the driver remains responsible for correcting FSD at all times.
In comparison, they talk about 22 million miles for Waymo. It looks like Bloomberg only used Waymo’s rider-only mileage in San Francisco, which is currently at 22 million miles, but when accounting all markets, Waymo is currently at more than 71 million miles:
It’s not clear why they would only use mileage in San Francisco for Waymo when they used Tesla’s global customer FSD mileage for Tesla.
Again, these are also “rider-only” miles, which means that there are only people riding inside the Waymo vehicles, compared to Tesla’s mileage being completely supervised by customer-drivers at all times.
We simply don’t know how many “rider-only” miles Tesla has, since it only started with one or two cars in Austin over the last few weeks. It is likely to have no more than a few hundred or a few thousand miles.
Regardless, it’s completely nonsensical to claim that Tesla is “ahead of its peers” in self-driving, especially Waymo, based on this report.
Tesla is currently only trying to launch something that Waymo has been doing for years.
The other argument the report attempts to make is that Tesla’s “self-driving” vehicles are approximately 7 times cheaper than Waymo’s.
Again, the problem is that Tesla’s vehicles are not self-driving. Tesla has yet to prove that, and that’s why it is using “plenty of teleoperation” in this launch in Austin. Mapping, optimizing for geo-fenced area, and teleoperations are the real limiting factors here. Not the cost of the vehicles.
Suppose Tesla has anything less than a 100-to-1 vehicle-to-teleoperator ratio. In that case, its system is not profitably scalable and I wouldn’t be surprised if it has a 1-to-1 ratio for the foreseeable future – at least on its current hardware.
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