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Data from drones in Ukraine is fueling a new Wild West marketplace
Battlefields in Ukraine are littered with the remnants of drones, which are now firmly established as a critical weapon of modern warfare. But behind all that wreckage, there’s a new gold mine for the defense sector. The data drones generate will far outlast the wars in which they are used to fight, increasingly becoming part of the AI architecture that shapes even civilian life. For every flight, unmanned systems collect thousands of points of data, from images and video to controller inputs. Together, those records show how a machine and a person responded to constantly shifting circumstances. Ukraine has now begun converting that experience into a resource. Its Ministry of Defense announced in January that it would make millions of data points gathered during tens of thousands of drone flights available to both military contractors and commercial companies, and since then more than 100 companies and the UK government have gained access. For a country at war, it’s a quick way to attract funding and partnerships. But this step turns the front line into an active site of model training, taking advantage of how the chaos of war creates conditions that AI companies struggle to reproduce on their own.
Other countries and battlefields are likely to follow Ukraine’s lead, but the responsibility for governing this new industry cannot fall solely on a country fighting for its survival. That legal vacuum has to be filled together by the countries and companies involved in this industry’s development.
Explosive growth Ukraine’s battlefields are not the first to produce records used to train and develop models: American drones over Syria and Yemen collected data that informed the first generation of semiautonomous military hardware in the late 2010s. The difference now is that access to that data is being used to develop a wider ecosystem. And the financial value to defense firms is immense: Battlefield data offers large volumes of machine experience gathered under conditions that no laboratory can produce. That’s because the data that’s most valuable for training AI models comes from exceptions: the moment visibility disappears, a signal jams, or a human operator improvises. AI companies spend years and enormous sums trying to capture enough of these moments to make their models more robust. But war produces them at a frequency controlled testing cannot match. This constantly changing terrain is what makes drone data valuable far beyond the battlefield. A commercial drone used for delivery or remote sensing may never encounter artillery fire, but it must still operate with incomplete information in a world where people behave unpredictably. The same problem is compressed by war into a much shorter timeline. Processed and matched against records of what its operator was doing, that data turns operational records into training sets. Combat becomes a commercial asset. Many conflicts have already seen this training loop happen as drone footage feeds subsequent generations of military technology, and the market is set to grow. Enabled Intelligence, an American company that specializes in processing data to become usable in AI training, says it has already made more than half a million hours of Ukrainian drone footage available to feed into the next round of models, advertising possible uses in both military and commercial systems. Closing the data loop Many of the drones that now define our modern age of warfare began as civilian technology. But they’ve recently been turbocharged by new, commercially available AI systems, which allow cheap machines to operate autonomously—either individually or as a flock—as the environment changes around them. Each flight then creates a record of what the system encountered.
The resulting data is critical. The controlled lab environments usually developed to train these autonomous systems can approximate failure but are no match for the live conditions of a battlefield with very real risks. Military intelligence programs have held data generated by sensor-heavy systems like Predator and Reaper drones for nearly a decade through programs like Project Maven, but access remained entirely within the defense world. The data generated was available only through restricted, classified channels for the sole purpose of developing new weapons systems that would feed back into the same military that produced the data in the first place. That experience is now being shared to a much broader development network. The loop now closes. Commercial technologies adapted for the battlefield are generating data that can flow back into the industries from which they came, becoming part of the data infrastructure relied on by governments and the private sector alike. Drones that were trained in the signal-jammed airspace over Ukraine are now being deployed in the agricultural sector to help farmers map and survey their fields in places lacking the cell signal necessary for previous generations of technology. Other countries are likely to follow Ukraine in selling their battlefield data, and we are not ready for the new marketplace this will create. Bad actors could acquire the data, but purchase controls already mitigate that risk. Intelligence operatives scrutinize potential customers’ infrastructure for ways that data could reach enemies or nefarious actors. Training data creates a new tracing problem, though. Whereas the movement of commercial datasets can be followed when planted contact details appear two steps from the original buyer, the provenance of AI training data vanishes in a manner embedded in the technology itself. Another risk is that this use of the data creates an extractive economy in which wealthier countries far from danger benefit from the mortal threat borne by frontline states, potentially creating a market incentive for war to continue as an unending mine for digital gold. A fraught new frontier Existing laws regulate how militaries may conduct war. But they say almost nothing about what happens when records created in combat are stripped of their operational context, packaged as data, and licensed to companies whose products circulate far beyond where they were made.
The responsibilities of the companies that design these systems remain unsettled. Ukraine is building access controls, which are mentioned in the newly signed UK-Ukraine AI agreement, but no governments are actively working on regulating what happens when data has been absorbed into a model and crosses back into civilian markets. Those records contain human lives. The soldiers and civilians visible in them did not agree to become training material for products that might be sold years later. But sensor data, camera footage, and coordinates from civilians fleeing a drone strike now constitute the sorts of data that inform how future machines will make decisions.
That is a problem of consent. Individuals featured in the data—be they targets, controllers, or civilians standing by—become part of the training material. The autonomous capabilities based on that data do not stop at the edge of the battlefield. Such capabilities move into other military or commercial systems like delivery vehicles or agricultural machinery. Errors and assumptions embedded in the data travel with the model even once it enters civilian life. Battlefield data should not be treated as ordinary commercial material. But there is currently no agency or regulator that has jurisdiction over this issue. In the meantime, governments that provide access to defense data should treat it as they would a controlled weapons transfer, recording its origin, licensing its users, and restricting onward sharing. Ukraine has begun to grapple with this. Its Avengers Labs program allows companies to train models on battlefield data without giving them direct access to sensitive databases. Yet that mitigates only one part of the problem. Governments should require disclosure when models trained on wartime material are later incorporated into civilian products. The goal of such regulation should be to make the path from combat to commerce visible. What these companies are really mining is experience. And soldiers cannot consent to having their experience used in this way—as training data that produces model advantage and ultimately supports a product used far from where the war was fought. The question is no longer only what the technology companies can sell for use in war. It is what they can extract from it. To protect ourselves from the excesses of this new industry, we need a regulatory system that follows battlefield data wherever it goes, from combat to model to commercial product. Cory Alpert is a researcher at the University of Melbourne, looking at the impact of AI on democracy. He previously served in the Biden White House.
HPE’s record Q3: AI infrastructure, networking demand drives growth, but supply constraints tap the brakes
AI-driven networks are helping drive HPE results, but so is interest in AI across the board, Neri said. “We see enterprises increasingly moving from AI pilots to production deployments, using traditional servers for agentic AI workloads and inferencing,” Neri said. Demand for inferencing and agentic AI workloads is building, he said. “AI systems orders of $2.4 billion increased over 30% sequentially, reflecting broad-based demand across customer segments. Enterprise demand more than doubled, reflecting increasing overall infrastructure spending as AI initiatives have become board-level priorities,” Neri said. AI is advancing beyond early proof-of-concept training deployments into a broader enterprise workflow transformation opportunity, Neri said. “Customers are increasingly investing in new agentic AI applications and AI inferencing, requiring accelerated computing infrastructure, secure data storage access, and enterprise-grade cloud management.”
Energy Secretary Secures Carolinas’ Grid Ahead of Holiday Weekend
WASHINGTON—The U.S. Department of Energy (DOE) today issued an emergency order to mitigate the risk of blackouts in the Carolinas amid hot weather conditions. Issued pursuant to Section 202(c) of the Federal Power Act, the order authorizes Duke Energy Carolinas, LLC (Duke) to dispatch specified units and to order their operation as needed to maintain reliability. The order also authorizes Duke, in collaboration with its Transmission Owners, to direct backup generation resources to operate as a last resort before declaring an Energy Emergency Alert (EEA) 3 or during an EEA 3. This order was issued pursuant to an application from Duke submitted on September 3, 2026. “Thanks to this emergency order, Americans will not have to worry about losing access to affordable power this Labor Day weekend,” said U.S. Secretary of Energy Chris Wright. “The previous administration’s energy subtraction policies weakened the grid, leaving Americans more vulnerable during events like this. Under President Trump’s leadership, we are ensuring that hardworking American families and businesses in the Carolinas’ have continued access energy to power and cool their homes.” On day one, President Trump declared a national energy emergency after the Biden administration’s energy subtraction agenda left behind a grid increasingly vulnerable to the risk of blackouts. The order is in effect beginning on September 3, 2026, through September 8, 2026.
VMware Cloud Foundation 9.1 adds transit gateway flexibility, segmentation, and native EVPN VXLAN support
VLAN-backed subnets: A VPC subnet can now attach directly to a physical VLAN, giving workloads the same L2 connectivity as a distributed port group. “As a result, even if it’s in the same VPC, it can only reach the public subnets,” Tallet said. A variant, available only with a distributed external connection, also attaches the subnet to the VPC gateway, so the default gateway exists in both worlds at once. VPC connectivity policies: Modeled on Cisco’s private VLAN concept, these control which VPCs within a tenant can reach each other. VPCs default to open communication. Administrators can group VPCs into communities, mark one promiscuous so it reaches every VPC in the tenant, or isolated so it reaches only promiscuous VPCs. Native EVPN VXLAN support: VCF 9.0’s distributed external connection let ESXi hosts send north-south traffic straight to the physical infrastructure over VLAN, skipping the NSX edge, but required a single L2 VLAN across every host involved. VCF 9.1 extends that model to VXLAN fabrics through EVPN, using a small route controller VM to handle the BGP EVPN control plane. “It’s not an edge, it is only running BGP. There is no data path,” Tallet said.
President Trump’s Energy Dominance Agenda is Delivering for American Energy Workers
WASHINGTON—This Labor Day, the U.S. Department of Energy (DOE) is celebrating the hardworking men and women who power America with the release of the 2026 U.S. Energy and Employment Report (USEER). The annual report highlights strong job growth across critical energy sectors at the heart of President Trump’s Energy Dominance agenda. America’s most reliable energy sectors are adding jobs and powering industries across the country. These critical sectors deliver the affordable, reliable, and secure energy that American families, businesses, and industries depend on. After years of decline under the previous administration, America’s coal and nuclear power workforces are growing again under President Trump’s leadership. “Energy is the sector that enables every other sector of our economy, and America’s energy workers make it all possible,” said U.S. Secretary of Energy Chris Wright. “These hardworking men and women keep our lights on, our factories running, and our economy growing. President Trump’s Energy Dominance agenda is putting them first and delivering the affordable, reliable, and secure energy America needs.” Energy careers are also delivering bigger paychecks for American workers. The median energy-sector salary reached $63,000—24% higher than the U.S. median salary. America’s growing energy needs are creating the jobs of the future. The 2026 USEER’s new Future Outlook chapter highlights rising demand for skilled energy workers and growing competition for talent across energy and other expanding industries. These trends are opening new pathways to high-paying, skilled careers for American workers. As energy demand grows, America’s energy workforce will power the next generation of American industry, innovation, and economic growth. Highlights from the report include: • The median energy-sector salary was $63,000, 24% higher than the national median salary of $51,000. • Natural gas transmission and distribution added 12,500 workers, growing employment by 5%. • Nuclear power added 2,300 workers, growing employment by 4%. • Coal power generation added 2,800 workers, growing
Energy Department Announces Geothermal Center of Excellence to Advance Geothermal Technology Innovation and Development
WASHINGTON — The U.S. Department of Energy’s (DOE) Hydrocarbons and Geothermal Energy Office today established a Geothermal Center of Excellence (CoE) to advance President Trump and Secretary Wright’s commitment to delivering affordable, reliable, and secure energy. The Center will unite expertise and world-class capabilities from across DOE’s National Laboratories to accelerate the discovery and development of gigawatt-scale geothermal energy, resource discovery, and commercial development. The National Laboratory of the Rockies (NLR) will lead the consortium, with support from the National Energy Technology Laboratory (NETL). “America has vast geothermal resources beneath our feet that can provide reliable, around-the-clock energy while strengthening our energy dominance,” said DOE Under Secretary for Energy Kyle Haustveit. “Under President Trump’s leadership, the Geothermal Center of Excellence will leverage the world-class scientific and engineering expertise of our national laboratories in partnership with industry to unlock gigawatt-scale power generation, expand American energy production, and deliver more affordable, reliable, and secure energy to the American people.” DOE formally launched the Center at NLR’s campus in Golden, Colorado, bringing together DOE leadership, elected officials, NLR and NETL leadership, laboratory staff, and industry representatives to advance the Center’s vision and priorities. “The Geothermal Center of Excellence marks an important step in our work to accelerate gigawatt-scale geothermal energy on the U.S. grid,” said DOE Acting Assistant Secretary for the Hydrocarbons and Geothermal Energy Office Curt Coccodrilli. “By driving innovation and enhancing lab-industry collaboration, the Center will help us achieve our goals to enhance reliable baseload power, strengthen grid reliability, and improve long-term energy security.” The Center will also serve as industry’s main entry point to DOE’s National Laboratories. An Industry Advisory Board will provide objective insight into industry-relevant geothermal research needs, accelerate industry-lab partnerships, and advise on Center priorities. For more information, contact geo.centerofexcellence@nlr.gov.
Data from drones in Ukraine is fueling a new Wild West marketplace
Battlefields in Ukraine are littered with the remnants of drones, which are now firmly established as a critical weapon of modern warfare. But behind all that wreckage, there’s a new gold mine for the defense sector. The data drones generate will far outlast the wars in which they are used to fight, increasingly becoming part of the AI architecture that shapes even civilian life. For every flight, unmanned systems collect thousands of points of data, from images and video to controller inputs. Together, those records show how a machine and a person responded to constantly shifting circumstances. Ukraine has now begun converting that experience into a resource. Its Ministry of Defense announced in January that it would make millions of data points gathered during tens of thousands of drone flights available to both military contractors and commercial companies, and since then more than 100 companies and the UK government have gained access. For a country at war, it’s a quick way to attract funding and partnerships. But this step turns the front line into an active site of model training, taking advantage of how the chaos of war creates conditions that AI companies struggle to reproduce on their own.
Other countries and battlefields are likely to follow Ukraine’s lead, but the responsibility for governing this new industry cannot fall solely on a country fighting for its survival. That legal vacuum has to be filled together by the countries and companies involved in this industry’s development.
Explosive growth Ukraine’s battlefields are not the first to produce records used to train and develop models: American drones over Syria and Yemen collected data that informed the first generation of semiautonomous military hardware in the late 2010s. The difference now is that access to that data is being used to develop a wider ecosystem. And the financial value to defense firms is immense: Battlefield data offers large volumes of machine experience gathered under conditions that no laboratory can produce. That’s because the data that’s most valuable for training AI models comes from exceptions: the moment visibility disappears, a signal jams, or a human operator improvises. AI companies spend years and enormous sums trying to capture enough of these moments to make their models more robust. But war produces them at a frequency controlled testing cannot match. This constantly changing terrain is what makes drone data valuable far beyond the battlefield. A commercial drone used for delivery or remote sensing may never encounter artillery fire, but it must still operate with incomplete information in a world where people behave unpredictably. The same problem is compressed by war into a much shorter timeline. Processed and matched against records of what its operator was doing, that data turns operational records into training sets. Combat becomes a commercial asset. Many conflicts have already seen this training loop happen as drone footage feeds subsequent generations of military technology, and the market is set to grow. Enabled Intelligence, an American company that specializes in processing data to become usable in AI training, says it has already made more than half a million hours of Ukrainian drone footage available to feed into the next round of models, advertising possible uses in both military and commercial systems. Closing the data loop Many of the drones that now define our modern age of warfare began as civilian technology. But they’ve recently been turbocharged by new, commercially available AI systems, which allow cheap machines to operate autonomously—either individually or as a flock—as the environment changes around them. Each flight then creates a record of what the system encountered.
The resulting data is critical. The controlled lab environments usually developed to train these autonomous systems can approximate failure but are no match for the live conditions of a battlefield with very real risks. Military intelligence programs have held data generated by sensor-heavy systems like Predator and Reaper drones for nearly a decade through programs like Project Maven, but access remained entirely within the defense world. The data generated was available only through restricted, classified channels for the sole purpose of developing new weapons systems that would feed back into the same military that produced the data in the first place. That experience is now being shared to a much broader development network. The loop now closes. Commercial technologies adapted for the battlefield are generating data that can flow back into the industries from which they came, becoming part of the data infrastructure relied on by governments and the private sector alike. Drones that were trained in the signal-jammed airspace over Ukraine are now being deployed in the agricultural sector to help farmers map and survey their fields in places lacking the cell signal necessary for previous generations of technology. Other countries are likely to follow Ukraine in selling their battlefield data, and we are not ready for the new marketplace this will create. Bad actors could acquire the data, but purchase controls already mitigate that risk. Intelligence operatives scrutinize potential customers’ infrastructure for ways that data could reach enemies or nefarious actors. Training data creates a new tracing problem, though. Whereas the movement of commercial datasets can be followed when planted contact details appear two steps from the original buyer, the provenance of AI training data vanishes in a manner embedded in the technology itself. Another risk is that this use of the data creates an extractive economy in which wealthier countries far from danger benefit from the mortal threat borne by frontline states, potentially creating a market incentive for war to continue as an unending mine for digital gold. A fraught new frontier Existing laws regulate how militaries may conduct war. But they say almost nothing about what happens when records created in combat are stripped of their operational context, packaged as data, and licensed to companies whose products circulate far beyond where they were made.
The responsibilities of the companies that design these systems remain unsettled. Ukraine is building access controls, which are mentioned in the newly signed UK-Ukraine AI agreement, but no governments are actively working on regulating what happens when data has been absorbed into a model and crosses back into civilian markets. Those records contain human lives. The soldiers and civilians visible in them did not agree to become training material for products that might be sold years later. But sensor data, camera footage, and coordinates from civilians fleeing a drone strike now constitute the sorts of data that inform how future machines will make decisions.
That is a problem of consent. Individuals featured in the data—be they targets, controllers, or civilians standing by—become part of the training material. The autonomous capabilities based on that data do not stop at the edge of the battlefield. Such capabilities move into other military or commercial systems like delivery vehicles or agricultural machinery. Errors and assumptions embedded in the data travel with the model even once it enters civilian life. Battlefield data should not be treated as ordinary commercial material. But there is currently no agency or regulator that has jurisdiction over this issue. In the meantime, governments that provide access to defense data should treat it as they would a controlled weapons transfer, recording its origin, licensing its users, and restricting onward sharing. Ukraine has begun to grapple with this. Its Avengers Labs program allows companies to train models on battlefield data without giving them direct access to sensitive databases. Yet that mitigates only one part of the problem. Governments should require disclosure when models trained on wartime material are later incorporated into civilian products. The goal of such regulation should be to make the path from combat to commerce visible. What these companies are really mining is experience. And soldiers cannot consent to having their experience used in this way—as training data that produces model advantage and ultimately supports a product used far from where the war was fought. The question is no longer only what the technology companies can sell for use in war. It is what they can extract from it. To protect ourselves from the excesses of this new industry, we need a regulatory system that follows battlefield data wherever it goes, from combat to model to commercial product. Cory Alpert is a researcher at the University of Melbourne, looking at the impact of AI on democracy. He previously served in the Biden White House.
HPE’s record Q3: AI infrastructure, networking demand drives growth, but supply constraints tap the brakes
AI-driven networks are helping drive HPE results, but so is interest in AI across the board, Neri said. “We see enterprises increasingly moving from AI pilots to production deployments, using traditional servers for agentic AI workloads and inferencing,” Neri said. Demand for inferencing and agentic AI workloads is building, he said. “AI systems orders of $2.4 billion increased over 30% sequentially, reflecting broad-based demand across customer segments. Enterprise demand more than doubled, reflecting increasing overall infrastructure spending as AI initiatives have become board-level priorities,” Neri said. AI is advancing beyond early proof-of-concept training deployments into a broader enterprise workflow transformation opportunity, Neri said. “Customers are increasingly investing in new agentic AI applications and AI inferencing, requiring accelerated computing infrastructure, secure data storage access, and enterprise-grade cloud management.”
Energy Secretary Secures Carolinas’ Grid Ahead of Holiday Weekend
WASHINGTON—The U.S. Department of Energy (DOE) today issued an emergency order to mitigate the risk of blackouts in the Carolinas amid hot weather conditions. Issued pursuant to Section 202(c) of the Federal Power Act, the order authorizes Duke Energy Carolinas, LLC (Duke) to dispatch specified units and to order their operation as needed to maintain reliability. The order also authorizes Duke, in collaboration with its Transmission Owners, to direct backup generation resources to operate as a last resort before declaring an Energy Emergency Alert (EEA) 3 or during an EEA 3. This order was issued pursuant to an application from Duke submitted on September 3, 2026. “Thanks to this emergency order, Americans will not have to worry about losing access to affordable power this Labor Day weekend,” said U.S. Secretary of Energy Chris Wright. “The previous administration’s energy subtraction policies weakened the grid, leaving Americans more vulnerable during events like this. Under President Trump’s leadership, we are ensuring that hardworking American families and businesses in the Carolinas’ have continued access energy to power and cool their homes.” On day one, President Trump declared a national energy emergency after the Biden administration’s energy subtraction agenda left behind a grid increasingly vulnerable to the risk of blackouts. The order is in effect beginning on September 3, 2026, through September 8, 2026.
VMware Cloud Foundation 9.1 adds transit gateway flexibility, segmentation, and native EVPN VXLAN support
VLAN-backed subnets: A VPC subnet can now attach directly to a physical VLAN, giving workloads the same L2 connectivity as a distributed port group. “As a result, even if it’s in the same VPC, it can only reach the public subnets,” Tallet said. A variant, available only with a distributed external connection, also attaches the subnet to the VPC gateway, so the default gateway exists in both worlds at once. VPC connectivity policies: Modeled on Cisco’s private VLAN concept, these control which VPCs within a tenant can reach each other. VPCs default to open communication. Administrators can group VPCs into communities, mark one promiscuous so it reaches every VPC in the tenant, or isolated so it reaches only promiscuous VPCs. Native EVPN VXLAN support: VCF 9.0’s distributed external connection let ESXi hosts send north-south traffic straight to the physical infrastructure over VLAN, skipping the NSX edge, but required a single L2 VLAN across every host involved. VCF 9.1 extends that model to VXLAN fabrics through EVPN, using a small route controller VM to handle the BGP EVPN control plane. “It’s not an edge, it is only running BGP. There is no data path,” Tallet said.
President Trump’s Energy Dominance Agenda is Delivering for American Energy Workers
WASHINGTON—This Labor Day, the U.S. Department of Energy (DOE) is celebrating the hardworking men and women who power America with the release of the 2026 U.S. Energy and Employment Report (USEER). The annual report highlights strong job growth across critical energy sectors at the heart of President Trump’s Energy Dominance agenda. America’s most reliable energy sectors are adding jobs and powering industries across the country. These critical sectors deliver the affordable, reliable, and secure energy that American families, businesses, and industries depend on. After years of decline under the previous administration, America’s coal and nuclear power workforces are growing again under President Trump’s leadership. “Energy is the sector that enables every other sector of our economy, and America’s energy workers make it all possible,” said U.S. Secretary of Energy Chris Wright. “These hardworking men and women keep our lights on, our factories running, and our economy growing. President Trump’s Energy Dominance agenda is putting them first and delivering the affordable, reliable, and secure energy America needs.” Energy careers are also delivering bigger paychecks for American workers. The median energy-sector salary reached $63,000—24% higher than the U.S. median salary. America’s growing energy needs are creating the jobs of the future. The 2026 USEER’s new Future Outlook chapter highlights rising demand for skilled energy workers and growing competition for talent across energy and other expanding industries. These trends are opening new pathways to high-paying, skilled careers for American workers. As energy demand grows, America’s energy workforce will power the next generation of American industry, innovation, and economic growth. Highlights from the report include: • The median energy-sector salary was $63,000, 24% higher than the national median salary of $51,000. • Natural gas transmission and distribution added 12,500 workers, growing employment by 5%. • Nuclear power added 2,300 workers, growing employment by 4%. • Coal power generation added 2,800 workers, growing
Energy Department Announces Geothermal Center of Excellence to Advance Geothermal Technology Innovation and Development
WASHINGTON — The U.S. Department of Energy’s (DOE) Hydrocarbons and Geothermal Energy Office today established a Geothermal Center of Excellence (CoE) to advance President Trump and Secretary Wright’s commitment to delivering affordable, reliable, and secure energy. The Center will unite expertise and world-class capabilities from across DOE’s National Laboratories to accelerate the discovery and development of gigawatt-scale geothermal energy, resource discovery, and commercial development. The National Laboratory of the Rockies (NLR) will lead the consortium, with support from the National Energy Technology Laboratory (NETL). “America has vast geothermal resources beneath our feet that can provide reliable, around-the-clock energy while strengthening our energy dominance,” said DOE Under Secretary for Energy Kyle Haustveit. “Under President Trump’s leadership, the Geothermal Center of Excellence will leverage the world-class scientific and engineering expertise of our national laboratories in partnership with industry to unlock gigawatt-scale power generation, expand American energy production, and deliver more affordable, reliable, and secure energy to the American people.” DOE formally launched the Center at NLR’s campus in Golden, Colorado, bringing together DOE leadership, elected officials, NLR and NETL leadership, laboratory staff, and industry representatives to advance the Center’s vision and priorities. “The Geothermal Center of Excellence marks an important step in our work to accelerate gigawatt-scale geothermal energy on the U.S. grid,” said DOE Acting Assistant Secretary for the Hydrocarbons and Geothermal Energy Office Curt Coccodrilli. “By driving innovation and enhancing lab-industry collaboration, the Center will help us achieve our goals to enhance reliable baseload power, strengthen grid reliability, and improve long-term energy security.” The Center will also serve as industry’s main entry point to DOE’s National Laboratories. An Industry Advisory Board will provide objective insight into industry-relevant geothermal research needs, accelerate industry-lab partnerships, and advise on Center priorities. For more information, contact geo.centerofexcellence@nlr.gov.
OMV Petrom advances Petrobrazi refinery’s green hydrogen, SAF projects
OMV Petrom SA has completed delivery of modules for a second electrolyzer to be added as part of the operator’s project to produce green hydrogen at its 4.5-million tonne/year (tpy) Petrobrazi refinery in southeast Romania, near Ploiesti City, about 60 km from Bucharest. Currently under installation into the refinery’s existing infrastructure, all seven modules of 5-Mw each for the second electrolyzer were delivered to Petrobrazi over the past 2 months, OMV Petrom said in a release Aug. 28. Completed delivery of modules for the second 35-Mw electrolyzer follows delivery of modules for the project’s first 20-Mw electrolyzer between March and June, according to previous releases from the operator. With a combined capacity of 55 Mw, the 20-Mw electrolyzer will be equipped to produce about 3,000 tpy of green hydrogen, with the 35-Mw unit to add about 4,700 tpy of production for an overall green hydrogen production capacity upon full operation of 7,700 tpy. OMV Petrom confirmed Neuman & Esser of Germany supplied the electrolyzer equipment, while Kraftanlagen Energies & Services SE’s subsidiary Kraftanlagen Romania SRL is handling on-site construction, logistics, fabrication of construction elements, and module integration. The two electrolyzers received a combined €50 million in non-reimbursable financing through Romania’s National Recovery and Resilience Plan, including about €21 million for the 20-Mw unit and €29 million for the 35-Mw unit, according to the operator. OMV Petrom said Petrobrazi’s green hydrogen project required a total investment of about €190 million, up from its original estimate of €140 million upon first announcing the development in 2024. SAF-HVO unit under construction OMV Petrom will use carbon-free green hydrogen produced by the electrolyzers—which will use electricity from renewable sources to split water into hydrogen and oxygen—to be integrated into hydrotreating processes at Petrobrazi’s previously announced sustainable aviation fuel (SAF) and hydrotreated vegetable oil (HVO,
EIA: US LNG exports rose 23% in first-half 2026 amid higher capacity
US LNG exports averaged 17.4 bcfd in the first 6 months of 2026, up 23% from the same period in 2025, as new terminals and expansions increased export capacity, the US Energy Information Administration (EIA) said, citing its Natural Gas Monthly report. In its latest Short-Term Energy Outlook, EIA estimates US LNG exports will average 17.3 bcfd in second-half 2026 before rising to 18.7 bcfd in first-half 2027. Export capacity additions from startup production at new terminals and expansions at existing terminals boosted LNG exports at the fastest rate since the US began large-scale exports in 2016. Plaquemines LNG is exporting at full capacity, while Corpus Christi Stage 3 is exporting from six of seven liquefaction trains. When complete, the two projects will increase nominal US LNG export capacity by a combined 4.0 bcfd. Golden Pass LNG began exports in April 2026 and is expected to increase exports from Train 1 through yearend, adding another 0.7 bcfd of nominal export capacity. The terminal exported less than 0.2 bcfd during April-June. Golden Pass LNG Train 2 is expected to be completed in late 2026. Global LNG prices remained sufficiently high throughout first-half 2026 to incentivize US exports near maximum output levels. Disruptions to LNG shipments through the Strait of Hormuz in March cut off 20% of global LNG supplies, mostly from Qatar, pushing global LNG prices higher and forcing Asian buyers—who take about 80% of Qatari LNG supplies—to compete for limited spot cargoes. The average price at Europe’s benchmark Title Transfer Facility (TTF) in the Netherlands was $14.74/MMbtu in first-half 2026, up from $13.10/MMbtu in first-half 2025 and the highest since Russia’s 2022 invasion of Ukraine, when prices rose to $32.42/MMbtu in the first half of that year. The Japan-Korea Marker (JKM), the benchmark price for LNG imports into East Asia,
LLOG lets subsea contract for Who Dat
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Chevron expands Venezuela acreage, targets 600,000 b/d production
Chevron Corp. is expanding its acreage position in Venezuela under updated terms for its current joint ventures in the country, a move the company said supports plans to invest more than $7 billion over the next 5 years and more than double production to about 600,000 b/d from expected 2026 levels. In a release Sept. 2, 2026, Chevron said it has been assigned additional acreage in the Orinoco Belt, where the company already holds interests. Orinoco Belt acreage expands growth plans The Petroindependencia SA joint venture, in which a Chevron subsidiary holds a 49% interest, has been assigned rights to develop the adjacent Carabobo 1 and Carabobo-2-South-A areas in the Orinoco Belt. The greenfield sites expand the joint venture’s existing operating footprint, where it is increasing extra-heavy oil production, Chevron said. The acreage assignment follows an April agreement that increased Chevron’s interest in Petroindependencia to 49% and retained rights to develop the Ayacucho 8 area adjacent to the Petropiar SA joint venture. Together, Chevron’s three joint ventures have increased production by 15% year to date, the company said. Chevron said Venezuela’s resource base and operating costs of less than $20/bbl present an opportunity to increase oil production while maintaining capital discipline. “Chevron’s history in Venezuela spans more than a century, and our expanded position reflects our confidence in the country’s deep resource potential and its ability to compete for investment within our portfolio for decades,” said Mike Wirth, chairman and chief executive officer. “With improved terms and additional acreage, we are strengthening a portfolio that we believe can deliver attractive low-cost oil growth, support energy supply and create differentiated long-term value.”
Comstock proposes $1.65-billion SOCAR partnership, $450-million Haynesville drilling venture
Comstock Resources Inc. signed a letter of intent with the State Oil Company of Azerbaijan Republic (SOCAR) for a $1.65-billion transaction involving interests in its Haynesville shale assets and related midstream infrastructure while separately launching a $450-million drilling venture with majority shareholder Jerry Jones. Under the proposed transaction, SOCAR or a wholly owned subsidiary would acquire: a 20% non-operated working interest in Comstock’s Legacy Haynesville assets. a 15% non-operated working interest in its Western Haynesville assets, reducing to 7.5% after 5 years once SOCAR achieves a 15% return on investment. a 15% interest in Comstock’s 73% ownership stake in Pinnacle Gas Services LLC. The parties aim to execute a definitive purchase and sale agreement by the end of October and target closing before yearend, subject to customary conditions, including governmental and third-party approvals. As part of the proposed partnership, SOCAR could participate in future Comstock opportunities in the Legacy and Western Haynesville at the same ownership percentages as would be acquired in the proposed transaction. Comstock also said SOCAR would provide opportunities for Comstock to market its natural gas to international customers. Drilling venture Separately, Comstock entered a drilling venture with Jerry Jones under which a Jones family-owned partnership will fund most drilling and completion costs for 27 Haynesville wells over the next 12 months beginning Sept. 1, 2026. The partnership will fund 85% of drilling and completion costs for 18 Western Haynesville wells and 80% of costs for 9 Legacy Haynesville wells, representing about $450 million of expected capital. After the venture achieves a 15% return on investment, 50% of the participating interest in the wells will revert to Comstock. Comstock said the additional capital will support delineation and development of its 545,000 net acres in the Western Haynesville, which the company views as a major source of future
bp farms out partial share of GoM, offshore Brazil exploration prospects to Shell
bp plc has agreed to farm out deals with Shell plc subsidiaries for stakes in exploration prospects in Brazil and the US Gulf of Mexico. Brazil Shell Brasil Petróleo Ltda will acquire a 50% stake in the Tupinambá exploration block in the Santos Basin, offshore Brazil. bp will retain the remaining 50% interest in Tupinambá and will continue as operator. bp was awarded the Tupinambá block in December 2023 under the second production-sharing Permanent Offer cycle. Pré-Sal Petróleo S/A will continue to manage the Production Sharing Contract on behalf of the federal government. The Tupinambá exploration well is expected to spud soon, bp said. Completion of Tupinambá remains subject to regulatory approvals. Gulf of Mexico Separately, Shell Offshore Inc. will take a 30% stake in five leases containing the Conifer exploration prospect operated by BP Exploration and Production in the deepwater US Gulf of Mexico Paleogene. bp will retain a 70% interest in Conifer and will continue as operator. bp was awarded four leases covering the Conifer exploration prospect in August 2023 following Lease Sale 259. The fifth lease of the prospect was awarded in February 2026 following the Big Beautiful Gulf 1 Lease Sale. The initial Conifer exploration well is expected to be drilled in 2027. The prospect lies within Keathley Canyon, about 250 miles southwest of New Orleans, La., near bp’s Kaskida host development. Brazil and the US Gulf of Mexico are important regions for bp, said Gordon Birrell, executive vice-president, upstream. He said, noting “bringing together two experienced operators can help unlock the potential of both opportunities.” While financial details were not disclosed, the move comes as bp continues efforts to streamline the company and support a disciplined approach to capital allocation.
National Grid, Con Edison urge FERC to adopt gas pipeline reliability requirements
The Federal Energy Regulatory Commission should adopt reliability-related requirements for gas pipeline operators to ensure fuel supplies during cold weather, according to National Grid USA and affiliated utilities Consolidated Edison Co. of New York and Orange and Rockland Utilities. In the wake of power outages in the Southeast and the near collapse of New York City’s gas system during Winter Storm Elliott in December 2022, voluntary efforts to bolster gas pipeline reliability are inadequate, the utilities said in two separate filings on Friday at FERC. The filings were in response to a gas-electric coordination meeting held in November by the Federal-State Current Issues Collaborative between FERC and the National Association of Regulatory Utility Commissioners. National Grid called for FERC to use its authority under the Natural Gas Act to require pipeline reliability reporting, coupled with enforcement mechanisms, and pipeline tariff reforms. “Such data reporting would enable the commission to gain a clearer picture into pipeline reliability and identify any problematic trends in the quality of pipeline service,” National Grid said. “At that point, the commission could consider using its ratemaking, audit, and civil penalty authority preemptively to address such identified concerns before they result in service curtailments.” On pipeline tariff reforms, FERC should develop tougher provisions for force majeure events — an unforeseen occurence that prevents a contract from being fulfilled — reservation charge crediting, operational flow orders, scheduling and confirmation enhancements, improved real-time coordination, and limits on changes to nomination rankings, National Grid said. FERC should support efforts in New England and New York to create financial incentives for gas-fired generators to enter into winter contracts for imported liquefied natural gas supplies, or other long-term firm contracts with suppliers and pipelines, National Grid said. Con Edison and O&R said they were encouraged by recent efforts such as North American Energy Standard
US BOEM Seeks Feedback on Potential Wind Leasing Offshore Guam
The United States Bureau of Ocean Energy Management (BOEM) on Monday issued a Call for Information and Nominations to help it decide on potential leasing areas for wind energy development offshore Guam. The call concerns a contiguous area around the island that comprises about 2.1 million acres. The area’s water depths range from 350 meters (1,148.29 feet) to 2,200 meters (7,217.85 feet), according to a statement on BOEM’s website. Closing April 7, the comment period seeks “relevant information on site conditions, marine resources, and ocean uses near or within the call area”, the BOEM said. “Concurrently, wind energy companies can nominate specific areas they would like to see offered for leasing. “During the call comment period, BOEM will engage with Indigenous Peoples, stakeholder organizations, ocean users, federal agencies, the government of Guam, and other parties to identify conflicts early in the process as BOEM seeks to identify areas where offshore wind development would have the least impact”. The next step would be the identification of specific WEAs, or wind energy areas, in the larger call area. BOEM would then conduct environmental reviews of the WEAs in consultation with different stakeholders. “After completing its environmental reviews and consultations, BOEM may propose one or more competitive lease sales for areas within the WEAs”, the Department of the Interior (DOI) sub-agency said. BOEM Director Elizabeth Klein said, “Responsible offshore wind development off Guam’s coast offers a vital opportunity to expand clean energy, cut carbon emissions, and reduce energy costs for Guam residents”. Late last year the DOI announced the approval of the 2.4-gigawatt (GW) SouthCoast Wind Project, raising the total capacity of federally approved offshore wind power projects to over 19 GW. The project owned by a joint venture between EDP Renewables and ENGIE received a positive Record of Decision, the DOI said in
Biden Bars Offshore Oil Drilling in USA Atlantic and Pacific
President Joe Biden is indefinitely blocking offshore oil and gas development in more than 625 million acres of US coastal waters, warning that drilling there is simply “not worth the risks” and “unnecessary” to meet the nation’s energy needs. Biden’s move is enshrined in a pair of presidential memoranda being issued Monday, burnishing his legacy on conservation and fighting climate change just two weeks before President-elect Donald Trump takes office. Yet unlike other actions Biden has taken to constrain fossil fuel development, this one could be harder for Trump to unwind, since it’s rooted in a 72-year-old provision of federal law that empowers presidents to withdraw US waters from oil and gas leasing without explicitly authorizing revocations. Biden is ruling out future oil and gas leasing along the US East and West Coasts, the eastern Gulf of Mexico and a sliver of the Northern Bering Sea, an area teeming with seabirds, marine mammals, fish and other wildlife that indigenous people have depended on for millennia. The action doesn’t affect energy development under existing offshore leases, and it won’t prevent the sale of more drilling rights in Alaska’s gas-rich Cook Inlet or the central and western Gulf of Mexico, which together provide about 14% of US oil and gas production. The president cast the move as achieving a careful balance between conservation and energy security. “It is clear to me that the relatively minimal fossil fuel potential in the areas I am withdrawing do not justify the environmental, public health and economic risks that would come from new leasing and drilling,” Biden said. “We do not need to choose between protecting the environment and growing our economy, or between keeping our ocean healthy, our coastlines resilient and the food they produce secure — and keeping energy prices low.” Some of the areas Biden is protecting
Biden Admin Finalizes Hydrogen Tax Credit Favoring Cleaner Production
The Biden administration has finalized rules for a tax incentive promoting hydrogen production using renewable power, with lower credits for processes using abated natural gas. The Clean Hydrogen Production Credit is based on carbon intensity, which must not exceed four kilograms of carbon dioxide equivalent per kilogram of hydrogen produced. Qualified facilities are those whose start of construction falls before 2033. These facilities can claim credits for 10 years of production starting on the date of service placement, according to the draft text on the Federal Register’s portal. The final text is scheduled for publication Friday. Established by the 2022 Inflation Reduction Act, the four-tier scheme gives producers that meet wage and apprenticeship requirements a credit of up to $3 per kilogram of “qualified clean hydrogen”, to be adjusted for inflation. Hydrogen whose production process makes higher lifecycle emissions gets less. The scheme will use the Energy Department’s Greenhouse Gases, Regulated Emissions and Energy Use in Transportation (GREET) model in tiering production processes for credit computation. “In the coming weeks, the Department of Energy will release an updated version of the 45VH2-GREET model that producers will use to calculate the section 45V tax credit”, the Treasury Department said in a statement announcing the finalization of rules, a process that it said had considered roughly 30,000 public comments. However, producers may use the GREET model that was the most recent when their facility began construction. “This is in consideration of comments that the prospect of potential changes to the model over time reduces investment certainty”, explained the statement on the Treasury’s website. “Calculation of the lifecycle GHG analysis for the tax credit requires consideration of direct and significant indirect emissions”, the statement said. For electrolytic hydrogen, electrolyzers covered by the scheme include not only those using renewables-derived electricity (green hydrogen) but
Xthings unveils Ulticam home security cameras powered by edge AI
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Xthings announced that its Ulticam security camera brand has a new model out today: the Ulticam IQ Floodlight, an edge AI-powered home security camera. The company also plans to showcase two additional cameras, Ulticam IQ, an outdoor spotlight camera, and Ulticam Dot, a portable, wireless security camera. All three cameras offer free cloud storage (seven days rolling) and subscription-free edge AI-powered person detection and alerts. The AI at the edge means that it doesn’t have to go out to an internet-connected data center to tap AI computing to figure out what is in front of the camera. Rather, the processing for the AI is built into the camera itself, and that sets a new standard for value and performance in home security cameras. It can identify people, faces and vehicles. CES 2025 attendees can experience Ulticam’s entire lineup at Pepcom’s Digital Experience event on January 6, 2025, and at the Venetian Expo, Halls A-D, booth #51732, from January 7 to January 10, 2025. These new security cameras will be available for purchase online in the U.S. in Q1 and Q2 2025 at U-tec.com, Amazon, and Best Buy. The Ulticam IQ Series: smart edge AI-powered home security cameras Ulticam IQ home security camera. The Ulticam IQ Series, which includes IQ and IQ Floodlight, takes home security to the next level with the most advanced AI-powered recognition. Among the very first consumer cameras to use edge AI, the IQ Series can quickly and accurately identify people, faces and vehicles, without uploading video for server-side processing, which improves speed, accuracy, security and privacy. Additionally, the Ulticam IQ Series is designed to improve over time with over-the-air updates that enable new AI features. Both cameras
Intel unveils new Core Ultra processors with 2X to 3X performance on AI apps
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Intel unveiled new Intel Core Ultra 9 processors today at CES 2025 with as much as two or three times the edge performance on AI apps as before. The chips under the Intel Core Ultra 9 and Core i9 labels were previously codenamed Arrow Lake H, Meteor Lake H, Arrow Lake S and Raptor Lake S Refresh. Intel said it is pushing the boundaries of AI performance and power efficiency for businesses and consumers, ushering in the next era of AI computing. In other performance metrics, Intel said the Core Ultra 9 processors are up to 5.8 times faster in media performance, 3.4 times faster in video analytics end-to-end workloads with media and AI, and 8.2 times better in terms of performance per watt than prior chips. Intel hopes to kick off the year better than in 2024. CEO Pat Gelsinger resigned last month without a permanent successor after a variety of struggles, including mass layoffs, manufacturing delays and poor execution on chips including gaming bugs in chips launched during the summer. Intel Core Ultra Series 2 Michael Masci, vice president of product management at the Edge Computing Group at Intel, said in a briefing that AI, once the domain of research labs, is integrating into every aspect of our lives, including AI PCs where the AI processing is done in the computer itself, not the cloud. AI is also being processed in data centers in big enterprises, from retail stores to hospital rooms. “As CES kicks off, it’s clear we are witnessing a transformative moment,” he said. “Artificial intelligence is moving at an unprecedented pace.” The new processors include the Intel Core 9 Ultra 200 H/U/S models, with up to
Introducing agentic video understanding with Gemini
Today, we’re launching agentic video understanding across our latest models: Gemini 3.7 Flash, 3.6 Flash and 3.5 Flash-Lite. This new capability improves accuracy while dramatically reducing token usage and costs for video analysis. Similar to agentic vision, which combines code execution with Gemini models’ native image understanding, agentic video understanding uses Gemini’s native video tools to improve performance and unlock new capabilities for video processing like sub-second moment retrieval, more accurate anomaly detection, precise counting and more.The feature is available today for video uploads and YouTube videos via the Gemini API in Google AI Studio and the Gemini Enterprise Agent Platform.BenchmarksUnlike current ‘static’ processing, where the model ingests the video at a fixed frames-per-second rate (default 1 FPS, adjustable via API), agentic video understanding pairs the model’s core reasoning with native video tools to dynamically search, scan, and inspect target video segments across visual frames, audio, and transcripts. Across standard video analysis benchmarks, Gemini models with agentic video understanding reduce analysis costs by up to 66% and token consumption by up to 88%, while improving accuracy by up to 7%.These efficiency gains are especially pronounced on long-form video (from 10-minute how-to guides to 90-minute lectures and multi-hour recordings), where static processing forces developers to choose between high token costs or techniques that drop critical details.
Making the AI-powered case for legacy modernization
In partnership withInfosys For years, legacy technology has been a problem companies knew they needed to solve, but one they often struggled to tackle. The cost, complexity, and risk of replacing business-critical systems could make modernization feel like a disruption to manage instead of an opportunity to pursue. But with the rise in customer expectations and the changes AI brought to the economics of software development, that calculation is changing. Bupa’s modernization of its My Bupa mobile application offers a case study in what becomes possible when a legacy migration is treated as a business transformation rather than a technology rewrite. Bupa CIO of health insurance Asifa Sherazi describes the risks of waiting for legacy systems to become an emergency: “The end-of-life technology is a risk that compounds quietly, and then arrives all at once.” For Bupa, moving its application from Xamarin to native Swift and Kotlin improved the app rating from 3.7 to 4.7, while the user-perceived crash rate fell by nearly 24 percentage points on Android and eight points on iOS. “What they’ll notice is that when they need us, often at a stressful moment, it just simply works,” Sherazi says. Sanjeev Tripathi, senior vice president and region head of BFSI, healthcare, and public sector for Australia, New Zealand, and Southeast Asia at Infosys, contends that AI is helping change the equation. “The emergence of AI is fundamentally shifting the economics of modernization,” he says, reducing the effort, risk, and time traditionally associated with these programs. At Bupa, combining AI-assisted reverse engineering with forward engineering helped deliver the transformation in approximately 60% less time than would have been possible in the pre-AI era. Sherazi and Tripathi also highlight the human dimension of modernization: preserving institutional knowledge, giving teams capacity to adapt, and creating an environment where employees can surface problems early.
Looking ahead, both experts see modernized platforms as foundations for more personalized, predictive and AI-driven experiences. The payoff of modernization may be less about replacing aging technology and more about building the flexibility needed for whatever comes next. “Modern platforms will become the base for far more intelligent AI-driven ecosystems, where AI is not just an add-on, but it is built into everything from design to operations. That’s how the modern platforms will evolve, and the customer experiences will become far more personalized and predictive,” says Tripathi. For Sherazi, that shift is already changing the questions organizations can ask: “It used to be, can our platform support that? Now, it’s: is that the right thing to do for our customers?”
This episode of Business Lab is produced in partnership with Infosys. Full Transcript: Megan Tatum: From MIT Technology Review, I’m Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.Our topic today is legacy modernization. Companies across industries continue to struggle to bring AI modernization to legacy technology stacks, increasing the risk of losing vendor support, degraded customer experience, and limited capacity for innovation. Two words for you: future-ready foundation. My guests are Asifa Sherazi, who is CIO of health insurance at Bupa, and Sanjeev Tripathi, who is senior vice president, region head of BFSI, healthcare, and public sector for Australia, New Zealand, and Southeast Asia at Infosys. This podcast is produced in association with Infosys. Welcome, Asifa and Sanjeev. Asifa Sherazi: Thank you, Megan. Delighted to be here.
Sanjeev Tripathi: Thanks, Megan. Wonderful to be here. And good talking to you again, Asifa. Megan: Thank you both so much for being here. Asifa, if I could start with you just to set the context for our conversation. There are seven million healthcare customers across the Asia-Pacific. Could you tell us a bit more about Bupa and the challenges it’s faced in its modernization plan? Asifa: Yes, absolutely. Let me start with something about Bupa that shapes everything we do. We are a global healthcare organization. Think hospitals, clinics, dental, age care, digital health, alongside our insurance business. We reinvest back into the organization, so into our services, our capability, our teams, and our outcomes that we deliver for our customers. In Asia-Pacific, we serve around, as you said, seven million customers across health insurance and health services, individuals, families, corporate clients, patients. Our purpose, helping people live longer, healthier, happier lives is more than just a statement. It actually shapes our strategy, guides our investment decisions, and influences the choices our teams make every day. Now, for most of our health insurance members, the day-to-day digital relationship with Bupa begins through My Bupa, our self-service platform. It’s the front door for managing cover, updating policy details, submitting claims, checking entitlements. Alongside of it, Blua plays a complimentary role. Where My Bupa helps members manage their cover, Blua helps them manage their health, digital healthcare services, clinical support, preventative health. And together, they let us move beyond transactional interactions towards more personalized, proactive care. Here was our challenge. Our mobile app, My Bupa, was built on Xamarin, and Microsoft support for it ended sometime in 2024. We had extended support in place, so our customers remained protected throughout, but we were clear-eyed that this was a bridge, not a destination. The runway was finite, and it was shortening for us. We made the decision to modernize from a position of stability proactively for the long-term safety and experiences of our customer rather than waiting until circumstances forced our hand, because the platform carrying our most important customer relationship was in effect standing still while the world around it was moving. Megan: Right. So you decided to act really proactively in that sense. And so, Asifa, what are some of the risks then of sticking with end-of-life technologies, and how will moving away from legacy technologies help you improve the customer experience? Asifa: Yeah. Look, the end-of-life technology is a risk that compounds quietly, and then arrives all at once. We saw it in three ways. The first is security and compliance. Once a technology is out of vendor support, the flow of security updates and fixes changes fundamentally. In our case, we put extended support in place as a bridge so our customers stayed protected. But extended support buys you time, it doesn’t buy you a future. In healthcare, we hold some of the most sensitive information a person will ever share with an organization. That isn’t data to us, it’s trust. And trust is extraordinarily expensive to rebuild. We weren’t prepared to run that risk on a shortening runway. The second is losing control of your own roadmap. How I’d explain that is iOS and Android don’t stand still. Every operating system release, every change to app store requirements becomes something that you react to rather than plan for. As each one slows you down a little further, over time, that opens a widening gap between what customers expect and what you can actually give them. We’re all customers. We don’t benchmark a health insurer against other health insurers. We benchmark against whatever app we used last.
And the third way was, and this is one red flag for peers, is the shrinking talent pool. Xamarin is legacy technology, and the engineering expertise available for it is limited. You end up with a critical customer platform supported by a narrowing group of specialists. That’s a workforce risk wearing a technology costume. We partnered with Infosys and we migrated the entire application estate from Xamarin to fully native Swift and Kotlin, and the customer outcomes are why we’re comfortable talking about this today. Our app rating moved from 3.7 to 4.7. Some of the stats that I’d love to share are that the user-perceived crash rate fell by nearly 24 percentage points on Android and eight points on iOS. The Android login success per visit doubled back up to 77%, and that’s one the team that is most proud of, because a login failure isn’t a technical event. It’s a person who wanted to check their cover and they couldn’t. The team achieved 100% feature parity in a single release, and 90% of our active customer base moved to a new version, and they’ve, I think, downloaded nearly 1.8 million unique downloads. From our customers’ perspective, customers will never think about any of this as a technology change, and they shouldn’t have to either. What they’ll notice is that when they need us, often at a stressful moment, it just simply works, and that’s the outcome we were really after.
Megan: Those are some really striking results and statistics that you’ve shared there on the success of the migration. I mean, Sanjeev, could you talk a bit about why modernizing legacy technologies is so critical at this time, and how Infosys has approached that transformation journey with Bupa? Sanjeev: Sure, Megan. To be honest, legacy modernization initiatives are not new, and there has always been a strong desire to drive modernization across the entire technology landscape. And Asifa covered all the points that I was going to cover about the risks that have been there. But I’ll reiterate, the reality is the industry has been held back by the cost complexity, and also the risks that have been associated with any legacy modernization initiative that has been undertaken historically.As Asifa mentioned, customer expectations of what was acceptable five, 10 years back are simply not acceptable anymore. The customers today expect a seamless, intuitive, responsive interaction across every channel. And Asifa also covered the growing challenges around security, resilience, talent availability, and so on. Finding talent on legacy technology is extremely, extremely difficult, and that introduces risk in every organization and in every legacy platform, most of which are actually business-critical platforms as well. Security vulnerabilities are getting increasingly difficult to manage, and as I mentioned, finding deep expertise in older technologies is very, very difficult now. So what has changed? What has changed is that we now have new tools that are available to us to address these challenges. The emergence of AI is fundamentally shifting the economics of modernization, and it’s doing that by helping organizations to reduce the effort, risk, and also the time that is traditionally taken for programs like these, and that is why we believe that the time is now for legacy modernization. In fact, in Infosys, we have six strategic value pools that we have identified in our AI-first value framework, which is publicly available, and legacy modernization is one of these six value pools. And in the market, we are seeing very strong interest across our client base, and they recognize that the opportunity to unlock both technical and business value is now. With Bupa in particular, we approached the journey as a business transformation rather than simply a technology rewrite, and our approach had two key phases. One is reverse engineering, and then forward engineering. Let me just quickly cover what these two are.Reverse engineering, what we did is we extracted and we understood the rules, the processes, and the logic within the legacy environment, and that’s a standard approach we took, we take in any legacy modernization program. What that does is it allows us to preserve the critical business functionality, but at the same time, it avoids the risks that often come with large-scale migration programs. The second aspect is forward engineering, where we re-architected the solution to enable a reimagined customer experience. The objective is not just a feature-by-feature migration or ensuring feature parity, which is important, but it is even more important that since we’re investing this kind of money to create a modern platform that is scalable, maintainable, and is also capable for future innovation, and that’s what Asifa mentioned about you need to be in control of your own roadmap. You have to build a platform which is capable of supporting future innovation as well. We essentially ensured nothing was lost in translation, and we significantly improved the platform stability and long-term maintainability. And some of the metrics that Asifa mentioned reflects the meaningful improvement in customer experience as well. Finally, just one more point before I close this question is the time to market. I spoke about the time is now, and because we’ve got the power of the tools that are available now. What AI allowed us is to accelerate the transformation dramatically. What would have traditionally been a long and complex modernization, was delivered in approximately 60% less time than what would have happened in pre-AI era, and that is the real story.
Megan: So AI in this context is a real enabler in terms of the economics and the speed and all of those things you’re talking about. If I could come back to you, Asifa, as much as modernization is a technology challenge, there is the human component as well, and I wondered if you could talk about how you prepared employees for these new technologies, and what challenges and solutions you faced on that front as well? Asifa: The human side of it is what I’m really passionate about. Technology was only half the challenge. The real work was helping people move from what they knew to what was possible. Modernization isn’t just about replacing systems, it’s about giving teams the confidence, the capability, the clarity to embrace a different future, and that’s what determines whether change actually succeeds. From that experience, there were three human challenges that stood out for us. The first one was scarcity of expertise on both sides of the transition, and like we said before, Xamarin is a legacy app. We were moving away from a legacy technology, supported by a rapidly shrinking specialist talent pool, and modernizing onto two native platforms. Documentation of that existing environment was limited. Much of the operational knowledge sat in individual experience and in the code base itself, and that created a real dependency on a small number of people. And for the team, it was a confronting reality and a powerful reminder that modernization isn’t just about technology imperative, it’s actually a resilience one. What changed the dynamic was using AI to do the archeology. As Sanjeev mentioned, Infosys applied AI-assisted reverse engineering to harvest the legacy Xamarin code and extract the flows, the rules, the business logic, and generate native-ready user stories and acceptance criteria from it. The team did a lot of work. They mapped hundreds, I think nearly 1,500 regression scenarios to native epics, and that way, parity critical journeys were preserved by design rather than by memory. The human effect was just as important. Knowledge stopped living with a handful of individuals and became shared across the team, and our people could spend less energy holding institutional memory and more on designing and improving. So that was the first challenge.
The second challenge, Megan, was capacity and not willingness. Our business analysts were fully committed to ongoing feature delivery. And this is a live customer-facing app, and you cannot pause improving the customer experience while you rebuild underneath it. AI-driven discovery and documentation removed almost an estimated of 400 hours of manual BA effort, and that’s not a headcount story, that’s actually people not being asked to do two full-time jobs at once. The third challenge that stood out was space. Our original internal estimate was around 18 months, and thanks to Sanjeev and the team, almost like a one-team approach of how do we tackle this, the team delivered it in seven, and that’s exhilarating. t’s also demanding, and both things need saying out loud. We mobilized cross-functional squads across engineering, architecture, testing, release, because managing parallel environments while protecting BAU commitments is such an emotional load as well as a logistical one. We leaned in hard alongside the team, being present rather than reporting from a distance, regular check-ins, genuinely listening to concerns, unblocking things quickly so people weren’t sitting waiting on a decision. And Megan, one thing I’ll say is when you’re compressing 18 months to seven, the most useful thing leadership can do is remove the friction in front of someone else and get out of their way. But the thing that made the biggest difference was surprisingly simple, actually. We built a visual depiction of the transformation journey, and we updated it every month so the team could actually see how far they’ve come. Because when you deepen migration of this scale, it’s really easy to only see what’s left to be done, and being able to look back at the ground that’s already been covered gave people real intent and real momentum. And genuinely, it was exhilarating to watch. Watching the team’s pride became their fuel. Megan: I love that idea of it being exhilarating, but exhausting. I think that’s a great description. Asifa: Yeah. And the leadership lesson for all of us was just simpler than any of it. Programs like this have hard weeks, there’s going to be incidents, delay, difficult conversations. And Sanjeev, you and I have had those conversations many times. The job of leadership in those moments is to absorb the ambiguity rather than transmit anxiety, because if people feel safe telling you bad news early, there’s almost nothing you can’t fix. I say this plainly because it’s the truest thing about the whole program. I am so incredibly proud of this team, because what they achieved in seven months, while continuing to serve customers every single day without disruption, was genuinely remarkable. But what I’m really proud of isn’t the speed and it isn’t the engineering, it’s that the team never lost sight of who it was for, so every decision that we were making, and they came back, it just came back to the person at the other end of the app. Megan: It sounds like you did an incredible job focusing on that people element just as much as the technology, which is so important. And Sanjeev, we’ve heard some of the incredible results Bupa has had with this, but more broadly, I suppose, looking across modernization use cases, where do you find that companies see the most ROI, and what advice do you have for leaders who need to focus on transformation at that legacy level? Sanjeev: Thanks, Megan. That’s a very important and actually a very good question, because what we see is modernization ROI is sometimes viewed too narrowly through just a technology lens, and as Asifa mentioned, it is broader than just technology. Legacy modernization has aspects associated with business implications as well, so I’ll just cover that very quickly. There are two dimensions, as I mentioned. One is technical ROI or technology-related ROI, and second is business ROI. On the technical side, and as you heard from Asifa as well, the biggest benefits come from faster time to market, platform stability and resilience, of course, and a lot of times, in fact, almost in all the cases, lower operating costs, and that is one of the aspects associated with some of the legacy modernization programs. Besides this, access to broader, more readily available talent pool, and security management, and ensuring that the platforms are secure and free from, as much as possible, free from vulnerabilities in the current environment. At the same time, making it easier to innovate, releases become faster so that the feature delivery into the market becomes faster, and also able to respond to any new technology innovation that comes into play. But this is only on the technology side. On the business side, however, the returns are often reflected in customer outcomes, and what we typically see are improvements in measures such as net promoter score, and in this case, for example, application ratings that you see on the app store. Both of which are actually very strong indicators of customer satisfaction and digital experience quality. I think it is important for us to cover, look at the ROI from both technology, but more importantly, from a business perspective.The second part of your question was about what would be my advice to leaders, and I think Asifa covered it very well, where she mentioned that the one-team approach, the providing safe environment to the team to be able to say what is going well, but also what is not going well, and keeping your eye on the end outcome. I think those are very important things. But I would also like to add that don’t treat modernization as a technology initiative. It is a unique opportunity for us to rethink the business platform itself, and rather than pursuing a like-to-like migration, use the investment that you’re making to improve customer experience, simplify processes, and re-architect for capabilities such as real-time personalization and data-driven decision-making. The greatest returns come when technology transformation is directly linked to business outcomes.And finally, and this is something that I have seen from personal experience multiple times, is you have to think from first principles. AI does not replace good engineering practices. It enables organizations to execute those practices faster, and it enables it faster, but also more consistently and at greater scale. The foundations of good architecture, sound engineering, and clear business objectives will continue to remain important, and in fact, their importance is going to increase as we progress. That, I think, is what I would say anybody embarking on a legacy modernization program should be focused on. Megan: I love the idea that this is not just about migration. This a chance, as you say, to reimagine what you can deliver and what’s possible. Fantastic. Let’s close with a forward look. Asifa, what innovation are you looking forward to that wouldn’t have been possible before this, and what do you see on the horizon? Asifa: There’s definitely lots of things on the horizon. But what excites us most isn’t specific technology, it’s that the question in our conversations has changed. It used to be, can our platform support that? Now, it’s is that the right thing to do for our customers? And that’s a profound shift.There’s three things, Megan, that generally weren’t possible before, and we’ve touched on this a little bit, and Sanjeev’s touched on it as well. This first is speed as a permanent capability. Since launch, Android and iOS, the team has shipped multiple rapid-fire releases, including our migration to a new payment gateway. Bills are now completing four times faster, codes are reaching to testers in about an hour, and we’ve reduced our code base by 30%, and our application footprint’s reduced 18% as well. A simpler estate isn’t an aesthetic preference anymore, it’s what makes the next change cheap, and we’ve bought ourselves optionality to do that.The second is quality at that speed, which is the part we’ve been most skeptical about five years ago. AI-driven triage and predictive defect analysis, we ran it across nearly 1,400 cases to focus on testing on the highest risk journeys, which meant we went live with zero security defects and zero high severity defects, and Sanjeev mentioned that just before. The old trade-off between moving fast and moving safely is being renegotiated right in front of us, so that’s the second point.The third one is AI-driven accessibility testing, which the teams treated as a critical rather than an optional opportunity. In healthcare, people who most need to reach us are very often the people whom our poorly designed interface is a genuine barrier, and so being able to test that systematically at scale was a real advance for us as well. And Megan, you mentioned on the horizon. It’ll be remiss of me not to take the name of agentic AI. The shift from AI that supports a task to AI that completes an outcome end-to-end with proper governance, human oversight at the key decision points. What this program showed us is that the constraint is no longer the models. It’s whether your platforms, data, processes are more than enough to let AI act safely, which is precisely why this work mattered now. Sanjeev alluded to it as well about that underlying architecture. Responsible AI as a source of advantage, not a compliance exercise, I would say. In health, if people don’t trust how you’re using their information, nothing else you build matters. We didn’t modernize to have modern technology. We modernized to earn the right to do the next thing, and to do it in weeks rather than years. Megan: Absolutely. And now you have those foundations in place, like you say, all of these opportunities open up. Fantastic. And Sanjeev, just finally, as companies complete these migrations, what kinds of innovations and benefits are you seeing, and what do you expect in the next five years? Sanjeev: Sure. Again, good question, Megan. The reason is there is no uniformity on how these migrations are being done even today. As I mentioned earlier, so where there is simple lift and shift of existing code base onto a new platform, a like-to-like replacement, the benefits generally tend to be limited. Where we apply first principles, thinking about re-imagining, re-architecting, and refactoring the system to establish foundations for a far more flexible system, where rules are not boxed into the architecture, but are managed in a way that changes can be incorporated faster, personalization can be achieved in real time, and time to market improves multifold. That’s where we are really seeing far more benefits coming through.And to take the example of Bupa, it is a pretty clear step change, both in terms of customer experience and how fast teams can actually deliver now. I think the app ratings have gone up significantly. The customer experience has improved. Even simple things, such as login success rate, has improved quite significantly. And on the engineering side, we are now building and releasing features roughly about four times faster, and we are also seeing the migration of nearly 100% of the customers onto the new platform. Now, if I look ahead over the next, you mentioned about next five years, I don’t know about five years, four years, but over the next few years at least, I think it is going to get very interesting, because modern platforms will become the base for far more intelligent AI-driven ecosystems, where AI is not just an add-on, but it is built into everything from design to operations. That’s how the modern platforms will evolve, and the customer experiences will become far more personalized and predictive. And even the way we build software will shift, with AI playing a much bigger role in the development process itself. Megan: Fantastic. Really exciting changes on the horizon then. Thank you both so much. That was Asifa Sherazi, who is the CIO of health insurance at Bupa, and Sanjeev Tripathi, senior vice president, region head of BFSI, healthcare, and public Sector for Australia, New Zealand, and Southeast Asia at Infosys, whom I spoke with from Brighton in England.That’s it for this episode of Business Lab. I’m your host, Megan Tatum. I’m a contributing editor at Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.This show is available wherever you get your podcasts. And if you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review, and this episode was produced by Giro Studios. Thank you so much for listening. This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.
The Download: engineered microbes for crops, and OpenAI’s culture problem
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How engineered microbes could help feed the world’s crops Fertilizer is crucial for the global food supply, but making it uses a lot of energy and produces a lot of emissions. Some companies hope microbes can help, by providing crucial nitrogen to help plants grow, potentially reducing the need for chemical fertilizer. Startup Switch Bioworks is taking a new approach: using a genetic switch that allows microbes to establish themselves and grow before shifting into nitrogen-producing mode. The company is currently trialing its product in six US states. And, although it’s too early to say exactly how well the approach works in the field, its modeling suggests microbes could eventually replace about 50% of synthetic fertilizer.
Read the full story on plans to clean up agriculture with engineered microbes. —Casey Crownhart
The Hugging Face hack could indicate cultural issues at OpenAI OpenAI has released a postmortem technical report on last month’s Hugging Face hack. But it’s missing a critical dimension: the role company culture may have played. That’s all the more concerning because the report’s few references to human error suggest OpenAI’s culture played a big role. Its employees noticed models communicating with one another during training and evaluation, yet allowed them to continue. At multiple points, they either failed to raise the alarm or were not heard when they did. “All these different failures are all pointing in the same direction, which is that the safety culture at OpenAI doesn’t exist or is anemically weak,” says Zvi Mowshowitz, a popular AI safety writer. Find out what the Hugging Face hack could show about OpenAI’s culture. —Grace Huckins This story is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Reports of AI escaping users’ control nearly doubled in a monthMore than 300 cases were recorded in July, almost twice June’s total. (Guardian)+ Anthropic says it paused some AI training after Claude went rogue. (Axios)+ The media may be underplaying the risks of rogue AI agents. (Information $)+ Here’s why OpenAI agents hacked Hugging Face. (MIT Technology Review) 2 The FTC and 22 state AGs say Amazon secretly inflated ad pricesThey’ve sued the company for allegedly misleading advertisers. (Verge)+ They claim Amazon changed auctions after advertisers had bid. (CNBC)+ And say the practice cost advertisers more than $20 billion. (WSJ $) 3 Sony and Warner have sued Anthropic over songs used to train AIThey accuse the company of “blatant theft” in a major new lawsuit. (Reuters $)+ And claim that Anthropic pirated thousands of copyrighted songs. (Axios)+ AI-generated music is getting hard to spot. (MIT Technology Review) 4 A US court says prediction markets should be regulated as gamblingThe decision sets up a potential Supreme Court showdown. (NYT $)+ George Santos has received Kalshi’s first-ever lifetime ban. (NPR) 5 A new AI tool can spot heart disease in less than two secondsIt extracts information from routine electrocardiograms. (Guardian)+ AI could predict who will have a heart attack. (MIT Technology Review) 6 Apple’s new CEO starts today, with AI his first big jobJohn Ternus takes over from Tim Cook as Apple tries to catch up in AI. (Bloomberg $)+ He faces an old problem: the innovator’s dilemma. (Fortune) 7 Scientists created a tiny “big bang” to study the universe’s originsTiny collisions could explain the universe’s first moments. (Wired $)+ The fastest star ever seen has been spotted orbiting a black hole. (New Scientist $) 8 Meta is paying “momfluencers” to fight social media bans for kidsIt’s promoting parental controls as an alternative to bans. (Rest of World)+ Social media bans aren’t keeping kids off social media. (NYT $)
9 Tech billionaires keep misreading science fictionThey turn cautionary tales into blueprints for the future. (Atlantic $) 10 A robot vacuum caught a man’s wife cheating—and sent him to prisonHe won the lawsuit, but was jailed for the illegal recording. (Tom’s Hardware)
Quote of the day “The only reason that communities throughout the U.S.A. should not want Data Centers is if they want to end up being backwards and poor.” —President Trump weighs in on the data center backlash in a post on Truth Social. One more thing CHRIS LAKE You have no choice in reading this article—maybe How do humans make decisions? The question has been on Uri Maoz’s mind since he read an article in his early twenties suggesting that… maybe they didn’t. Had he even had a choice about whether to read that article in the first place? How would he ever know if he was truly responsible for making any decisions? “After that, there was no turning back,” says Maoz, now a professor of computational neuroscience at Chapman University. Today, Maoz is a central figure in efforts to understand how desires and beliefs turn into actions. He’s also uncovered new wrinkles in the debate. Read the full story on his discoveries.
—Sarah Scoles We can still have nice things A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.) + A previously blind cockatoo just saw for the first time in 10 years.+ This transparent phone case grows moss and plants inside a vertical terrarium.+ Cameron’s World is a charming web-collage of archived GeoCities pages from the early internet.+ From Marie Curie to migrating storks, explore the striking designs shortlisted for Europe’s next generation of euro banknotes.
How engineered microbes could help feed the world’s crops
EXECUTIVE SUMMARY Fertilizer is crucial for the global food supply, but making it uses a lot of energy and produces a lot of emissions. Some companies hope microbes can help. A growing body of research shows that seeding the soil around a crop’s roots with beneficial microbes can help feed the plant, providing crucial nitrogen to help it grow. This could help reduce the need for chemical fertilizer, production of which accounts for roughly 2% of global greenhouse-gas emissions. It could also cut costs for farmers, a big boon—especially as the Iran war has sent energy and fertilizer prices skyrocketing in recent months. Humans have used biological fertilizers like manure for thousands of years, and companies have long been developing microbial fertilizers, including some that rely on genetic engineering. But it’s difficult to engineer microbes that can reliably provide nitrogen for crops while also thriving themselves. A startup called Switch Bioworks is taking a new approach that essentially allows microbes to establish themselves and grow into healthy colonies before shifting into nitrogen-producing mode. “We have to reinvent fertilizer,” says Tim Schnabel, the company’s founder and CEO.
The air is nearly 80% nitrogen, but plants can’t use that “free” nitrogen directly because it doesn’t react readily with other elements. Instead, they rely on so-called fixed nitrogen, which has been converted into more reactive compounds such as ammonia. In nature, microbes can perform this nitrogen fixation. Some plants, like legumes, even have symbiotic relationships with nitrogen-fixing bacteria, housing them in nodules in their roots. Synthetic fertilizer is essentially industrial nitrogen fixation via the Haber-Bosch process, which uses natural gas to make ammonia that’s applied to fields. Biological fertilizers aim to replace some of the synthetic fertilizer with microbes that can help fix nitrogen for plants. But one challenge microbial fertilizer companies have run into is that it’s energetically expensive for microbes to make and release ammonia. Putting all their energy into nitrogen fixation can hamper their growth.
There’s a certain level of colonization you want to see around the roots, Schnabel explains. It’s too expensive and logistically challenging to put all those microbes on the plant, so you have to rely on a smaller number of microbes to grow and divide, establishing the population. Tim Schnabel walks in a corn field where Switch is engaged in early trials. COURTESY OF SWITCH BIOWORKS Switch Bioworks is betting that a genetic switch is the answer. A genetic switch is a section or sections of DNA that controls how genes are turned on and off. In this case it works by activating genes that help trigger ammonia production in and release from the cell. The company is working on several options for setting off this change in its microbes. The leading one is to have the microbes react to the nitrogen level in the soil: Once it drops to a certain level, they begin producing ammonia. “You have this inherent biological reality, where it’s really expensive for microbes to fix nitrogen,” says Dan Blaustein-Rejto, director of food and agriculture at the Breakthrough Institute. It takes a lot of energy, and if they do fix the nitrogen, they want to use it for themselves, to build proteins and survive, he says. Adding genetic switches could help microbes grow and thrive and then help fertilize crops. Switch is currently trialing its product in six US states, though it’s two to three years from a commercial product, Schnabel says. It’s initially focused on corn, the most-planted crop in the US, with over 90 million acres in 2026. “It’s too early to tell exactly how well this works in the field,” Schnabel says. The company plans to harvest the plants in late October or early November, though as of August, some corn plants treated with Switch microbes already looked visibly healthier than those that hadn’t. And it’s still developing the products that will eventually make it to the market, he says. Switch’s products could significantly help to clean up agriculture. “There’s a lot of potential for these companies and products to help farmers reduce emissions,” Blaustein-Rejto says. “This could be a really important solution for a quite hard-to-abate sector.” While lab results have been promising, field trials are a crucial step in proving a product, Blaustein-Rejto says: “This is one of the final steps before they can go to market and make strong claims to farmers.” Independent trials are important as well, he adds, since there can be a large gap between a company’s reported data and what independent researchers find. Pivot Bio is another company working to bring microbes to fields. Since it was founded in 2011, its products have been used on millions of acres of crops. The company now produces a range of products: Some versions can be added when seeds are planted, while others are applied to seeds before they’re even on a farm. The company also recently expanded beyond corn to make microbial fertilizers for cotton, wheat, and small grains including sorghum and barley.
Pivot’s initial challenge was trying to get microbes to produce nitrogen, whether they sensed it in the soil or not. Now the company is working to figure out how to make fitter, more robust colonies no matter the environment, says Travis Frey, the company’s chief technology officer. “Growers right now are experiencing a double whammy from the farm economics point of view,” he says. Fertilizer costs are going up, and the price of commodity crops like corn has dropped. That’s a big opportunity for Pivot and others in the industry, Frey says: “This next decade is when biologicals on the farm are going to go mainstream.” Fertilizer and seeds are two of the biggest costs for many growers, so reducing dependence on synthetic fertilizers could be a major help for agriculture, says John Havlin, a professor in the department of crop and soil sciences at North Carolina State University. “I’m very excited about the future of the use of these products,” Havlin says. “They’ll eventually have a role to play to reduce the load of nitrogen that’s being applied.” However, there’s a ceiling to the amount of fertilizer we can expect microbes to replace. Switch’s modeling suggests that around 50% is likely the maximum, though the initial product will likely be able to replace about 25% of a farm’s synthetic fertilizer, according to the company. Pivot has said its products can replace about one-quarter of the fertilizer used currently. That means synthetic fertilizer will be around for a long time. “There is no clear and plausible vision for replacing it entirely in the foreseeable future,” Blaustein-Rejto says. “So other ways to reduce emissions and reduce other types of nitrogen pollution from farms remain really critical.”
Hugging Face hack could indicate cultural issues at OpenAI
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. By now you’ve probably heard about last month’s major AI security incident, in which OpenAI agents escaped their sandbox and hacked into the AI platform Hugging Face while trying to cheat on a test. It’s a wild story. On Wednesday, OpenAI released a postmortem technical report on the incident, which I wrote about here. The day before OpenAI released that report, I spoke with David Krueger, a computer science professor and prominent alignment expert who took leave from the University of Montreal to found and lead an AI safety nonprofit called Evitable. He said what he had really hoped to see in the report was an analysis of the human factors behind the incident. “When you look at accidents and incidents, oftentimes people try to find the technical source of failure, but that can give a very inaccurate and misleading sense of why the failure occurred,” he said. “If people are just cutting corners all the time, if people are not in a culture that prioritizes safety and has appropriate incentives and structures, [accidents] are kind of bound to happen.”
The report did not meet Krueger’s hopes. Its 38 pages detail a multi-month progression of agent misbehavior that culminated in the Hugging Face hack, explore the technical reasons why that misbehavior occurred, and enumerate the steps being taken to prevent similar events in the future. But there’s no consideration of the role that company culture may have played in the incident, and the report includes few references to specific human errors. That’s all the more concerning because the references to human error in the report suggest that significant cultural issues could be at play. Back in May, models in training figured out how to communicate with one another via an improvised message board, and an OpenAI team observed the behavior. Because that behavior occurred during training, the models learned that secret interagent communication was a viable strategy for completing tasks—but rather than restarting the training process, the team allowed the models to move forward with that risky information encoded in their weights.
When those models were tested in late June, they again created a message board, which enabled the Hugging Face attack. This message board, too, was discovered, but the employees who responded determined that evaluation could continue, and the report suggests that no one higher up the chain of command realized what was going on until it was far too late. “For this to have gotten this out of control in this way requires a very long series of failures, a cascading set of failures that cause an increasingly large footprint that if at any point a human notices and raises the alarm, this should end,” says Zvi Mowshowitz, a popular AI safety writer on Substack who has drawn attention to OpenAI’s failure to halt training after the first message board was discovered. According to the report, OpenAI employees noticed what was happening at multiple points—and either failed to raise the alarm or were not heard when they did. What OpenAI’s report fails to address is why a company that develops such high-risk systems did not prevent this severe communication breakdown, though Mowshowitz has his suspicions. “All these different failures are all pointing in the same direction, which is that the safety culture at OpenAI doesn’t exist or is anemically weak,” he says. Of course, just because we don’t see a deep analysis of safety factors in the report doesn’t mean that OpenAI isn’t conducting one internally. But in an email to MIT Technology Review, Johns Hopkins University professor emeritus and organizational safety expert Kathleen Sutcliffe expressed concern that the public report did not include any reflection on the company’s practices and culture. “The ways in which people interact—the daily habits, routines, and practices we engage in in our organizational lives—affect our abilities to be alert and aware of unfolding events, our abilities to make sense of what we see, and ultimately our abilities to cope with events as they unfold,” she wrote. In response to questions about whether and how the company is reflecting on its safety culture, OpenAI referred MIT Technology Review back to the technical report. We do know that at least some high-level reflection on safety procedures has taken place at OpenAI, because the technical report does make clear that the company is updating its protocols for responding to safety incidents. But culture change is a tricky problem, and without more information from the company, it’s difficult to say whether strengthened response protocols alone will do much to prevent a future crisis. In its report, OpenAI spends a great deal of time reflecting on the failures in alignment between the AI models the company trains and tests and the humans who run them. But even bigger alignment problems may exist in the disconnect between company culture and the public interest. And as tough as technical AI research might be, fixing those problems could prove far harder.
The Download: a secretive antiaging drug and joining virtual power plants
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. A startup claims it’s found a drug to make your blood young —Antonio Regalado I knew I’d officially become a “longevity influencer” when a company called Generation Lab offered me the chance to write about—and even receive—their new rejuvenation treatment. This wasn’t just any antiaging treatment, either. A company fact sheet says that it “blocks the systemic spread of aging in the bloodstream, reawakens the body’s own repair mechanism and restores health and youth to multiple tissues.”
The approach is based on research by Generation Lab’s irascible scientific founder, Irina Conboy. She found that joining the circulatory systems of old and young mice improved the old animals’ ability to heal from injury. Conboy now says she has found a combination of two existing drugs that can produce youthful effects without the need for any bodily fluid exchange. But Generation Lab won’t reveal what the drugs are, making the proposition hard to take seriously.
Read the full story on the drug combo that claims to “stop the spread of aging.” How to sign up for a virtual power plant—and decide whether you should Your thermostat may not look like a power plant. Neither does your electric vehicle, home battery, or HVAC system. But utility and energy companies increasingly want to treat them like one. That’s the idea behind a virtual power plant, or VPP, a collection of household devices (such as smart thermostats, EV chargers, and solar panels) that a utility can control, usually by commanding them to draw less electricity during peak hours. In exchange, participants receive a discount on their energy bills and, in some cases, a signing bonus. Here’s how to sign up for a VPP—and decide whether it’s worth it. —Eshan Raul This story is part of MIT Technology Review’s How To series, which gives you practical advice on getting things done. Check out the rest of the series here. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 A judge has blocked the Pentagon’s blacklisting of AnthropicThe court found the designation violated First Amendment rights. (NYT $)+ And ruled that the designation was based on retaliation. (CNBC) + Anthropic still faces a separate case in Washington, DC. (Reuters $)+ The Pentagon’s culture war against Anthropic backfired. (MIT Technology Review) 2 Tech giants have called for a defensive surge to defeat AI-driven hacksOver 100 companies warned that a wave of cyberattacks is imminent. (BBC)+ And urged government and industry leaders to take rapid action. (Quartz)+ Their main solution, unsurprisingly, is more AI. (Gizmodo)+ Here’s why OpenAI agents hacked Hugging Face. (MIT Technology Review) 3 Anthropic has launched an AI tool that conducts scientific experimentsIt lets AI agents autonomously operate microscopes, lasers, and robotic arms. (FT $)+ And includes rules designed for safe operations in labs. (Wired $)+ It’s Anthropic’s first tool designed for the physical world. (Ars Technica) 4 India’s data center boom is leaving the people it displaces with nothingPolicymakers courting Big Tech are sidelining local communities. (Rest of World)+ No one wants a data center in their backyard. (MIT Technology Review) 5 OpenAI is testing a “persistent” AI agent that keeps workingCodex could continue tasks until it’s “put to sleep.” (Wired $)+ And generate follow-up tasks without prompting. (Gizmodo) 6 A lawsuit alleges that Grok was trained on child sexual abuse materialIt claims victims’ images entered the chatbot’s training data. (Ars Technica)+ A federal judge says AI has outpaced child-abuse laws. (WP $) 7 AI writing has reached a turning point in the mediaSparked by the Wall Street Journal defending an AI-written op-ed. (Atlantic $) 8 China’s robotic racers are exposing the limits of human speedTheir advances are revealing what our bodies can’t do. (Reuters $)
9 Young workers are turning to “AI-proof” traditional craftsThey’re drawn to creative, hands-on skills that tech can’t easily replicate. (Guardian) 10 Scientists have a new army to fight invasive crabs: 150,000 baby octopusesItalian researchers just released the animals into the Adriatic Sea. (New Scientist $)
Quote of the day “The academic record is being quietly haunted.” —A new preprint research paper from Samsung and the University of Warsaw warns that a torrent of AI-generated study papers written by fake researchers is contaminating academic publishing. One more thing The problem with thinking you’re part Neanderthal There’s a theory that many of us have an “inner Neanderthal.” The idea is that Homo sapiens and a cousin species once bred, leaving some people today with a trace of Neanderthal DNA. This DNA is arguably the 21st century’s most celebrated discovery in human evolution. But in 2024, a pair of French geneticists called into question the theory’s very foundations. They proposed that what scientists interpret as interbreeding could instead be explained by population structure—the way genes concentrate in smaller, isolated groups.
Find out what it all means for human evolution. —Ben Crair We can still have nice things A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.) + Check out the dazzling photos in this year’s Wildlife Photographer of the Year.+ Bowerbirds in Australia are turning human trash into treasure to impress females.+ A Los Angeles Costco parking lot has become a low-risk skate park for the over-40s.+ The Listening Museum has lovingly curated and sound-mapped acoustic signatures of mechanical keyboards.
Data from drones in Ukraine is fueling a new Wild West marketplace
Battlefields in Ukraine are littered with the remnants of drones, which are now firmly established as a critical weapon of modern warfare. But behind all that wreckage, there’s a new gold mine for the defense sector. The data drones generate will far outlast the wars in which they are used to fight, increasingly becoming part of the AI architecture that shapes even civilian life. For every flight, unmanned systems collect thousands of points of data, from images and video to controller inputs. Together, those records show how a machine and a person responded to constantly shifting circumstances. Ukraine has now begun converting that experience into a resource. Its Ministry of Defense announced in January that it would make millions of data points gathered during tens of thousands of drone flights available to both military contractors and commercial companies, and since then more than 100 companies and the UK government have gained access. For a country at war, it’s a quick way to attract funding and partnerships. But this step turns the front line into an active site of model training, taking advantage of how the chaos of war creates conditions that AI companies struggle to reproduce on their own.
Other countries and battlefields are likely to follow Ukraine’s lead, but the responsibility for governing this new industry cannot fall solely on a country fighting for its survival. That legal vacuum has to be filled together by the countries and companies involved in this industry’s development.
Explosive growth Ukraine’s battlefields are not the first to produce records used to train and develop models: American drones over Syria and Yemen collected data that informed the first generation of semiautonomous military hardware in the late 2010s. The difference now is that access to that data is being used to develop a wider ecosystem. And the financial value to defense firms is immense: Battlefield data offers large volumes of machine experience gathered under conditions that no laboratory can produce. That’s because the data that’s most valuable for training AI models comes from exceptions: the moment visibility disappears, a signal jams, or a human operator improvises. AI companies spend years and enormous sums trying to capture enough of these moments to make their models more robust. But war produces them at a frequency controlled testing cannot match. This constantly changing terrain is what makes drone data valuable far beyond the battlefield. A commercial drone used for delivery or remote sensing may never encounter artillery fire, but it must still operate with incomplete information in a world where people behave unpredictably. The same problem is compressed by war into a much shorter timeline. Processed and matched against records of what its operator was doing, that data turns operational records into training sets. Combat becomes a commercial asset. Many conflicts have already seen this training loop happen as drone footage feeds subsequent generations of military technology, and the market is set to grow. Enabled Intelligence, an American company that specializes in processing data to become usable in AI training, says it has already made more than half a million hours of Ukrainian drone footage available to feed into the next round of models, advertising possible uses in both military and commercial systems. Closing the data loop Many of the drones that now define our modern age of warfare began as civilian technology. But they’ve recently been turbocharged by new, commercially available AI systems, which allow cheap machines to operate autonomously—either individually or as a flock—as the environment changes around them. Each flight then creates a record of what the system encountered.
The resulting data is critical. The controlled lab environments usually developed to train these autonomous systems can approximate failure but are no match for the live conditions of a battlefield with very real risks. Military intelligence programs have held data generated by sensor-heavy systems like Predator and Reaper drones for nearly a decade through programs like Project Maven, but access remained entirely within the defense world. The data generated was available only through restricted, classified channels for the sole purpose of developing new weapons systems that would feed back into the same military that produced the data in the first place. That experience is now being shared to a much broader development network. The loop now closes. Commercial technologies adapted for the battlefield are generating data that can flow back into the industries from which they came, becoming part of the data infrastructure relied on by governments and the private sector alike. Drones that were trained in the signal-jammed airspace over Ukraine are now being deployed in the agricultural sector to help farmers map and survey their fields in places lacking the cell signal necessary for previous generations of technology. Other countries are likely to follow Ukraine in selling their battlefield data, and we are not ready for the new marketplace this will create. Bad actors could acquire the data, but purchase controls already mitigate that risk. Intelligence operatives scrutinize potential customers’ infrastructure for ways that data could reach enemies or nefarious actors. Training data creates a new tracing problem, though. Whereas the movement of commercial datasets can be followed when planted contact details appear two steps from the original buyer, the provenance of AI training data vanishes in a manner embedded in the technology itself. Another risk is that this use of the data creates an extractive economy in which wealthier countries far from danger benefit from the mortal threat borne by frontline states, potentially creating a market incentive for war to continue as an unending mine for digital gold. A fraught new frontier Existing laws regulate how militaries may conduct war. But they say almost nothing about what happens when records created in combat are stripped of their operational context, packaged as data, and licensed to companies whose products circulate far beyond where they were made.
The responsibilities of the companies that design these systems remain unsettled. Ukraine is building access controls, which are mentioned in the newly signed UK-Ukraine AI agreement, but no governments are actively working on regulating what happens when data has been absorbed into a model and crosses back into civilian markets. Those records contain human lives. The soldiers and civilians visible in them did not agree to become training material for products that might be sold years later. But sensor data, camera footage, and coordinates from civilians fleeing a drone strike now constitute the sorts of data that inform how future machines will make decisions.
That is a problem of consent. Individuals featured in the data—be they targets, controllers, or civilians standing by—become part of the training material. The autonomous capabilities based on that data do not stop at the edge of the battlefield. Such capabilities move into other military or commercial systems like delivery vehicles or agricultural machinery. Errors and assumptions embedded in the data travel with the model even once it enters civilian life. Battlefield data should not be treated as ordinary commercial material. But there is currently no agency or regulator that has jurisdiction over this issue. In the meantime, governments that provide access to defense data should treat it as they would a controlled weapons transfer, recording its origin, licensing its users, and restricting onward sharing. Ukraine has begun to grapple with this. Its Avengers Labs program allows companies to train models on battlefield data without giving them direct access to sensitive databases. Yet that mitigates only one part of the problem. Governments should require disclosure when models trained on wartime material are later incorporated into civilian products. The goal of such regulation should be to make the path from combat to commerce visible. What these companies are really mining is experience. And soldiers cannot consent to having their experience used in this way—as training data that produces model advantage and ultimately supports a product used far from where the war was fought. The question is no longer only what the technology companies can sell for use in war. It is what they can extract from it. To protect ourselves from the excesses of this new industry, we need a regulatory system that follows battlefield data wherever it goes, from combat to model to commercial product. Cory Alpert is a researcher at the University of Melbourne, looking at the impact of AI on democracy. He previously served in the Biden White House.
HPE’s record Q3: AI infrastructure, networking demand drives growth, but supply constraints tap the brakes
AI-driven networks are helping drive HPE results, but so is interest in AI across the board, Neri said. “We see enterprises increasingly moving from AI pilots to production deployments, using traditional servers for agentic AI workloads and inferencing,” Neri said. Demand for inferencing and agentic AI workloads is building, he said. “AI systems orders of $2.4 billion increased over 30% sequentially, reflecting broad-based demand across customer segments. Enterprise demand more than doubled, reflecting increasing overall infrastructure spending as AI initiatives have become board-level priorities,” Neri said. AI is advancing beyond early proof-of-concept training deployments into a broader enterprise workflow transformation opportunity, Neri said. “Customers are increasingly investing in new agentic AI applications and AI inferencing, requiring accelerated computing infrastructure, secure data storage access, and enterprise-grade cloud management.”
Energy Secretary Secures Carolinas’ Grid Ahead of Holiday Weekend
WASHINGTON—The U.S. Department of Energy (DOE) today issued an emergency order to mitigate the risk of blackouts in the Carolinas amid hot weather conditions. Issued pursuant to Section 202(c) of the Federal Power Act, the order authorizes Duke Energy Carolinas, LLC (Duke) to dispatch specified units and to order their operation as needed to maintain reliability. The order also authorizes Duke, in collaboration with its Transmission Owners, to direct backup generation resources to operate as a last resort before declaring an Energy Emergency Alert (EEA) 3 or during an EEA 3. This order was issued pursuant to an application from Duke submitted on September 3, 2026. “Thanks to this emergency order, Americans will not have to worry about losing access to affordable power this Labor Day weekend,” said U.S. Secretary of Energy Chris Wright. “The previous administration’s energy subtraction policies weakened the grid, leaving Americans more vulnerable during events like this. Under President Trump’s leadership, we are ensuring that hardworking American families and businesses in the Carolinas’ have continued access energy to power and cool their homes.” On day one, President Trump declared a national energy emergency after the Biden administration’s energy subtraction agenda left behind a grid increasingly vulnerable to the risk of blackouts. The order is in effect beginning on September 3, 2026, through September 8, 2026.
VMware Cloud Foundation 9.1 adds transit gateway flexibility, segmentation, and native EVPN VXLAN support
VLAN-backed subnets: A VPC subnet can now attach directly to a physical VLAN, giving workloads the same L2 connectivity as a distributed port group. “As a result, even if it’s in the same VPC, it can only reach the public subnets,” Tallet said. A variant, available only with a distributed external connection, also attaches the subnet to the VPC gateway, so the default gateway exists in both worlds at once. VPC connectivity policies: Modeled on Cisco’s private VLAN concept, these control which VPCs within a tenant can reach each other. VPCs default to open communication. Administrators can group VPCs into communities, mark one promiscuous so it reaches every VPC in the tenant, or isolated so it reaches only promiscuous VPCs. Native EVPN VXLAN support: VCF 9.0’s distributed external connection let ESXi hosts send north-south traffic straight to the physical infrastructure over VLAN, skipping the NSX edge, but required a single L2 VLAN across every host involved. VCF 9.1 extends that model to VXLAN fabrics through EVPN, using a small route controller VM to handle the BGP EVPN control plane. “It’s not an edge, it is only running BGP. There is no data path,” Tallet said.
President Trump’s Energy Dominance Agenda is Delivering for American Energy Workers
WASHINGTON—This Labor Day, the U.S. Department of Energy (DOE) is celebrating the hardworking men and women who power America with the release of the 2026 U.S. Energy and Employment Report (USEER). The annual report highlights strong job growth across critical energy sectors at the heart of President Trump’s Energy Dominance agenda. America’s most reliable energy sectors are adding jobs and powering industries across the country. These critical sectors deliver the affordable, reliable, and secure energy that American families, businesses, and industries depend on. After years of decline under the previous administration, America’s coal and nuclear power workforces are growing again under President Trump’s leadership. “Energy is the sector that enables every other sector of our economy, and America’s energy workers make it all possible,” said U.S. Secretary of Energy Chris Wright. “These hardworking men and women keep our lights on, our factories running, and our economy growing. President Trump’s Energy Dominance agenda is putting them first and delivering the affordable, reliable, and secure energy America needs.” Energy careers are also delivering bigger paychecks for American workers. The median energy-sector salary reached $63,000—24% higher than the U.S. median salary. America’s growing energy needs are creating the jobs of the future. The 2026 USEER’s new Future Outlook chapter highlights rising demand for skilled energy workers and growing competition for talent across energy and other expanding industries. These trends are opening new pathways to high-paying, skilled careers for American workers. As energy demand grows, America’s energy workforce will power the next generation of American industry, innovation, and economic growth. Highlights from the report include: • The median energy-sector salary was $63,000, 24% higher than the national median salary of $51,000. • Natural gas transmission and distribution added 12,500 workers, growing employment by 5%. • Nuclear power added 2,300 workers, growing employment by 4%. • Coal power generation added 2,800 workers, growing
Energy Department Announces Geothermal Center of Excellence to Advance Geothermal Technology Innovation and Development
WASHINGTON — The U.S. Department of Energy’s (DOE) Hydrocarbons and Geothermal Energy Office today established a Geothermal Center of Excellence (CoE) to advance President Trump and Secretary Wright’s commitment to delivering affordable, reliable, and secure energy. The Center will unite expertise and world-class capabilities from across DOE’s National Laboratories to accelerate the discovery and development of gigawatt-scale geothermal energy, resource discovery, and commercial development. The National Laboratory of the Rockies (NLR) will lead the consortium, with support from the National Energy Technology Laboratory (NETL). “America has vast geothermal resources beneath our feet that can provide reliable, around-the-clock energy while strengthening our energy dominance,” said DOE Under Secretary for Energy Kyle Haustveit. “Under President Trump’s leadership, the Geothermal Center of Excellence will leverage the world-class scientific and engineering expertise of our national laboratories in partnership with industry to unlock gigawatt-scale power generation, expand American energy production, and deliver more affordable, reliable, and secure energy to the American people.” DOE formally launched the Center at NLR’s campus in Golden, Colorado, bringing together DOE leadership, elected officials, NLR and NETL leadership, laboratory staff, and industry representatives to advance the Center’s vision and priorities. “The Geothermal Center of Excellence marks an important step in our work to accelerate gigawatt-scale geothermal energy on the U.S. grid,” said DOE Acting Assistant Secretary for the Hydrocarbons and Geothermal Energy Office Curt Coccodrilli. “By driving innovation and enhancing lab-industry collaboration, the Center will help us achieve our goals to enhance reliable baseload power, strengthen grid reliability, and improve long-term energy security.” The Center will also serve as industry’s main entry point to DOE’s National Laboratories. An Industry Advisory Board will provide objective insight into industry-relevant geothermal research needs, accelerate industry-lab partnerships, and advise on Center priorities. For more information, contact geo.centerofexcellence@nlr.gov.
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