Walmart cracks enterprise AI at scale: Thousands of use cases, one framework

Electricity powers modern life. And we’re accelerating a wide range of technologies, from enhanced geothermal to advanced nuclear to even fusion technologies, that can enable a future where on-demand electricity needs are met with clean energy, every hour of every day.Today, we’re adding another technology to our portfolio: long duration energy storage (LDES). Through a new long-term partnership with Energy Dome, we plan to support multiple commercial projects globally to deploy their LDES technology.Energy Dome’s novel CO₂ Battery can store excess clean energy and then dispatch it back to the grid for 8-24 hours, bridging the gap between when renewable energy is generated and when it is needed. With this commercial partnership, as well as an investment in the company, we believe these projects can unlock new clean energy for grids where we operate before 2030, helping meet near-term electricity system needs and moving us closer to our 24/7 carbon-free energy goal.By bringing this first-of-a-kind LDES technology to market faster, we aim to rapidly bring its potential to communities everywhere — making reliable, affordable electricity available around the clock and supporting the resilience of grids as they integrate growing amounts of renewable energy sources.Why it’s importantLithium-ion batteries, which typically store and dispatch power for 4 hours or less, have been critical for adding electricity capacity to grids and managing short-term fluctuations in renewable generation — when the sun isn’t shining or the wind isn’t blowing. Google’s support for these shorter-duration batteries has helped the grids we rely on, from Belgium to Nevada, meet peak electricity demand and reduce the need to ramp up fossil fuel power plants.But what if we could store and dispatch clean energy for more than a few hours, or even a full day? Studies by the Electric Power Research Institute show that LDES technologies can cost-effectively integrate a growing volume of renewables onto power systems and contribute to more flexible, reliable grids. The LDES Council estimates that deploying up to 8 terawatts (TW) of LDES by 2040 could result in $540 billion in annual savings globally, thanks in part to their ability to optimize grids.How the technology worksEnergy Dome’s novel approach to energy storage uses carbon dioxide (CO₂) held in a unique dome-shaped battery. When there’s an abundance of renewable energy on the grid, the system uses that power to compress CO₂ gas into a liquid. When the grid needs more clean power, the liquid CO₂ expands back into a hot gas under pressure, creating a powerful force — much like steam escaping a pressure cooker — which spins a turbine. This spinning turbine generates carbon-free energy that can flow directly back into the grid for durations ranging from 8 to 24 hours.Energy Dome has already signed contracts to build commercial scale projects in Italy, the U.S., and India. And their technology has already proven successful, having injected electrons into the Italian grid for more than three years, thanks to their commercial demonstration facility and now with their full-scale 20 megawatt (MW) commercial plant in Sardinia, Italy.Why scale is crucialLDES has the potential to commercialize much faster than some of the other advanced clean energy technologies in our portfolio. This means we can use it in the near term to help the electricity system grow more flexibly and reliably, alongside other tools we’re developing such as data center demand response.By supporting multiple commercial deployments of Energy Dome’s technology globally, we aim to bring this technology to scale faster and at lower costs. Beyond our long-term collaboration with Energy Dome, we plan to support a growing range of LDES technologies under development through both commercial agreements that can catalyze wider market adoption of more mature technologies, like Energy Dome’s, as well as earlier-stage investments.To remove barriers to the deployment and commercialization of LDES and other advanced carbon-free energy technologies, we’re also advocating for clean energy policies, ensuring that energy markets fully value firm, flexible carbon-free technologies, and advancing policy measures that enable infrastructure essential for grid decarbonization and energy security.We’re excited to take this first step with Energy Dome to unlock the full potential of LDES. Our partnership will strengthen grid resilience while enabling us to power our technologies, grow our economies and keep the lights on in our homes with 24/7 clean energy.

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On Wednesday, President Trump issued three executive orders, delivered a speech, and released an action plan, all on the topic of continuing American leadership in AI.  The plan contains dozens of proposed actions, grouped into three “pillars”: accelerating innovation, building infrastructure, and leading international diplomacy and security. Some of its recommendations are thoughtful even if incremental, some clearly serve ideological ends, and many enrich big tech companies, but the plan is just a set of recommended actions.  The three executive orders, on the other hand, actually operationalize one subset of actions from each pillar:  One aims to prevent “woke AI” by mandating that the federal government procure only large language models deemed “truth-seeking” and “ideologically neutral” rather than ones allegedly favoring DEI. This action purportedly accelerates AI innovation. A second aims to accelerate construction of AI data centers. A much more industry-friendly version of an order issued under President Biden, it makes available rather extreme policy levers, like effectively waiving a broad swath of environmental protections, providing government grants to the wealthiest companies in the world, and even offering federal land for private data centers. A third promotes and finances the export of US AI technologies and infrastructure, aiming to secure American diplomatic leadership and reduce international dependence on AI systems from adversarial countries. This flurry of actions made for glitzy press moments, including an hour-long speech from the president and onstage signings. But while the tech industry cheered these announcements (which will swell their coffers), they obscured the fact that the administration is currently decimating the very policies that enabled America to become the world leader in AI in the first place.
To maintain America’s leadership in AI, you have to understand what produced it. Here are four specific long-standing public policies that helped the US achieve this leadership—advantages that the administration is undermining.  Investing federal funding in R&D  Generative AI products released recently by American companies, like ChatGPT, were developed with industry-funded research and development. But the R&D that enables today’s AI was actually funded in large part by federal government agencies—like the Defense Department, the National Science Foundation, NASA, and the National Institutes of Health—starting in the 1950s. This includes the first successful AI program in 1956, the first chatbot in 1961, and the first expert systems for doctors in the 1970s, along with breakthroughs in machine learning, neural networks, backpropagation, computer vision, and natural-language processing.
American tax dollars also funded advances in hardware, communications networks, and other technologies underlying AI systems. Public research funding undergirded the development of lithium-ion batteries, micro hard drives, LCD screens, GPS, radio-frequency signal compression, and more in today’s smartphones, along with the chips used in AI data centers, and even the internet itself. Instead of building on this world-class research history, the Trump administration is slashing R&D funding, firing federal scientists, and squeezing leading research universities. This week’s action plan recommends investing in R&D, but the administration’s actual budget proposes cutting nondefense R&D by 36%. It also proposed actions to better coordinate and guide federal R&D, but coordination won’t yield more funding. Some say that companies’ R&D investments will make up the difference. However, companies conduct research that benefits their bottom line, not necessarily the national interest. Public investment allows broad scientific inquiry, including basic research that lacks immediate commercial applications but sometimes ends up opening massive markets years or decades later. That’s what happened with today’s AI industry. Supporting immigration and immigrants Beyond public R&D investment, America has long attracted the world’s best researchers and innovators. Today’s generative AI is based on the transformer model (the T in ChatGPT), first described by a team at Google in 2017. Six of the eight researchers on that team were born outside the US, and the other two are children of immigrants.  This isn’t an exception. Immigrants have been central to American leadership in AI. Of the 42 American companies included in the 2025 Forbes ranking of the 50 top AI startups, 60% have at least one immigrant cofounder, according to an analysis by the Institute for Progress. Immigrants also cofounded or head the companies at the center of the AI ecosystem: OpenAI, Anthropic, Google, Microsoft, Nvidia, Intel, and AMD. “Brain drain” is a term that was first coined to describe scientists’ leaving other countries for the US after World War II—to the Americans’ benefit. Sadly, the trend has begun reversing this year. Recent studies suggest that the US is already losing its AI talent edge through the administration’s anti-immigration actions (including actions taken against AI researchers) and cuts to R&D funding. Banning noncompetes Attracting talented minds is only half the equation; giving them freedom to innovate is just as crucial.

Silicon Valley got its name because of mid-20th-century companies that made semiconductors from silicon, starting with the founding of Shockley Semiconductor in 1955. Two years later, a group of employees, the “Traitorous Eight,” quit to launch a competitor, Fairchild Semiconductor. By the end of the 1960s, successive groups of former Fairchild employees had left to start Intel, AMD, and others collectively dubbed the “Fairchildren.”  Software and internet companies eventually followed, again founded by people who had worked for their predecessors. In the 1990s, former Yahoo employees founded WhatsApp, Slack, and Cloudera; the “PayPal Mafia” created LinkedIn, YouTube, and fintech firms like Affirm. Former Google employees have launched more than 1,200 companies, including Instagram and Foursquare. AI is no different. OpenAI has founders that worked at other tech companies and alumni who have gone on to launch over a dozen AI startups, including notable ones like Anthropic and Perplexity. This labor fluidity and the innovation it has created were possible in large part, according to many historians, because California’s 1872 constitution has been interpreted to prohibit noncompete agreements in employment contracts—a statewide protection the state originally shared only with North Dakota and Oklahoma. These agreements bind one in five American workers. Last year, the Federal Trade Commission under President Biden moved to ban noncompetes nationwide, but a Trump-appointed federal judge has halted the action. The current FTC has signaled limited support for the ban and may be comfortable dropping it. If noncompetes persist, American AI innovation, especially outside California, will be limited. Pursuing antitrust actions One of this week’s announcements requires the review of FTC investigations and settlements that “burden AI innovation.” During the last administration the agency was reportedly investigating Microsoft’s AI actions, and several big tech companies have settlements that their lawyers surely see as burdensome, meaning this one action could thwart recent progress in antitrust policy. That’s an issue because, in addition to the labor fluidity achieved by banning noncompetes, antitrust policy has also acted as a key lubricant to the gears of Silicon Valley innovation.  Major antitrust cases in the second half of the 1900s, against AT&T, IBM, and Microsoft, allowed innovation and a flourishing market for semiconductors, software, and internet companies, as the antitrust scholar Giovanna Massarotto has described. William Shockley was able to start the first semiconductor company in Silicon Valley only because AT&T had been forced to license its patent on the transistor as part of a consent decree resolving a DOJ antitrust lawsuit against the company in the 1950s. 
The early software market then took off because in the late 1960s, IBM unbundled its software and hardware offerings as a response to antitrust pressure from the federal government. As Massarotto explains, the 1950s AT&T consent decree also aided the flourishing of open-source software, which plays a major role in today’s technology ecosystem, including the operating systems for mobile phones and cloud computing servers. Meanwhile, many attribute the success of early 2000s internet companies like Google to the competitive breathing room created by the federal government’s antitrust lawsuit against Microsoft in the 1990s. 
Over and over, antitrust actions targeting the dominant actors of one era enabled the formation of the next. And today, big tech is stifling the AI market. While antitrust advocates were rightly optimistic about this administration’s posture given key appointments early on, this week’s announcements should dampen that excitement.  I don’t want to lose focus on where things are: We should want a future in which lives are improved by the positive uses of AI.  But if America wants to continue leading the world in this technology, we must invest in what made us leaders in the first place: bold public research, open doors for global talent, and fair competition.  Prioritizing short-term industry profits over these bedrock principles won’t just put our technological future at risk—it will jeopardize America’s role as the world’s innovation superpower.  Asad Ramzanali is the director of artificial intelligence and technology policy at the Vanderbilt Policy Accelerator. He previously served as the chief of staff and deputy director of strategy of the White House Office of Science and Technology Policy under President Biden.

Hyperscalers and data center developers are in a pickle: They all want to add computing power tomorrow, but utilities frequently play hard to get, citing years-long waits for grid connections.

“All the AI data centers are struggling to get connected,” Amit Narayan, founder and CEO of Gridcare, told TechCrunch. “They’re so desperate. They are looking for solutions, which may or may not happen. Certainly not in the five-year timelines they cite.”

That has led many data centers to pursue what’s called “behind the meter” power sources — basically, they build their own power plants, a costly endeavor that hints at just how desperate they are for electricity.

But Narayan knew there was plenty of slack in the system, even if utilities themselves haven’t discovered it yet. He has studied the grid for the last 15 years, first as a Stanford researcher then as a founder of another company. “How do we create more capacity when everyone thinks that there is no capacity on the grid?” he said.

Narayan said that Gridcare, which has been operating in stealth, has already discovered several places where extra capacity exists, and it’s ready to play matchmaker between data centers and utilities.

Gridcare recently closed an oversubscribed $13.5 million seed round, the company told TechCrunch. The round was led by Xora, Temasek’s deep tech venture firm, with participation from Acclimate Ventures, Aina Climate AI Ventures, Breakthrough Energy Discovery, Clearvision, Clocktower Ventures, Overture Ventures, Sherpalo Ventures, and WovenEarth.

For Narayan and his colleagues at Gridcare, the first step to finding untapped capacity was to map the existing grid. Then the company used generative AI to help forecast what changes might be implemented in the coming years. It also layers on other details, including the availability of fiber optic connections, natural gas, water, extreme weather, permitting, and community sentiment around data center construction and expansion. 

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“There are 200,000-plus scenarios that you have to consider every time you’re running this study,” Narayan said.

To make sure it’s not running afoul of regulations, Gridcare then takes that data and weighs it against federal guidelines that dictate grid usage. Once it finds a spot, it starts talking with the relevant utility to verify the data.

“We’ll find out where the maximum bang for the buck is,” Narayan said.

At the same time, Gridcare works with hyperscalers and data center developers to identify where they are looking to expand operations or build new ones. “They have already told us what they’re willing to do. We know the parameters under which they can operate,” he said.

That’s when the matchmaking begins.

Gridcare sells its services to data center developers, charging them a fee based on how many megawatts of capacity the startup can unlock for them. “That fee is significant for us, but it’s negligible for data centers,” Narayan said.

For some data centers, the price of admission might be forgoing grid power for a few hours here and there, relying on on-site backup power instead. For others, the path might be clearer if their demand helps green-light a new grid-scale battery installation nearby. In the future, the winner might be the developer that is willing to pay more. Utilities have already approached Gridcare inquiring about auctioning access to newfound capacity.

Regardless of how it happens, Narayan thinks that Gridcare can unlock more than 100 gigawatts of capacity using its approach. “We don’t have to solve nuclear fusion to do this,” he said.

Update: Corrected spare capacity on the grid to gigawatts from megawatts.