Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Snowflake has thousands of enterprise customers that use the company’s data and AI technologies. Though many issues with generative AI are solved there is still lots of room for improvement. Two such issues are text-to-SQL query and AI inference. SQL is the query language used for databases and it has been around in various forms for over 50 years. Existing large language models (LLMs) have text-to-SQL capabilities that can help users to write SQL queries. Vendors including Google have introduced advanced natural language SQL capabilities. Inference is also a mature capability with common technologies including Nvidia’s TensorRT being widely deployed. While enterprises have widely deployed both technologies, they still face unresolved issues that demand solutions. Existing text-to-SQL capabilities in LLMs can generate plausible-looking queries, however they often break when executed against real enterprise databases. When it comes to inference, speed and cost efficiency are always areas where every enterprise is looking to do better. That’s where a pair of new open-source efforts from Snowflake are aiming to make a difference: Arctic-Text2SQL-R1 and Arctic Inference. Snowflake’s approach to AI research is all about the enterprise Snowflake AI Research is tackling the issues of text-to-SQL and inference optimization by fundamentally rethinking the optimization targets. Instead of chasing academic benchmarks, the team focused on what actually matters in enterprise deployment. One issue is making sure the system can adapt to real traffic patterns without forcing costly trade-offs. The other issue is understanding if the generated SQL actually execute correctly against real databases? The result is two breakthrough technologies that address persistent enterprise pain points rather than incremental research advances. “We want to deliver practical, real-world AI research that solves critical enterprise challenges,” Dwarak Rajagopal,

EnCharge AI, an AI chip startup that raised $144 million to date, announced the EnCharge EN100, an AI accelerator built on precise and scalable analog in-memory computing.

Designed to bring advanced AI capabilities to laptops, workstations, and edge devices, EN100leverages transformational efficiency to deliver 200-plus TOPS (a measure of AI performance) of total compute power within the power constraints of edge and client platforms such as laptops.

The company spun out of Princeton University on the bet that its analog memory chips will speed up AI processing and cut costs too.

“EN100 represents a fundamental shift in AI computing architecture, rooted in hardware and software innovations that have been de-risked through fundamental research spanning multiple generations of silicon development,” said Naveen Verma, CEO at EnCharge AI, in a statement. “These innovations are now being made available as products for the industry to use, as scalable, programmable AI inference solutions that break through the energy efficient limits of today’s digital solutions. This means advanced, secure, and personalized AI can run locally, without relying on cloud infrastructure. We hope this will radically expand what you can do with AI.”

Previously, models driving the next generation of AI economy—multimodal and reasoning systems—required massive data center processing power. Cloud dependency’s cost, latency, and security drawbacks made countless AI applications impossible.

EN100 shatters these limitations. By fundamentally reshaping where AI inference happens, developers can now deploy sophisticated, secure, personalized applications locally.

This breakthrough enables organizations to rapidly integrate advanced capabilities into existing products—democratizing powerful AI technologies and bringing high-performance inference directly to end-users, the company said.

EN100, the first of the EnCharge EN series of chips, features an optimized architecture that efficiently processes AI tasks while minimizing energy. Available in two form factors – M.2 for laptops and PCIe for workstations – EN100 is engineered to transform on-device capabilities:

● M.2 for Laptops: Delivering up to 200+ TOPS of AI compute power in an 8.25W power envelope, EN100 M.2 enables sophisticated AI applications on laptops without compromising battery life or portability.

● PCIe for Workstations: Featuring four NPUs reaching approximately 1 PetaOPS, the EN100 PCIe card delivers GPU-level compute capacity at a fraction of the cost and power consumption, making it ideal for professional AI applications utilizing complex models and large datasets.

EnCharge AI’s comprehensive software suite delivers full platform support across the evolving model landscape with maximum efficiency. This purpose-built ecosystem combines specialized optimization tools, high-performance compilation, and extensive development resources—all supporting popular frameworks like PyTorch and TensorFlow.

Compared to competing solutions, EN100 demonstrates up to ~20x better performance per watt across various AI workloads. With up to 128GB of high-density LPDDR memory and bandwidth reaching 272 GB/s, EN100 efficiently handles sophisticated AI tasks, such as generative language models and real-time computer vision, that typically require specialized data center hardware. The programmability of EN100 ensures optimized performance of AI models today and the ability to adapt for the AI models of tomorrow.

“The real magic of EN100 is that it makes transformative efficiency for AI inference easily accessible to our partners, which can be used to help them achieve their ambitious AI roadmaps,” says Ram Rangarajan, Senior Vice President of Product and Strategy at EnCharge AI. “For client platforms, EN100 can bring sophisticated AI capabilities on device, enabling a new generation of intelligent applications that are not only faster and more responsive but also more secure and personalized.”

Early adoption partners have already begun working closely with EnCharge to map out how EN100 will deliver transformative AI experiences, such as always-on multimodal AI agents and enhanced gaming applications that render realistic environments in real-time.

While the first round of EN100’’s Early Access Program is currently full, interested developers and OEMs can sign up to learn more about the upcoming Round 2 Early Access Program, which provides a unique opportunity to gain a competitive advantage by being among the first to leverage EN100’s capabilities for commercial applications at www.encharge.ai/en100.

Competition

EnCharge doesn’t directly compete with many of the big players, as we have a slightly different focus and strategy. Our approach prioritizes the rapidly growing AI PC and edge device market, where our energy efficiency advantage is most compelling, rather than competing directly in data center markets.

That said, EnCharge does have a few differentiators that make it uniquely competitive within the chip landscape. For one, EnCharge’s chip has dramatically higher energy efficiency (approximately 20 times greater) than the leading players. The chip can run the most advanced AI models using about as much energy as a light bulb, making it an extremely competitive offering for any use case that can’t be confined to a data center.

Secondly, EnCharge’s analog in-memory computing approach makes its chips far more compute dense than conventional digital architectures, with roughly 30 TOPS/mm2 versus 3. This allows customers to pack significantly more AI processing power into the same physical space, something that’s particularly valuable for laptops, smartphones, and other portable devices where space is at a premium. OEMs can integrate powerful AI capabilities without compromising on device size, weight, or form factor, enabling them to create sleeker, more compact products while still delivering advanced AI features.

Origins

Encharge AI has raised $144 million.

In March 2024, EnCharge partnered with Princeton University to secure an $18.6 million grant from DARPA Optimum Processing Technology Inside Memory Arrays (OPTIMA) program Optima is a $78 million effort to develop fast, power-efficient, and scalable compute-in-memory accelerators that can unlock new possibilities for commercial and defense-relevant AI workloads not achievable with current technology.

EnCharge’s inspiration came from addressing a critical challenge in AI: the inability of traditional computing architectures to meet the needs of AI. The company was founded to solve the problem that, as AI models grow exponentially in size and complexity, traditional chip architectures (like GPUs) struggle to keep pace, leading to both memory and processing bottlenecks, as well as associated skyrocketing energy demands. (For example, training a single large language model can consume as much electricity as 130 U.S. households use in a year.)

The specific technical inspiration originated from the work of EnCharge ‘s founder, Naveen Verma, and his research at Princeton University in next generation computing architectures. He and his collaborators spent over seven years exploring a variety of innovative computing architectures, leading to a breakthrough in analog in-memory computing.

This approach aimed to significantly enhance energy efficiency for AI workloads while mitigating the noise and other challenges that had hindered past analog computing efforts. This technical achievement, proven and de-risked over multiple generations of silicon, was the basis for founding EnCharge AI to commercialize analog in-memory computing solutions for AI inference.

Encharge AI launched in 2022, led by a team with semiconductor and AI system experience. The team spun out of Princeton University, with a focus on a robust and scalable analog in-memory AI inference chip and accompanying software.

The company was able to overcome previous hurdles to analog and in-memory chip architectures by leveraging precise metal-wire switch capacitors instead of noise-prone transistors. The result is a full-stack architecture that is up to 20 times more energy efficient than currently available or soon-to-be-available leading digital AI chip solutions.

With this tech, EnCharge is fundamentally changing how and where AI computation happens. Their technology dramatically reduces the energy requirements for AI computation, bringing advanced AI workloads out of the data center and onto laptops, workstations, and edge devices. By moving AI inference closer to where data is generated and used, EnCharge enables a new generation of AI-enabled devices and applications that were previously impossible due to energy, weight, or size constraints while improving security, latency, and cost.

Why it matters

Encharge AI is striving to get rid of memory bottlenecks in AI computing.

As AI models have grown exponentially in size and complexity, their chip and associated energy demands have skyrocketed. Today, the vast majority of AI inference computation is accomplished with massive clusters of energy-intensive chips warehoused in cloud data centers. This creates cost, latency, and security barriers for applying AI to use cases that require on-device computation.

Only with transformative increases in compute efficiency will AI be able to break out of the data center and address on-device AI use-cases that are size, weight, and power constrained or have latency or privacy requirements that benefit from keeping data local. Lowering the cost and accessibility barriers of advanced AI can have dramatic downstream effects on a broad range of industries, from consumer electronics to aerospace and defense.

The reliance on data centers also present supply chain bottleneck risks. The AI-driven surge in demand for high-end graphics processing units (GPUs) alone could increase total demand for certain upstream components by 30% or more by 2026. However, a demand increase of about 20% or more has a high likelihood of upsetting the equilibrium and causing a chip shortage. The company is already seeing this in the massive costs for the latest GPUs and years-long wait lists as a small number of dominant AI companies buy up all available stock.

The environmental and energy demands of these data centers are also unsustainable with current technology. The energy use of a single Google search has increased over 20x from 0.3 watt-hours to 7.9 watt-hours with the addition of AI to power search. In aggregate, the International Energy Agency (IEA) projects that data centers’ electricity consumption in 2026 will be double that of 2022 — 1K terawatts, roughly equivalent to Japan’s current total consumption.

Investors include Tiger Global Management, Samsung Ventures, IQT, RTX Ventures, VentureTech Alliance, Anzu Partners, VentureTech Alliance, AlleyCorp and ACVC Partners. The company has 66 people.

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The whale has returned. After rocking the global AI and business community early this year with the January 20 initial release of its hit open source reasoning AI model R1, the Chinese startup DeepSeek — a spinoff of formerly only locally well-known Hong Kong quantitative analysis firm High-Flyer Capital Management — has released DeepSeek-R1-0528, a significant update that brings DeepSeek’s free and open model near parity in reasoning capabilities with proprietary paid models such as OpenAI’s o3 and Google Gemini 2.5 Pro This update is designed to deliver stronger performance on complex reasoning tasks in math, science, business and programming, along with enhanced features for developers and researchers. Like its predecessor, DeepSeek-R1-0528 is available under the permissive and open MIT License, supporting commercial use and allowing developers to customize the model to their needs. Open-source model weights are available via the AI code sharing community Hugging Face, and detailed documentation is provided for those deploying locally or integrating via the DeepSeek API. Existing users of the DeepSeek API will automatically have their model inferences updated to R1-0528 at no additional cost. The current cost for DeepSeek’s API is For those looking to run the model locally, DeepSeek has published detailed instructions on its GitHub repository. The company also encourages the community to provide feedback and questions through their service email. Individual users can try it for free through DeepSeek’s website here, though you’ll need to provide a phone number or Google Account access to sign in. Enhanced reasoning and benchmark performance At the core of the update are significant improvements in the model’s ability to handle challenging reasoning tasks. DeepSeek explains in its new model card on HuggingFace that these enhancements

It’s been a little over a week since we published Power Hungry, a package that takes a hard look at the expected energy demands of AI. Last week in this newsletter, I broke down the centerpiece of that package, an analysis I did with my colleague James O’Donnell. (In case you’re still looking for an intro, you can check out this Roundtable discussion with James and our editor in chief Mat Honan, or this short segment I did on Science Friday.) But this week, I want to talk about another story that I also wrote for that package, which focused on nuclear energy. I thought this was an important addition to the mix of stories we put together, because I’ve seen a lot of promises about nuclear power as a saving grace in the face of AI’s energy demand. My reporting on the industry over the past few years has left me a little skeptical.  As I discovered while I continued that line of reporting, building new nuclear plants isn’t so simple or so fast. And as my colleague David Rotman lays out in his story for the package, the AI boom could wind up relying on another energy source: fossil fuels. So what’s going to power AI? Let’s get into it.  When we started talking about this big project on AI and energy demand, we had a lot of conversations about what to include. And from the beginning, the climate team was really focused on examining what, exactly, was going to be providing the electricity needed to run data centers powering AI models. As we wrote in the main story: 
“A data center humming away isn’t necessarily a bad thing. If all data centers were hooked up to solar panels and ran only when the sun was shining, the world would be talking a lot less about AI’s energy consumption.”  But a lot of AI data centers need to be available constantly. Those that are used to train models can arguably be more responsive to the changing availability of renewables, since that work can happen in bursts, any time. Once a model is being pinged with questions from the public, though, there needs to be computing power ready to run all the time. Google, for example, would likely not be too keen on having people be able to use its new AI Mode only during daylight hours.
Solar and wind power, then, would seem not to be a great fit for a lot of AI electricity demand, unless they’re paired with energy storage—and that increases costs. Nuclear power plants, on the other hand, tend to run constantly, outputting a steady source of power for the grid.  As you might imagine, though, it can take a long time to get a nuclear power plant up and running.  Large tech companies can help support plans to reopen shuttered plants or existing plants’ efforts to extend their operating lifetimes. There are also some existing plants that can make small upgrades to improve their output. I just saw this news story from the Tri-City Herald about plans to upgrade the Columbia Generating Station in eastern Washington—with tweaks over the next few years, it could produce an additional 162 megawatts of power, over 10% of the plant’s current capacity.  But all that isn’t going to be nearly enough to meet the demand that big tech companies are claiming will materialize in the future. (For more on the numbers here and why new tech isn’t going to come online fast enough, check out my full story.)  Instead, natural gas has become the default to meet soaring demand from data centers, as David lays out in his story. And since the lifetime of plants built today is about 30 years, those new plants could be running past 2050, the date the world needs to bring greenhouse-gas emissions to net zero to meet the goals set out in the Paris climate agreement.  One of the bits I found most interesting in David’s story is that there’s potential for a different future here: Big tech companies, with their power and influence, could actually use this moment to push for improvements. If they reduced their usage during peak hours, even for less than 1% of the year, it could greatly reduce the amount of new energy infrastructure required. Or they could, at the very least, push power plant owners and operators to install carbon capture technology, or ensure that methane doesn’t leak from the supply chain. AI’s energy demand is a big deal, but for climate change, how we choose to meet it is potentially an even bigger one. 

Imagine: China deploys hundreds of thousands of autonomous drones in the air, on the sea, and under the water—all armed with explosive warheads or small missiles. These machines descend in a swarm toward military installations on Taiwan and nearby US bases, and over the course of a few hours, a single robotic blitzkrieg overwhelms the US Pacific force before it can even begin to fight back.  Maybe it sounds like a new Michael Bay movie, but it’s the scenario that keeps the chief technology officer of the US Army up at night. “I’m hesitant to say it out loud so I don’t manifest it,” says Alex Miller, a longtime Army intelligence officer who became the CTO to the Army’s chief of staff in 2023. Even if World War III doesn’t break out in the South China Sea, every US military installation around the world is vulnerable to the same tactics—as are the militaries of every other country around the world. The proliferation of cheap drones means just about any group with the wherewithal to assemble and launch a swarm could wreak havoc, no expensive jets or massive missile installations required. 
While the US has precision missiles that can shoot these drones down, they don’t always succeed: A drone attack killed three US soldiers and injured dozens more at a base in the Jordanian desert last year. And each American missile costs orders of magnitude more than its targets, which limits their supply; countering thousand-dollar drones with missiles that cost hundreds of thousands, or even millions, of dollars per shot can only work for so long, even with a defense budget that could reach a trillion dollars next year. The US armed forces are now hunting for a solution—and they want it fast. Every branch of the service and a host of defense tech startups are testing out new weapons that promise to disable drones en masse. There are drones that slam into other drones like battering rams; drones that shoot out nets to ensnare quadcopter propellers; precision-guided Gatling guns that simply shoot drones out of the sky; electronic approaches, like GPS jammers and direct hacking tools; and lasers that melt holes clear through a target’s side.
Then there are the microwaves: high-powered electronic devices that push out kilowatts of power to zap the circuits of a drone as if it were the tinfoil you forgot to take off your leftovers when you heated them up.  That’s where Epirus comes in.  When I went to visit the HQ of this 185-person startup in Torrance, California, earlier this year, I got a behind-the-scenes look at its massive microwave, called Leonidas, which the US Army is already betting on as a cutting-edge anti-drone weapon. The Army awarded Epirus a $66 million contract in early 2023, topped that up with another $17 million last fall, and is currently deploying a handful of the systems for testing with US troops in the Middle East and the Pacific. (The Army won’t get into specifics on the location of the weapons in the Middle East but published a report of a live-fire test in the Philippines in early May.)  Up close, the Leonidas that Epirus built for the Army looks like a two-foot-thick slab of metal the size of a garage door stuck on a swivel mount. Pop the back cover, and you can see that the slab is filled with dozens of individual microwave amplifier units in a grid. Each is about the size of a safe-deposit box and built around a chip made of gallium nitride, a semiconductor that can survive much higher voltages and temperatures than the typical silicon.  Leonidas sits on top of a trailer that a standard-issue Army truck can tow, and when it is powered on, the company’s software tells the grid of amps and antennas to shape the electromagnetic waves they’re blasting out with a phased array, precisely overlapping the microwave signals to mold the energy into a focused beam. Instead of needing to physically point a gun or parabolic dish at each of a thousand incoming drones, the Leonidas can flick between them at the speed of software. The Leonidas contains dozens of microwave amplifier units and can pivot to direct waves at incoming swarms of drones. Of course, this isn’t magic—there are practical limits on how much damage one array can do, and at what range—but the total effect could be described as an electromagnetic pulse emitter, a death ray for electronics, or a force field that could set up a protective barrier around military installations and drop drones the way a bug zapper fizzles a mob of mosquitoes. I walked through the nonclassified sections of the Leonidas factory floor, where a cluster of engineers working on weaponeering—the military term for figuring out exactly how much of a weapon, be it high explosive or microwave beam, is necessary to achieve a desired effect—ran tests in a warren of smaller anechoic rooms. Inside, they shot individual microwave units at a broad range of commercial and military drones, cycling through waveforms and power levels to try to find the signal that could fry each one with maximum efficiency.  On a live video feed from inside one of these foam-padded rooms, I watched a quadcopter drone spin its propellers and then, once the microwave emitter turned on, instantly stop short—first the propeller on the front left and then the rest. A drone hit with a Leonidas beam doesn’t explode—it just falls. Compared with the blast of a missile or the sizzle of a laser, it doesn’t look like much. But it could force enemies to come up with costlier ways of attacking that reduce the advantage of the drone swarm, and it could get around the inherent limitations of purely electronic or strictly physical defense systems. It could save lives. Epirus CEO Andy Lowery, a tall guy with sparkplug energy and a rapid-fire southern Illinois twang, doesn’t shy away from talking big about his product. As he told me during my visit, Leonidas is intended to lead a last stand, like the Spartan from whom the microwave takes its name—in this case, against hordes of unmanned aerial vehicles, or UAVs. While the actual range of the Leonidas system is kept secret, Lowery says the Army is looking for a solution that can reliably stop drones within a few kilometers. He told me, “They would like our system to be the owner of that final layer—to get any squeakers, any leakers, anything like that.”

Now that they’ve told the world they “invented a force field,” Lowery added, the focus is on manufacturing at scale—before the drone swarms really start to descend or a nation with a major military decides to launch a new war. Before, in other words, Miller’s nightmare scenario becomes reality.  Why zap? Miller remembers well when the danger of small weaponized drones first appeared on his radar. Reports of Islamic State fighters strapping grenades to the bottom of commercial DJI Phantom quadcopters first emerged in late 2016 during the Battle of Mosul. “I went, ‘Oh, this is going to be bad,’ because basically it’s an airborne IED at that point,” he says. He’s tracked the danger as it’s built steadily since then, with advances in machine vision, AI coordination software, and suicide drone tactics only accelerating.  Then the war in Ukraine showed the world that cheap technology has fundamentally changed how warfare happens. We have watched in high-definition video how a cheap, off-the-shelf drone modified to carry a small bomb can be piloted directly into a faraway truck, tank, or group of troops to devastating effect. And larger suicide drones, also known as “loitering munitions,” can be produced for just tens of thousands of dollars and launched in massive salvos to hit soft targets or overwhelm more advanced military defenses through sheer numbers.  As a result, Miller, along with large swaths of the Pentagon and DC policy circles, believes that the current US arsenal for defending against these weapons is just too expensive and the tools in too short supply to truly match the threat. Just look at Yemen, a poor country where the Houthi military group has been under constant attack for the past decade. Armed with this new low-tech arsenal, in the past 18 months the rebel group has been able to bomb cargo ships and effectively disrupt global shipping in the Red Sea—part of an effort to apply pressure on Israel to stop its war in Gaza. The Houthis have also used missiles, suicide drones, and even drone boats to launch powerful attacks on US Navy ships sent to stop them. The most successful defense tech firm selling anti-drone weapons to the US military right now is Anduril, the company started by Palmer Luckey, the inventor of the Oculus VR headset, and a crew of cofounders from Oculus and defense data giant Palantir. In just the past few months, the Marines have chosen Anduril for counter-drone contracts that could be worth nearly $850 million over the next decade, and the company has been working with Special Operations Command since 2022 on a counter-drone contract that could be worth nearly a billion dollars over a similar time frame. It’s unclear from the contracts what, exactly, Anduril is selling to each organization, but its weapons include electronic warfare jammers, jet-powered drone bombs, and propeller-driven Anvil drones designed to simply smash into enemy drones. In this arsenal, the cheapest way to stop a swarm of drones is electronic warfare: jamming the GPS or radio signals used to pilot the machines. But the intense drone battles in Ukraine have advanced the art of jamming and counter-jamming close to the point of stalemate. As a result, a new state of the art is emerging: unjammable drones that operate autonomously by using onboard processors to navigate via internal maps and computer vision, or even drones connected with 20-kilometer-long filaments of fiber-optic cable for tethered control.
But unjammable doesn’t mean unzappable. Instead of using the scrambling method of a jammer, which employs an antenna to block the drone’s connection to a pilot or remote guidance system, the Leonidas microwave beam hits a drone body broadside. The energy finds its way into something electrical, whether the central flight controller or a tiny wire controlling a flap on a wing, to short-circuit whatever’s available. (The company also says that this targeted hit of energy allows birds and other wildlife to continue to move safely.) Tyler Miller, a senior systems engineer on Epirus’s weaponeering team, told me that they never know exactly which part of the target drone is going to go down first, but they’ve reliably seen the microwave signal get in somewhere to overload a circuit. “Based on the geometry and the way the wires are laid out,” he said, one of those wires is going to be the best path in. “Sometimes if we rotate the drone 90 degrees, you have a different motor go down first,” he added.
The team has even tried wrapping target drones in copper tape, which would theoretically provide shielding, only to find that the microwave still finds a way in through moving propeller shafts or antennas that need to remain exposed for the drone to fly.  Leonidas also has an edge when it comes to downing a mass of drones at once. Physically hitting a drone out of the sky or lighting it up with a laser can be effective in situations where electronic warfare fails, but anti-drone drones can only take out one at a time, and lasers need to precisely aim and shoot. Epirus’s microwaves can damage everything in a roughly 60-degree arc from the Leonidas emitter simultaneously and keep on zapping and zapping; directed energy systems like this one never run out of ammo. As for cost, each Army Leonidas unit currently runs in the “low eight figures,” Lowery told me. Defense contract pricing can be opaque, but Epirus delivered four units for its $66 million initial contract, giving a back-of-napkin price around $16.5 million each. For comparison, Stinger missiles from Raytheon, which soldiers shoot at enemy aircraft or drones from a shoulder-mounted launcher, cost hundreds of thousands of dollars a pop, meaning the Leonidas could start costing less (and keep shooting) after it downs the first wave of a swarm. Raytheon’s radar, reversed Epirus is part of a new wave of venture-capital-backed defense companies trying to change the way weapons are created—and the way the Pentagon buys them. The largest defense companies, firms like Raytheon, Boeing, Northrop Grumman, and Lockheed Martin, typically develop new weapons in response to research grants and cost-plus contracts, in which the US Department of Defense guarantees a certain profit margin to firms building products that match their laundry list of technical specifications. These programs have kept the military supplied with cutting-edge weapons for decades, but the results may be exquisite pieces of military machinery delivered years late and billions of dollars over budget. Rather than building to minutely detailed specs, the new crop of military contractors aim to produce products on a quick time frame to solve a problem and then fine-tune them as they pitch to the military. The model, pioneered by Palantir and SpaceX, has since propelled companies like Anduril, Shield AI, and dozens of other smaller startups into the business of war as venture capital piles tens of billions of dollars into defense. Like Anduril, Epirus has direct Palantir roots; it was cofounded by Joe Lonsdale, who also cofounded Palantir, and John Tenet, Lonsdale’s colleague at the time at his venture fund, 8VC. (Tenet, the son of former CIA director George Tenet, may have inspired the company’s name—the elder Tenet’s parents were born in the Epirus region in the northwest of Greece. But the company more often says it’s a reference to the pseudo-mythological Epirus Bow from the 2011 fantasy action movie Immortals, which never runs out of arrows.) 
While Epirus is doing business in the new mode, its roots are in the old—specifically in Raytheon, a pioneer in the field of microwave technology. Cofounded by MIT professor Vannevar Bush in 1922, it manufactured vacuum tubes, like those found in old radios. But the company became synonymous with electronic defense during World War II, when Bush spun up a lab to develop early microwave radar technology invented by the British into a workable product, and Raytheon then began mass-producing microwave tubes—known as magnetrons—for the US war effort. By the end of the war in 1945, Raytheon was making 80% of the magnetrons powering Allied radar across the world. From padded foam chambers at the Epirus HQ, Leonidas devices can be safely tested on drones. Large tubes remained the best way to emit high-power microwaves for more than half a century, handily outperforming silicon-based solid-state amplifiers. They’re still around—the microwave on your kitchen counter runs on a vacuum tube magnetron. But tubes have downsides: They’re hot, they’re big, and they require upkeep. (In fact, the other microwave drone zapper currently in the Pentagon pipeline, the Tactical High-power Operational Responder, or THOR, still relies on a physical vacuum tube. It’s reported to be effective at downing drones in tests but takes up a whole shipping container and needs a dish antenna to zap its targets.) By the 2000s, new methods of building solid-state amplifiers out of materials like gallium nitride started to mature and were able to handle more power than silicon without melting or shorting out. The US Navy spent hundreds of millions of dollars on cutting-edge microwave contracts, one for a project at Raytheon called Next Generation Jammer—geared specifically toward designing a new way to make high-powered microwaves that work at extremely long distances. Lowery, the Epirus CEO, began his career working on nuclear reactors on Navy aircraft carriers before he became the chief engineer for Next Generation Jammer at Raytheon in 2010. There, he and his team worked on a system that relied on many of the same fundamentals that now power the Leonidas—using the same type of amplifier material and antenna setup to fry the electronics of a small target at much closer range rather than disrupting the radar of a target hundreds of miles away. 
The similarity is not a coincidence: Two engineers from Next Generation Jammer helped launch Epirus in 2018. Lowery—who by then was working at the augmented-reality startup RealWear, which makes industrial smart glasses—joined Epirus in 2021 to run product development and was asked to take the top spot as CEO in 2023, as Leonidas became a fully formed machine. Much of the founding team has since departed for other projects, but Raytheon still runs through the company’s collective CV: ex-Raytheon radar engineer Matt Markel started in January as the new CTO, and Epirus’s chief engineer for defense, its VP of engineering, its VP of operations, and a number of employees all have Raytheon roots as well. Markel tells me that the Epirus way of working wouldn’t have flown at one of the big defense contractors: “They never would have tried spinning off the technology into a new application without a contract lined up.” The Epirus engineers saw the use case, raised money to start building Leonidas, and already had prototypes in the works before any military branch started awarding money to work on the project. Waiting for the starting gun On the wall of Lowery’s office are two mementos from testing days at an Army proving ground: a trophy wing from a larger drone, signed by the whole testing team, and a framed photo documenting the Leonidas’s carnage—a stack of dozens of inoperative drones piled up in a heap.  Despite what seems to have been an impressive test show, it’s still impossible from the outside to determine whether Epirus’s tech is ready to fully deliver if the swarms descend.  The Army would not comment specifically on the efficacy of any new weapons in testing or early deployment, including the Leonidas system. A spokesperson for the Army’s Rapid Capabilities and Critical Technologies Office, or RCCTO, which is the subsection responsible for contracting with Epirus to date, would only say in a statement that it is “committed to developing and fielding innovative Directed Energy solutions to address evolving threats.”  But various high-ranking officers appear to be giving Epirus a public vote of confidence. The three-star general who runs RCCTO and oversaw the Leonidas testing last summer told Breaking Defense that “the system actually worked very well,” even if there was work to be done on “how the weapon system fits into the larger kill chain.” And when former secretary of the Army Christine Wormuth, then the service’s highest-ranking civilian, gave a parting interview this past January, she mentioned Epirus in all but name, citing “one company” that is “using high-powered microwaves to basically be able to kill swarms of drones.” She called that kind of capability “critical for the Army.”  The Army isn’t the only branch interested in the microwave weapon. On Epirus’s factory floor when I visited, alongside the big beige Leonidases commissioned by the Army, engineers were building a smaller expeditionary version for the Marines, painted green, which it delivered in late April. Videos show that when it put some of its microwave emitters on a dock and tested them out for the Navy last summer, the microwaves left their targets dead in the water—successfully frying the circuits of outboard motors like the ones propelling Houthi drone boats.  Epirus is also currently working on an even smaller version of the Leonidas that can mount on top of the Army’s Stryker combat vehicles, and it’s testing out attaching a single microwave unit to a small airborne drone, which could work as a highly focused zapper to disable cars, data centers, or single enemy drones.  Epirus’s microwave technology is also being tested in devices smaller than the traditional Leonidas. While neither the Army nor the Navy has yet to announce a contract to start buying Epirus’s systems at scale, the company and its investors are actively preparing for the big orders to start rolling in. It raised $250 million in a funding round in early March to get ready to make as many Leonidases as possible in the coming years, adding to the more than $300 million it’s raised since opening its doors in 2018. “If you invent a force field that works,” Lowery boasts, “you really get a lot of attention.” The task for Epirus now, assuming that its main customers pull the trigger and start buying more Leonidases, is ramping up production while advancing the tech in its systems. Then there are the more prosaic problems of staffing, assembly, and testing at scale. For future generations, Lowery told me, the goal is refining the antenna design and integrating higher-powered microwave amplifiers to push the output into the tens of kilowatts, allowing for increased range and efficacy.  While this could be made harder by Trump’s global trade war, Lowery says he’s not worried about their supply chain; while China produces 98% of the world’s gallium, according to the US Geological Survey, and has choked off exports to the US, Epirus’s chip supplier uses recycled gallium from Japan.  The other outside challenge may be that Epirus isn’t the only company building a drone zapper. One of China’s state-owned defense companies has been working on its own anti-drone high-powered microwave weapon called the Hurricane, which it displayed at a major military show in late 2024.  It may be a sign that anti-electronics force fields will become common among the world’s militaries—and if so, the future of war is unlikely to go back to the status quo ante, and it might zag in a different direction yet again. But military planners believe it’s crucial for the US not to be left behind. So if it works as promised, Epirus could very well change the way that war will play out in the coming decade.  While Miller, the Army CTO, can’t speak directly to Epirus or any specific system, he will say that he believes anti-drone measures are going to have to become ubiquitous for US soldiers. “Counter-UAS [Unmanned Aircraft System] unfortunately is going to be like counter-IED,” he says. “It’s going to be every soldier’s job to think about UAS threats the same way it was to think about IEDs.”  And, he adds, it’s his job and his colleagues’ to make sure that tech so effective it works like “almost magic” is in the hands of the average rifleman. To that end, Lowery told me, Epirus is designing the Leonidas control system to work simply for troops, allowing them to identify a cluster of targets and start zapping with just a click of a button—but only extensive use in the field can prove that out. Epirus CEO Andy Lowery sees the Leonidas as providing a last line of defense against UAVs. In the not-too-distant future, Lowery says, this could mean setting up along the US-Mexico border. But the grandest vision for Epirus’s tech that he says he’s heard is for a city-scale Leonidas along the lines of a ballistic missile defense radar system called PAVE PAWS, which takes up an entire 105-foot-tall building and can detect distant nuclear missile launches. The US set up four in the 1980s, and Taiwan currently has one up on a mountain south of Taipei. Fill a similar-size building full of microwave emitters, and the beam could reach out “10 or 15 miles,” Lowery told me, with one sitting sentinel over Taipei in the north and another over Kaohsiung in the south of Taiwan. Riffing in Greek mythological mode, Lowery said of drones, “I call all these mischief makers. Whether they’re doing drugs or guns across the border or they’re flying over Langley [or] they’re spying on F-35s, they’re all like Icarus. You remember Icarus, with his wax wings? Flying all around—‘Nobody’s going to touch me, nobody’s going to ever hurt me.’” “We built one hell of a wax-wing melter.”  Sam Dean is a reporter focusing on business, tech, and defense. He is writing a book about the recent history of Silicon Valley returning to work with the Pentagon for Viking Press and covering the defense tech industry for a number of publications. Previously, he was a business reporter at the Los Angeles Times.

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The recent takedown of DanaBot, a Russian malware platform responsible for infecting over 300,000 systems and causing more than $50 million in damage, highlights how agentic AI is redefining cybersecurity operations. According to a recent Lumen Technologies post, DanaBot actively maintained an average of 150 active C2 servers per day, with roughly 1,000 daily victims across more than 40 countries.   Last week, the U.S. Department of Justice unsealed a federal indictment in Los Angeles against 16 defendants of DanaBot, a Russia-based malware-as-a-service (MaaS) operation responsible for orchestrating massive fraud schemes, enabling ransomware attacks and inflicting tens of millions of dollars in financial losses to victims.   DanaBot first emerged in 2018 as a banking trojan but quickly evolved into a versatile cybercrime toolkit capable of executing ransomware, espionage and distributed denial-of-service (DDoS) campaigns. The toolkit’s ability to deliver precise attacks on critical infrastructure has made it a favorite of state-sponsored Russian adversaries with ongoing cyber operations targeting Ukrainian electricity, power and water utilities. DanaBot sub-botnets have been directly linked to Russian intelligence activities, illustrating the merging boundaries between financially motivated cybercrime and state-sponsored espionage. DanaBot’s operators, SCULLY SPIDER, faced minimal domestic pressure from Russian authorities, reinforcing suspicions that the Kremlin either tolerated or leveraged their activities as a cyber proxy. As illustrated in the figure below, DanaBot’s operational infrastructure involved complex and dynamically shifting layers of bots, proxies, loaders and C2 servers, making traditional manual analysis impractical. Overview of DanaBot pipeline and management infrastructure. Source: Team Cymru and Lumen Technologies DanaBot shows why agentic AI is the new front line against automated threats Agentic AI played a central role in dismantling DanaBot, orchestrating predictive threat modeling, real-time telemetry correlation, infrastructure analysis and autonomous