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Podcast: Vaire Computing Bets on Reversible Logic for ‘Near Zero Energy’ AI Data Centers

The AI revolution is charging ahead—but powering it shouldn’t cost us the planet. That tension lies at the heart of Vaire Computing’s bold proposition: rethinking the very logic that underpins silicon to make chips radically more energy efficient. Speaking on the Data Center Frontier Show podcast, Vaire CEO Rodolfo Rossini laid out a compelling case […]

The AI revolution is charging ahead—but powering it shouldn’t cost us the planet. That tension lies at the heart of Vaire Computing’s bold proposition: rethinking the very logic that underpins silicon to make chips radically more energy efficient.

Speaking on the Data Center Frontier Show podcast, Vaire CEO Rodolfo Rossini laid out a compelling case for why the next era of compute won’t just be about scaling transistors—but reinventing the way they work.

“Moore’s Law is coming to an end, at least for classical CMOS,” Rossini said. “There are a number of potential architectures out there—quantum and photonics are the most well known. Our bet is that the future will look a lot like existing CMOS, but the logic will look very, very, very different.”

That bet is reversible computing—a largely untapped architecture that promises major gains in energy efficiency by recovering energy lost during computation.

A Forgotten Frontier

Unlike conventional chips that discard energy with each logic operation, reversible chips can theoretically recycle that energy. The concept, Rossini explained, isn’t new—but it’s long been overlooked.

“The tech is really old. I mean really old,” Rossini said. “The seeds of this technology were actually at the very beginning of the industrial revolution.”

Drawing on the work of 19th-century mechanical engineers like Sadi Carnot and later insights from John von Neumann, the theoretical underpinnings of reversible computing stretch back decades. A pivotal 1961 paper formally connected reversibility to energy efficiency in computing. But progress stalled—until now.

“Nothing really happened until a team of MIT students built the first chip in the 1990s,” Rossini noted. “But they were trying to build a CPU, which is a world of pain. There’s a reason why I don’t think there’s been a startup trying to build CPUs for a very, very long time.”

AI, the Great Enabler

The rise of AI may have unlocked reversible computing’s real-world potential. Rossini described how the shift from CPUs to GPUs and specialized AI accelerators created an opening for new logic architectures to gain traction.

“It’s still general-purpose enough to run a model that can do a lot of different things,” he said, “But the underlying architecture is much simpler.”

Rossini founded Vaire after a personal epiphany. “I saw that the future of AI was going to be brilliant—everything was going to be AI. But at the same time, we were going to basically boil the oceans to power our data centers.”

So he started looking for alternatives. One obscure MIT project—and a conspicuous lack of competition—led him to reversible computing.

“Nobody was working on this, which is usually peculiar,” he said. “Dozens of quantum computing companies, hundreds of photonic companies… and this area was a bit orphan.”

From Theory to Silicon

Today, Vaire has completed a tape-out of its first test chip. It’s not commercial yet, but it proves the design works in silicon.

“We proved the theory. Great. Now we proved the practice. And we are on to proving the commercial viability,” Rossini said.

And while he’s quick to acknowledge the difficulty of building chips from scratch, Rossini is bullish on the comparative simplicity of their approach.

“I’m not saying it’s super simple,” he said. “But it’s not the order of magnitude of building a quantum computer. It’s basically CMOS.”

Vaire has also secured foundational patents in a space that remains curiously underexplored.

“We found all the basic patents and everything in a space where no one was working,” Rossini said.

Product, Not IP

Unlike some chip startups focused on licensing intellectual property, Vaire is playing to win with full-stack product development.

“Right now we’re not really planning to license. We really want to build product,” Rossini emphasized. “It’s very important today, especially from the point of view of the customer. It’s not just the hardware—it’s the hardware and software.”

Rossini points to Nvidia’s CUDA ecosystem as the gold standard for integrated hardware/software development.

“The reason why Nvidia is so great is because they spent a decade perfecting their CUDA stack,” he said. “You can’t really think of a chip company being purely a hardware company anymore. Better hardware is the ticket to the ball—and the software is how you get to dance.”

A great metaphor for a company aiming to rewrite the playbook on compute logic.

The Long Game: Reimagining Chips Without Breaking the System

In an industry where even incremental change can take years to implement, Vaire Computing is taking a pragmatic approach to a deeply ambitious goal: reimagining chip architecture through reversible computing — but without forcing the rest of the computing stack to start over.

“We call it the Near-Zero Energy Chip,” said Rossini. “And by that we mean a chip that operates at the lowest possible energy point compared to classical chips—one that dissipates the least amount of energy, and where you can reuse the software and the manufacturing supply chain.”

That last point is crucial. Vaire isn’t trying to uproot the hyperscale data center ecosystem — it’s aiming to integrate into it. The company’s XPU architecture is designed to deliver breakthrough efficiency while remaining compatible with existing tooling, manufacturing processes, and software paradigms.

Build the Future—But Don’t Break the Present

Rossini acknowledged that other new compute architectures, from FET variants to exotic materials, often fall short not because of technical limitations, but because they require sweeping changes in manufacturing or software support.

“You might have the best product in the world. It doesn’t matter,” Rossini said. “If it requires a change in manufacturing, you have to spend a decade convincing a leading foundry to adopt it—and that’s a killer.”

Instead, Vaire is working within the existing CMOS ecosystem—what Rossini calls “the 800-pound gorilla in chip manufacturing”—and limiting how many pieces they move on the board at once.

“We could, in theory, achieve much better performance if we changed everything: software stack, manufacturing method, and computer architecture,” he said. “But the more you change, the more problems you create for the customer. Suddenly they need to support a new architecture, test it, and port applications. That’s a huge opportunity cost.”

One Vaire customer, Rossini noted, is locked in a tight competitive race. If they spent even six months porting software to a novel architecture, “they basically lost the race.”

So Vaire’s first objective is to prove that its chip can meet or exceed current expectations while fitting into existing systems as seamlessly as possible.

A Roadmap, Not a Revolution

The company’s ambitions stretch over the next 10 to 15 years—but Rossini was quick to clarify that this isn’t a long wait for a product.

“I don’t mean we ship a product in 10 years. We want to ship a product in three,” he said. “But we think we’ll have multiple iterations of products before we reach the true end of CMOS.”

He likened the strategy to Apple’s evolution of the iPhone—from version 1 to version 16, the progress was steady, deliberate, and transformative without ever being jarring.

“There isn’t really a quantum jump from one version to another,” Rossini said. “But there’s constant improvement. You ship a product every year or 18 months, and customers keep getting better and better performance.”

Software Matters—But So Does Timing

Asked whether Vaire is building a parallel software stack, Rossini responded with a nuanced yes.

“The greatest thing is that creating a software stack in 2025 is not the same thing as doing it in 2006,” he said. “There’s a very clear dominant open-source ecosystem that we have to support. That means PyTorch, possibly TensorFlow. But we don’t have to support everything.”

Vaire is focusing first on inference workloads rather than training—an important decision that narrows the company’s engineering scope and accelerates go-to-market potential.

“Training is a really complicated beast,” Rossini admitted. “Inference is where we want to play.”

Even more interesting is Vaire’s counterintuitive take on software efficiency. The company is betting that even if the software isn’t fully optimized at launch, the chip’s energy efficiency and performance gains will be so significant that users will adopt it anyway.

“Basically, the idea is that the car is inefficient, but it’s so comfortable and beautiful that—even if the software isn’t perfect—it’s still a net gain,” Rossini said. “And over time, you refine the software, you push the limits of the hardware.”

Reversible, Relatable, Ready for Reality: Disruptive—but Not Destructive

As Vaire moves from test chip to commercial product, Rossini’s team is walking a tightrope: introducing genuinely novel compute logic in a way that doesn’t break developer workflows, supply chains, or hyperscaler procurement models.

“Reversible computing is disrupting enough,” said Rossini. “We don’t want to break everything at the same time.”

In an AI-powered world where compute demand is exploding and energy is the limiting factor, that kind of surgical disruption may be exactly what the industry needs.

So what do you call a chip that’s not quite a CPU, not exactly a GPU, and is building a new compute architecture without breaking the software world? For now, it’s “XPU,” says Rodolfo. “It’s CPU-esque, GPU-esque… but we’re not building a graphics pipeline.”

Getting that XPU into data centers, though, is not about trying to reinvent the wheel—or the hyperscale rack. Rodolfo’s playbook draws on his experience working with the Department of Defense, “probably the most complex supply chain in the world.” You don’t sell directly to the big players; instead, you embed into the existing machinery. “You partner with Tier 2 and Tier 3 suppliers,” he explains. “They already know the power budgets, size, space, design constraints… so we co-design with them to get into the customer’s hands.”

You can’t stop the train of computing, he adds. “We still use COBOL and Fortran in production. You can’t say, ‘Hey, let’s stop everything for a better mousetrap.’” But you can jump onboard, just in the right railcar. “We’re injecting ourselves into existing supply chains,” he says. “Maybe we’re selling IP, maybe chips—but always in the path of upgrade, not disruption for disruption’s sake.”

Less Heat, More Compute

One of the central advantages of reversible computing is thermal efficiency. Traditional chips discard charge with every gate switch. That’s fine when you’ve got a handful of transistors—but in the modern era of ultra-dense chips, that adds up fast. “If you have low-leakage nodes,” Rodolfo notes, “that discarded charge is actually the majority of the energy dissipated.”

Reversible chips operate a little slower, but they recover that charge, making them ideal for multicore environments. “That’s why Nvidia chips are slower than Intel’s, but use tons of cores,” he adds. “Same idea—slow down a little, parallelize a lot.”

The result is a system that’s more thermally efficient and more sustainable in the long run. “We’ve figured out how to make a reversible system talk to classical systems—without them even knowing it’s a different architecture. It’s completely alien under the hood, but seamless on the outside.”

And while ultra-low power use cases are on the roadmap, the short-term problem is more about power envelopes. “Customers already have power coming into the building,” he says. “What they don’t have is headroom. They want more compute without increasing draw. That’s the opportunity.”

Modular Visions and the Edge

When asked if this technology lends itself to edge inference, Rodolfo steers the conversation toward modularity—but not edge computing. “We’re talking about data centers here,” he says. “The market is clear. The price per chip is higher. And the pain points—power, cooling—are urgent.”

But he sees that modular vision evolving. “If you don’t need massive centralized cooling, maybe you don’t need massive centralized data centers either,” he muses. “We could be heading toward a new generation of modular units, kind of like the old Sun Microsystems container idea.”

That’s down the line, though. Edge devices still face huge cost constraints and BOM sensitivities. “Consumer electronics can’t just absorb premium chip prices—even if the chip is magic.”

A New Path, Rooted in Reversibility

Zooming out, Rodolfo sees this as a foundational transition in computing. “In 15 years, every chip will be reversible,” he predicts. “Not all made by us, of course—but that’s the direction. CMOS is bound by thermal limits. Photonics will help, but computing with light still needs breakthroughs. Quantum? It’s still niche. But here’s the thing—quantum computers are also reversible. They have to be.”

That’s not a coincidence. It’s a trend line. “Reversible is the way forward,” says Rossini

To be clear, Rodolfo’s company isn’t in the quantum business. “We have nothing to do with quantum,” he says. “They use reversibility for coherence. We do it for heat.” But both share a deeper implication: Moore’s Law has run its course. Classical computing gave us an extraordinary 50-year run—but scaling it further demands a new direction. And for Rodolfo and his team, that direction is irreversibility’s mirror twin: reversible logic.

That’s not the mainstream view in the chip world, of course. “This is very much not the orthodoxy,” he admits. But rather than chasing academic acceptance, VAR’s strategy was all-in from day one. “We didn’t want to write papers—we did publish, but that’s not the goal,” he says. “We raised money, built a team, and spent three years in stealth. Because once you put silicon in a customer’s hands, the argument ends.”

Pacing Progress: Balancing Ambition and Real-World Delivery

So when will that first chip hit real workloads? The company’s current thinking pegs 2027 as the start of commercial availability—but it’s a moving target. “We’re in that tension of ‘How cool can this car be?’ versus ‘Can we just ship the damn motorcycle?’” Rodolfo jokes.

As VAR works closely with early customers, new ideas and possibilities keep pushing the roadmap out. “We’re wrestling with scope—people see what we’re building and say, ‘Can it do this? Can it do that?’ And suddenly the product gets bigger and more ambitious.”

Still, VAR has a structure. They plan on taping out at least one new chip per year leading up to 2027, each tackling a specific, foundational breakthrough—resonators, adiabatic switching, reversible clocking, and other techniques that make this novel architecture viable in silicon. “Every year, we take on a hard problem we think we can solve on paper. Then we build it,” he explains. “If it works—great. If it doesn’t—we go back to the drawing board.”

So far, they’re on track. And that steady drumbeat is what gave the team the confidence to finally come out of stealth. “We didn’t start talking until we were sure we wouldn’t fall flat on our faces.”

From Moonshot to Minimum Viable Product

When DCF asked Rossini whether Vaire Computing’s technology could be considered a moonshot, the CEO didn’t hesitate: “Yes, it absolutely is,” he affirmed.

This candid acknowledgment speaks volumes about Vaire’s bold approach. The company is attempting nothing less than the reinvention of chip architecture itself, with a vision to fundamentally reshape the energy efficiency and scalability of computing in a way that could transcend the limitations of current technologies. While the challenge is daunting, it’s a calculated moonshot—one that strives to deliver a game-changing solution without requiring a complete overhaul of the ecosystem that currently supports data centers, software, and hardware.

Why a moonshot? The idea of reversible computing, which Vaire is pushing forward, is uncharted territory for most of the chip industry. It holds the promise of dramatically reduced energy consumption, enabling the next generation of computing to scale beyond the thermal limits of Moore’s Law. However, building such an architecture is no small feat. It involves overcoming deep technical challenges—ranging from charge recovery at the gate level to making this revolutionary architecture compatible with classical systems. It’s a high-risk, high-reward effort, and success could redefine how we think about the energy demands of computing.

For now, Vaire is tackling the challenge with incremental steps, working closely with customers and partners to ensure that their innovations are practical and scalable. But the long-term goal remains clear: to reshape the computing landscape by delivering chips that push beyond the boundaries of today’s energy constraints.

And if they succeed, Vaire Computing could very well light the way for the next major leap in data center technology, marking the beginning of a new era in energy-efficient computing.

While the long-term vision is big—“Every chip will be reversible,” Rodolfo repeats—it’s grounded in pragmatism. The Vaire team isn’t trying to boil the ocean. “We try to scope problems the customer has right now,” he says. “Maybe our tech can solve something today—in a year, not five.”

Along the way, they may find simpler, more narrowly scoped chips that solve very specific problems—side quests that still push the architecture forward, even if they’re not the ultimate XPU. “There’s a whole range of complexity in chip design,” Rodolfo notes. “We always try to build the least complex product that gets us to market fastest.”

The Beginning of Something Big

Reversible computing might not be orthodox, but it’s starting to sound inevitable. If heat is the ultimate barrier to scale—and it increasingly is—then it’s time to rethink what chips can do, and how they do it. For Vaire, that rethink has already started.

“We’re not here to stop the train,” Rodolfo says. “We’re jumping on board—and building the engine for what comes next.”

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Qualcomm’s $3.9 billion purchase of Modular aims to change the data center dynamic

“Nvidia has something like 85% of the AI accelerator chip market,” he pointed out. “Sure, they have nowhere to go but down, but that’s still going to take them a while. More importantly, they have literally spent decades working with practitioners in AI and ML and compute-intensive fields, indoctrinating them into their CUDA software ecosystem. Rewriting that tool chain will take institutional change at most organizations, which means years, if not decades, to uncouple.” “Organizations that think they’ve achieved agnosticism because they’re using high-level abstractions like PyTorch, well,  they have come closest,” he observed. “But just cutting and pasting the same code into AMD Instinct can lead to memory and dependency errors. It’s like VM lift and shifts to the public cloud 10 years ago. Easier, but still possible to screw up.” Nonetheless, Annand said that the deal, if it goes through, is still good news for enterprises. 

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KKR Bets Big on AI Infrastructure With Helix Launch, Tapping Former AWS CEO Adam Selipsky to Build a New Hyperscale Model

To close industry watchers, it’s really no secret that the AI infrastructure race has entered another phase; one where capital formation itself may become as strategically important as GPUs, power procurement, or liquid cooling. And in launching Helix Digital Infrastructure, investment giant KKR is making a calculated wager that hyperscalers no longer simply need developers or financiers. They need a partner capable of orchestrating capital, energy, connectivity, and data center execution as a unified platform. The significance of that strategy is underscored by the executive chosen to lead it. Adam Selipsky, the former CEO of Amazon Web Services and one of the industry’s most experienced cloud operators, will serve as Co-Founder and CEO of Helix, bringing firsthand experience from the very class of customers the new venture intends to serve. A New Model for AI Infrastructure Helix launches with more than $10 billion in long-duration committed capital from founding investors including KKR, the Kuwait Investment Authority (KIA), NVIDIA, and Vistra. But the headline number tells only part of the story. The company has been structured around an increasingly important thesis: that AI infrastructure can no longer be assembled piecemeal. Rather than treating data centers, electrical supply, transmission capacity, and fiber connectivity as separate procurement exercises, Helix proposes a vertically coordinated approach in which a single organization manages and finances the entire infrastructure stack. According to KKR, the objective is to reduce execution risk and accelerate deployment for hyperscale customers facing unprecedented AI demand. As AI factories grow from hundreds of megawatts toward gigawatt-scale campuses, synchronization among land acquisition, utility planning, financing, construction, and technology deployment has emerged as one of the industry’s defining challenges. Helix is effectively positioning itself as an operating platform designed to simplify that complexity. Why Selipsky Matters The appointment of Adam Selipsky may be the announcement’s

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Beyond Hyperscale: Why Enterprise Data Centers Still Matter in the AI Era

“The enterprise data centers, even the new ones, tend to be far, far smaller than new hyperscale deployments,” Killian said. “Not uncommon to see enterprises deploy a quarter meg or one meg or two, maybe up to 10 megs. Whereas the hyperscale guys are deploying 40 up to 300 meg facilities.” But scale alone does not tell the story. For every one of the roughly 20 hyperscale users that dominate headlines, Killian noted, there may be 50 to 100 times as many large and mid-sized enterprise users. Those companies run critical business systems, purchase hardware, software, telecom and services, employ large data center teams, and often operate multiple facilities across domestic, edge, EMEA and Asia-Pacific footprints. In other words, enterprise demand may be smaller in unit size, but it remains massive in aggregate. And as AI shifts from training to inference, the enterprise data center could become newly strategic. Enterprise AI Is Not Hyperscale AI Killian’s central point is that enterprise infrastructure requirements differ materially from hyperscale requirements. Hyperscalers are primarily optimizing for massive scale and speed to market. Enterprises, by contrast, tend to prioritize reliability, flexibility, integration into broader IT systems, and audit and compliance. That difference has major implications for developers and colocation providers. “The real industry opportunity is to take some of the innovation and the economies of scale that we’re seeing from the hyperscale builds to deliver smaller chunks of data center capacity,” Killian said. That might mean adapting lessons from 40 MW or 100 MW campuses into enterprise-ready deployments of 2 MW, 4 MW or 8 MW. Killian pointed to providers such as DataBank and Flexential as examples of companies working to deliver hyperscale-derived efficiencies in smaller enterprise increments. He also noted that QTS and other large campus developers may reserve portions of multi-building campuses

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Revolutionizing Data Center Cooling: Innovations for AI and HPC Growth

This is a crucial point for AI infrastructure. In some markets, water can be as politically and operationally difficult as power. Evaporative cooling and cooling towers can consume large volumes of water, while discharge permits can slow projects or limit operations. Gradiant claims HyperSolved can expand access to alternative sources such as municipal reuse and impaired supplies, reduce reliance on freshwater, protect cooling performance through integrated treatment and AI-enabled operations, and minimize discharge through high-recovery concentration and reuse. The platform uses containerized systems for immediate or temporary capacity while also supporting permanent infrastructure and lifecycle operations from commissioning onward. That fits the AI data center buildout, where developers may need bridge capacity during construction, phased water infrastructure, or interim systems while permanent treatment plants are completed. This can address the speed of deployment issue that plagues many data center solutions. Water is becoming a siting and scaling variable that has to be addressed. A site may have land and power prospects, but if water sourcing, reuse, or discharge cannot be solved, the project will face higher costs, delays, and local opposition. Gradiant is positioning itself as the managed water layer for hyperscale AI, similar to how power providers, cooling vendors, and network suppliers each own critical infrastructure domains. The Pattern: Hybridization, Standardization, and Industrial Scale The announcements included here make it clear that cooling is seeing significant attention from technology vendors, and not just state-of-the-art new technologies such as direct-to-chip, but also traditional data center air cooling. T-Global and SiPearl are working on high-conductivity materials and two-phase modules for HPC chips. Castrol is providing fluids for direct-to-chip and immersion environments. These are technologies aimed at the heat source itself, where higher chip power and rack density are overwhelming conventional approaches. The reference design offerings from Johnson Controls acknowledges the importance

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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