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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More AI is advancing at a rapid clip for businesses, and that’s especially true of speech and voice AI models. Case in point: Today, ElevenLabs, the well-funded voice and AI sound effects startup founded by former Palantir engineers, debuted Conversational AI 2.0, a significant upgrade to its platform for building advanced voice agents for enterprise use cases, such as customer support, call centers, and outbound sales and marketing. This update introduces a host of new features designed to create more natural, intelligent, and secure interactions, making it well-suited for enterprise-level applications. The launch comes just four months after the debut of the original platform, reflecting ElevenLabs’ commitment to rapid development, and a day after rival voice AI startup Hume launched its own new, turn-based voice AI model, EVI 3. It also comes after new open source AI voice models hit the scene, prompting some AI influencers to declare ElevenLabs dead. It seems those declarations were, naturally, premature. According to Jozef Marko from ElevenLabs’ engineering team, Conversational AI 2.0 is substantially better than its predecessor, setting a new standard for voice-driven experiences. Enhancing naturalistic speech A key highlight of Conversational AI 2.0 is its state-of-the-art turn-taking model. This technology is designed to handle the nuances of human conversation, eliminating awkward pauses or interruptions that can occur in traditional voice systems. By analyzing conversational cues like hesitations and filler words in real-time, the agent can understand when to speak and when to listen. This feature is particularly relevant for applications such as customer service, where agents must balance quick responses with the natural rhythms of a conversation. Multilingual support Conversational AI 2.0 also introduces integrated language detection, enabling seamless multilingual discussions without the need for manual configuration. This capability ensures that the agent can recognize the language spoken by the user and respond accordingly within the same interaction. The feature caters to global enterprises seeking consistent service for diverse customer bases, removing language barriers and fostering more inclusive experiences. Enterprise-grade One of the more powerful additions is the built-in Retrieval-Augmented Generation (RAG) system. This feature allows the AI to access external knowledge bases and retrieve relevant information instantly, while maintaining minimal latency and strong privacy protections. For example, in healthcare settings, this means a medical assistant agent can pull up treatment guidelines directly from an institution’s database without delay. In customer support, agents can access up-to-date product details from internal documentation to assist users more effectively. Multimodality and alternate personas In addition to these core features, ElevenLabs’ new platform supports multimodality, meaning agents can communicate via voice, text, or a combination of both. This flexibility reduces the engineering burden on developers, as agents only need to be defined once to operate across different communication channels. Further enhancing agent expressiveness, Conversational AI 2.0 allows multi-character mode, enabling a single agent to switch between different personas. This capability could be valuable in scenarios such as creative content development, training simulations, or customer engagement campaigns. Batch outbound calling For enterprises looking to automate large-scale outreach, the platform now supports batch calls.\ Organizations can initiate multiple outbound calls simultaneously using Conversational AI agents, an approach well-suited for surveys, alerts, and personalized messages. This feature aims to increase both reach and operational efficiency, offering a more scalable alternative to manual outbound efforts. Enterprise-grade standards and pricing plans Beyond the features that enhance communication and engagement, Conversational AI 2.0 places a strong emphasis on trust and compliance. The platform is fully HIPAA-compliant, a critical requirement for healthcare applications that demand strict privacy and data protection. It also supports optional EU data residency, aligning with data sovereignty requirements in Europe. ElevenLabs reinforces these compliance-focused features with enterprise-grade security and reliability. Designed for high availability and integration with third-party systems, Conversational AI 2.0 is positioned as a secure and dependable choice for businesses operating in sensitive or regulated environments. As far as pricing is concerned, here are the available subscription plans that include Conversational AI currently listed on ElevenLabs’ website: Free: $0/month, includes 15 minutes, 4 concurrency limit, requires attribution and no commercial licensing. Starter: $5/month, includes 50 minutes, 6 concurrency limit. Creator: $11/month (discounted from $22), includes 250 minutes, 6 concurrency limit, ~$0.12 per additional minute. Pro: $99/month, includes 1,100 minutes, 10 concurrency limit, ~$0.11 per additional minute. Scale: $330/month, includes 3,600 minutes, 20 concurrency limit, ~$0.10 per additional minute. Business: $1,320/month, includes 13,750 minutes, 30 concurrency limit, ~$0.096 per additional minute. A new chapter in realistic, naturalistic AI voice interactions As stated in the company’s video introducing the new release, “The potential of conversational AI has never been greater. The time to build is now.” With Conversational AI 2.0, ElevenLabs aims to provide the tools and infrastructure for enterprises to create truly intelligent, context-aware voice agents that elevate the standard of digital interactions. For those interested in learning more, ElevenLabs encourages developers and organizations to explore its documentation, visit the developer portal, or reach out to the sales team to see how Conversational AI 2.0 can enhance their customer experiences. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More AI is advancing at a rapid clip for businesses, and that’s especially true of speech and voice AI models. Case in point: Today, ElevenLabs, the well-funded voice and AI sound effects startup founded by former Palantir engineers, debuted Conversational AI 2.0, a significant upgrade to its platform for building advanced voice agents for enterprise use cases, such as customer support, call centers, and outbound sales and marketing. This update introduces a host of new features designed to create more natural, intelligent, and secure interactions, making it well-suited for enterprise-level applications. The launch comes just four months after the debut of the original platform, reflecting ElevenLabs’ commitment to rapid development, and a day after rival voice AI startup Hume launched its own new, turn-based voice AI model, EVI 3. It also comes after new open source AI voice models hit the scene, prompting some AI influencers to declare ElevenLabs dead. It seems those declarations were, naturally, premature. According to Jozef Marko from ElevenLabs’ engineering team, Conversational AI 2.0 is substantially better than its predecessor, setting a new standard for voice-driven experiences. Enhancing naturalistic speech A key highlight of Conversational AI 2.0 is its state-of-the-art turn-taking model. This technology is designed to handle the nuances of human conversation, eliminating awkward pauses or interruptions that can occur in traditional voice systems. By analyzing conversational cues like hesitations and filler words in real-time, the agent can understand when to speak and when to listen. This feature is particularly relevant for applications such as customer service, where agents must balance quick responses with the natural rhythms of a conversation. Multilingual support Conversational AI 2.0 also introduces integrated language detection, enabling seamless multilingual discussions without the need for manual configuration. This capability ensures that the agent can recognize the language spoken by the user and respond accordingly within the same interaction. The feature caters to global enterprises seeking consistent service for diverse customer bases, removing language barriers and fostering more inclusive experiences. Enterprise-grade One of the more powerful additions is the built-in Retrieval-Augmented Generation (RAG) system. This feature allows the AI to access external knowledge bases and retrieve relevant information instantly, while maintaining minimal latency and strong privacy protections. For example, in healthcare settings, this means a medical assistant agent can pull up treatment guidelines directly from an institution’s database without delay. In customer support, agents can access up-to-date product details from internal documentation to assist users more effectively. Multimodality and alternate personas In addition to these core features, ElevenLabs’ new platform supports multimodality, meaning agents can communicate via voice, text, or a combination of both. This flexibility reduces the engineering burden on developers, as agents only need to be defined once to operate across different communication channels. Further enhancing agent expressiveness, Conversational AI 2.0 allows multi-character mode, enabling a single agent to switch between different personas. This capability could be valuable in scenarios such as creative content development, training simulations, or customer engagement campaigns. Batch outbound calling For enterprises looking to automate large-scale outreach, the platform now supports batch calls.\ Organizations can initiate multiple outbound calls simultaneously using Conversational AI agents, an approach well-suited for surveys, alerts, and personalized messages. This feature aims to increase both reach and operational efficiency, offering a more scalable alternative to manual outbound efforts. Enterprise-grade standards and pricing plans Beyond the features that enhance communication and engagement, Conversational AI 2.0 places a strong emphasis on trust and compliance. The platform is fully HIPAA-compliant, a critical requirement for healthcare applications that demand strict privacy and data protection. It also supports optional EU data residency, aligning with data sovereignty requirements in Europe. ElevenLabs reinforces these compliance-focused features with enterprise-grade security and reliability. Designed for high availability and integration with third-party systems, Conversational AI 2.0 is positioned as a secure and dependable choice for businesses operating in sensitive or regulated environments. As far as pricing is concerned, here are the available subscription plans that include Conversational AI currently listed on ElevenLabs’ website: Free: $0/month, includes 15 minutes, 4 concurrency limit, requires attribution and no commercial licensing. Starter: $5/month, includes 50 minutes, 6 concurrency limit. Creator: $11/month (discounted from $22), includes 250 minutes, 6 concurrency limit, ~$0.12 per additional minute. Pro: $99/month, includes 1,100 minutes, 10 concurrency limit, ~$0.11 per additional minute. Scale: $330/month, includes 3,600 minutes, 20 concurrency limit, ~$0.10 per additional minute. Business: $1,320/month, includes 13,750 minutes, 30 concurrency limit, ~$0.096 per additional minute. A new chapter in realistic, naturalistic AI voice interactions As stated in the company’s video introducing the new release, “The potential of conversational AI has never been greater. The time to build is now.” With Conversational AI 2.0, ElevenLabs aims to provide the tools and infrastructure for enterprises to create truly intelligent, context-aware voice agents that elevate the standard of digital interactions. For those interested in learning more, ElevenLabs encourages developers and organizations to explore its documentation, visit the developer portal, or reach out to the sales team to see how Conversational AI 2.0 can enhance their customer experiences. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

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 official 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.EPIRUS 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.  EPIRUS 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.EPIRUS 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. EPIRUS 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.EPIRUS 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. This piece has been updated to clarify that Alex Miller is a civilian intelligence official. 

The Trump administration has expanded Palantir’s work with the government, spreading the company’s technology — which could easily merge data on Americans — throughout agencies.

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Chief Technology Officer Shyam Sankar, co-founder and president Stephen Cohen and other executives also dumped millions worth of shares.

Weapon of choice? Musk’s DOGE used Meta’s Llama 2—not Grok—for gov’t slashing, report says Grok apparently wasn't an option. Ashley Belanger – May 22, 2025 5:12 pm | 19 Credit: Anadolu / Contributor | Anadolu Credit: Anadolu / Contributor | Anadolu Story text Size Small Standard Large Width * Standard Wide Links Standard Orange * Subscribers only   Learn more An outdated Meta AI model was apparently at the center of the Department of Government Efficiency's initial ploy to purge parts of the federal government. Wired reviewed materials showing that affiliates of Elon Musk's DOGE working in the Office of Personnel Management "tested and used Meta’s Llama 2 model to review and classify responses from federal workers to the infamous 'Fork in the Road' email that was sent across the government in late January." The "Fork in the Road" memo seemed to copy a memo that Musk sent to Twitter employees, giving federal workers the choice to be "loyal"—and accept the government's return-to-office policy—or else resign. At the time, it was rumored that DOGE was feeding government employee data into AI, and Wired confirmed that records indicate Llama 2 was used to sort through responses and see how many employees had resigned. Llama 2 is perhaps best known for being part of another scandal. In November, Chinese researchers used Llama 2 as the foundation for an AI model used by the Chinese military, Reuters reported. Responding to the backlash, Meta told Reuters that the researchers' reliance on a “single" and "outdated" was "unauthorized," then promptly reversed policies banning military uses and opened up its AI models for US national security applications, TechCrunch reported. "We are pleased to confirm that we’re making Llama available to US government agencies, including those that are working on defense and national security applications, and private sector partners supporting their work," a Meta blog said. "We’re partnering with companies including Accenture, Amazon Web Services, Anduril, Booz Allen, Databricks, Deloitte, IBM, Leidos, Lockheed Martin, Microsoft, Oracle, Palantir, Scale AI, and Snowflake to bring Llama to government agencies." Because Meta's models are open-source, they "can easily be used by the government to support Musk’s goals without the company’s explicit consent," Wired suggested. It's hard to track where Meta's models may have been deployed in government so far, and it's unclear why DOGE relied on Llama 2 when Meta has made advancements with Llama 3 and 4. Not much is known about DOGE's use of Llama 2. Wired's review of records showed that DOGE deployed the model locally, "meaning it’s unlikely to have sent data over the Internet," which was a privacy concern that many government workers expressed. In an April letter sent to Russell Vought, director of the Office of Management and Budget, more than 40 lawmakers demanded a probe into DOGE's AI use, which, they warned—alongside "serious security risks"—could "have the potential to undermine successful and appropriate AI adoption." That letter called out a DOGE staffer and former SpaceX employee who supposedly used Musk’s xAI Grok-2 model to create an "AI assistant," as well as the use of a chatbot named "GSAi"—"based on Anthropic and Meta models"—to analyze contract and procurement data. DOGE has also been linked to a software called AutoRIF that supercharges mass firings across the government. In particular, the letter emphasized the "major concerns about security" swirling DOGE's use of "AI systems to analyze emails from a large portion of the two million person federal workforce describing their previous week’s accomplishments," which they said lacked transparency. Those emails came weeks after the "Fork in the Road" emails, Wired noted, asking workers to outline weekly accomplishments in five bullet points. Workers fretted over responses, worried that DOGE might be asking for sensitive information without security clearances, Wired reported. Wired could not confirm if Llama 2 was also used to parse these email responses, but federal workers told Wired that if DOGE was "smart," then they'd likely "reuse their code" from the "Fork in the Road" email experiment. Why didn’t DOGE use Grok? It seems that Grok, Musk's AI model, wasn't available for DOGE's task because it was only available as a proprietary model in January. Moving forward, DOGE may rely more frequently on Grok, Wired reported, as Microsoft announced it would start hosting xAI’s Grok 3 models in its Azure AI Foundry this week, The Verge reported, which opens the models up for more uses. In their letter, lawmakers urged Vought to investigate Musk's conflicts of interest, while warning of potential data breaches and declaring that AI, as DOGE had used it, was not ready for government. "Without proper protections, feeding sensitive data into an AI system puts it into the possession of a system’s operator—a massive breach of public and employee trust and an increase in cybersecurity risks surrounding that data," lawmakers argued. "Generative AI models also frequently make errors and show significant biases—the technology simply is not ready for use in high-risk decision-making without proper vetting, transparency, oversight, and guardrails in place." Although Wired's report seems to confirm that DOGE did not send sensitive data from the "Fork in the Road" emails to an external source, lawmakers want much more vetting of AI systems to deter "the risk of sharing personally identifiable or otherwise sensitive information with the AI model deployers." A seeming fear is that Musk may start using his own models more, benefiting from government data his competitors cannot access, while potentially putting that data at risk of a breach. They're hoping that DOGE will be forced to unplug all its AI systems, but Vought seems more aligned with DOGE, writing in his AI guidance for federal use that "agencies must remove barriers to innovation and provide the best value for the taxpayer." "While we support the federal government integrating new, approved AI technologies that can improve efficiency or efficacy, we cannot sacrifice security, privacy, and appropriate use standards when interacting with federal data," their letter said. "We also cannot condone use of AI systems, often known for hallucinations and bias, in decisions regarding termination of federal employment or federal funding without sufficient transparency and oversight of those models—the risk of losing talent and critical research because of flawed technology or flawed uses of such technology is simply too high." Ashley Belanger Senior Policy Reporter Ashley Belanger Senior Policy Reporter Ashley is a senior policy reporter for Ars Technica, dedicated to tracking social impacts of emerging policies and new technologies. She is a Chicago-based journalist with 20 years of experience. 19 Comments

I just returned from SAP Sapphire 2025 in Orlando, and while SAP painted a compelling vision of an AI-powered future, I couldn’t help but think about the gap between their shiny new announcements and where most SAP customers actually are today. Let me cut through the marketing hype and give you the analyst perspective on what really matters. The Cloud Migration Elephant in the Room SAP’s biggest challenge isn’t building cool AI features – it’s that the vast majority of their customer base is still running on-premise ERP systems. While SAP was busy showcasing their AI Foundation and enhanced Joule capabilities, I kept thinking about the thousands of companies still on SAP ECC 6.0 or older versions, some of which haven’t been updated in years. Here’s the reality check: nearly every exciting AI announcement at Sapphire requires SAP’s cloud solutions. The AI Foundation? Cloud-based. Enhanced Joule with proactive capabilities? Needs cloud infrastructure. The new Business Data Cloud intelligence offerings? You guessed it – cloud only. For the average SAP shop running on-premise systems, these announcements might as well be science fiction. They’re dealing with basic integration challenges, struggling with outdated user interfaces, and fighting to get reliable reports out of their current systems. The idea of AI agents autonomously managing their supply chain seems laughably distant. AI: Useful Tool, Not Magic Wand Don’t get me wrong – the AI capabilities SAP demonstrated are genuinely impressive. The ability for Joule to anticipate user needs and provide contextual insights could indeed improve productivity. But let’s pump the brakes on SAP’s claim of “up to 30% productivity gains.” I’ve been analyzing enterprise software implementations for years, and productivity gains of that magnitude typically come from process improvements and workflow optimization, not just from adding AI on top of existing inefficiencies. If your procurement process is broken, an AI agent won’t fix it – it’ll just automate the broken process faster. The more realistic wins will come from: Reducing time spent searching for information across multiple systems Automating routine data analysis and report generation Providing better decision support through predictive analytics Streamlining repetitive tasks in finance, HR, and supply chain operations These are valuable improvements, but they’re evolutionary, not revolutionary. The Partnership Strategy: Hedging Their Bets SAP’s partnerships tell an interesting story. The Accenture ADVANCE program acknowledges that many mid-market companies need significant hand-holding to modernize their SAP environments. The Palantir integration suggests SAP recognizes they can’t be everything to everyone in the data analytics space. The Perplexity collaboration admits that their AI needs external data sources to be truly useful. These partnerships are smart business moves, but they also highlight SAP’s dependencies. If you’re planning an SAP transformation, you’re not just buying SAP – you’re buying into an ecosystem of partners and integrations that adds complexity and cost. What This Means for Your SAP Strategy If you’re currently running SAP on-premise, Sapphire 2025 should reinforce one key message: the innovation train is leaving the station, and it’s heading to the cloud. But before you panic about missing out on AI capabilities, consider these pragmatic steps: For On-Premise SAP Customers: Audit your current state first. Most companies I work with aren’t maximizing their existing SAP capabilities, let alone ready for AI enhancements. Plan your cloud migration timeline. SAP’s 2030 end-of-support deadline for older systems isn’t going away. Use that as your forcing function. Focus on data quality. AI is only as good as the data it works with. If your master data is a mess, AI won’t help. Start small with cloud integration. Consider hybrid approaches that connect your on-premise core with cloud-based analytics and AI tools. For Companies Already in SAP Cloud: Evaluate which AI features actually solve business problems you have today, not theoretical future use cases. Pilot before you scale. The productivity claims sound great, but test them in your environment with your data. Invest in change management. The biggest barrier to AI adoption isn’t technical – it’s getting people to change how they work. The Bottom Line: Evolution, Not Revolution SAP Sapphire 2025 showcased legitimate innovations that will improve how businesses operate, but let’s keep expectations realistic. The companies that will benefit most from these AI capabilities are those that have already modernized their SAP infrastructure and cleaned up their business processes. For the majority of SAP customers still on legacy systems, the real question isn’t whether AI will transform their business – it’s whether they can execute a successful modernization program that positions them to eventually take advantage of these capabilities. Your Next Steps Here’s what I recommend you do this week: Assess where you stand on your SAP modernization journey. Are you cloud-ready, or do you have years of technical debt to address first? Map your business cases for the AI capabilities that caught your attention. Can you quantify the value they’d deliver in your specific environment? Build a realistic roadmap that acknowledges both the exciting possibilities and the practical constraints of your current SAP landscape. Start the conversation with your leadership about long-term SAP strategy. The decisions you make in the next two years will determine whether you’re positioned to benefit from the AI revolution or left behind with legacy systems. The AI future SAP is promising will arrive eventually, but for most companies, the path there runs through cloud migration, data governance, and process optimization. Focus on building that foundation first, and the AI capabilities will follow when you’re actually ready to use them effectively.   The post SAP Sapphire 2025 appeared first on Gigaom.

SAP executives, led by CEO Christian Klein, told IT and business leaders attending this year’s SAP Sapphire event that by tapping into SAP’s “flywheel” combination of the broadest suite of enterprise apps, context-aware data, and world-class Business AI, they can conquer uncertainty. In a kickoff keynote at the Orlando event, they unveiled a raft of new AI tools, business apps, and partnerships designed to help customers overcome these challenges, transform their operations, and “bring out your best.” Uncertainty “All of us face one problem – every company, no matter which industry or geography. And that problem is uncertainty,” said Klein. “Uncertainty about trade disruption, about new regulatory requirements, and about how AI will transform my business and impact my workforce.” SAP can’t take away macroeconomic uncertainty, he said, “But you have my commitment that we will help your business become more resilient and more resilient – especially in times like these. We will help you bring out your best.” Flywheel effect SAP can do this by utilizing what Klein and other senior SAP executives called the “flywheel effect,” explaining that in physics this is about bringing individual elements together to create new energy. “The first element of the SAP flywheel is our applications,” Klein explained. “Not only do we have the broadest application portfolio in our industry, we also run the most-mission critical business processes end-to-end. No one understands business and industry like SAP does.” The second element of the flywheel is data. “The apps give us the richest treasure of mission-critical business data, and no other tech company has more access to semantically rich business data,” he said. This data then feeds into the third element of the flywheel: AI. Having access to an enormous amount of business data is key to developing extremely powerful Business AI. SAP Business Suite “Then we make the flywheel spin by infusing AI back into the apps, which run your company,” Klein said before introducing the first of several on-stage demos showing how a company can manage uncertainty like shifting tariffs with the power of SAP Business Suite and AI. Expanding the flywheel theme, Muhammad Alam, member of the Executive Board of SAP SE leading SAP Product and Engineering, noted that the demo highlighted how the flywheel of apps, data, and AI is critical to the company’s quest to reimagine business applications. To achieve this, a company needs an application suite that is end-to-end and seamlessly integrated; a data layer that provides a harmonized data model with high-quality data, strong governance and a rich semantical context; and AI that is built on high-quality data and natively available in the apps used every day. Alam noted that while the concept of the flywheel effect isn’t new and most company CIOs have been working hard to achieve it, they have been frustrated by the disparate and heterogeneous applications landscape often built up over time out of necessity. Fragile balance Most companies spend a significant amount of time and money integrating the apps they use together and extracting and bringing together disparate data sets. “In fact,” Alam said, “We estimate that organizations can spend up to 80 percent of their effort and budget in putting together this fragile balance of apps and data, leaving only 20 percent of your resources to focus on value creation.” While this fragile balance has existed for a while, he explained that it breaks down completely in the age of AI, because to create exponential value you need to have your data available with the end-to-end business process context. “Enabling this in a disparate, disconnected landscape is close to impossible,” he said. The only real solution is to move to a best of breed as a suite, or what SAP calls “suite-as-a- service,” Alam told the audience. “The new SaaS is what will allow you to take advantage of the flywheel effect.” “In the age of AI, true differentiation will lie in how you create value from this end-to-end context for your organization,” he said. “This can only happen when you simplify and innovate your core, and not add another layer of complexity to your landscape.” This is what the SAP Business Suite offers. SAP Business Data Cloud Klein and Alam also emphasized the importance of the launch of SAP Business Data Cloud in February and its positive reception from customers and partners. Klein described it as “the crystal ball we need in times of uncertainty, because it offers you the biggest semantical data layer in the industry harmonizing SAP and non-SAP data.” Klein also announced the expansion of business data analysis capabilities in SAP Business Data Cloud through a new partnership with Palantir, while Alam unveiled a new partnership with Adobe to develop an intelligent application on SAP Business Data Cloud that combines SAP Cloud ERP data with data in the Adobe Experience Platform. To enhance the power of the flywheel, Klein and Philipp Herzig, CTO and chief AI officer of SAP, also made a series of AI-related announcements. “At SAP, our goal is to make every end user 30 percent more productive,” said Klein. Joule To achieve this goal, SAP is enabling end users to use human language to interact with Joule, SAP’s generative AI copilot, as our new UX, he said. Joule will provide answers to any business questions they have, while in the backend, very smart Joule Agents will take over tasks from employees across end-to-end processes. “Today we are proud to announce that we are making Joule available to you everywhere and giving you answers on everything,” the SAP CEO said. By combining Joule with WalkMe, the adoption platform acquired by SAP last year, Joule becomes an omnipresent, always-on, proactive, and personalized AI assistant. “No matter in which app the end user is working, SAP or non-SAP, Joule will be everywhere,” Klein said. In addition to making Joule available everywhere, SAP is also enabling Joule to answer everything through a new partnership with Perplexity whose CEO, Aravind Srinivas, joined Klein onstage to demonstrate how Perplexity’s answer engine will integrate with SAP data to provide trusted answers to everyday business questions. Business AI During his keynote section, Herzig highlighted the progress made with SAP Business AI. “We committed that 80 percent of our most-used transactions would be managed via Joule, and we have delivered more than 1,600 skills supporting a wide range of tasks across HR, finance, supply chain, and more out-of-the-box,” he said. “We also introduced Joule for developers and Joule for consultants, helping tens of thousands of people every day to achieve more.” Herzig said SAP is on track to deliver more than 400 AI scenarios by the end of this year, and he is thrilled to see more than 34,000 customers are already using SAP Business AI every day to transform the way they work. For example, he shared that British Telecom is already saving 85 percent of the time it took to find the right skilled candidates for jobs with Joule, which is also helping employees to complete HR tasks in 40 percent less time. “Our AI strategy is super simple, it’s really about three things,” Herzig said. “Joule as the new UI in the age of AI, its Joule Agents to reimagine business processes and AI Foundation on SAP BTP, which is becoming the operating system for AI.” Joule Agents While Joule transforms the experience layer, Herzig said, Joule Agents are transforming the business process layer, “reimagining business processes from the ground up.” Instead of designing enterprise apps around the principle of “insight-to-action,” Joule Agents streamline decision-making through reasoning and action. “With this, we are making a paradigm shift in enterprise software, going really from insight to action to reason and action with AI,” Herzig said. “Joule Agents will take your business processes to a completely new level.” Soon, he said, companies will have a huge team of agents – Joule Agents, third-party agents, and custom agents built in Joule Studio – as part of their workforce. To control and govern these agents, Herzig announced the launch of AI agent hub in SAP LeanIX. “SAP LeanIX already knows your business processes and enterprise application landscape, so it was just a logical step to expand the methodology to Joule Agents too,” he said. Among other innovations, Herzig announced that SAP is introducing more than 60 new and updated capabilities into AI Foundation, including a prompt optimizer in AI Foundation developed in joint collaboration with Not Diamond, a frontier AI lab out of San Francisco. Prompt optimizerautomatically converts existing prompts to different models, and a futuristic new partnership with Neura Robotics and Nvidia is designed to blend digital and physical worlds and help empower humanoid robots to achieve business outcomes. SAP runs SAP Rounding out the kickoff keynote, Sebastian Steinhaeuser, chief operating officer and newest member of the Executive Board of SAP SE, explained how the company has achieved considerable gains by running its own operations on SAP using the combined power of apps, data, and AI. “With Joule, our developers, consultants, and HR are seeing strong efficiency benefits today. Across SAP, we track several hundreds of millions in AI-powered efficiency gains committed to our budget.” Steinhaeuser also discussed the benefits of SAP Business Data Cloud, noting, “Earlier this year, we became customer zero of SAP Business Data Cloud, offering us distinct advantages: First, we can connect all data in one semantically rich data layer. Second, this helps us generate better insights faster, and across our financial, workforce, and sales planning. And third, we can fuel even better Business AI.” Next up, he said, we are excited to go live with the first set of intelligent applications, starting with People Intelligence. Moving to SAP’s own app layer, Steinhaeuser shared more about SAP’s own RISE with SAP journey. He also emphasized the importance of tools like SAP Signavio and SAP LeanIX in mapping process landscapes and enterprise architecture, for SAP’s own transformation journey. “Many of our meetings no longer start with PowerPoint, instead we use Signavio and LeanIX to discuss how we can improve our processes and architecture,” he said. Summary Summing up the keynote messages, Christian Klein said: “Together we have seen how the SAP Business Suite infused with Business AI can help you in times of uncertainty, and how we help you to get there faster, simpler, and at lower cost.” “Let me finish this keynote with a personal commitment which is really dear to my heart: We will continue to listen to your needs, we will continue to deliver great innovations, and we will stay by your side along the entire journey – especially in times of uncertainty – so that you can run your business in sheer harmony.” This story also appears on SAP.com.