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Four things we’d need to put data centers in space

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here. In January, Elon Musk’s SpaceX filed an application with the US Federal Communications Commission to launch up to one million data centers into Earth’s orbit. The goal? To fully unleash the potential of AI without triggering an environmental crisis on Earth. But could it work? SpaceX is the latest in a string of high-tech companies extolling the potential of orbital computing infrastructure. Last year, Amazon founder Jeff Bezos said that the tech industry will move toward large-scale computing in space. Google has plans to loft data-crunching satellites, aiming to launch a test constellation of 80 as early as next year. And last November Starcloud, a startup based in Washington State, launched a satellite fitted with a high-performance Nvidia H100 GPU, marking the first orbital test of an advanced AI chip. The company envisions orbiting data centers as large as those on Earth by 2030. Proponents believe that putting data centers in space makes sense. The current AI boom is straining energy grids and adding to the demand for water, which is needed to cool the computers. Communities in the vicinity of large-scale data centers worry about increasing prices for those resources as a result of the growing demand, among other issues. In space, advocates say, the water and energy problems would be solved. In constantly illuminated sun-synchronous orbits, space-borne data centers would have uninterrupted access to solar power. At the same time, the excess heat they produce would be easily expelled into the cold vacuum of space. And with the cost of space launches decreasing, and mega-rockets such as SpaceX’s Starship promising to push prices even lower, there could be a point at which moving the world’s data centers into space makes sound business sense. Detractors, on the other hand, tell a different story and point to a variety of technological hurdles, though some say it’s possible they may be surmountable in the not-so-distant future. Here are four of the must-haves we’d need to make space-based data centers a reality.  A way to carry away heat  AI data centers produce a lot of heat. Space might seem like a great place to dispel that heat without using up massive amounts of water. But it’s not so simple. To get the power needed to run 24-7, a space-based data center would have to be in a constantly illuminated orbit, circling the planet from pole to pole, and never hide in Earth’s shadow. And in that orbit, the temperature of the equipment would never drop below 80 °C, which is way too hot for electronics to operate safely in the long term.  Getting the heat out of such a system is surprisingly challenging. “Thermal management and cooling in space is generally a huge problem,” says Lilly Eichinger, CEO of the Austrian space tech startup Satellives. On Earth, heat dissipates mostly through the natural process of convection, which relies on the movement of gases and liquids like air and water. In the vacuum of space, heat has to be removed through the far less efficient process of radiation. Safely removing the heat produced by the computers, as well as what’s absorbed from the sun, requires large radiative surfaces. The bulkier the satellite, the harder it is to send all the heat inside it out into space. But Yves Durand, former director of technology at the European aerospace giant Thales Alenia Space, says that technology already exists to tackle the problem. The company previously developed a system for large telecommunications satellites that can pipe refrigerant fluid through a network of tubing using a mechanical pump, ultimately transferring heat from within a spacecraft to radiators on the exterior. Durand led a 2024 feasibility study on space-based data centers, which found that although challenges exist, it should be possible for Europe to put gigawatt-scale data centers (on par with the largest Earthbound facilities) into orbit before 2050. These would be considerably larger than those envisioned by SpaceX, featuring solar arrays hundreds of meters in size—larger than the International Space Station. Computer chips that can withstand a radiation onslaught The space around Earth is constantly battered by cosmic particles and lashed by solar radiation. On Earth’s surface, humans and their electronic devices are protected from this corrosive soup of charged particles by the planet’s atmosphere and magnetosphere. But the farther away from Earth you venture, the weaker that protection becomes. Studies show that aircraft crews have a higher risk of developing cancer because of their frequent exposure to high radiation at cruising altitude, where the atmosphere is thin and less protective. Electronics in space are at risk of three types of problems caused by high radiation levels, says Ken Mai, a principal systems scientist in electrical and computer engineering at Carnegie Mellon University. Phenomena known as single-event upsets can cause bit flips and corrupt stored data when charged particles hit chips and memory devices. Over time, electronics in space accumulate damage from ionizing radiation that degrades their performance. And sometimes a charged particle can strike the component in a way that physically displaces atoms on the chip, creating permanent damage, Mai explains. Traditionally, computers launched to space had to undergo years of testing and were specifically designed to withstand the intense radiation present in Earth’s orbit. These space-hardened electronics are much more expensive, though, and their performance is also years behind the state-of-the-art devices for Earth-based computing. Launching conventional chips is a gamble. But Durand says cutting-edge computer chips use technologies that are by default more resistant to radiation than past systems. And in mid-March, Nvidia touted hardware, including a new GPU, that is “bringing AI compute to orbital data centers.”  Nvidia’s head of edge AI marketing, Chen Su, told MIT Technology Review, that “Nvidia systems are inherently commercial off the shelf, with radiation resilience achieved at the system level rather than through radiation‑hardened silicon alone.” He added that satellite makers increase the chips’ resiliency with the help of shielding, advanced software for error detection, and architectures that combine the consumer-grade devices with bespoke, hardened technologies. Still, Mai says that the data-crunching chips are only one issue. The data centers would also need memory and storage devices, both of which are vulnerable to damage by excessive radiation. And operators would need the ability to swap things out or adapt when issues arise. The feasibility and affordability of using robots or astronaut missions for maintenance is a major question mark hanging over the idea of large-scale orbiting data centers. “You not only need to throw up a data center to space that meets your current needs; you need redundancy, extra parts, and reconfigurability, so when stuff breaks, you can just change your configuration and continue working,” says Mai. “It’s a very challenging problem because on one hand you have free energy and power in space, but there are a lot of disadvantages. It’s quite possible that those problems will outweigh the advantages that you get from putting a data center into space.” In addition to the need for regular maintenance, there’s also the potential for catastrophic loss. During periods of intense space weather, satellites can be flooded with enough radiation to kill all their electronics. The sun has just passed the most active phase of its 11-year cycle with relatively little impact on satellites. Still, experts warn that since the space age began, the planet has not experienced the worst the sun is capable of. Many doubt whether the low-cost new space systems that dominate Earth’s orbits today are prepared for that. A plan to dodge space debris Both large-scale orbiting data centers such as those envisioned by Thales Alenia Space and the mega-constellations of smaller satellites as proposed by SpaceX give a headache to space sustainability experts. The space around Earth is already quite crowded with satellites. Starlink satellites alone perform hundreds of thousands of collision avoidance maneuvers every year to dodge debris and other spacecraft. The more stuff in space, the higher the likelihood of a devastating collision that would clutter the orbit with thousands of dangerous fragments. Large structures with hundreds of square meters of solar arrays would quickly suffer damage from small pieces of space debris and meteorites, which would over time degrade the performance of their solar panels and create more debris in orbit. Operating one million satellites in low Earth orbit, the region of space at the altitude of up to 2,000 kilometers, might be impossible to do safely unless all satellites in that area are part of the same network so they can communicate effectively to maneuver around each other, Greg Vialle, the founder of the orbital recycling startup Lunexus Space, told MIT Technology Review. “You can fit roughly four to five thousand satellites in one orbital shell,” Vialle says. “If you count all the shells in low Earth orbit, you get to a number of around 240,000 satellites maximum.” And spacecraft must be able to pass each other at a safe distance to avoid collisions, he says.  “You also need to be able to get stuff up to higher orbits and back down to de-orbit,” he adds. “So you need to have gaps of at least 10 kilometers between the satellites to do that safely. Mega-constellations like Starlink can be packed more tightly because the satellites communicate with each other. But you can’t have one million satellites around Earth unless it’s a monopoly.” On top of that, Starlink would likely want to regularly upgrade its orbiting data centers with more modern technology. Replacing a million satellites perhaps every five years would mean even more orbital traffic—and it could increase the rate of debris reentry into Earth’s atmosphere from around three or four pieces of junk a day to about one every three minutes, according to a group of astronomers who filed objections against SpaceX’s FCC application. Some scientists are concerned that reentering debris could damage the ozone layer and alter Earth’s thermal balance.  Economical launch and assembly The longer hardware survives in orbit, the better the return on investment. But for orbital data centers to make economic sense, companies will have to find a relatively cheap way to get that hardware in orbit. SpaceX is betting on its upcoming Starship mega-rocket, which will be able to carry up to six times as much payload as the current workhorse, Falcon 9. The Thales Alenia Space study concluded that if Europe were to build its own orbital data centers, it would have to develop a similarly potent launcher.  But launch is only part of the equation. A large-scale orbital data center won’t fit in a rocket—even a mega-rocket. It will need to be assembled in orbit. And that will likely require advanced robotic systems that do not exist yet. Various companies have conducted Earth-based tests with precursors of such systems, but they are still far from real-world use. Durand says that in the short term, smaller-scale data centers are likely to establish themselves as an integral part of the orbital infrastructure, by processing images from Earth-observing satellites directly in space without having to send them to Earth. That would be a huge help for companies selling insights from space, as many of these data sets are extremely large, and competition for opportunities to downlink them to Earth for processing via ground stations is growing. “The good thing with orbital data centers is that you can start with small servers and gradually increase and build up larger data centers,” says Durand. “You can use modularity. You can learn little by little and gradually develop industrial capacity in space. We have all the technology, and the demand for space-based data processing infrastructure is huge, so it makes sense to think about it.” Smaller facilities probably won’t do much to offset the strain that terrestrial data centers are placing on the planet’s water and electricity, though. That vision of the future might take decades to come to fruition, some critics think—if it even gets off the ground at all. 

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

In January, Elon Musk’s SpaceX filed an application with the US Federal Communications Commission to launch up to one million data centers into Earth’s orbit. The goal? To fully unleash the potential of AI without triggering an environmental crisis on Earth. But could it work?

SpaceX is the latest in a string of high-tech companies extolling the potential of orbital computing infrastructure. Last year, Amazon founder Jeff Bezos said that the tech industry will move toward large-scale computing in space. Google has plans to loft data-crunching satellites, aiming to launch a test constellation of 80 as early as next year. And last November Starcloud, a startup based in Washington State, launched a satellite fitted with a high-performance Nvidia H100 GPU, marking the first orbital test of an advanced AI chip. The company envisions orbiting data centers as large as those on Earth by 2030.

Proponents believe that putting data centers in space makes sense. The current AI boom is straining energy grids and adding to the demand for water, which is needed to cool the computers. Communities in the vicinity of large-scale data centers worry about increasing prices for those resources as a result of the growing demand, among other issues.

In space, advocates say, the water and energy problems would be solved. In constantly illuminated sun-synchronous orbits, space-borne data centers would have uninterrupted access to solar power. At the same time, the excess heat they produce would be easily expelled into the cold vacuum of space. And with the cost of space launches decreasing, and mega-rockets such as SpaceX’s Starship promising to push prices even lower, there could be a point at which moving the world’s data centers into space makes sound business sense. Detractors, on the other hand, tell a different story and point to a variety of technological hurdles, though some say it’s possible they may be surmountable in the not-so-distant future. Here are four of the must-haves we’d need to make space-based data centers a reality. 

A way to carry away heat 

AI data centers produce a lot of heat. Space might seem like a great place to dispel that heat without using up massive amounts of water. But it’s not so simple. To get the power needed to run 24-7, a space-based data center would have to be in a constantly illuminated orbit, circling the planet from pole to pole, and never hide in Earth’s shadow. And in that orbit, the temperature of the equipment would never drop below 80 °C, which is way too hot for electronics to operate safely in the long term. 

Getting the heat out of such a system is surprisingly challenging. “Thermal management and cooling in space is generally a huge problem,” says Lilly Eichinger, CEO of the Austrian space tech startup Satellives.

On Earth, heat dissipates mostly through the natural process of convection, which relies on the movement of gases and liquids like air and water. In the vacuum of space, heat has to be removed through the far less efficient process of radiation. Safely removing the heat produced by the computers, as well as what’s absorbed from the sun, requires large radiative surfaces. The bulkier the satellite, the harder it is to send all the heat inside it out into space.

But Yves Durand, former director of technology at the European aerospace giant Thales Alenia Space, says that technology already exists to tackle the problem.

The company previously developed a system for large telecommunications satellites that can pipe refrigerant fluid through a network of tubing using a mechanical pump, ultimately transferring heat from within a spacecraft to radiators on the exterior. Durand led a 2024 feasibility study on space-based data centers, which found that although challenges exist, it should be possible for Europe to put gigawatt-scale data centers (on par with the largest Earthbound facilities) into orbit before 2050. These would be considerably larger than those envisioned by SpaceX, featuring solar arrays hundreds of meters in size—larger than the International Space Station.

Computer chips that can withstand a radiation onslaught

The space around Earth is constantly battered by cosmic particles and lashed by solar radiation. On Earth’s surface, humans and their electronic devices are protected from this corrosive soup of charged particles by the planet’s atmosphere and magnetosphere. But the farther away from Earth you venture, the weaker that protection becomes. Studies show that aircraft crews have a higher risk of developing cancer because of their frequent exposure to high radiation at cruising altitude, where the atmosphere is thin and less protective.

Electronics in space are at risk of three types of problems caused by high radiation levels, says Ken Mai, a principal systems scientist in electrical and computer engineering at Carnegie Mellon University. Phenomena known as single-event upsets can cause bit flips and corrupt stored data when charged particles hit chips and memory devices. Over time, electronics in space accumulate damage from ionizing radiation that degrades their performance. And sometimes a charged particle can strike the component in a way that physically displaces atoms on the chip, creating permanent damage, Mai explains.

Traditionally, computers launched to space had to undergo years of testing and were specifically designed to withstand the intense radiation present in Earth’s orbit. These space-hardened electronics are much more expensive, though, and their performance is also years behind the state-of-the-art devices for Earth-based computing. Launching conventional chips is a gamble. But Durand says cutting-edge computer chips use technologies that are by default more resistant to radiation than past systems. And in mid-March, Nvidia touted hardware, including a new GPU, that is “bringing AI compute to orbital data centers.” 

Nvidia’s head of edge AI marketing, Chen Su, told MIT Technology Review, that “Nvidia systems are inherently commercial off the shelf, with radiation resilience achieved at the system level rather than through radiation‑hardened silicon alone.” He added that satellite makers increase the chips’ resiliency with the help of shielding, advanced software for error detection, and architectures that combine the consumer-grade devices with bespoke, hardened technologies.

Still, Mai says that the data-crunching chips are only one issue. The data centers would also need memory and storage devices, both of which are vulnerable to damage by excessive radiation. And operators would need the ability to swap things out or adapt when issues arise. The feasibility and affordability of using robots or astronaut missions for maintenance is a major question mark hanging over the idea of large-scale orbiting data centers.

“You not only need to throw up a data center to space that meets your current needs; you need redundancy, extra parts, and reconfigurability, so when stuff breaks, you can just change your configuration and continue working,” says Mai. “It’s a very challenging problem because on one hand you have free energy and power in space, but there are a lot of disadvantages. It’s quite possible that those problems will outweigh the advantages that you get from putting a data center into space.”

In addition to the need for regular maintenance, there’s also the potential for catastrophic loss. During periods of intense space weather, satellites can be flooded with enough radiation to kill all their electronics. The sun has just passed the most active phase of its 11-year cycle with relatively little impact on satellites. Still, experts warn that since the space age began, the planet has not experienced the worst the sun is capable of. Many doubt whether the low-cost new space systems that dominate Earth’s orbits today are prepared for that.

A plan to dodge space debris

Both large-scale orbiting data centers such as those envisioned by Thales Alenia Space and the mega-constellations of smaller satellites as proposed by SpaceX give a headache to space sustainability experts. The space around Earth is already quite crowded with satellites. Starlink satellites alone perform hundreds of thousands of collision avoidance maneuvers every year to dodge debris and other spacecraft. The more stuff in space, the higher the likelihood of a devastating collision that would clutter the orbit with thousands of dangerous fragments.

Large structures with hundreds of square meters of solar arrays would quickly suffer damage from small pieces of space debris and meteorites, which would over time degrade the performance of their solar panels and create more debris in orbit. Operating one million satellites in low Earth orbit, the region of space at the altitude of up to 2,000 kilometers, might be impossible to do safely unless all satellites in that area are part of the same network so they can communicate effectively to maneuver around each other, Greg Vialle, the founder of the orbital recycling startup Lunexus Space, told MIT Technology Review.

“You can fit roughly four to five thousand satellites in one orbital shell,” Vialle says. “If you count all the shells in low Earth orbit, you get to a number of around 240,000 satellites maximum.”

And spacecraft must be able to pass each other at a safe distance to avoid collisions, he says. 

“You also need to be able to get stuff up to higher orbits and back down to de-orbit,” he adds. “So you need to have gaps of at least 10 kilometers between the satellites to do that safely. Mega-constellations like Starlink can be packed more tightly because the satellites communicate with each other. But you can’t have one million satellites around Earth unless it’s a monopoly.”

On top of that, Starlink would likely want to regularly upgrade its orbiting data centers with more modern technology. Replacing a million satellites perhaps every five years would mean even more orbital traffic—and it could increase the rate of debris reentry into Earth’s atmosphere from around three or four pieces of junk a day to about one every three minutes, according to a group of astronomers who filed objections against SpaceX’s FCC application. Some scientists are concerned that reentering debris could damage the ozone layer and alter Earth’s thermal balance. 

Economical launch and assembly

The longer hardware survives in orbit, the better the return on investment. But for orbital data centers to make economic sense, companies will have to find a relatively cheap way to get that hardware in orbit. SpaceX is betting on its upcoming Starship mega-rocket, which will be able to carry up to six times as much payload as the current workhorse, Falcon 9. The Thales Alenia Space study concluded that if Europe were to build its own orbital data centers, it would have to develop a similarly potent launcher. 

But launch is only part of the equation. A large-scale orbital data center won’t fit in a rocket—even a mega-rocket. It will need to be assembled in orbit. And that will likely require advanced robotic systems that do not exist yet. Various companies have conducted Earth-based tests with precursors of such systems, but they are still far from real-world use.

Durand says that in the short term, smaller-scale data centers are likely to establish themselves as an integral part of the orbital infrastructure, by processing images from Earth-observing satellites directly in space without having to send them to Earth. That would be a huge help for companies selling insights from space, as many of these data sets are extremely large, and competition for opportunities to downlink them to Earth for processing via ground stations is growing.

“The good thing with orbital data centers is that you can start with small servers and gradually increase and build up larger data centers,” says Durand. “You can use modularity. You can learn little by little and gradually develop industrial capacity in space. We have all the technology, and the demand for space-based data processing infrastructure is huge, so it makes sense to think about it.”

Smaller facilities probably won’t do much to offset the strain that terrestrial data centers are placing on the planet’s water and electricity, though. That vision of the future might take decades to come to fruition, some critics think—if it even gets off the ground at all. 

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Building the AI Optical Layer: Connectivity, Standards, and the Future of AI Infrastructure

As AI data centers push past the limits of traditional compute architecture, the industry’s attention is moving deeper into the physical layer. GPUs, accelerators, power systems and cooling platforms still dominate the headlines, but the network fabric that connects those systems is becoming just as critical. A wave of recent announcements points to the same conclusion: future growth will depend not only on more compute, but on faster, denser, more efficient and more scalable optical connectivity. A new multi-source agreement is bringing together major technology companies to standardize expanded beam optical connectivity for AI data centers. University of Arizona research is powering a new optical switching technology designed to reduce the energy consumed by data center networks. STL is planning to invest up to $100 million in U.S. manufacturing capacity to support AI data center and telecom customers with optical connectivity products. Those developments are now being reinforced by a broader series of moves across the optical ecosystem: Corning’s major AI infrastructure partnerships with NVIDIA and Amazon, GlobalFoundries’ push into co-packaged optics, Sivers’ laser-array collaboration with GlobalFoundries, Wiwynn’s co-packaged optics demonstration at Computex, Credo’s acquisition of DustPhotonics, and emerging near-packaged optical interconnect designs from LightSpeed Photonics. Taken together, these announcements highlight a maturing market around the optical layer of AI infrastructure. The value is not simply faster data movement. It is about reducing deployment complexity, lowering operating overhead, supporting higher-density clusters, improving energy efficiency and strengthening the domestic supply chain behind AI-ready networks. Let’s drill down into what these announcements mean. Standards for the AI Optical Layer The launch of a new coalition focused on expanded beam optical, or EBO, connectivity reflects a practical challenge facing AI deployments: as clusters grow larger and more bandwidth-intensive, physical connections become harder to deploy, maintain and scale. 3M announced that it has joined

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Data Center Jobs: Engineering, Construction, Commissioning, Sales, Field Service and Facility Tech Jobs Available in Major Data Center Hotspots

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist CFD Engineer – Data Center Mechanical DesignNew York, NY (remote)This position is also available as a remote role anywhere in the US, in addition to key markets such as Cedar Rapids, IA; Kansas City, MO or White Plains, NY. Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, and sustainable design expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer New Albany, OH (limited travel)Non-Traveling CxA positions available in: Indianapolis, IN; Cedar Rapids, IA and Austin, TX. Traveling CxA Roles: New York, NY; White Plain, NY; Morristown, NJ; Dallas, TX; Richmond, VA; Ashburn, VA; Montvale, NJ; Charlotte, NC; Atlanta, GA; Phoenix, AZ; Salt Lake City, UT;  Kansas City, MO; Omaha, NE; Chesterton, IN or Chicago, IL. *** ALSO looking for a LEAD EE and ME CxA Agents and CxA PMs. *** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services

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H5 Data Centers’ 325 Hudson: A Manhattan Carrier Hotel with SoHo DNA – DCF Tours

A Carrier Hotel Reimagined for Modern Colocation H5 formally announced its expansion into 325 Hudson, a 225,000 square-foot mixed-use building comprised of office, lab and data center uses, in 2021 through a partnership with real estate investment firm DivcoWest, describing its new location as a data center and carrier hotel in one of the world’s largest communications markets. At the time, H5 founder and CEO Josh Simms framed the move as an opportunity to expand an already-established interconnection ecosystem while supporting growing demand from cloud providers, content delivery networks, and communications carriers. That vision now appears fully realized inside the building. The facility today operates as both a traditional carrier hotel and a modern enterprise colocation environment. H5’s infrastructure footprint supports high-density deployments, A/B UPS power architecture, N+1 emergency generators, N+1 CRAC systems, and energy-efficient in-row cooling with cold aisle containment. The building also reflects the physical realities of Manhattan infrastructure engineering. Operators work within vertical constraints rather than sprawling horizontal campuses. Freight access, riser strategy, structured cabling pathways, and efficient floor utilization become critical operational variables. H5 highlighted several features tailored for those realities, including 13-foot slab-to-slab heights, 150 pounds-per-square-foot floor loading capability, secure loading access, and extensive pre-built conduit infrastructure.

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AI Infrastructure Demands a New Operating System for Project Delivery

For much of the data center industry’s history, project management has largely been viewed as an execution discipline: a collection of schedules, milestones, spreadsheets, and status meetings designed to shepherd individual facilities from groundbreaking to commissioning. The AI era is rapidly rendering that model obsolete. As hyperscalers, developers, utilities, EPC firms, telecom providers, equipment suppliers, and local governments converge around increasingly complex AI campuses, the challenge is no longer simply delivering projects on time. Rather, it is orchestrating an infrastructure manufacturing process that stretches from land acquisition and permitting through construction, operations, and ultimately asset modernization years later. That changing reality was a central theme during Data Center Frontier’s conversation with Sitetracker at Fiber Connect 2026. The company’s perspective reflects a broader shift underway across digital infrastructure: project management is evolving into lifecycle management, where financial planning, regulatory coordination, supply chain visibility, and operational readiness become inseparable parts of the same platform. Complexity Begins Before Construction Much of the attention surrounding AI infrastructure focuses on GPU deployments, liquid cooling, and power availability. Yet Sitetracker argues that many of today’s greatest operational headaches begin much earlier in the development process. According to Reilly McClure, Sr. Product Marketing Manager – Digital Infrastructure with Sitetracker, operators are increasingly seeking help with land acquisition, parcel management, and site identification as AI infrastructure expands into new markets. “There are so many variables that we need to track,” he explained. “The demand and the growth and the build-out for where all that infrastructure is going is becoming increasingly complex. They’re finding it just cannot be done on a spreadsheet.” That observation resonates across the industry. Finding suitable sites now requires simultaneously evaluating power availability, transmission timelines, fiber access, permitting requirements, environmental studies, municipal approvals, and community considerations; all while competing developers race to secure the same

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Gartner: Data center electricity consumption to grow 26% in 2026

AI-optimized servers are a relatively new phenomenon but they have rapidly gained uh traditional data centers in terms of power use. Gartner estimates AI-optimized server adoption will account for 31% of data center power consumption in 2026, and that by 2027 their power consumption will surpass that of conventional servers. “Surging demand for compute-intensive AI workloads is driving unprecedented data center power growth, while AI capacity is now constrained by power availability, making data center power security the new battle ground for scaling and protecting margins in the global AI race,” said Wang in a statement. Wang said of the 565TWh consumed this year, the U.S. will account for about 204TWh, or 36% of the total amount consumed. And of the 204TWh consumed this year, dedicated AI data centers will consume 68TWh, or one-third of the total. So in just five years, AI data centers have gone from zero to of the total power consumption in the US.

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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