A new Microsoft chip could lead to more stable quantum computers

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A new Microsoft chip could lead to more stable quantum computers

Microsoft announced today that it has made significant progress in its 20-year quest to make topological quantum bits, or qubits—a special approach to building quantum computers that could make them more stable and easier to scale up. Researchers and companies have been working for years to build quantum computers, which could unlock dramatic new abilities to simulate complex materials and discover new ones, among many other possible applications. To achieve that potential, though, we must build big enough systems that are stable enough to perform computations. Many of the technologies being explored today, such as the superconducting qubits pursued by Google and IBM, are so delicate that the resulting systems need to have many extra qubits to correct errors. Microsoft has long been working on an alternative that could cut down on the overhead by using components that are far more stable. These components, called Majorana quasiparticles, are not real particles. Instead, they are special patterns of behavior that may arise inside certain physical systems and under certain conditions. The pursuit has not been without setbacks, including a high-profile paper retraction by researchers associated with the company in 2018. But the Microsoft team, which has since pulled this research effort in house, claims it is now on track to build a fault-tolerant quantum computer containing a few thousand qubits in a matter of years and that it has a blueprint for building out chips that each contain a million qubits or so, a rough target that could be the point at which these computers really begin to show their power. This week the company announced a few early successes on that path: piggybacking on a Nature paper published today that describes a fundamental validation of the system, the company says it has been testing a topological qubit, and that it has wired up a chip containing eight of them. “You don’t get to a million qubits without a lot of blood, sweat, and tears and solving a lot of really difficult technical challenges along the way. And I do not want to understate any of that,” says Chetan Nayak, a Microsoft technical fellow and leader of the team pioneering this approach. That said, he says, “I think that we have a path that we very much believe in, and we see a line of sight.” Researchers outside the company are cautiously optimistic. “I’m very glad that [this research] seems to have hit a very important milestone,” says computer scientist Scott Aaronson, who heads the Quantum Information Center at the University of Texas at Austin. “I hope that this stands, and I hope that it’s built up.” Even and odd The first step in building a quantum computer is constructing qubits that can exist in fragile quantum states—not 0s and 1s like the bits in classical computers, but rather a mixture of the two. Maintaining qubits in these states and linking them up with one another is delicate work, and over the years a significant amount of research has gone into refining error correction schemes to make up for noisy hardware. For many years, theorists and experimentalists alike have been intrigued by the idea of creating topological qubits, which are constructed through mathematical twists and turns and have protection from errors essentially baked into their physics. “It’s been such an appealing idea to people since the early 2000s,” says Aaronson. “The only problem with it is that it requires, in a sense, creating a new state of matter that’s never been seen in nature.” Microsoft has been on a quest to synthesize this state, called a Majorana fermion, in the form of quasiparticles. The Majorana was first proposed nearly 90 years ago as a particle that is its own antiparticle, which means two Majoranas will annihilate when they encounter one another. With the right conditions and physical setup, the company has been hoping to get behavior matching that of the Majorana fermion within materials. In the last few years, Microsoft’s approach has centered on creating a very thin wire or “nanowire” from indium arsenide, a semiconductor. This material is placed in close proximity to aluminum, which becomes a superconductor close to absolute zero, and can be used to create superconductivity in the nanowire. Ordinarily you’re not likely to find any unpaired electrons skittering about in a superconductor—electrons like to pair up. But under the right conditions in the nanowire, it’s theoretically possible for an electron to hide itself, with each half hiding at either end of the wire. If these complex entities, called Majorana zero modes, can be coaxed into existence, they will be difficult to destroy, making them intrinsically stable. ”Now you can see the advantage,” says Sankar Das Sarma, a theoretical physicist at the University of Maryland, College Park, who did early work on this concept. “You cannot destroy a half electron, right? If you try to destroy a half electron, that means only a half electron is left. That’s not allowed.” In 2023, the Microsoft team published a paper in the journal Physical Review B claiming that this system had passed a specific protocol designed to assess the presence of Majorana zero modes. This week in Nature, the researchers reported that they can “read out” the information in these nanowires—specifically, whether there are Majorana zero modes hiding at the wires’ ends. If there are, that means the wire has an extra, unpaired electron. “What we did in the Nature paper is we showed how to measure the even or oddness,” says Nayak. “To be able to tell whether there’s 10 million or 10 million and one electrons in one of these wires.” That’s an important step by itself, because the company aims to use those two states—an even or odd number of electrons in the nanowire—as the 0s and 1s in its qubits. If these quasiparticles exist, it should be possible to “braid” the four Majorana zero modes in a pair of nanowires around one another by making specific measurements in a specific order. The result would be a qubit with a mix of these two states, even and odd. Nayak says the team has done just that, creating a two-level quantum system, and that it is currently working on a paper on the results. Researchers outside the company say they cannot comment on the qubit results, since that paper is not yet available. But some have hopeful things to say about the findings published so far. “I find it very encouraging,” says Travis Humble, director of the Quantum Science Center at Oak Ridge National Laboratory in Tennessee. “It is not yet enough to claim that they have created topological qubits. There’s still more work to be done there,” he says. But “this is a good first step toward validating the type of protection that they hope to create.” Others are more skeptical. Physicist Henry Legg of the University of St Andrews in Scotland, who previously criticized Physical Review B for publishing the 2023 paper without enough data for the results to be independently reproduced, is not convinced that the team is seeing evidence of Majorana zero modes in its Nature paper. He says that the company’s early tests did not put it on solid footing to make such claims. “The optimism is definitely there, but the science isn’t there,” he says. One potential complication is impurities in the device, which can create conditions that look like Majorana particles. But Nayak says the evidence has only grown stronger as the research has proceeded. “This gives us confidence: We are manipulating sophisticated devices and seeing results consistent with a Majorana interpretation,” he says. “They have satisfied many of the necessary conditions for a Majorana qubit, but there are still a few more boxes to check,” Das Sarma said after seeing preliminary results on the qubit. “The progress has been impressive and concrete.” Scaling up On the face of it, Microsoft’s topological efforts seem woefully behind in the world of quantum computing—the company is just now working to combine qubits in the single digits while others have tied together more than 1,000. But both Nayak and Das Sarma say other efforts had a strong head start because they involved systems that already had a solid grounding in physics. Work on the topological qubit, on the other hand, has meant starting from scratch. “We really were reinventing the wheel,” Nayak says, likening the team’s efforts to the early days of semiconductors, when there was so much to sort out about electron behavior and materials, and transistors and integrated circuits still had to be invented. That’s why this research path has taken almost 20 years, he says: “It’s the longest-running R&D program in Microsoft history.” Some support from the US Defense Advanced Research Projects Agency could help the company catch up. Early this month, Microsoft was selected as one of two companies to continue work on the design of a scaled-up system, through a program focused on underexplored approaches that could lead to utility-scale quantum computers—those whose benefits exceed their costs. The other company selected is PsiQuantum, a startup that is aiming to build a quantum computer containing up to a million qubits using photons. Many of the researchers MIT Technology Review spoke with would still like to see how this work plays out in scientific publications, but they were hopeful. “The biggest disadvantage of the topological qubit is that it’s still kind of a physics problem,” says Das Sarma. “If everything Microsoft is claiming today is correct … then maybe right now the physics is coming to an end, and engineering could begin.” This story was updated with Henry Legg’s current institutional affiliation.

Researchers and companies have been working for years to build quantum computers, which could unlock dramatic new abilities to simulate complex materials and discover new ones, among many other possible applications.

To achieve that potential, though, we must build big enough systems that are stable enough to perform computations. Many of the technologies being explored today, such as the superconducting qubits pursued by Google and IBM, are so delicate that the resulting systems need to have many extra qubits to correct errors.

Microsoft has long been working on an alternative that could cut down on the overhead by using components that are far more stable. These components, called Majorana quasiparticles, are not real particles. Instead, they are special patterns of behavior that may arise inside certain physical systems and under certain conditions.

The pursuit has not been without setbacks, including a high-profile paper retraction by researchers associated with the company in 2018. But the Microsoft team, which has since pulled this research effort in house, claims it is now on track to build a fault-tolerant quantum computer containing a few thousand qubits in a matter of years and that it has a blueprint for building out chips that each contain a million qubits or so, a rough target that could be the point at which these computers really begin to show their power.

This week the company announced a few early successes on that path: piggybacking on a Nature paper published today that describes a fundamental validation of the system, the company says it has been testing a topological qubit, and that it has wired up a chip containing eight of them.

“You don’t get to a million qubits without a lot of blood, sweat, and tears and solving a lot of really difficult technical challenges along the way. And I do not want to understate any of that,” says Chetan Nayak, a Microsoft technical fellow and leader of the team pioneering this approach. That said, he says, “I think that we have a path that we very much believe in, and we see a line of sight.”

Researchers outside the company are cautiously optimistic. “I’m very glad that [this research] seems to have hit a very important milestone,” says computer scientist Scott Aaronson, who heads the Quantum Information Center at the University of Texas at Austin. “I hope that this stands, and I hope that it’s built up.”

Even and odd

The first step in building a quantum computer is constructing qubits that can exist in fragile quantum states—not 0s and 1s like the bits in classical computers, but rather a mixture of the two. Maintaining qubits in these states and linking them up with one another is delicate work, and over the years a significant amount of research has gone into refining error correction schemes to make up for noisy hardware.

For many years, theorists and experimentalists alike have been intrigued by the idea of creating topological qubits, which are constructed through mathematical twists and turns and have protection from errors essentially baked into their physics. “It’s been such an appealing idea to people since the early 2000s,” says Aaronson. “The only problem with it is that it requires, in a sense, creating a new state of matter that’s never been seen in nature.”

Microsoft has been on a quest to synthesize this state, called a Majorana fermion, in the form of quasiparticles. The Majorana was first proposed nearly 90 years ago as a particle that is its own antiparticle, which means two Majoranas will annihilate when they encounter one another. With the right conditions and physical setup, the company has been hoping to get behavior matching that of the Majorana fermion within materials.

In the last few years, Microsoft’s approach has centered on creating a very thin wire or “nanowire” from indium arsenide, a semiconductor. This material is placed in close proximity to aluminum, which becomes a superconductor close to absolute zero, and can be used to create superconductivity in the nanowire.

Ordinarily you’re not likely to find any unpaired electrons skittering about in a superconductor—electrons like to pair up. But under the right conditions in the nanowire, it’s theoretically possible for an electron to hide itself, with each half hiding at either end of the wire. If these complex entities, called Majorana zero modes, can be coaxed into existence, they will be difficult to destroy, making them intrinsically stable.

”Now you can see the advantage,” says Sankar Das Sarma, a theoretical physicist at the University of Maryland, College Park, who did early work on this concept. “You cannot destroy a half electron, right? If you try to destroy a half electron, that means only a half electron is left. That’s not allowed.”

In 2023, the Microsoft team published a paper in the journal Physical Review B claiming that this system had passed a specific protocol designed to assess the presence of Majorana zero modes. This week in Nature, the researchers reported that they can “read out” the information in these nanowires—specifically, whether there are Majorana zero modes hiding at the wires’ ends. If there are, that means the wire has an extra, unpaired electron.

“What we did in the Nature paper is we showed how to measure the even or oddness,” says Nayak. “To be able to tell whether there’s 10 million or 10 million and one electrons in one of these wires.” That’s an important step by itself, because the company aims to use those two states—an even or odd number of electrons in the nanowire—as the 0s and 1s in its qubits.

If these quasiparticles exist, it should be possible to “braid” the four Majorana zero modes in a pair of nanowires around one another by making specific measurements in a specific order. The result would be a qubit with a mix of these two states, even and odd. Nayak says the team has done just that, creating a two-level quantum system, and that it is currently working on a paper on the results.

Researchers outside the company say they cannot comment on the qubit results, since that paper is not yet available. But some have hopeful things to say about the findings published so far. “I find it very encouraging,” says Travis Humble, director of the Quantum Science Center at Oak Ridge National Laboratory in Tennessee. “It is not yet enough to claim that they have created topological qubits. There’s still more work to be done there,” he says. But “this is a good first step toward validating the type of protection that they hope to create.”

Others are more skeptical. Physicist Henry Legg of the University of St Andrews in Scotland, who previously criticized Physical Review B for publishing the 2023 paper without enough data for the results to be independently reproduced, is not convinced that the team is seeing evidence of Majorana zero modes in its Nature paper. He says that the company’s early tests did not put it on solid footing to make such claims. “The optimism is definitely there, but the science isn’t there,” he says.

One potential complication is impurities in the device, which can create conditions that look like Majorana particles. But Nayak says the evidence has only grown stronger as the research has proceeded. “This gives us confidence: We are manipulating sophisticated devices and seeing results consistent with a Majorana interpretation,” he says.

“They have satisfied many of the necessary conditions for a Majorana qubit, but there are still a few more boxes to check,” Das Sarma said after seeing preliminary results on the qubit. “The progress has been impressive and concrete.”

Scaling up

On the face of it, Microsoft’s topological efforts seem woefully behind in the world of quantum computing—the company is just now working to combine qubits in the single digits while others have tied together more than 1,000. But both Nayak and Das Sarma say other efforts had a strong head start because they involved systems that already had a solid grounding in physics. Work on the topological qubit, on the other hand, has meant starting from scratch.

“We really were reinventing the wheel,” Nayak says, likening the team’s efforts to the early days of semiconductors, when there was so much to sort out about electron behavior and materials, and transistors and integrated circuits still had to be invented. That’s why this research path has taken almost 20 years, he says: “It’s the longest-running R&D program in Microsoft history.”

Some support from the US Defense Advanced Research Projects Agency could help the company catch up. Early this month, Microsoft was selected as one of two companies to continue work on the design of a scaled-up system, through a program focused on underexplored approaches that could lead to utility-scale quantum computers—those whose benefits exceed their costs. The other company selected is PsiQuantum, a startup that is aiming to build a quantum computer containing up to a million qubits using photons.

Many of the researchers MIT Technology Review spoke with would still like to see how this work plays out in scientific publications, but they were hopeful. “The biggest disadvantage of the topological qubit is that it’s still kind of a physics problem,” says Das Sarma. “If everything Microsoft is claiming today is correct … then maybe right now the physics is coming to an end, and engineering could begin.”

This story was updated with Henry Legg’s current institutional affiliation.

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When Facts Aren’t Enough Few places offer a more revealing test case than Loudoun County, Virginia. Data Center Alley has spent decades living with data center development at a scale most emerging markets will never approach. Rizer said Loudoun’s experience gives the county an unusually deep record with which to answer questions about environmental impacts, infrastructure and economic benefits. But those facts increasingly struggle to penetrate the broader public debate. Rizer said Loudoun today has more than 250 data centers, while the entire sector uses less than 10% of the county water system. He also pointed to improved air quality over the past decade and approximately $1.2 billion in tax revenue from the industry. Yet he acknowledged that simply producing another data point does little good when residents no longer trust the people presenting it. “I call it community concern whack-a-mole, because every time you address one thing, there are three others that pop up,” Rizer said. The problem, in his view, has become partly emotional rather than informational. “You can’t change how people think until you change how they feel,” he said. “And right now they feel angry, they feel confused, they are fearful, they are mistrustful, both of government and the big tech industry.” That distinction matters. The industry’s instinct has often been to counter criticism with facts: tax receipts, job numbers, water-use calculations, emissions data or explanations of how a particular cooling system works. Those facts remain important. But Rizer’s argument is that the industry must first rebuild enough credibility for communities to hear them. The Industry’s Unforced Errors Not all of the distrust has arrived from outside the industry. Rizer and Waitkunas were equally pointed about mistakes by developers and operators that have given opponents powerful examples to use against data center projects elsewhere. “The industry

Reports: Data Center Expansion Finds Its Contours

AI Density Is Arriving Unevenly Inside the data center, the AI transition remains equally uneven. Uptime’s 2026 survey found the average modal, or most common, rack density across respondents exceeding 11 kW for the first time, up from 9 kW in 2025. But that number requires context. A relatively small group of very high-density facilities pulls the average upward. Without those facilities, Uptime puts average modal rack density at 7.8 kW, only modestly higher than 7.5 kW in 2025. The industry therefore continues to operate two realities at once: a vast installed base running conventional rack densities and a rapidly emerging class of AI facilities pushing far beyond them. The latter is becoming more visible. Some 24% of Uptime respondents now report racks at 30 kW or higher, up from 19% last year. Much of the increase occurred above 50 kW, and some operators reported deployments exceeding 100 kW. Still, most surveyed facilities have no racks at 30 kW or above. AI inference is also moving up the density curve. For the first time in Uptime’s survey, generative AI inference matched AI training as a driver of respondents’ highest-density deployments, with 21% citing each workload. That matters because inference potentially pushes AI infrastructure requirements beyond a relatively concentrated population of model-training campuses and into a broader set of facilities and markets. Power Is Both Constraint and Risk No issue connects the three reports more consistently than power. It limits new site availability. It redirects development toward emerging markets. It shapes community debates. It affects density and cooling architecture. And once a facility is operating, it remains the largest source of outage risk. Uptime says 56% of operators who experienced an impactful outage identified power as the primary cause of their most recent incident. The institute cautions against treating the increase

DCF Poll: What Will Constrain Data Center Growth Next?

Matt Vincent is Editor in Chief of Data Center Frontier, where he leads editorial strategy and coverage focused on the infrastructure powering cloud computing, artificial intelligence, and the digital economy. A veteran B2B technology journalist with more than two decades of experience, Vincent specializes in the intersection of data centers, power, cooling, and emerging AI-era infrastructure. Since assuming the EIC role in 2023, he has helped guide Data Center Frontier’s coverage of the industry’s transition into the gigawatt-scale AI era, with a focus on hyperscale development, behind-the-meter power strategies, liquid cooling architectures, and the evolving energy demands of high-density compute, while working closely with the Digital Infrastructure Group at Endeavor Business Media to expand the brand’s analytical and multimedia footprint. Vincent also hosts The Data Center Frontier Show podcast, where he interviews industry leaders across hyperscale, colocation, utilities, and the data center supply chain to examine the technologies and business models reshaping digital infrastructure. Since its inception he serves as Head of Content for the Data Center Frontier Trends Summit. Before becoming Editor in Chief, he served in multiple senior editorial roles across Endeavor Business Media’s digital infrastructure portfolio, with coverage spanning data centers and hyperscale infrastructure, structured cabling and networking, telecom and datacom, IP physical security, and wireless and Pro AV markets. He began his career in 2005 within PennWell’s Advanced Technology Division and later held senior editorial positions supporting brands such as Cabling Installation & Maintenance, Lightwave Online, Broadband Technology Report, and Smart Buildings Technology. Vincent is a frequent moderator, interviewer, and keynote speaker at industry events including the HPC Forum, where he delivers forward-looking analysis on how AI and high-performance computing are reshaping digital infrastructure. He graduated with honors from Indiana University Bloomington with a B.A. in English Literature and Creative Writing and lives in southern New Hampshire with

Is your networking built for AI’s traffic patterns and data volumes?

As data centers evolve into AI factories, compute has shifted from a cost center to a revenue driver. “Compute is revenue,” said Jensen Huang, co-founder and CEO of NVIDIA. “Without compute, there is no way to generate tokens. Without tokens, there’s no way to generate revenue. So, in this new world of AI, compute equals revenue.” This reframe changes an organizations’ calculus. If compute is revenue, what do you optimize for? Here are 5 questions to consider: Are you measuring what actually drives AI factory revenue? Most AI factories are power-constrained, so tokens per watt dictate how much revenue you can generate and the cost per token impacts the AI factory profit margin. But neither of these metrics should be evaluated at a single operating point. Batch jobs, real-time chat, and agentic workloads demand different points on the throughput-latency curve. AI chips that perform well at only a few points will underserve the full range of workloads. Additional key operational metrics like time to first token (TTFT), mean time between interruptions (MTBI), and platform useful life are the bedrock of AI factory efficiency. They dictate how quickly an AI factory comes online to generate tokens, the reliability of its revenue streams, and its long-term ability to remain productive as AI workloads evolve. How does agentic AI change what your CPU needs to deliver? Data center CPUs have historically been optimized for parallel throughput, where more cores improve aggregate capacity. Agentic workloads run in loops and make different demands. The model reasons on the GPU, the CPU executes tool calls such as code compilation and data retrieval, and the result returns to the GPU so the model can reason again. Every step runs in sequence, gated by the one before it. Per-core performance and memory latency determine how fast each step

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.

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

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

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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