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This road map could help us decide whether to deploy solar geoengineering

A San Francisco nonprofit has published a detailed road map of the experiments, studies, and infrastructure that it says would be needed to make informed decisions about the use of solar geoengineering, MIT Technology Review can reveal. Scientists have now spent half a century exploring the possibility that we could counteract climate change by releasing reflective particles into the stratosphere, mimicking the cooling effects of volcanic eruptions.  But even after at least hundreds of studies on the concept, known as stratospheric aerosol injection (SAI), big gaps remain in the scientific understanding of how well it would work and what else it might do—and there has been no systematic plan for clearing up that uncertainty. Reflective, a research organization that funds studies on solar geoengineering, has today attempted to fill that gap with the release of its SAI Research Roadmap. “Our mission is to equip the world with the data and tools required for informed decision-making about sunlight reflection fast enough to matter,” says Dakota Gruener, the organization’s cofounder and chief executive. “Our sense is the world may need to make very consequential decisions on timelines far shorter than our research system is prepared for.” The hope is the exercise will guide scientific efforts and encourage philanthropies or government agencies to fund high-priority work and “responsibly accelerate research,” says Gruener. If all the work is done in a coordinated way, it would take about a decade and cost around $370 million—and if it’s not, it would require roughly 20 years and nearly $1.4 billion, the report estimates. While Gruener stresses that Reflective doesn’t advocate using this form of solar geoengineering, the report does make the case for conducting outdoor experiments, which would release successively larger amounts of sulfur dioxide (or materials that would convert into it) in the stratosphere to observe what happens. That is a controversial standpoint. Since 2002, hundreds of academics have signed an open letter calling for a ban on outdoor experiments and an “international non-use agreement,” arguing that such a powerful technology could never be governed in a globally equitable way. And some signatories argue that more studies can never address one of the biggest questions about using solar geoengineering: Who gets to do it.   “The first-order questions, from my perspective, are not technical,” Aarti Gupta, co-initiator of the non-use initiative and professor of global environmental governance at Wageningen University in the Netherlands, told me in a recent on-stage interview.  “The core question is: Who would control a planet-altering technology like stratospheric aerosol injection? Who would develop it, and who would deploy it, and to what end? To serve what purposes, and whose purposes? Those questions are very fundamental, because this planet-altering technology will have winners and losers.” ‘Fast enough to matter’ Since Gruener incorporated Reflective in late 2023, the nonprofit has quickly become an important  player in solar geoengineering research. It has now raised more than $20 million from a number of prominent charities and individuals, and it’s provided around $4 million to several dozen research groups. Reflective has also undertaken a handful of its own projects to promote research, including the development of an open-source solar geoengineering simulator and an online hub for collaborative research. Earlier this year, Reflective released its SAI Uncertainties database, which identified a long list of scientific unknowns and  engineering obstacles that would need to be addressed before even a small-scale solar geoengineering effort could move ahead. (I wrote about the specific scenario and the unknowns in this earlier piece.) Some of the biggest uncertainties involve what gas or particles would make the most sense to use and what would happen once they were released in the dry stratosphere. It’s not clear, for example, whether they’d spread out in a way that maximizes the reflectivity—or clump together and quickly fall out into the troposphere, the lowest layer of Earth’s atmosphere.  The road map builds upon the database, highlighting the path to addressing most of those questions.  The road map The initial phase in Reflective’s road map, labeled “foundational knowledge,” includes additional computer simulation studies and lab experiments designed to shed light on the potential impacts on different regions, ecosystems, and phenomena, including ocean circulation patterns, ice sheets, and crop yields.  The report also notes the need to begin developing more observational tools during this phase to improve understanding of the baseline conditions of the stratosphere—and, in turn, our ability to assess any effects from the eventual release of materials. This first stage would last two to three years and cost $30 million to $75 million, though some of the analysis and observational work would continue into subsequent phases.  The next stage would include using modified aircraft to release 10 metric tons of sulfur dioxide into the stratosphere, four times over the course of two seasons. The full research stage could take four to eight years and cost $70 million to $150 million, the report says. The work during it may reduce uncertainty about the “cooling efficacy” of solar geoengineering, or how much the planet would cool per ton of sulfur released, by about 25%. The experiments during the next phase would step those levels up dramatically, releasing 25,000 tons of sulfur dioxide over the course of one season, at least once but possibly twice. That research stage, which includes other work as well, would last four to 11 years, run $270 million to $1.1 billion, and decrease efficacy uncertainty by around 66%, according to the road map. The final phase of research would be ongoing monitoring of full-scale solar geoengineering, if the world goes ahead with it. The goal would be to gather real-life data on the technology in action, update estimates of the effects in models, and spot any “unexpected or undesired consequences.” Gruener says that the road map is intended as a Version 1, meant to be “concrete enough for people to argue with.” But Reflective intends to update the plan as it receives additional reactions from researchers and other observers, and it will invite such feedback through a mechanism on the site. She also notes that there are firm “stage gates,” set up between the latter stages—in other words, research shouldn’t proceed to the next phase if the experiments suggest that the releases don’t have the hoped-for impact, show worrisome downsides, or fail to resolve crucial uncertainties. “Our road map has these gates precisely because there may be points where the answer is ‘You should stop,’” she says. Termination shock Most observers I spoke to about the report agree that these studies could reduce uncertainty about the effectiveness of solar geoengineering and our technical ability to carry it out.  But highlighting the scientific importance of outdoor experiments won’t necessarily make them any easier to move ahead with. Several earlier proposals to carry out such experiments, including Harvard’s SCoPEx and the UK-based SPICE project, were ultimately halted amid opposition from environmentalists or policymakers. In addition, not everyone agrees that experiments at those scales will get us to the point where we’re capable of making an “informed decision.”  Wil Burns, a research professor and legal scholar at American University and a signatory to the International Non-Use Agreement, fears that scientists won’t be able to understand the extent of the potential downsides, including impacts on the protective ozone layer and changes to regional precipitation patterns, until we’re carrying out full-fledged solar geoengineering. “The research would give you some answers,” he says. “I just don’t think it gives you answers that are that relevant. To get to those relevant answers, you have to deploy at scale—and I just don’t think that’s ever tenable.” That’s because, in his view, using the technology would violate principles of intergenerational equity: If the world continues emitting greenhouse gases, increased levels of solar geoengineering would merely mask the continued warming of the planet. Burns says that means future generations—people who had no say in its use—couldn’t turn it off without triggering a sudden surge of warming, known as termination shock.  “What that would do, in my mind, is put a sword of Damocles over future generations,” he says. “So even if you could, quote-unquote, ‘prove it works,’ I don’t think from an intergenerational perspective it would ever be tenable.” (Some researchers, however, have argued that the risks of termination shock are less likely than often assumed—and that solar geoengineering could be slowly dialed down over time.) ‘The right approach’ Ilan Gur, the former CEO of the Advanced Research and Invention Agency (ARIA), the UK research department that funded 21 geoengineering research projects last year, applauds Reflective’s road map.  “Whether you’re a scientist or a policymaker or just a concerned citizen, our goal should be as quickly and efficiently as possible to answer the biggest questions scientifically that would tell us [whether] this is an approach that might work or that would never work,” he says. “We should all want to spend the effort and money to buy down that uncertainty, so my view is 100% the approach that Reflective is taking is the right one.” Sebastian Eastham, an associate professor in sustainable aviation at Imperial College London who is leading an ARIA-funded research project exploring another approach to engineered cooling, agrees that the outdoor experiments described in the Reflective road map can’t resolve all the unknowns. But he says the map helps begin a conversation about how to make decisions concerning the use of a tool with potential benefits and risks, in the face of escalating climate dangers. “Every hard decision that has ever been taken has been in the context of unresolved uncertainty,” he says. “That’s just the nature of things.” Eastham adds that it’s become essential to move beyond computer simulations to address some of the key questions, arguing that appropriately designed and executed outdoor experiments can teach us so much more than millions of hours of computational processing time “that it almost becomes irresponsible to say, ‘Well, there cannot be ever any experiment.’” The risk is “that we spin our wheels running the same computational simulations over and over and over again,” he says. That could prevent researchers from learning essential things about the effectiveness or the dangers of stratospheric aerosol injection.  Weighing the risks Gruener says the risks that solar geoengineering could exacerbate inequality need to be considered, but notes that unchecked warming also threatens to disproportionately harm developing regions. She also acknowledges that outdoor experiments won’t fully address the scientific unknowns but stresses that they can answer a lot—and carry little environmental risk. She notes that 10 tons of sulfur dioxide is less than 2% of the amount that the global aviation industry releases into the atmosphere each day. “Some people will be uncomfortable with any discussion of any outdoor experiment, but if we want decisions made on good science … then these are questions that an experiment will be necessary to address,” Gruener says. She fears that the rising dangers of climate change will put growing pressure on nations and other actors to move forward with solar geoengineering, even if no one has done the necessary research to reduce scientific uncertainty and sort out the technical challenges. “We don’t think the alternative is decisions not happening at all,” she says. “We think the alternative is decisions being made in a panic or on lack of evidence.”

A San Francisco nonprofit has published a detailed road map of the experiments, studies, and infrastructure that it says would be needed to make informed decisions about the use of solar geoengineering, MIT Technology Review can reveal.

Scientists have now spent half a century exploring the possibility that we could counteract climate change by releasing reflective particles into the stratosphere, mimicking the cooling effects of volcanic eruptions. 

But even after at least hundreds of studies on the concept, known as stratospheric aerosol injection (SAI), big gaps remain in the scientific understanding of how well it would work and what else it might do—and there has been no systematic plan for clearing up that uncertainty.

Reflective, a research organization that funds studies on solar geoengineering, has today attempted to fill that gap with the release of its SAI Research Roadmap.

“Our mission is to equip the world with the data and tools required for informed decision-making about sunlight reflection fast enough to matter,” says Dakota Gruener, the organization’s cofounder and chief executive. “Our sense is the world may need to make very consequential decisions on timelines far shorter than our research system is prepared for.”

The hope is the exercise will guide scientific efforts and encourage philanthropies or government agencies to fund high-priority work and “responsibly accelerate research,” says Gruener.

If all the work is done in a coordinated way, it would take about a decade and cost around $370 million—and if it’s not, it would require roughly 20 years and nearly $1.4 billion, the report estimates.

While Gruener stresses that Reflective doesn’t advocate using this form of solar geoengineering, the report does make the case for conducting outdoor experiments, which would release successively larger amounts of sulfur dioxide (or materials that would convert into it) in the stratosphere to observe what happens.

That is a controversial standpoint. Since 2002, hundreds of academics have signed an open letter calling for a ban on outdoor experiments and an “international non-use agreement,” arguing that such a powerful technology could never be governed in a globally equitable way. And some signatories argue that more studies can never address one of the biggest questions about using solar geoengineering: Who gets to do it.  

“The first-order questions, from my perspective, are not technical,” Aarti Gupta, co-initiator of the non-use initiative and professor of global environmental governance at Wageningen University in the Netherlands, told me in a recent on-stage interview. 

“The core question is: Who would control a planet-altering technology like stratospheric aerosol injection? Who would develop it, and who would deploy it, and to what end? To serve what purposes, and whose purposes? Those questions are very fundamental, because this planet-altering technology will have winners and losers.”

‘Fast enough to matter’

Since Gruener incorporated Reflective in late 2023, the nonprofit has quickly become an important  player in solar geoengineering research. It has now raised more than $20 million from a number of prominent charities and individuals, and it’s provided around $4 million to several dozen research groups. Reflective has also undertaken a handful of its own projects to promote research, including the development of an open-source solar geoengineering simulator and an online hub for collaborative research.

Earlier this year, Reflective released its SAI Uncertainties database, which identified a long list of scientific unknowns and  engineering obstacles that would need to be addressed before even a small-scale solar geoengineering effort could move ahead. (I wrote about the specific scenario and the unknowns in this earlier piece.)

Some of the biggest uncertainties involve what gas or particles would make the most sense to use and what would happen once they were released in the dry stratosphere. It’s not clear, for example, whether they’d spread out in a way that maximizes the reflectivity—or clump together and quickly fall out into the troposphere, the lowest layer of Earth’s atmosphere. 

The road map builds upon the database, highlighting the path to addressing most of those questions. 

The road map

The initial phase in Reflective’s road map, labeled “foundational knowledge,” includes additional computer simulation studies and lab experiments designed to shed light on the potential impacts on different regions, ecosystems, and phenomena, including ocean circulation patterns, ice sheets, and crop yields. 

The report also notes the need to begin developing more observational tools during this phase to improve understanding of the baseline conditions of the stratosphere—and, in turn, our ability to assess any effects from the eventual release of materials.

This first stage would last two to three years and cost $30 million to $75 million, though some of the analysis and observational work would continue into subsequent phases. 

The next stage would include using modified aircraft to release 10 metric tons of sulfur dioxide into the stratosphere, four times over the course of two seasons. The full research stage could take four to eight years and cost $70 million to $150 million, the report says. The work during it may reduce uncertainty about the “cooling efficacy” of solar geoengineering, or how much the planet would cool per ton of sulfur released, by about 25%.

The experiments during the next phase would step those levels up dramatically, releasing 25,000 tons of sulfur dioxide over the course of one season, at least once but possibly twice. That research stage, which includes other work as well, would last four to 11 years, run $270 million to $1.1 billion, and decrease efficacy uncertainty by around 66%, according to the road map.

The final phase of research would be ongoing monitoring of full-scale solar geoengineering, if the world goes ahead with it. The goal would be to gather real-life data on the technology in action, update estimates of the effects in models, and spot any “unexpected or undesired consequences.”

Gruener says that the road map is intended as a Version 1, meant to be “concrete enough for people to argue with.” But Reflective intends to update the plan as it receives additional reactions from researchers and other observers, and it will invite such feedback through a mechanism on the site.

She also notes that there are firm “stage gates,” set up between the latter stages—in other words, research shouldn’t proceed to the next phase if the experiments suggest that the releases don’t have the hoped-for impact, show worrisome downsides, or fail to resolve crucial uncertainties.

“Our road map has these gates precisely because there may be points where the answer is ‘You should stop,’” she says.

Termination shock

Most observers I spoke to about the report agree that these studies could reduce uncertainty about the effectiveness of solar geoengineering and our technical ability to carry it out. 

But highlighting the scientific importance of outdoor experiments won’t necessarily make them any easier to move ahead with. Several earlier proposals to carry out such experiments, including Harvard’s SCoPEx and the UK-based SPICE project, were ultimately halted amid opposition from environmentalists or policymakers.

In addition, not everyone agrees that experiments at those scales will get us to the point where we’re capable of making an “informed decision.” 

Wil Burns, a research professor and legal scholar at American University and a signatory to the International Non-Use Agreement, fears that scientists won’t be able to understand the extent of the potential downsides, including impacts on the protective ozone layer and changes to regional precipitation patterns, until we’re carrying out full-fledged solar geoengineering.

“The research would give you some answers,” he says. “I just don’t think it gives you answers that are that relevant. To get to those relevant answers, you have to deploy at scale—and I just don’t think that’s ever tenable.”

That’s because, in his view, using the technology would violate principles of intergenerational equity: If the world continues emitting greenhouse gases, increased levels of solar geoengineering would merely mask the continued warming of the planet. Burns says that means future generations—people who had no say in its use—couldn’t turn it off without triggering a sudden surge of warming, known as termination shock. 

“What that would do, in my mind, is put a sword of Damocles over future generations,” he says. “So even if you could, quote-unquote, ‘prove it works,’ I don’t think from an intergenerational perspective it would ever be tenable.”

(Some researchers, however, have argued that the risks of termination shock are less likely than often assumed—and that solar geoengineering could be slowly dialed down over time.)

‘The right approach’

Ilan Gur, the former CEO of the Advanced Research and Invention Agency (ARIA), the UK research department that funded 21 geoengineering research projects last year, applauds Reflective’s road map. 

“Whether you’re a scientist or a policymaker or just a concerned citizen, our goal should be as quickly and efficiently as possible to answer the biggest questions scientifically that would tell us [whether] this is an approach that might work or that would never work,” he says. “We should all want to spend the effort and money to buy down that uncertainty, so my view is 100% the approach that Reflective is taking is the right one.”

Sebastian Eastham, an associate professor in sustainable aviation at Imperial College London who is leading an ARIA-funded research project exploring another approach to engineered cooling, agrees that the outdoor experiments described in the Reflective road map can’t resolve all the unknowns. But he says the map helps begin a conversation about how to make decisions concerning the use of a tool with potential benefits and risks, in the face of escalating climate dangers.

“Every hard decision that has ever been taken has been in the context of unresolved uncertainty,” he says. “That’s just the nature of things.”

Eastham adds that it’s become essential to move beyond computer simulations to address some of the key questions, arguing that appropriately designed and executed outdoor experiments can teach us so much more than millions of hours of computational processing time “that it almost becomes irresponsible to say, ‘Well, there cannot be ever any experiment.’”

The risk is “that we spin our wheels running the same computational simulations over and over and over again,” he says. That could prevent researchers from learning essential things about the effectiveness or the dangers of stratospheric aerosol injection. 

Weighing the risks

Gruener says the risks that solar geoengineering could exacerbate inequality need to be considered, but notes that unchecked warming also threatens to disproportionately harm developing regions.

She also acknowledges that outdoor experiments won’t fully address the scientific unknowns but stresses that they can answer a lot—and carry little environmental risk. She notes that 10 tons of sulfur dioxide is less than 2% of the amount that the global aviation industry releases into the atmosphere each day.

“Some people will be uncomfortable with any discussion of any outdoor experiment, but if we want decisions made on good science … then these are questions that an experiment will be necessary to address,” Gruener says.

She fears that the rising dangers of climate change will put growing pressure on nations and other actors to move forward with solar geoengineering, even if no one has done the necessary research to reduce scientific uncertainty and sort out the technical challenges.

“We don’t think the alternative is decisions not happening at all,” she says. “We think the alternative is decisions being made in a panic or on lack of evidence.”

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US BLM considers expanding oil and gas leasing to Utah’s Ouray National Wildlife Refuge

The US Bureau of Land Management (BLM) Sept. 2 said it was considering opening about 5,200 acres of federal land under the Ouray National Wildlife Refuge (ONWR) near Vernal in northeast Utah to oil and gas leasing. The BLM proposal contains a “no surface occupancy” restriction that bars drilling inside the refuge. Instead, if the proposal is approved, leaseholders could use only directional or horizontal drilling from well pads outside ONWR to develop leases. BLM opened a 30-day public scoping period to seek public input on the proposal and BLM’s preparation of a resource management plan amendment and associated environmental analysis. Comments are due Oct. 5, 2026.  ONWR lies entirely within the oil and gas rich Uinta basin, which produced a record 67.9 million bbl of oil in 2025 along with 328 bcf of gas, according to the Utah Division of Oil, Gas and Mining. The basin is known for its abundance of waxy crude oil, coveted by refineries because of its low sulfur levels, making it optimal for lubricants and engine oils. 

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ERCOT Puts Texas AI Megawatts to the Test

Texas has no shortage of proposed data center megawatts. The harder question is how many of them are real. That distinction is becoming central to the Electric Reliability Council of Texas (ERCOT) as the state works through an unprecedented wave of AI, hyperscale and other large-load requests. In June, ERCOT said it was tracking more than 438 GW of proposed large loads, nearly 89% associated with data centers. By Aug. 3, Gov. Greg Abbott said ERCOT was considering approximately 474 GW of connection requests, roughly 90% from data centers and more than five times the system’s record peak demand. Neither figure represents a forecast of what will actually get built. And that is increasingly the point. ERCOT’s new Batch Zero process is beginning to put harder boundaries around Texas’ enormous development pipeline, asking which projects have enough maturity, technical information and commitment to warrant space in the transmission plan. At the same time, new requirements surrounding voltage ride-through and dynamic modeling are forcing another realization on the AI infrastructure industry: at hundreds of megawatts, a data center is no longer simply a customer at the edge of the grid. Its behavior can affect the grid itself. For developers, utilities and investors, Texas is becoming a large-scale test of what separates an announced AI campus from executable infrastructure. The Queue Is Not the Grid The sheer scale of ERCOT’s large-load queue can obscure how early many projects remain. ERCOT’s April 2026 monthly report offered a revealing snapshot. Large-load applications totaled 445.8 GW through 2033, but 321 GW had no studies submitted to ERCOT. Another 93.7 GW was under ERCOT review, while 22 GW had met the applicable Section 9.5 requirements. Against that enormous development funnel, ERCOT reported just 5.9 GW of observed energized large loads, with another 3.2 GW approved to

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DCF Trends Summit: AI Compresses the Data Center Hardware Lifecycle and Raises the Stakes for ITAD

The AI infrastructure race is largely a story about getting more computing into data centers faster. But the accelerated hardware cycle is creating an equally consequential problem at the other end of the rack: getting yesterday’s equipment back out while it is still valuable. GPU systems built around increasingly dense and specialized AI architectures are beginning to challenge traditional assumptions about IT asset disposition, or ITAD. Where conventional enterprise infrastructure might remain in service for three to five years, newer GPU platforms can face refresh cycles of 18 to 24 months, according to Josh Humm, Data Center Solutions Manager at Dynamic Lifecycle Innovations. That compression changes the economics as well as the mechanics of decommissioning. “The faster we can get the materials out of your building, the more it’s worth, the more we can return to your program,” Humm said. Humm joined DCF Contributing Editor Doug Black for a DCF Show podcast recorded at the third annual Data Center Frontier Trends Summit, held Aug. 4-6 in Reston, Virginia. Their conversation focused on a less visible part of the AI infrastructure buildout: what happens to servers, accelerators, memory, storage and networking gear when the next generation arrives. The answer increasingly touches facility operations, data security, logistics, sustainability and potentially millions of dollars in recoverable hardware value. AI Hardware Changes the Exit Path AI systems create some obvious physical challenges for decommissioning. Traditional ITAD teams accustomed to pulling 1U and 2U servers out of air-cooled racks may instead encounter liquid-cooling manifolds, substantially heavier systems and equipment requiring specialized rigging and handling procedures. Humm said some systems can weigh between 5,000 and 6,000 pounds. “We’re not pulling out just 1U, 2U servers out of racks anymore,” he said. Liquid cooling adds another layer. Removing infrastructure designed around direct-to-chip or other liquid-cooling architectures can

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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 Design New York, NY (remote)This position is also available as a remote role anywhere in the U.S. in addition to key markets such as Cedar Rapids, IA; Kansas City, CA 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 Agent – Data Centers Columbus, OH (limited travel) Non-traveling CxA positions available in: Indianapolis, IN; Cedar Rapids, IA; Phoenix, AZ; Atlanta, GA and Austin, TX. Traveling CxA based near any major airport, otherwise traveling to: New York, NY; White Plains, NY; Dallas, TX; Richmond, VA; Montvale, NJ; Charlotte, NC; Salt Lake City, UT; Kansas City, MO; Chesterton, IN or Chicago, IL. *** Also looking for a lead EE, ME CxA agents and CxA PMs. *** This opportunity is with a leading EPC company of data center design / build / commissioning solutions. This company provides a complete life cycle of solutions that are custom-fit to the requirements of their client’s mission-critical facilities. This opportunity provides a career-growth minded role with exciting projects with

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DCFTS 2026: Data Center Development Moves From Projection to Execution

The Data Center Map Gets More Selective For EdgeCore, finding viable development locations has become an exercise in aggressive filtering. Kestler said the company evaluated 172 sites during the previous 12 months to narrow the field to seven locations it wanted to actively manage. Its requirements include roughly 100 acres or more, the ability to support a 300-MVA-or-larger substation, credible utility development timelines and sufficient network proximity to support what Kestler called “interdependent compute” locations. The distinction matters. Not every AI workload needs the same geography, and not every site marketed as available for AI infrastructure can support the combination of land, network, power and timing required to make a project real. Miller placed that process in the context of a data center map already being redrawn by power availability. Northern Virginia’s power constraints in 2022 provided an early warning, redirecting capacity into markets including Atlanta and driving developers farther afield in search of large blocks of electricity. AI has intensified the process. As campus requirements move toward hundreds of megawatts and, in some cases, gigawatt scale, Miller said, fewer locations can satisfy all of the requirements simultaneously. Community acceptance is narrowing the map further. At the same time, Miller pointed to a potential countertrend: the growth of AI inference could create another layer of data center geography. Some inference architectures may favor smaller, distributed facilities rather than concentrating every workload inside enormous campuses. The result could be a more stratified infrastructure market. “Everything everywhere all at once,” Miller said. Build Where Data Centers Are Wanted For large campus development, Kestler offered another increasingly important filter. EdgeCore wants to build where it is wanted. In practical terms, that means targeting municipalities and jurisdictions that have already made deliberate decisions about where data center or other light industrial development belongs. Kestler

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How States Are Rewriting the Rules for Data Center Growth

Pennsylvania has moved from courting data center investment to setting much stricter terms for how the industry grows. Governor Josh Shapiro’s August 18 executive order creates one of the country’s most comprehensive state-level frameworks for large data centers, linking a more favorable environmental permitting process and state tax treatment to requirements covering power supply, grid costs, local approval, workforce commitments, water use and environmental performance. The order is the latest stage of Shapiro’s Governor’s Responsible Infrastructure Development, or GRID, initiative. GRID was announced in February, detailed in May and partially reinforced through Pennsylvania’s 2026-27 budget in July. The Pennsylvania House also passed legislation intended to codify the standards, but the Senate did not act. Shapiro has now used existing executive and agency authority to put much of the framework into effect immediately. Pennsylvania’s debate has also produced more direct proposals to slow development. Senate Bill 1359 would impose a statewide moratorium on hyperscale data center development and permitting, although the measure remains in the Senate Local Government Committee. A separate measure, Senate Bill 1345, would authorize municipalities to temporarily stop accepting or considering new applications for high-impact data centers for up to 18 months. SB 1345 advanced to second consideration in the Senate in July. Neither measure has become law. What is the Impact on Data Center Development? For data center projects with peak demand exceeding 25 MW, Pennsylvania’s template GRID Consent Order and Agreement provides the mechanism for binding developers to the requirements while allowing the states Department of Environmental Protection (DEP) to review qualifying permit applications on a rolling basis. Developers that decline to sign can still seek permits, but DEP will not begin reviewing their applications until local approvals and required water or wastewater authorizations are secured, and permits will not be handled on a rolling basis.

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PwC Maps $31.6 Trillion AI Data Center Buildout Through 2050

The scale of the AI infrastructure buildout is becoming easier to describe in trillions than billions. PwC’s inaugural Global Data Centre Outlook 2026–50 projects $31.6 trillion in cumulative global data center capital expenditure through 2050 under its central scenario, with annual spending rising from roughly $800 billion in 2026 to $1.1 trillion in 2030 and $1.8 trillion by 2050. There is also an enormous range around that central case. PwC, working with Oxford Economics, puts plausible cumulative investment at roughly $22 trillion to nearly $50 trillion, depending primarily on how quickly AI adoption progresses. But the most important finding may not be the $31.6 trillion headline. PwC argues that the economics of AI infrastructure are creating a fundamentally different capital cycle from previous infrastructure booms. Data centers are long-lived assets, but the increasingly expensive computing equipment inside them is not. Servers, GPUs, networking systems and other information and communications technology equipment are expected to require replacement on roughly four- to six-year cycles. PwC calculates that every $1 of construction spending can effectively commit the market to approximately $12 of subsequent ICT investment. ICT equipment accounts for about 70% of total data center CapEx in 2026 under its model, rising to 93% by 2050. That creates something closer to a continuously renewing technology platform than a conventional construction cycle. Over a 20-year data center asset life, PwC estimates that a facility could undergo three to five rounds of ICT investment. Increasing rack densities can force corresponding power and cooling upgrades, but the largest recurring expense remains the compute hardware itself. For data center developers and operators, that distinction matters. The economic life of the building increasingly diverges from the technical and financial life of the infrastructure filling it. AI Fragments the Data Center Demand Model The report also sees AI broadening

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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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