Insights – Page 2

It’s not your model — AI has a data movement problem

But that can be the wrong lever. When you examine the full end-to-end transaction, the model usually isn’t the problem. Take the extraordinary scale of AI traffic as it moves from a branch office to a centralized security stack, then to a cloud region, then to a data-hosted backend somewhere — and then add inconsistent local internet performance, packet loss and congestion — and the AI experience deteriorates. The challenge also varies widely depending on where in the world you’re standing. At an AI symposium in Africa recently, the attendees might have been expected to discuss which model to use, but the conversation instead was “how do we actually deploy this.” In Latin America, the discussion I hear most often from customers isn’t about raw bandwidth, but latency, specifically, because of how the region’s infrastructure routes traffic back to major cloud regions. Silicon Valley and São Paulo are not the same infrastructure conversation, and treating them as though they were is exactly how well-intentioned rollouts stall. This is not because the model got worse, but because everything underneath it was never built to carry this kind of load across this kind of distance. When an AI application performs poorly, our instinct is to look up the stack, but the answer is often underneath it.

The ecologist revealing the invisible networks that revive rainforests

In a research station in central Panama, you can walk through a century of forest time, watching scrubby plots of adolescent trees give way to cathedral-like canopies of mature growth. Forest ecologist Daisy Dent, a senior scientist at ETH Zurich, oversees this sequence of plots across the Barro Colorado Nature Monument, some of which date as far back as the 19th century. Not one of the trees she surveys was planted by human hands. She maps, measures, and tracks each trunk that emerges, revealing how waves of tree life rise from bare earth. Tropical forests like these are our planet’s biggest terrestrial carbon sink, accounting for around 40% of carbon stored on land. As part of an international network of researchers, Dent is deciphering the networks of animal and plant interactions that enable some forests to recover after destruction. The goal is a low-cost, high-impact alternative to typical carbon-crediting schemes, which plant huge areas with a single species of tree. For over a decade, her research has built a body of evidence that such large “monocrop” programs could be expensive mistakes—and that more resilient forests can rebound naturally in just a few decades, re-creating ecosystems that suck up vast quantities of carbon dioxide. Few places are better to demonstrate this than Barro Colorado, a reserve covering the forested peninsulas and islands that formed when construction of the Panama Canal flooded the area in 1914. Under the management of the Smithsonian Tropical Research Institute, generations of ecologists have amassed more than a century of records. That means Dent can trace why some plots rebound after clear-cutting for farming and forestry while others falter. Fruit-eating animals—including spider monkeys, bats, and parrots—are responsible for planting more than 70% of trees in tropical forests, and Dent’s dataset can tell which built each patch of trees by depositing seeds in their nutrient-rich droppings. Many of these creatures worked together to plant a diverse 70-year-old patch whose canopy is approaching the stunning structure and richness of an ancient jungle. In contrast, a patch of similar age populated by narrow trunks of a single long-lived species, Gustavia superba, hints that tropical rodents called agoutis are more or less the sole animal architects behind this more homogeneous forest.
The data she collects helps write a playbook for when to let ecosystems regenerate and when to give them a helping hand. One 2024 estimate in Nature found that some 215 million hectares of forest worldwide (an area larger than Mexico) could regenerate naturally. “Tropical forests are underpinned by this massive network of interactions,” Dent says—interactions between life-forms like the fungi that help trees take up nutrients, as well as pollinators and seed-distributors. Supporting those relationships is key to growing forests that lock up carbon and help sustain local communities and wildlife. Filling in the gaps Just over a decade ago, as the landmark Paris Agreement was being codified in 2015, forest recovery seized global attention as a solution to the climate crisis. Global leaders rallied behind plans to regrow a trillion trees, after the ecologist Thomas Crowther highlighted their potential to capture a decade’s worth of global carbon emissions. But the biggest question has lingered: How, exactly, do you regrow two trees for every star in the Milky Way?
While startups have proposed seed-spraying drones and nutrient-coated seed bombs, Dent has spent two decades studying what happens when we let forests rebuild themselves. As a PhD student around the turn of the millennium, she researched old-growth forests in northern Borneo but felt hopeless watching entire landscapes felled for palm oil plantations. Rather than despair, she turned to restoration. Then, as a postdoc in 2005, she joined Panama’s Prorena, a joint initiative between the Smithsonian Tropical Research Institute and the Yale School of Forestry and Environmental Studies, which aimed at determining how best to establish diverse native forests on clear-cut land. Back then, secondary forests—ones that grow naturally after the original forest was lost—had been “a bit of a niche research area,” Dent says. But as farming and human infrastructure have driven deforestation, resurgent forests now account for some 70% of all tropical canopies. Prorena initially followed the usual playbook of transplanting nursery trees to rangelands, but Dent began to question whether planting was necessary. In 2009, she published an encouraging paper showing that animal populations rebound quickly as forests naturally regrow. In 2010, she began working in Barro Colorado on the plots established by the pioneering American ecologist Julie Denslow. In 1994, Denslow identified plots that had been regenerating naturally for 20 to 100 years, and Dent would add younger ones to continue the sequence. The site provides not just general data on forest regrowth but in-depth demographic information over time, says Robin Chazdon, a professor emeritus of ecology and evolutionary biology at the University of Connecticut who collaborated with Denslow when she established the plots. “It tells you more about the actual processes that are happening in the site,” she says. For example, previous research had shown that fast-growing “pioneer trees,” the first to thrive in the damaged land, are eventually overtaken by larger carbon-storing trees. Dent’s research helped fill in gaps about the mechanics of how this happens—or doesn’t happen—in individual cases. In the Gustavia-dominated patch, for instance, stubborn grasses planted by cattle ranchers smothered most other trees before they could take root. These trees can hang around, crowding out the light, for 150 years, blocking the second wave of large-trunked trees. To plant or not to plant Dent found herself well placed for the massive influx of interest in reforestation that began in 2015. She’d already become a principal investigator for 2ndFor, an international research network aiming to learn how best to manage secondary forests. Using data gathered from the Barro Colorado plots, the team authored a 2016 paper in Nature showing that a hectare of naturally regenerated secondary forest can capture 11 times as much carbon each year as an old-growth one, with young trees absorbing it quickly as they compete to grow toward the sun. Then, in a 2022 study published by the Royal Society, Dent and colleagues showed that locations such as Barro Colorado are among the best-case scenarios for what can be achieved without planting a single seed. Clearings are often surrounded by lush environments filled with birds, bats, and other animals, all of which help tree cover regain old-growth levels of richness and abundance within a few decades. Using data from Panama and Brazil, Dent published a follow-up in 2024 showing that plots with as little as 35% of tree cover can regenerate as forest. “Having these long-term mapped forest plots is really valuable,” says Liza Comita, a professor of tropical forest ecology at Yale University, who is collaborating with Dent on projects that translate her findings into action. “It provides this really nice research infrastructure where you can build projects on top of that.”

In Panama, Dent works with nonprofits and carbon-crediting schemes to fast-track recovery. They’ve mapped corridors to reconnect forest fragments and made plans to reintroduce animals in “silent forests” that have lost much of their wildlife. They’re also working to replant tree species that can accelerate growth—what Comita calls assisted natural regeneration. Leaving plots alone may seem like the simpler option, but it can’t be done everywhere, says Dent. Many sites are too degraded. Clearings are often surrounded by lush environments filled with birds, bats, and other animals, all of which helps tree cover regain old-growth levels of richness. The two are working with the London-based carbon-crediting startup Ponterra to develop solutions for a region of southern Panama where remnant forests line the edges of abandoned ranches. Such sites almost always need some intervention, Comita explains. Many demand hands-on actions, such as enrichment planting. Today, the questions projects like Ponterra are asking are closer to her own research, says Dent. For instance, they want to understand if solitary standing trees, often left by ranchers to shade cows, help sites regenerate faster. Those insights can help Ponterra sell high-quality credits and avoid the embarrassing pitfalls that can lead fresh plantations to literally go up in smoke. While credits can be bought for as little as $5 per metric ton, corporate giants such as Microsoft—one of Ponterra’s clients—have paid more than 10 times as much for high-quality removal. There are plenty of places where a level of natural regeneration can work well. So far, millions of hectares of rainforest are naturally regenerating in Brazil, according to the Brazilian Restoration and Reforestation Observatory. Still, Dent is mystified as to why the people in charge of large-scale projects continue to think the best solution is the one that imposes humans’ will on the landscape. The conversation may be shifting. As droughts hit Latin America, policymakers and carbon creditors might be forced to face the shortcomings of cookie-cutter plantations. “You have much reduced resilience in the system when it comes to extreme events like droughts or floods or extreme heat or pathogens,” Dent says. The coming year’s “mega El Niño” could be a moment of reckoning, she says: “I think there’s an increasing recognition that we can’t just plant trees. We have to rebuild a system.” Matthew Ponsford is a freelance reporter based in London.

Energy Department Authorizes Strategic Petroleum Reserve Exchange to Address Supply Disruptions Due to Hurricane

WASHINGTON—The U.S. Department of Energy (DOE) today announced the authorization of an emergency exchange of up to 4 million barrels of crude oil from the Strategic Petroleum Reserve (SPR) to address supply disruptions due to inclement weather in the Gulf of America. Under the terms of the exchanges, participating companies will return the borrowed crude oil with additional premium barrels in 2027, helping replenish and strengthen the SPR at no additional cost to taxpayers. “With this emergency exchange, the Energy Department is acting swiftly to help maintain refinery operations, limit disruptions to fuel supplies, and ensure American families and businesses continue to have access to affordable, reliable, and secure energy” said U.S. Secretary of Energy Chris Wright. “In addition to helping minimize supply disruptions, these emergency exchanges will also require Exxon and BP to return more barrels to the Reserve than are currently being withdrawn, saving taxpayers millions of dollars. Rest assured, the Trump Administration is committed to strengthening America’s energy security at every opportunity.” The Department has authorized exchanges of up to 2 million barrels each for ExxonMobil and BP America. ExxonMobil’s exchange will accelerate deliveries of previously awarded SPR crude oil to address immediate supply needs. DOE will continue working with industry partners to support reliable fuel supplies and respond to hurricane-related disruptions. For more information about the Strategic Petroleum Reserve, visit energy.gov.

Energy Department Supports FERC’s Call for PJM to Protect Ratepayers and Build More Reliable Power

WASHINGTON—The U.S. Department of Energy (DOE) announced its support for the Federal Energy Regulatory Commission’s (FERC) guidance that would help ensure lower electricity costs for American families and businesses across the PJM Interconnection LLC’s (PJM) region spanning across the mid-Atlantic. DOE filed a Notice of Intervention and Statement of Position with FERC supporting its recommendation for PJM’s proposed Reliability Backstop Procurement. FERC recommends PJM submit revised tariff provisions to implement appropriate cost allocation and other reforms. FERC’s recommendations are consistent with President Trump’s Ratepayer Protection Pledge, ensuring large electricity users pay for the generation and infrastructure needed to serve them. This action follows the Trump Administration’s call in January 2026 for PJM to adopt market rules to strengthen grid reliability and reduce electricity costs. Specifically, the Administration asked PJM to hold an emergency power auction and to accelerate the development of reliable power generation. The Trump administration was joined by governors from all 13 states who participate in the PJM grid. “The Trump Administration remains committed to doing everything it can to lower electricity costs, grow the supply of American energy and strengthen grid reliability,” said U.S. Secretary of Energy Chris Wright. “That’s why President Trump created the Ratepayer Protection Pledge, and the Energy Department supports having large electricity users pay for reliable new power generation.” “In January, the Trump Administration recognized early warning signs of capacity shortfalls in PJM and joined the Governors of all 13 PJM states in issuing a Statement of Principles to PJM, calling for PJM to adopt market rules to strengthen grid reliability and reduce electricity costs for American families and businesses,” said U.S. Deputy Secretary of Energy James P. Danly. “By increasing transparency and preventing unjust cost shifts onto existing customers, the proposed reforms advance President Trump’s Ratepayer Protection Pledge while ensuring Americans continue

ICANN set to expand gTLD: Brands, AI, and crypto all get new domains

For the first time in 14 years, the internet is getting a ton more generic Top Level Domains (gTLDs). When the internet’s domain system started back in 1985, there were only a few primary TLDs: .com, .net, .org, .edu, .gov and .mil. The number of TLDs grew in small fits and spurts until 2012, when 1,241 new gTLDs were added. The internet has changed over the last 14 years, and now ICANN is opening up the process for the next round of new gTLD, which could bring 1,600 new names to the internet. This time around, big brands are in the mix trying to get their own corporate name as a TLD, and cryptocurrency is well represented. And, of course, it wouldn’t be a tech story if there wasn’t AI in the mix, too. Among the applications for new gTLDs are:

Shell agrees to acquire interest in Bay du Nord project

Shell plc and Equinor ASA have signed an agreement under which Shell will acquire a 30% interest in the Bay du Nord project offshore Newfoundland and Labrador in Canada. Equinor will retain a 70% interest and remain as the operator of the project. The transaction supports the continued maturation of Bay du Nord towards an investment decision currently targeted for early 2027, Equinor said. Bay du Nord is in Flemish Pass basin, about 500 km offshore Newfoundland and Labrador in 600–1,170 m of water. The development concept is based on a floating production, storage, and offloading vessel (FPSO). Bay du Nord and Cambriol discoveries are included in the initial phase, with potential future subsea tiebacks to Cappahayden, Harpoon, and Baccalieu. Estimated recoverable resources in the initial phase are more than 400 MMbbl of oil. First oil is anticipated in 2031, Equinor said. The project is currently finalizing front-end engineering and design (FEED), with continued work focused on strengthening capital efficiency, execution planning, and overall project robustness. Engagement with provincial and federal governments has supported progress through key milestones, according to the company. Equinor and Shell said they will continue to mature the project towards an investment decision subject to market conditions, regulatory approvals, and the companies’ internal decision processes. The project will require a toal investment of about $14 billion (Can.; US$12.6 billion), Equinor said.

It’s not your model — AI has a data movement problem

The ecologist revealing the invisible networks that revive rainforests

Energy Department Authorizes Strategic Petroleum Reserve Exchange to Address Supply Disruptions Due to Hurricane

Energy Department Supports FERC’s Call for PJM to Protect Ratepayers and Build More Reliable Power

ICANN set to expand gTLD: Brands, AI, and crypto all get new domains

Shell agrees to acquire interest in Bay du Nord project

Energy Department Launches Genesis Mission Graduate Fellowship to Advance American Scientific Leadership

WASHINGTON—The U.S. Department of Energy (DOE) today announced the Genesis Mission Graduate Fellowship Pilot, a new funding opportunity designed to develop the next generation of American scientists and engineers and strengthen U.S. scientific leadership. Through accelerated, four-year doctoral pathways, the program will equip domestic graduate students with critical expertise in both Super Intelligence (SI) and core scientific and engineering disciplines. The inaugural fellowship advances President Trump’s historic Genesis Mission by investing in the education, development, and retention of domestic talent needed to accelerate U.S. scientific discovery and innovation. “To secure America’s position at the forefront of global scientific innovation, we must cultivate a workforce that is fluent in both foundational sciences and advanced Super Intelligence,” said DOE Under Secretary for Science Dr. Darío Gil. “The Genesis Mission Graduate Fellowship will bridge this critical gap by combining rigorous academic training with hands-on, high-impact experiences across our National Laboratories and industry partners.” DOE will fund an inaugural group of pilot projects led by domestic doctoral-granting institutions, supporting an initial cohort of approximately 100 Ph.D. fellows. As part of their four-year doctoral pathways, fellows will complete two high-impact, hands-on research experiences—one at a DOE National Laboratory or DOE User Facility and one with an industry partner—preparing them to lead the next generation of scientific discovery and innovation. Total planned funding is up to $100 million, with $20 million in Fiscal Year 2027, and outyear funding contingent on congressional appropriations. A webinar will be held on November 4, 2026, 4:00 PM ET. Register on Zoom. For more information, please visit the DOE Office of Science Funding Opportunities page.

U.S. Department of Energy Awards $96 Million to 67 Early Career Scientists

WASHINGTON—The U.S. Department of Energy’s (DOE) Office of Science today announced the selection of 67 early career scientists to receive a combined $96 million through the 2026 Early Career Research Program (ECRP). Today’s announcement advances President Trump’s Executive Order Restoring Gold Standard Science by supporting exceptional researchers pursuing rigorous, mission-driven research in areas critical to America’s scientific and technological leadership, including Super Intelligence (SI), fusion energy, and quantum science. “Groundbreaking science begins with empowering extraordinary people,” said DOE Under Secretary for Science Dr. Darío Gil. “These 67 early career researchers represent the very best of American ingenuity. DOE is proud to invest in their vision as they push the boundaries of knowledge and cement U.S. leadership in the technologies that will define our century.” The 2026 Early Career Research Program (ECRP) selectees represent 39 universities, 11 DOE National Laboratories and Office of Science User Facilities, and 26 states. By investing in exceptional researchers at a pivotal stage in their careers, ECRP empowers the next generation of STEM leaders to pursue bold, innovative research that drives DOE’s mission and ensures America continues to lead the world in science and innovation. Since its inception in 2010, the program has made 1,238 awards, including 797 to university researchers and 441 to DOE National Laboratory researchers. Awards to scientists at institutions of higher education are approximately $875,000 over five years, while awards to scientists at DOE National Laboratories or Office of Science User Facilities are approximately $2,750,000 over five years. Information about the 67 selectees and their research projects is available on the Office of Science Awards page. Profiles of previous award recipients, including information about how the program helped advance their research and careers, are available on the Early Career Program profiles page. Selection for award negotiations is not a commitment by DOE

National Hydrogen and Fuel Cell Day 2026: Celebrating the “Swiss Army Knife” of the Periodic Table

Today is National Hydrogen and Fuel Cell Day, a daylong celebration of all things hydrogen. Those new to the event will naturally ask, “why today?” Because October 8 is 10/08, and 1.008 is hydrogen’s atomic weight. This annual event is recognized and celebrated by interested communities around the world. As any hydrogen researcher can confirm, hydrogen is the Swiss Army knife of the energy world. It is the oldest and most abundant element in the universe, comprising about 90% of all atoms. It sits atop the periodic table as element number one. Like the Swiss Army knife, its versatility enables a wide range of applications. Hydrogen can serve as a feedstock for industrial and manufacturing applications, including steelmaking and high-heat processes. It can be combined with carbon monoxide to form electrofuels. It can support grid balancing by storing surplus energy for long periods of time. It can serve as transportation fuel for a wide range of applications. Hydrogen fuel cells are increasingly valuable as both primary and backup power sources for large facilities such as data centers and can deliver reliable power to hard-to-electrify remote regions. Hydrogen and Fuel Cell Day celebrates that versatility. This year, the Department of Energy’s Alternative Fuels and Feedstocks Office (AFFO) is highlighting its updated key areas of research, development, and demonstration, which directly support DOE’s objective to expand American energy abundance. These areas include: Strengthening Domestic Chemicals Production DOE’s Billion-Ton Report establishes the potential for more than one billion tons of domestic biomass production annually. These resources are distributed across the country in agricultural and forestry residues, wastes, and energy crops. That creates an opportunity to turn domestic feedstocks into higher-value chemicals and materials while reducing import dependence, improving supply-chain resilience, creating economic opportunities, and finding productive uses for waste streams. America has both the feedstock resources and

Rompetrol targets 2028 startup for two refinery solar projects

Rompetrol Rafinare SA—jointly owned by Kazakhstan’s state-owned JSC NC KazMunayGas (KMG) subsidiary KMG International NV (54.63%) and Romania’s Ministry of Economy, Energy & Business Environment (44.7%)—is advancing a project involving installation of a 9.4-Mw photovoltaic plant at its 5-million tonne/year (tpy) Petromidia refinery in Năvodari, Constanța County, on Romania’s Black Sea coast. Designed to generate more than 9,800 Mw-hr/year of electricity for direct use by the refinery, the project aims to reduce greenhouse gas (GHG) emissions at the site by about 6,024 tpy, Rompetrol Rafinare said in a release Oct. 8. Budgeted at an overall investment of about $9.2 million—including about $2.9 million in nonrefundable modernization fund support and a $6.3-million company contribution—the project is targeted for completion by Dec. 31, 2028, under the funding call deadline. Petromidia’s solar project will complement the site’s 80-Mw cogeneration plant—operated by Kazakh-Romanian Energy Investment Fund (KREIF) subsidiary Rompetrol Energy SA—which entered commercial operation at the end of 2025 and supplies the refinery’s electricity and process steam requirements. Rompetrol reported a 98.11% capacity utilization rate at Petromidia in first-half 2026. The refinery also recorded an 86.54% white-product yield and a 92-point Energy Intensity Index during the first 6 months of 2026, according to the Oct. 8 announcement. Vega project adds battery storage Announcement of the proposed project at Petromidia follows Rompetrol Rafinare’s Aug. 21 confirmation of a separate solar-and-storage project at its Vega Ploieşti niche refinery—specialized in the production of solvents, hexane, and the only Romanian-produced bitumen—in Ploiesti, Prahova County, Romania, about 60 km from Bucharest. The planned Vega installation will combine 4.8 Mw of photovoltaic capacity with a 9-Mw-hr energy storage system. It is expected to produce more than 6,200 Mw-hr/year, equivalent to about two-thirds of the refinery’s budgeted 2025 electricity use. The battery-based storage component of the project is intended to help

Equinor discovers gas at Gullfaks South

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Crescent Energy to acquire Eagle Ford assets from Devon for $4.2 billion

The acquisition continues a rapid buildout of Crescent’s Eagle Ford position, where the company has completed nine acquisitions since June 2023. Following closing, Crescent expects total production of about 400,000 boe/d, including roughly 170,000 b/d of oil. Management said the value proposition centers on operational improvements rather than a material increase in activity levels. Operations upside Asked whether Crescent planned to maintain Devon’s current 2-3-rig development program, Rockecharlie said the company expects to keep activity at roughly the same pace. By folding the assets into the company, he said, “we see significant capital allocation flexibility,” he said. “We think we’re going to deliver effectively the same production with less capital, just more capital efficiency, which is greater free cash flow for investors.” Crescent expects to realize about $140 million in annual synergies, fully captured by the end of 2027, across drilling and completions, lease operating expenses, and marketing. Management said most of those gains are expected to come from drilling and completion efficiencies, including longer laterals and improved surface designs. “Across our five most recent acquisitions, we’ve increased average lateral length by more than 25% versus previous operators, driving a step change in cost structure, increasing returns, and lowering breakevens,” Rynd said. “Most of this is pretty simple and pretty straightforward… increasing lateral lengths and taking advantage of scale,” said chief operating officer Joey Hall. Calling the acreage a familiar operating area, Hall added: “This is our backyard. We understand these assets. We’ve had line of sight into them for over 10 years.” Hall expanded on the opportunity presented. “Going from an average of 6,500 ft to 11,500 ft, that’s a step change in cost structure. We’ve been doing this across our acreage, so we’re confident we can execute on it and [we’re] looking forward to hitting that target.” Rockecharlie

LG rolls out new AI services to help consumers with daily tasks

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More LG kicked off the AI bandwagon today with a new set of AI services to help consumers in their daily tasks at home, in the car and in the office. The aim of LG’s CES 2025 press event was to show how AI will work in a day of someone’s life, with the goal of redefining the concept of space, said William Joowan Cho, CEO of LG Electronics at the event. The presentation showed LG is fully focused on bringing AI into just about all of its products and services. Cho referred to LG’s AI efforts as “affectionate intelligence,” and he said it stands out from other strategies with its human-centered focus. The strategy focuses on three things: connected devices, capable AI agents and integrated services. One of things the company announced was a strategic partnership with Microsoft on AI innovation, where the companies pledged to join forces to shape the future of AI-powered spaces. One of the outcomes is that Microsoft’s Xbox Ultimate Game Pass will appear via Xbox Cloud on LG’s TVs, helping LG catch up with Samsung in offering cloud gaming natively on its TVs. LG Electronics will bring the Xbox App to select LG smart TVs. That means players with LG Smart TVs will be able to explore the Gaming Portal for direct access to hundreds of games in the Game Pass Ultimate catalog, including popular titles such as Call of Duty: Black Ops 6, and upcoming releases like Avowed (launching February 18, 2025). Xbox Game Pass Ultimate members will be able to play games directly from the Xbox app on select LG Smart TVs through cloud gaming. With Xbox Game Pass Ultimate and a compatible Bluetooth-enabled

Big tech must stop passing the cost of its spiking energy needs onto the public

Julianne Malveaux is an MIT-educated economist, author, educator and political commentator who has written extensively about the critical relationship between public policy, corporate accountability and social equity. The rapid expansion of data centers across the U.S. is not only reshaping the digital economy but also threatening to overwhelm our energy infrastructure. These data centers aren’t just heavy on processing power — they’re heavy on our shared energy infrastructure. For Americans, this could mean serious sticker shock when it comes to their energy bills. Across the country, many households are already feeling the pinch as utilities ramp up investments in costly new infrastructure to power these data centers. With costs almost certain to rise as more data centers come online, state policymakers and energy companies must act now to protect consumers. We need new policies that ensure the cost of these projects is carried by the wealthy big tech companies that profit from them, not by regular energy consumers such as family households and small businesses. According to an analysis from consulting firm Bain & Co., data centers could require more than $2 trillion in new energy resources globally, with U.S. demand alone potentially outpacing supply in the next few years. This unprecedented growth is fueled by the expansion of generative AI, cloud computing and other tech innovations that require massive computing power. Bain’s analysis warns that, to meet this energy demand, U.S. utilities may need to boost annual generation capacity by as much as 26% by 2028 — a staggering jump compared to the 5% yearly increases of the past two decades. This poses a threat to energy affordability and reliability for millions of Americans. Bain’s research estimates that capital investments required to meet data center needs could incrementally raise consumer bills by 1% each year through 2032. That increase may

Final 45V hydrogen tax credit guidance draws mixed response

Dive Brief: The final rule for the 45V clean hydrogen production tax credit, which the U.S. Treasury Department released Friday morning, drew mixed responses from industry leaders and environmentalists. Clean hydrogen development within the U.S. ground to a halt following the release of the initial guidance in December 2023, leading industry participants to call for revisions that would enable more projects to qualify for the tax credit. While the final rule makes “significant improvements” to Treasury’s initial proposal, the guidelines remain “extremely complex,” according to the Fuel Cell and Hydrogen Energy Association. FCHEA President and CEO Frank Wolak and other industry leaders said they look forward to working with the Trump administration to refine the rule. Dive Insight: Friday’s release closed what Wolak described as a “long chapter” for the hydrogen industry. But industry reaction to the final rule was decidedly mixed, and it remains to be seen whether the rule — which could be overturned as soon as Trump assumes office — will remain unchanged. “The final 45V rule falls short,” Marty Durbin, president of the U.S. Chamber’s Global Energy Institute, said in a statement. “While the rule provides some of the additional flexibility we sought, … we believe that it still will leave billions of dollars of announced projects in limbo. The incoming Administration will have an opportunity to improve the 45V rules to ensure the industry will attract the investments necessary to scale the hydrogen economy and help the U.S. lead the world in clean manufacturing.” But others in the industry felt the rule would be sufficient for ending hydrogen’s year-long malaise. “With this added clarity, many projects that have been delayed may move forward, which can help unlock billions of dollars in investments across the country,” Kim Hedegaard, CEO of Topsoe’s Power-to-X, said in a statement. Topsoe

Texas, Utah, Last Energy challenge NRC’s ‘overburdensome’ microreactor regulations

Dive Brief: A 69-year-old Nuclear Regulatory Commission rule underpinning U.S. nuclear reactor licensing exceeds the agency’s statutory authority and creates an unreasonable burden for microreactor developers, the states of Texas and Utah and advanced nuclear technology company Last Energy said in a lawsuit filed Dec. 30 in federal court in Texas. The plaintiffs asked the Eastern District of Texas court to exempt Last Energy’s 20-MW reactor design and research reactors located in the plaintiff states from the NRC’s definition of nuclear “utilization facilities,” which subjects all U.S. commercial and research reactors to strict regulatory scrutiny, and order the NRC to develop a more flexible definition for use in future licensing proceedings. Regardless of its merits, the lawsuit underscores the need for “continued discussion around proportional regulatory requirements … that align with the hazards of the reactor and correspond to a safety case,” said Patrick White, research director at the Nuclear Innovation Alliance. Dive Insight: Only three commercial nuclear reactors have been built in the United States in the past 28 years, and none are presently under construction, according to a World Nuclear Association tracker cited in the lawsuit. “Building a new commercial reactor of any size in the United States has become virtually impossible,” the plaintiffs said. “The root cause is not lack of demand or technology — but rather the [NRC], which, despite its name, does not really regulate new nuclear reactor construction so much as ensure that it almost never happens.” More than a dozen advanced nuclear technology developers have engaged the NRC in pre-application activities, which the agency says help standardize the content of advanced reactor applications and expedite NRC review. Last Energy is not among them. The pre-application process can itself stretch for years and must be followed by a formal application that can take two

Qualcomm unveils AI chips for PCs, cars, smart homes and enterprises

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Qualcomm unveiled AI technologies and collaborations for PCs, cars, smart homes and enterprises at CES 2025. At the big tech trade show in Las Vegas, Qualcomm Technologies showed how it’s using AI capabilities in its chips to drive the transformation of user experiences across diverse device categories, including PCs, automobiles, smart homes and into enterprises. The company unveiled the Snapdragon X platform, the fourth platform in its high-performance PC portfolio, the Snapdragon X Series, bringing industry-leading performance, multi-day battery life, and AI leadership to more of the Windows ecosystem. Qualcomm has talked about how its processors are making headway grabbing share from the x86-based AMD and Intel rivals through better efficiency. Qualcomm’s neural processing unit gets about 45 TOPS, a key benchmark for AI PCs. The Snapdragon X family of AI PC processors. Additionally, Qualcomm Technologies showcased continued traction of the Snapdragon X Series, with over 60 designs in production or development and more than 100 expected by 2026. Snapdragon for vehicles Qualcomm demoed chips that are expanding its automotive collaborations. It is working with Alpine, Amazon, Leapmotor, Mobis, Royal Enfield, and Sony Honda Mobility, who look to Snapdragon Digital Chassis solutions to drive AI-powered in-cabin and advanced driver assistance systems (ADAS). Qualcomm also announced continued traction for its Snapdragon Elite-tier platforms for automotive, highlighting its work with Desay, Garmin, and Panasonic for Snapdragon Cockpit Elite. Throughout the show, Qualcomm will highlight its holistic approach to improving comfort and focusing on safety with demonstrations on the potential of the convergence of AI, multimodal contextual awareness, and cloudbased services. Attendees will also get a first glimpse of the new Snapdragon Ride Platform with integrated automated driving software stack and system definition jointly

Oil, Gas Execs Reveal Where They Expect WTI Oil Price to Land in the Future

Executives from oil and gas firms have revealed where they expect the West Texas Intermediate (WTI) crude oil price to be at various points in the future as part of the fourth quarter Dallas Fed Energy Survey, which was released recently. The average response executives from 131 oil and gas firms gave when asked what they expect the WTI crude oil price to be at the end of 2025 was $71.13 per barrel, the survey showed. The low forecast came in at $53 per barrel, the high forecast was $100 per barrel, and the spot price during the survey was $70.66 per barrel, the survey pointed out. This question was not asked in the previous Dallas Fed Energy Survey, which was released in the third quarter. That survey asked participants what they expect the WTI crude oil price to be at the end of 2024. Executives from 134 oil and gas firms answered this question, offering an average response of $72.66 per barrel, that survey showed. The latest Dallas Fed Energy Survey also asked participants where they expect WTI prices to be in six months, one year, two years, and five years. Executives from 124 oil and gas firms answered this question and gave a mean response of $69 per barrel for the six month mark, $71 per barrel for the year mark, $74 per barrel for the two year mark, and $80 per barrel for the five year mark, the survey showed. Executives from 119 oil and gas firms answered this question in the third quarter Dallas Fed Energy Survey and gave a mean response of $73 per barrel for the six month mark, $76 per barrel for the year mark, $81 per barrel for the two year mark, and $87 per barrel for the five year mark, that

We’re still figuring out the side effects of GLP-1 weight-loss drugs

EXECUTIVE SUMMARY This week my colleague Antonio Regalado had an interesting update on GLP-1 weight-loss drugs. According to research presented at an aging meeting in Boston, these drugs seem to affect at least some measures of biological age. Overweight and diabetic people who take GLP-1s have a biological age around two to three years younger than similar individuals who take a placebo, according to findings from Eli Lilly and Novo Nordisk, two pharmaceutical companies that make the drugs. I’ve heard people in the aging field describe GLP-1s as longevity drugs for a while now (even if they only benefit people who are overweight or diabetic in the first place). This latest announcement comes after we’ve seen a raft of other benefits associated with taking these drugs. But there are also some weird side effects. So far, GLP-1 agonist drugs—which repress hunger—have been approved for treating diabetes, chronic kidney disease, and obesity, and they can also be prescribed for some people who are overweight or at risk of certain cardiovascular diseases. But there is research to suggest they might also help people with substance-use disorders, and they’ve been investigated for potential benefits in neurodegenerative disease and cancer.
And a lot of people are taking them. According to a recent Gallup poll, 11% of Americans are taking a GLP-1 drug for weight loss, up from 3% in 2024. And 15% of Americans say they’ve used one for weight loss at some point. They can be incredibly effective. But, like any drug, they can come with side effects. And we’re still figuring out exactly what those might be, and how widespread they are.
The most common side effects are gastrointestinal—nausea, vomiting, diarrhea, and constipation. The drugs have also been linked to pancreatitis and gallstone disease. Some claim that, in rare cases, the drugs might cause damage to nerves behind the eye. Dozens of people are suing Novo and Eli Lilly after developing a condition that can lead to sudden vision loss. They’re claiming that their symptoms were brought on by the GLP-1 drugs that these companies manufacture. Both companies dispute the claims. Research into the link is mixed. We’re still getting to grips with the effects of weight-loss drugs on hair and nails, too. When Savanna Vidal at the George Washington University School of Medicine and Health Sciences in Washington, DC, and colleagues assessed electronic health data from 67 US health-care organizations, they found that GLP-1 use was associated with an increased risk of hair loss. It’s still not thought to be common, however. And at a conference in Austria last week, another group presented the results of a smaller study suggesting that 66% of GLP-1 users reported at least one nail disorder—in some cases involving nail detachment. Nail conditions were also fairly common among people who didn’t take the drugs, though. The authors don’t claim that the drugs are causing nails to fall out (as I’m sure 11% of the American population will be relieved to hear). The side effects might look different in children, but there’s not much data on those at the moment. Still, pediatric side effects will be something to watch given that, between 2019 and 2026, there was a 310-fold increase in GLP-1 prescriptions for eight- to 11-year-old children with obesity in the US. Child prescriptions are still rare, but they’ve been rising. On Wednesday, the World Health Organization released its first guidelines on managing obesity in children and adolescents. Despite the scale of the issue—the WHO estimates that 70 million children aged five to nine were living with obesity in 2024—the organization explicitly says it does not recommend pharmacological treatment for kids under 10. “This recommendation accounts for the lack of evidence on both benefits and the potential adverse events and consequences of the different medications and/or their active ingredients on normal growth, development, physiological parameters, and mental health,” the guidelines state. I get press releases about GLP-1s on an almost daily basis. There’s been a ton of research into these drugs, and there are probably hundreds of other studies underway. There will likely be plenty more on this from The Checkup as the evidence evolves. This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

Roundtables: A Conversation With the Creator of AI-Designed Viruses

Available only for MIT Technology Review subscribers.
Friday, October 16, 2026 Can AI design new life forms? In 2025, Stanford University PhD student Samuel King came up with a preliminary answer when he used a generative AI model to propose genetic blueprints for microscopic viruses. It isn’t yet an example of AI-generated life, but that could be next. Join senior AI reporter James O’Donnell as he interviews King about his work, being named one of MIT Technology Review’s Innovators Under 35 and new ways of seeing biology. Going live on October 16th at 18:30 BST / 1:30pm EDT / 10:30am PDT Speakers: James O’Donnell, AI Reporter, and Samuel King, Bioengineering PhD Candidate, Stanford University/Arc Institute Related Stories

The Download: AI roadblocks for humanoids and portable rubber dams

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. AI breakthroughs in robotics won’t change your life any time soon The hype around humanoid robots is reaching fever pitch. Much of it comes from the idea that the same AI advances behind ChatGPT and Claude could enable robots to imitate human movement the way chatbots imitate human language. But many researchers are skeptical. They argue that such assumptions minimize the challenges of using an intelligence built on language and images to master the infinite variability of the physical world. They also point out that conflating humanoids with so-called generalist machines is misleading. Across robotics labs, tensions have emerged over whether current forms of AI are all that’s needed to perfect all-purpose humanoids or whether an entirely new path is required.
Read the full story on what it will take to build truly general-purpose robots. —Jamie Condliffe
This story is a collaboration between MIT Technology Review and Aventine, a non-profit research foundation that creates and supports content about how technology and science are changing the way we live. 10 Climate Tech Companies to Watch: WaveSave and its portable rubber dam WaveSave is one of MIT Technology Review’s10 Climate Tech Companies to Watch 2026, available exclusively to subscribers. As sea levels rise and flood risks worsen, WaveSave has emerged with an ingeniously simple tool: a portable rubber dam that’s quick to install when flooding starts. Anyone can unfurl the SlamDam on the ground along a riverbank or shoreline and pump it full of water until it swells into place. And there it stands, up to 1.3 meters high, shielding nearby property or structures from damage as waters rise. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Scientists in Austria and China have built the first nuclear clocksThey could become the world’s most precise timekeepers. (NYT $)+ They could also help scientists search for dark matter. (Reuters $)+ China’s nuclear clock is six times more stable than Austria’s. (SCMP)+ A space clock could help measure Earth. (MIT Technology Review) 2 Three fired OpenAI researchers want to protect AI reasoning monitoringThey warn that AI companies could lose a key safety tool. (WSJ $)+ OpenAI fired them for allegedly sharing sensitive information. (Gizmodo)+ Don’t be fooled—LLMs don’t reason. (MIT Technology Review)
3 Samsung has forecast the highest quarterly profit ever for a tech firmThe AI chip boom has sent its projected profits to $80 billion. (CNBC)+ Can AI labs ever turn a profit? (Reuters $)+ What even is the AI bubble? (MIT Technology Review) 4 OpenAI’s math breakthroughs point beyond mathematicsAI is progressing into increasingly specialised fields. (Axios)+ OpenAI is catching up with Anthropic on coding. (WSJ $)+ A new AI tool could speed up math research.(MIT Technology Review)
5 A 194-year-old tortoise has had its genome sequencedHis longevity could reveal ways to extend human lifespan. (Guardian)+ Jonathan is the world’s oldest known land animal. (404 Media)+ His youthful mitochondria may help repair DNA. (New Scientist $) 6 A fraudster who used AI to outstream Taylor Swift has been jailedHe used AI-generated songs and bots to earn $8 million. (NYT $)+ But prosecutors said he stole royalty payments. (Ars Technica) 7 TikTok allegedly gave kids fake safety features to study engagementThe tests measured time spent and ad revenue. (Reuters $) 8 Burglars could be identified from DNA floating in the airThanks to a new device that collects airborne genetic material. (Economist $) 9 Pioneers of mirror-image molecules have won the chemistry NobelTheir work has become a key tool in developing medicines. (Guardian) 10 California has declared a human-vs-robot fight illegalThe state’s combat-sports laws were never written for machines. (NYT $)
Quote of the day “We grew a mind.” —David Robinson, who last week quit his job as OpenAI safety transparency lead, tells The New York Times that his former company has built models that develop capabilities their creators did not explicitly design. One more thing We need a moonshot for computing The US government is organizing itself for the next era of computing. Ultimately, it has one big choice to make: adopt a conservative strategy that aims to preserve its lead for the next five years—or orient itself toward genuine computing moonshots.
There is no shortage of candidates, including quantum computing, neuromorphic computing and reversible computing. And there are plenty of novel materials and devices. These possibilities could even be combined to form hybrid computing systems. The National Semiconductor Technology Center can drive these ideas forward. To be successful, it would do well to follow DARPA’s lead by focusing on moonshot programs. Read the full story. —Brady Helwig & PJ Maykish 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.) + An experimental pair of contact lenses can repair themselves with UV light.+ Meet Rich, the rescued squirrel who visits the woman who saved him every day.+ Reminisce about the lost joy of music piracy with this wistful reflection on a time before streaming. + What would rainbows look like on Tatooine? Randall Munroe, a former NASA roboticist, has the answer.

Why we’re watching these climate tech companies

EXECUTIVE SUMMARY This week, we released our 2026 version of our Climate Tech Companies to Watch list. It’s an annual project that the MIT Technology Review team puts together. Our goal is to highlight some of the promising, interesting advances and firms we think are worth paying attention to in the world of climate and energy technology. The list is always a little ray of hope for me—there are some fascinating ideas out there for changing how we power our world or moving away from fossil fuels to reduce the greenhouse-gas emissions that cause climate change. Our chosen companies tend to reflect several aspects of the current moment. Here are some of the companies we picked and the key themes worth highlighting. China is the center of the climate and energy tech world Forgive me—I know I’ve made this point before. Feel free to call me Captain Obvious. But I think China’s dominance in energy technology is a global trend that bears repeated mentioning.
China is home to the world’s largest grid, and renewables are coming online more quickly than I can wrap my head around. Contributing to that boom is Envision Energy, a renewables giant appearing on our list for the second time. The company has installed over 100 gigawatts of wind power worldwide, as well as over 50 gigawatt-hours of battery storage. Like many other Chinese companies right now, Envision is looking beyond the crowded Chinese market and setting its sights abroad, with projects in Germany, Brazil, and Australia and upcoming work in Vietnam.
China is also leading innovation in key technologies. I’ve been fascinated by the potential for next-generation batteries coming out of its companies: All the big ones, along with a host of smaller ones, are racing to develop solid-state batteries. They could be both safer than existing EV batteries and longer in range. WeLion New Energy is particularly interesting. The battery company announced a big lab result last year, achieving 824 watt-hours per kilogram—more than three times the energy density of a standard lithium-ion battery today. Lab results in battery research are notoriously difficult to scale, but WeLion is also working to deploy semi-solid-state batteries, which could come to fruition faster than purely solid-state cells while still improving on lithium-ion’s performance. Everybody wants a piece of the data center pie We included just three companies from the US on the list, and two of them have received a major boost from Big Tech as companies look to power data centers. (The third, Brimstone, is leaning into another political hot topic, critical minerals.) Technologies that can provide consistent power are in high demand. Fervo Energy is bringing next-generation geothermal power to the grid by using horizontal drilling and hydraulic fracturing to make it practical in more places. The company went public in May, raising $2.2 billion in its IPO, and plans to have a gigawatt’s worth of plants operational by the end of 2030. X-energy is building helium-cooled small modular nuclear reactors. The reactors’ small size lends flexibility, and the company is currently working on a 320-megawatt cluster of them in collaboration with Amazon in Richland, Washington. (For what it’s worth, I’m interested in what this technology could do for industrial facilities—the company has a deal with Dow that could see it deployed at one of the company’s chemical plants in the early 2030s.) The grid needs support, and energy storage will be a key piece moving forward I know, I know—I haven’t stopped talking about batteries since I started writing this newsletter four years ago. But the energy storage market has surpassed even my expectations over that time. And as solar and wind quickly come online, they need storage to help them meet more of the grid’s demand. Form Energy is on the list for a third time for its iron-air batteries, which could enable cheaper long-duration energy storage. The company has a massive project in the works, to the tune of 30 gigawatt-hours, which will be used to help power a Google data center. It could be the world’s largest battery project in terms of capacity by the time it comes online, which is expected to happen in phases between 2028 and 2031. Taking another tack, Energy Dome is using compressed carbon dioxide to store energy on the grid. This technology is already cheaper than lithium-ion batteries for longer durations (think 10 hours or more), and the company is working to scale quickly. I covered the company in 2022, before it had its first commercial plant running. Now it has plans for over 30 gigawatt-hours’ worth of plants around the world, including one with Google in Ireland that’s slated to come online in 2028.

And Moment Energy is working to use the million EV batteries that are expected to reach the end of their life by 2030. The company has a facility in Vancouver, British Columbia, that repurposes used batteries for backup power. This year hasn’t been all sunshine and rainbows for climate tech. But I’m hopeful that some of the companies on this list, and the technologies they build, will keep nudging us in the right direction. This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

AI breakthroughs in robotics won’t change your life any time soon

The story is a collaboration between MIT Technology Review and Aventine, a non-profit research foundation that creates and supports content about how technology and science are changing the way we live. A robot shaped like a human—white with a black head and torso—has been popping up on video feeds. Perhaps you’ve seen it dance or pass popcorn, put trash in a bin, vacuum, or press the button of a microwave. Or maybe you’ve watched it fall backward while handing out water bottles or struggle to iron a shirt. This would be Tesla’s Optimus, an AI-powered humanoid robot that Elon Musk, the company’s CEO, believes will be “not just Tesla’s biggest product ever, but probably the biggest product ever,” headed to work on factory floors and, later, in our homes. Eventually it “will have human and then superhuman dexterity,” he told shareholders in July. Optimus robots could automate almost all human labor—from hauling sheet metal to folding laundry—for as little as $20,000 each, Musk argues. Speaking at the World Economic Forum’s annual meeting in Davos, Switzerland, in January, he predicted they could be on sale to the public by the end of 2027. Musk is not alone in his evangelism. Marc Andreessen, cofounder and general partner of the Silicon Valley venture capital firm Andreessen Horowitz, has said that robotics could become the “biggest industry in the history of the planet.” In January, Jensen Huang, CEO of Nvidia, said that humanoid robots would match human-level ability this year. According to Morgan Stanley, the number of robots that “resemble and act like humans” is likely to reach nearly 1 billion by 2050, creating a market worth over $5 trillion. Elon Musk predicts that Tesla’s Optimus humanoid could be one the world’s best-selling products. For now, it’s most often seen handing out food and drinks at Tesla events.SIPA VIA AP IMAGES Such proclamations are in large part fueled by the idea that the same AI advances behind tools like OpenAI’s ChatGPT and Anthropic’s Claude will enable a new generation of robots to imitate human movement the way chatbots imitate human language. But many robotics researchers are skeptical, arguing that such assumptions minimize the challenges of using an intelligence built on language and images to master the infinite variability of the physical world. “None of those companies [building humanoid robots]—absolutely none of them—has any idea how to make those robots smart enough to be useful,” Yann LeCun, often referred to as one of the godfathers of AI, said at another event during the January Davos conference. Researchers also point out that the tendency to conflate humanoid robots made to resemble people with so-called generalist machines able to learn and perform multiple tasks is misleading. ”It’s very easy to make a robot that looks like a person,” explains Jonathan Hurst, cofounder and chief robot officer of Agility Robotics and professor of robotics at Oregon State University. “It is dramatically more difficult to make a machine that moves or behaves dynamically or physically like a person.”
These tensions—over whether all-purpose humanoid robots are just around the corner or nowhere in sight, and whether current forms of AI are all that’s needed to perfect them—are playing out in robotics labs across the country, where the hype over timelines is obscuring painstaking but meaningful progress. A decade or so ago, a series of breakthroughs led to a generative AI revolution that turned the long-imagined possibility of artificial intelligence into reality. Roboticists—though they disagree on exactly when this will happen—believe that an equally transformative revolution is possible in robotics, one that will endow machines with physical intuition and fluidity that has long been out of reach. As progress in robotics inches forward, the question is whether the same methods and tools that fueled advances in AI are enough to get there, or if an entirely new path is required.
Robots meet advanced AI To see one of the smartest robot brains working today, it’s worth looking at what Google DeepMind can do with a piece of equipment called ALOHA 2, short for “A Low-cost Open-source Hardware System for Bimanual Teleoperation.” Roboticists have long clashed over whether a humanlike form is necessary for generalist robots, with proponents arguing that it will help them slot into the world as it exists and detractors saying it’s not worth the trouble. ALOHA 2 reflects this second way of thinking. Not much to look at, it’s just a pair of arms, some grippers, and a couple of cameras. But despite its seeming simplicity, it is a workhorse for researchers at Google DeepMind, who use it to test their most advanced AI for robotics system, Gemini Robotics, in their various labs. When controlled by Gemini Robotics, ALOHA 2 becomes more of a generalist robot, in the sense that it can perform any number of tasks based on examples it’s been trained on. Ask it to pack a lunchbox and, as evidenced by a video of this exercise, it can use two pincer grippers to delicately place a piece of white bread into a Ziploc bag, close it, place a bunch of grapes in a Tupperware container, secure the lid, and then carefully move the items into a lunchbox before zipping it up. It’s not a great lunch. But the fact that the robot can put it together represents an objective step forward from what was possible even, say, three years ago. This is in large part due to AI and its impact on what are known as robot policies, which controls how a general-purpose robot will need to assess and understand its surroundings, plan how to move within them, and then perform its task correctly. The ALOHA 2 robot isn’t much more than two mechanical arms on a bench top, but it serves as a testbed for cutting-edge AI robotics models. These animations are based on human teleoperation of the robot arms, data that is used to train Google DeepMind’s models. (Video: Google DeepMind / Stanford University / Hoku Labs) Historically, these policies were based on rules developed by engineers who hard-coded them into the robot’s software—thousands of lines of code that would determine each millimeter of a robot’s movements in hundreds of tasks. What’s been happening for the last few years—and what is largely responsible for the optimism about generalist robots—is that robot policies are being handed over to advanced AI systems instead of being coded into the robot’s software. This first happened with VLMs, or vision-language models. These are similar to large language models, but they’re trained on images as well as words. Show a VLM a picture of a coffee spill and ask it to find a tool to clean up the mess, and it can identify a nearby cloth. This sort of immediate contextual understanding didn’t exist a couple of years ago when robot policies were hard-coded.
Next came vision-language-action models, which enable robots to assess their environment and take action within it. The models do this by adding yet another component: motion commands. VLAs are trained on a series of images or videos related to performing a given task along with associated data about how a robot arm moves to perform it. That movement data is typically collected through teleoperation, in which a human uses remote controls to lead a robot through an action. This sort of training allows the AI to learn how to command the robot to move and operate during a given task. Place a VLA-powered robot in front of a desk and tell it to “close a laptop” or “wrap up the headphone wire,” and it will survey the scene, identify the relevant object, plan a way to execute the request, and then swing its arms into action—at least if it has seen this task accomplished before. The Gemini Robotics model is a VLA, trained on many hours of human demonstrations depicting a vast array of different actions. As a result, it can perform relatively complex tasks like picking up snow peas with kitchen tongs, doing origami, or putting together a simple lunch. It’s impressive, but there’s a glaring limitation: For now, if a robot controlled by a VLA is asked to perform a task that falls outside its training set, it’s highly likely to fail. “Thinking about the space of all tasks, a real generalist policy would be able to do everything along that spectrum,” says Edward Johns, a robotics professor at Imperial College London. Today, though, a Gemini Robotics model can do only “a few things here and a few things there.” The search for true generality So how do we get robots to be able to do more things? The usual answer is probably not surprising: Train them on more data.
More data, the thinking goes, equals more examples, and more examples equals more generality. Google DeepMind, for instance, wants to pull together “as much data as possible,” says Pannag Sanketi, a former tech lead in robotics at the company who’s currently working on his own AI robotics project. But where to get it? Large language models had the benefit of oceans of existing text for training. There is no corresponding pool of high-quality physical demonstrations on which to train robots. Researchers have a few ways to make up for this, but all have flaws. One is to employ large numbers of people to create and collect teleoperation data (costly and time-consuming). Another is to train VLAs on videos of people performing activities (the resulting data quality is poor). Yet another is to deploy robots in the real world and use data collected from those experiences to further refine AI models (robots aren’t safe or reliable outside labs). Sanketi thinks a “multi-prong” approach that uses data collected from all these sources is the most likely path forward. But the belief that training data alone is the answer is far from universal. Agility’s Hurst describes it as “a fundamentally flawed premise.” The issue is that tasks in the real world quickly explode in complexity. If you’re trying to, say, make coffee, there are myriad variables: No two kitchens are identical; coffee machines work in different ways; different cups require different grips; coffee grounds, hot water, and milk all need to be handled differently. Even this simple task requires understanding an ever-changing menu of possibilities. Achieving generality through VLAs, Hurst argues, would require “complete data coverage of all of the things that [a robot] could ever do.” Or, in other words, an almost infinite pool of training data. LeCun is dismissive of the whole approach. “The [AI] approaches that have been successful for language do not work for high-dimensional, continuous, noisy data”—the kind of data that is commonplace in robotics, he said in Davos. “You have to use something else.” The leading contender for “something else” is the so-called world model—a form of AI trained less on text than on a combination of video, three-dimensional scans, and sensor data and built to predict the outcomes of actions in the real world. The aim is to build models that possess an internal representation of reality precise enough to capture how the physical world actually operates—how objects move, collide, fall, and deform. If roboticists could train machines in simulations faithful enough to real-world physics, development would become faster, cheaper, and safer, reducing the need for real-world testing. Even more transformative, robots equipped with world models could reason about their surroundings rather than merely reacting to them, helping them anticipate the consequences of an action before taking it.

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Google DeepMind’s latest AI models for robots are increasingly dextrous, if rather slow and erratic
Companies like Nvidia and Google are working on the technology, and investor cash is pouring into high-profile startups. World Labs, cofounded by the Stanford AI researcher Fei-Fei Li, raised $1 billion in funding in February and was acquired by AMD at the end of September for $8.2 billion. AMI Labs, cofounded by LeCun (formerly Meta’s chief AI scientist), also raised $1 billion in March. Yet by their own admission, it is still early days. Late last year Li described the field as “nascent,” adding that “foundational approaches are still being established.” In a June Substack she described daunting challenges. For now, world models are a promising area of research rather than an immediate route to general-purpose robotics, but we are beginning to see glimmers of what they could achieve. One such glimpse came with a small but potentially significant leap forward that took place in a San Francisco robotics lab last April. A breakthrough? In the heart of San Francisco’s Mission District, the startup Physical Intelligence—or PI (as in π), as it likes to be known—is focused on developing a universal brain that could, theoretically, turn any robot into a generalist. Using an everything-including-the-kitchen sink approach to training AI models for robots, the company recently observed a hint of what a robotic brain equipped with a world model could be capable of.
In 2024, PI published details of its first generalist robotics system, called π0, a VLA it claimed was the “most capable and dexterous generalist robot policy to date.” The model was initially trained on a 10,000-hour proprietary collection of human demonstrations gathered through teleoperation as well as several open-source robot datasets. A version released in spring 2025, π0.5, was trained on a wider variety of datasets, including labeled images from the web, lending it more versatility. A fall 2025 update, π0.6, added reinforcement learning to the model. Each update yielded important improvements to the model’s performance, increasing its menu of abilities from slowly folding laundry to putting things away in new environments to completing tasks like folding boxes with a higher success rate. Then, in April 2026, π0.7 seemed to catapult PI into new territory. This version makes use of a less powerful world model that generates images of steps necessary to perform a task. As the robot undertakes the job, this “lightweight” model feeds it snapshots of what to do next. How do you teach a robot to use a knife? At the startup Physical Intelligence, it begins with designing the right AI architecture, which includes components dedicated to language, vision and motion. This will help it relate commands — “hey robot, chop my vegetables!” — to appropriate actions.WINNI WINTERMEYER Data to train the AI can come from many sources, but one of the most important is human demonstrations. An employee at the startup controls a robot arm through teleoperation, exposing the AI to the task of slicing a zucchini.WINNI WINTERMEYER Researchers train the AI model on hundred of examples of human demonstrations, as well images from the web and first-person video. WINNI WINTERMEYER Once the AI is trained, the team presents it a task it has not seen before, such as chopping this summer squash. When a robot hasn’t seen the exact task before — it may wonder if that’s a yellow zucchini, or an unusual banana — it can mess up. But any failures can be used to help refine the model.WINNI WINTERMEYER The company claims that the model exhibits the first signs of compositional generalization, a term for AI systems’ ability to perform skills they’ve never been exposed to by recombining ones learned in their training data. One test involved asking a model to “load a sweet potato into the air fryer”—a task it had never previously encountered. In a demonstration video, the machine futzes around a little, makes a few false starts, and eventually manages a reasonable effort, though it doesn’t finish the task completely. Sergey Levine, a professor at the University of California, Berkeley, and a cofounder of PI, is excited by the potential: “It’s actually the first time that we’ve convincingly seen that kind of compositional generalization, where we can basically ask the model to do tasks that we did not specifically collect data for and train it to do, and it’ll actually make a passable attempt.” The success led the team to wonder how the model was able to achieve such a feat. After some digging, they found snippets of relevant labeled teleoperation data lurking in the training material, including two examples of a human controller using the robot to push an air fryer basket into the fryer. Those shreds of data might have been enough to enable π0.7 to almost air-fry a sweet potato. For now, it remains unclear just how impressive π0.7’s abilities to generalize are. Still, given how fleeting the model’s exposure to air fryers had been, it offers a glimpse into how far cutting-edge research can currently take robots.
“70% success is like it doesn’t work” You might be sensing a disconnect between the halting baby steps robots are making in labs—“Look! It put a sweet potato into an air fryer!”—and the dazzling, lifelike nimbleness on view during many demonstrations and videos, where robots are seen doing everything from dancing on a stage to courteously serving drinks. Such demos often don’t clearly reveal a key fact: In many instances, humans are controlling the robot or have carefully scripted its actions. (The robot that appeared onstage with Nvidia CEO Jensen Huang in March 2025, for example, seemingly responding to his instructions and following him around, was remote-controlled by what its makers called “a puppeteer behind the scenes.”) For now, fully autonomous motion planning so that a robot knows where it should go—especially in new, chaotic environments like a construction site or a unfamiliar home—remains a largely unsolved challenge. A bigger challenge still—albeit one that is often related—lies in getting robots to tackle larger, more ambiguous jobs that include multiple tasks and require decisions about how and in what order they’re done. This would be the difference between a robot that can put a plate into a microwave and one that can successfully respond to the prompt “Make dinner” by exploring the refrigerator, chopping ingredients, and firing up the stove. Google DeepMind’s best attempts at something like this—which involved asking its robot to survey a kitchen and pack all the ingredients for a mushroom risotto into a basket—have so far resulted in failure.
Adding to the challenge, a practical robot must essentially get it right every time. With VLAs, “people are very excited when their result goes from 50% success to 70% success,” says Marc Raibert, founder of Boston Dynamics. “But 70% success is like it doesn’t work, right?” Demonstrations often make robots look useful, but the machines still mess up far too often to be used reliably in homes and factories.AP IMAGES, SHUTTERSTOCK, 1X, AGILITY ROBOTICS, GOOGLE DEEPMIND The few humanoids that are being tested in real-life settings are undertaking extremely limited tasks in tightly controlled environments. They’re far from generalists. Agility has hundreds of robots deployed across trials in facilities owned by GXO Logistics, Amazon, and Schaeffler, according to the company. But for now, Hurst says, the robots are targeting simple tasks such as moving bins and totes around. Even then, he adds, it took years to develop robots safe enough for logistics firms to even contemplate using them. For his part, Elon Musk claimed in May 2025 that “thousands” of his Optimus robots would be working at Tesla factories by the end of the year, but in January of this year he said that the company had only “some of the Tesla Optimus robots doing simple tasks in the factory.” While humanoids are starting to venture onto the factory floor, making the jump to households will be even more difficult. Right now, should you so desire, you can preorder the 1X Neo home robot, expected to be ready for delivery sometime later this year. Yours for $20,000, it promises to take on “the boring and mundane tasks around the house”—putting away dishes, answering the door, tidying the living room—“so you can focus on what matters to you.” The idea is for this five-foot-six-inch robot to one day perform all those tasks autonomously, but for now a remote human operator is needed for it to do most things. (Yes, a person would need permission to peer into your home through the robot’s cameras.) Asked how long it will be until fully autonomous robots are ready for domestic work, Hurst said, “If I had to pick a number, I’d say it’s 10 years before robots are … actually doing useful things in people’s homes.” When that happens, the robots might well be Chinese, as China is well ahead of the West in terms of production. Nearly 90% of the roughly 15,000 humanoid robots shipped in 2025 were made by Chinese companies, according to the market intelligence company Omdia and the Chinese robotics firm Unitree. One model produced by Unitree, which shipped more humanoid robots than any other company last year, costs less than $6,000. Such an affordable price could go a long way toward making robots more attractive to consumers, though the company expects its machines to be used in industrial applications first. (If you’re wondering who is buying all these Chinese robots, by the way, the AP recently reported that orders come predominantly from corporate and academic labs and state-owned enterprises.) We’ve been here before The dream of building a humanoid robot runs deep: As far back as 1495, Leonardo da Vinci sketched out designs for a mechanical knight, controlled by cables and pulleys. Through the 20th century, machines of sci-fi fever dreams have come and gone. Westinghouse’s Elektro was a sensation at the 1939 World Fair. GETTY IMAGES Westinghouse’s seven-foot-tall box on legs, Elektro, hit the New York World’s Fair in 1939, smoking a cigarette. WABOT-1, built by Waseda University in Japan in 1973, was the first full-scale, programmable humanoid robot. Honda’s ASIMO, unveiled in 2000, was probably the first such machine to prove at all competent—it could, at least to some degree, climb steps, recognize faces, and autonomously move through spaces. But the robot was discontinued in 2018, unable to advance far enough beyond what it could do in demonstrations to be useful. All, at the time, were impressive—even jaw-dropping—feats of engineering. But none were ready to navigate the real world. Today’s robots, even with the transformative power of advanced AI, still face the same existential challenge.

Building a safer path to autonomous industrial AI

In partnership withAVEVA Industrial AI is entering a new phase. After decades of predictive analytics and other specialized applications, advances in foundation models, physical AI, and agentic AI are making it possible to automate more complex tasks across industrial environments. But unlike AI that operates purely in the digital world, industrial AI can interact directly with physical systems, where an unexpected decision can have consequences for safety, reliability, and critical infrastructure. That makes responsible deployment central to the next wave of industrial automation. “How do we leverage these technologies while maintaining safety, while maintaining reliable operations, while still being able to deliver on the promises of the new capabilities?” asks Arti Garg, chief technologist at AVEVA. The challenge is particularly acute as newer AI systems become more capable but also harder to predict and explain. One foundation for making that transition work is data. Industrial systems often contain information across telemetry, service logs, engineering documents, and other disparate sources. Newer technologies can help connect and correlate that information more quickly, giving operators real-time support when diagnosing problems. AI-powered robots could take that a step further by gathering information in hazardous environments without requiring workers to enter them. But greater autonomy also requires new approaches to governance. AVEVA’s framework for responsible AI emphasizes security, efficiency, and human safety and oversight. Garg argues that AI should augment rather than replace people in critical decision loops, with guardrails determining where automated systems can act and where human supervisors remain responsible.
Sustainability is another part of that equation. AI can help manage complex power systems as renewable generation grows, while organizations also need better ways to understand AI’s own environmental footprint. Garg is involved in an IEEE working group developing a standard methodology for measuring that impact across electricity, energy, resources, water, and carbon. The next phase could bring industrial AI further into the physical world, from autonomous robots and drones to AI-assisted coding that allows domain experts to build new applications. But realizing that potential will require more than deploying new technology, says Garg. Organizations will need to rethink business processes, establish appropriate safeguards, and give experienced workers new ways to apply their expertise, creating a model of automation that is not only more autonomous, but safer, more efficient, and more sustainable.
“Autonomous systems, whether they’re robots or drones, are really going to change the way that we work in plants, in power systems, on mining sites,” says Garg. “In a way, that will make these types of operations more efficient, much safer for the human beings involved and more productive.” This episode of Business Lab is produced in partnership with AVEVA. 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. Industrial AI may not be new, but it is developing at a breakneck pace. From modern predictive analytics that can preempt equipment failures, to autonomous robots tasked with inspecting high risk machinery, emerging applications have the potential to transform operational efficiencies. But often used in high consequence environments with minimal margin for error, industrial AI deployed without clarity, security, or accountability could also have catastrophic consequences for critical infrastructure over time. As industries rush to embrace the latest technologies, the goal should always be responsible AI designed to support, rather than replace that all important human judgment.Two words for you: sustainable automation. My guest today is Arti Garg, chief technologist at AVEVA. This podcast is produced in partnership with AVEVA. Welcome, Arti.

Arti Garg: Thank you, Megan. It’s great to be here. Megan: Thank you so much for joining us. And just to start, if we could set some context for our discussion, can you share a bit about the current state of industrial AI and what AVEVA is working on at the moment as well? Arti: Yeah, more than happy to. And I want to go back to what you already stated, which is that in some ways, industrial AI is not new. It’s something that, even here at AVEVA, we’ve been working on for more than 20 years, really thinking about how AI can enhance applications in the industrial sector. But what has changed quite substantially in maybe the last few years is the type of AI. We’ve really moved to these general purpose foundation models, which enable a lot more people to leverage advanced AI technologies. In addition to that, in just the last couple of years, there’s been an explosion in what’s known as physical AI and the types of AI that can really accelerate the capabilities of robotics and autonomous systems. And also agentic AI, which also allows for automation at more of the software level. And what we’re seeing is that while in the past there has been some hesitancy to adopt models that don’t totally have predictable outcomes, there’s been a rapid acceleration in adoption of AI even in the industrial sector. One study suggests maybe that’s almost a 78% increase over the past just two years within the industrial sector. So yes, that’s an inflection point, but it’s almost like a step function if you think about it with respect to AI adoption. And what we’re really thinking about now at AVEVA is, how do we think about this? How do we think about incorporating these new and very powerful AI technologies in environments where customers have mission-critical operations, human safety is a significant factor? Those are some of the primary concerns that our customers have when they think about adopting any new technology. We’re doing that in narrow where these newer types of AI aren’t necessarily predictable, in terms of how they behave. That’s one of the areas we’re really thinking about is, how do we leverage these technologies while maintaining safety, while maintaining reliable operations, while still being able to deliver on the promises of the new capabilities? Megan: There’s so many things to consider at once, isn’t it? And you outlined there some of the huge developments we’ve seen in this space in recent years. What makes this such a pivotal moment for industrial AI specifically? What has changed to make some of those perhaps more out there hypotheticals much more plausible? Arti: Well, I think one of the big things, one of the big challenges within industrial AI, and this is something that as AVEVA, it’s in our blood, it’s in our core DNA, is that to really leverage any kind of digital technology in the industrial space, one of the first important things you often need to do is gather and correlate data from disparate systems, so that maybe I have telemetry coming off of a pump or a mixer, and I want to be able to correlate that with service logs so I know maybe the last time that someone had done some maintenance on that pump or mixer. And maybe I also want to be able to correlate that with the original design documentation and some of the original documentation on how to maintain that, so that if something is happening, I can do a better job diagnosing what’s going wrong and how to address it. This has always been a core challenge in the industrial space.
I think that what’s happening now is that with newer technologies, even things like graph databases and being able to leverage AI to really match disparate data sets much faster, we’re able to get to the point where if something goes wrong, instead of an operator maybe needing to spend a little bit of time doing this research to really understand how do these different components fit together of information, they may have just an iPad with an AI that can very quickly go and fetch that information, correlate it and help in real time, almost, diagnose what is happening. Even a little bit more out there, but not as out there as you might think is, I still describe this with a human operator with potentially a mobile tablet, but imagine now I can also have a robot moving through that environment also gathering data and either with onboard computation or a quick connection to an operator who doesn’t necessarily now have to go out into a dangerous, potentially somewhat hazardous space, be able to gather data and enable that very quick diagnostic as well. And I think that’s really what’s exciting right now and what all of these new technologies that I’ve talked about are helping to enable.
And I think then from our perspective, trying to really leverage all of those means that we have to think a lot now about our governance approach to this. How do we leverage AI in a way that keeps it contained, if you will? Megan: Yeah, absolutely. And on that governance point, how does AVEVA define responsible AI and what do you see as the primary benefits? Arti: Yeah, so I think for us, we think about a triple mandate around responsible AI, and that means that it’s secure, it’s efficient, and that includes environmentally efficient, and then it really preserves human safety and human oversight above all. And for us, that’s what some of our core pillars of responsible AI include. And for us, that means we think about internally as we build tools, a multi-layered governance approach where we’ve got both for how we use AI as a company and then how we deploy AI into our products. We’ve got a similar framework that we leverage across both, a kind of joint governance model, both around how we’re leveraging AI and also how we’re infusing AI into our products to allow our customers to leverage AI in their operations. But I would say one of the core foundational principles underneath all of these is that human beings still remain centered in how we think about how AI is leveraged. And so human judgment, human responsibility, human ethics are still core to how we think about where AI can add benefits, and we think about it more as augmenting rather than replacing human beings in critical decision loops, if you will. Megan: Right, which is such an important distinction, isn’t it? And in terms of the next phase of development, there’s growing sentiment that agentic AI will soon allow these industrial systems to operate much more autonomously. I wonder, what makes the risks in industrial settings different from AI risk in a purely digital context? And I suppose on the flip side, what are the benefits and opportunities of that same software defined automation? Arti: I think we can start with the risks and then go to the benefits. I think ultimately, I would say the biggest risk in the industrial setting is that we are interacting with real physical systems, often physical systems that are very capable of delivering important outcomes that keep our world running, whether that’s delivering power, whether that’s mining for natural resources. But usually. Those physical systems are also operating in hazardous environments, the equipment itself is capable of also having human safety implications. Anytime you’re interfacing software with a physical system that can have real world consequences, you have to be extra careful. And we’ve always had that, again, we’ve had that in our DNA at AVEVA from the beginning, being mindful of that end user and that end application, which is not just something on a computer screen.
Even as we’ve deployed AI over the past few decades into our systems, we’ve typically leaned toward really trying to pick the best fit model, really understand that model’s behavior if we’re going to use, for example, we have a proprietary anomaly detection model that works in a lot of operational environments. Now, as we’re thinking about some of these newer AI capabilities, which by design are hard to understand how they behave, they’re not fundamentally explainable in the way we’ve thought about even AI or statistical models in the past, and actually their behavior can change over time as they learn and get tuned to new capabilities. That’s really the potential risk of automating a physical system based on capabilities that can evolve over time is one of the things that we have to be really mindful of and really thoughtful around, where are we willing to put some of that increased automation into practice? But at the same time, I think there’s a real opportunity there, because I glossed over this idea that, okay, well, models evolve, but so do human beings, and sometimes that’s a good thing. We talk about humans having expertise and they gain understanding over their careers of how a system works. If we can actually leverage the ability of some of these newer AI capabilities, these sort of reasoning models often have underlying agentic capabilities, to learn and gather experience faster, and potentially even more importantly or more valuably, take experience that’s learned at one site and apply it to another site, then that’s a real opportunity to take what we already know works for human beings, which is that sometimes you just have to learn by doing and be able to apply that and scale that through AI. That’s where I see potentially a real opportunity in this space. One of the things that we’re really aware of in the industrial sector is just the way that our workforce is changing. I think that almost half, not quite half, of the industrial workforce is set to retire in the next five years, and that’s a lot of expertise and experience that means that we’re going to lose in the sector. If there’s ways to make sure that we can capture that in ways that are actionable, in ways that can also help a newer generation of workers that are used to experience things in a different way, apply that expertise, apply that knowledge, then I think that’s a huge opportunity within the industrial space. Megan: Yeah, absolutely. Clearly, some huge opportunities there particularly against the backdrop of other market and workforce changes as you’ve outlined there. I suppose building on that, what is the potential for these autonomous industrial AI systems when built and deployed responsibly to facilitate even faster, more sustainable industrial processes?
Arti: I think there’s a couple different ways to think about this. One is, what are the demands of potentially more environmentally sustainable processes? I’ll use one example of something that my team has been working on in partnership with Idaho National Laboratory here in the U.S. as part of their testing for AI grid resilience project. One of the challenges in the electric grid as we’re moving toward a future where we have a lot more intermittent renewable power generation sources, often distributed rooftop solar for example, is it’s becoming much more challenging to manage the grid both from really understanding where electric capacity is coming in, electric load is pulling off of the grid. In addition to that, being able to maintain just power quality because instead of having one spinning asset delivering the frequency of electricity that’s going across the grid, you’ve got a lot of smaller systems. AI is actually quite critical for being able to manage that grid of the future. And this is one of the things that we’ve been working in partnership is, how can we do that? How can we help grid operators better understand what’s happening across their system, identify where things might be behaving anomalously so they can detect that early and then remediate for it? At the same time though, I always talk about that’s an example of where AI can potentially help us accelerate the transition to a lower carbon energy future. At the same time, I think there’s a lot of conversation around the sustainability of AI itself. What are the power requirements? What are the resource requirements needed to run AI? And before I get into how do we think about that at AVEVA, because it is definitely something we think about and I think a lot of actors in the space are thinking about, I want to point out, is one of the challenges is that there’s really no agreed upon method to measure the environmental impact of AI. You see a lot of these stories, like one query on some kind of chat interface is X amount of gallons of water or this amount of electricity use. But the truth is that community-wide, there’s no agreed upon standard. One of the things that I’m involved with is a standards working group that was launched by the IEEE two years ago, a little over two years ago. I’m the chair of that working group, it’s called the P7100 Standards Working Group on measuring the environmental impact of AI. And one of the things we’re trying to do is really identify all the different areas over which we want to think about environmental sustainability associated with AI, and then having one standard methodology that works and that can be adopted both from a reporting perspective and potentially also from an oversight or regulatory perspective. That becomes really important for any sort of real understanding of how AI impacts the environment is just we have to know, what does it use? We want to look holistically at that. Our standards cover electricity and energy consumption, it covers resource usage, it covers water consumption, and it also covers carbon. But all of that said, I think it’s important for us to understand the footprint of AI, but I think it’s also important for us to simultaneously recognize that a bigger model uses more compute and more compute probably leverages more resources. The more that we can be intentional and pick the right size model for the right application, the more we can already start down that path of being more environmentally efficient in the AI that we run. One of the potentially nice side effects of that is the more purpose-built a model, the less likely it is to misbehave in unpredictable ways, if you choose correctly your architecture. There’s some sort of ancillary benefits that go beyond environmental sustainability. Megan: Fascinating. It’s really, really interesting to hear some of the work going on behind the scenes there, because I think we’ll all have heard some of the statistics around the environmental impact, as you say. So, it’s great to understand some of the work going on there to really clarify that. And we’ve talked a little bit about AI augmenting humans earlier. As systems gain greater autonomy, how should organizations think about that balance between closed loop automation, human in the loop accountability? What guardrails need to be in place? And how are governments and cross-border actors approaching those concerns as well? Arti: There’s a lot packed into that question, so I’ll try to answer it somewhat one by one. First, starting with that sort of balance between closed loop and human in the loop, automation and accountability. One of the things that we talk about in the industrial sector is from human operator to human supervisor of systems, of industrial systems. And if I’m honest about that, we’re still trying to figure that out. When a human’s in a loop in an automated system, you’re really thinking about a system may process all the data and then make a recommendation. We’ve got a solution that we’ve been working on more jointly with a few different customers where we’re able to take in a lot of their operational data, also some simulations of how their systems work, and be able to provide, say, recommendations around, you should now operate at this set point instead of that set point based on some of the other conditions that are changing in your plant. There’s a lot of interest in moving from having that be a recommended set point to have an automation that can automatically adjust the set point. And we’ve actually had some successful real-world pilots around that as well. But when you get into that, obviously it makes people very nervous if you’re changing set points on industrial equipment. That’s where you maybe continue to have some guardrails around like, you can’t go outside of a certain band, for example, of operations, or we only allow the automation in certain areas. That’s where the human supervisor, the same way a human supervisor might give employees a lot of bandwidth or a lot of flexibility to make decisions around certain things, around other things, they’re hard and fast, like this is the deadline or this is the sort of production target. It’s like that. So thinking about, how do you put the appropriate guardrails? The challenge is, AIs are not human beings, and so the guardrails look different, and I think that’s one of the things that there’s still to think about. That question of, what guardrails should be put in place? I think that it’s going to be probably dependent a little bit on the application, but over time I think we’re all going to learn, and so being really upfront and thoughtful about how we do this I think is important. But the opportunity though, potentially, is really great. I’ve already mentioned that AI can be very useful in synthesizing a lot of information and bringing to the forefront, this is what matters. That’s something that we do want to create some space to experiment around, but what’s then important is making sure that a human being understands the risk. I’ll give a little bit of a personal story because it might be illustrative. This weekend, I bought a little toy robot and decided I wanted to program it to do some stuff, and I found AI very, very helpful to get me started to read all the documentation. Putting the robot together was straightforward, they had nice instructions, but there’s a pretty heavy software developer kit that’s already there, but going through and reading all that documentation can take a while. AI was super helpful to help me surface, this is the function that does this, to help me get started. But occasionally, it made really bad recommendations on the best way to troubleshoot something. That’s where I think having the human being in the loop, being able to say, “I know that it’s not going to be the best software architect or the best troubleshooter,” is really helpful because I had the opportunity to leverage AI for what it’s good for, synthesizing and surfacing a lot of information, but being able to say, “I don’t think that’s the most efficient first step. Let’s try something else.” That’s where it’s a really different way of working, but it’s something that I think as humans get more comfortable with the power and limitations of AI, you can start to teach people how to work with it, and it’s very different from how you would work with other digital tools. One of the things that is important when it comes to AI is recognizing just the breadth of things that it touches and the breadth of impacts that it’s going to have, whether it’s on the environment, as we’ve discussed, whether it’s on productivity as sort of implicit in this entire discussion, whether it’s on labor force, whether it’s on just how economies work. From my eye, and I’m by no means a policy expert in this area, but from my eye, what I’m seeing is that different governments are prioritizing different aspects of that from how they’re thinking about regulatory and other kind of governance approaches. Megan: We’re absolutely seeing some really vastly different approaches to this, aren’t we, around the world? I wondered to illustrate some of this, if you could share perhaps some case studies you’ve seen, maybe talking us through what lessons they could hold for other organizations perhaps a little earlier in their own AI journeys. Arti: I think a lot of the key areas where we’re seeing very clear benefits of adopting AI in the industrial sector, the core to all of them is actually getting the data right and getting the right foundation of data so that you can build AI on top of it. One of our customers, SCG Chemicals, which is a petrochemical company in Thailand, they had this vision of producing a reliability platform for their operations that was as AI-driven as possible. But a core part of that was actually getting the data right, being able to have all of their information in the right place, leveraging some of AVEVA’s tools to do that. Bringing together operational data and also engineering data. And then putting on top of that AI capabilities, in this case, one of our capabilities called AVEVA Predictive Analytics that deploys some proprietary models to do things like detect anomalies, so that they could really much earlier identify potential operational risks. And instead of having unplanned downtime, translate that to planned downtime. When you translate unplanned downtime to planned downtime, you can get a huge improvement in plant reliability. At this point they’re targeting something like 99% plant reliability and a very, very high return on investment from the platform that they put in place. Again, in early pilot days, it was almost a 9x ROI, and so that’s really quite impressive. But what I want to emphasize is that it’s kind of a multi-layered problem to get it right. Megan: Those are some really striking results, though. I mean, for industrial leaders who perhaps are still hesitant to explore AI, I wonder, what do you think is the cost of taking a more wait and see approach? Arti: I think, again, this is an area where the industrial sector has a little bit of a different calculus to apply to this type of problem. In general, I think you would hear most business experts say that you can’t afford to take a wait and see approach to AI because it is so transformative across every sector of society and economy, and certainly in the industrial space, we’re not immune to that. But I do think that some of the challenges that I’ve outlined today also puts leaders in the industrial space into a mindset of sometimes potentially being a fast follower rather than the first adopter of newer technologies, just because the risk is so high. But that being said, I think one of the challenges is that the risks that we’ve covered today around infusing systems that don’t always behave predictably with physical systems is that I don’t know that there’s a lot of other sectors that are going to be solving those problems. That’s where what I’m seeing is actually a lot of interest and excitement in trying new things and willingness to do that because there’s a recognition that applied correctly and applied with the right guardrails and safeguards in place, these technologies really have truly transformative potential from a resource usage standpoint, from a human safety standpoint, from a productivity standpoint. I think the calculus has shifted a little bit in the sector to we want to actually try these things out. But then it becomes more, how do we try these things out in environments that we can make a little bit more sandboxed or safe to test out what some of the unique challenges we’re going to face in the industrial sector are? Megan: Yeah, absolutely. It’s just hard to ignore the potential nowadays, isn’t it? And just to close with a slightly future forward look, I suppose, what are you most enthusiastic about in terms of the long-term potential of responsible autonomous industrial AI systems? Arti: Yeah. Well, I think that there’s a couple of different trajectories. One of the things that I think within the next 18 months for sure, we’re going to see an increase of people with deep domain expertise who maybe didn’t grow up with a software background, be able to adopt AI assisted coding techniques to really be able to build the things that they weren’t able to build before.That’s really exciting in my career, which way back when I was actually an industrial data scientist, I would say I always felt the most energized and that I learned the most talking to the people that were on the front lines of operations, having to monitor a lot of different equipment and understanding how it all worked together. They really have a lot of expertise, and being able to put in their hands the ability to very quickly develop new applications is I think going to really lead to a lot of new ideas that many of us in the industrial software space maybe wouldn’t even have thought about and really understood how to put. I think that’s really exciting. I think then looking beyond that, I was a little bit of a, not to say a robotic skeptic, because clearly robotics and autonomous systems are going to be important, especially given the types of environments, whether they’re hazardous or remote, that industrial equipment operates in. But I just actually think things are moving a lot faster than I had anticipated. I’m really interested to see how these physical embodiments of AI systems start to really transform, again, how we think about operations. One of the things about any new technology in any environment is that to really gain value from it, you have to change the way you do things. I would say for close to 10 years now, I’ve been giving talks on AI adoption and I always say the biggest barrier to AI adoption is not anything to do with the technology. It’s not even to do with the data, although data are often the biggest sticking point, it’s really to do with, are you going to change your business processes to be able to work with the way this technology is good or not good at things? Autonomous systems, whether they’re robots or drones, are really going to change the way that we work in plants, in power systems, on mining sites. And I think that will make these types of operations more efficient, much safer for the human beings involved and more productive. So, I’m quite excited about that. I don’t know entirely what direction it will go, but one of the things that I think about a lot is that if you go back to I, Robot and Isaac Asimov’s book, the premise of those was that robotics would be the first broadly adopted AI, not computer-based systems. We went in the other direction and I think it’s really interesting to now see all of this converging. Megan: Yeah, absolutely. See robotics catches up a bit. So many exciting things on the horizon, that’s for sure. Thank you so much, for your time. That was Arti Garg, chief technologist at AVEVA, 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 us, 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. Goodbye. Learn more at aveva.com. 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.

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