What’s next for smart glasses

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What’s next for smart glasses

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here. For every technological gadget that becomes a household name, there are dozens that never catch on. This year marks a full decade since Google confirmed it was stopping production of Google Glass, and for a long time it appeared as though mixed-reality products—think of the kinds of face computers that don’t completely cover your field of view they way a virtual-reality headset does—would remain the preserve of enthusiasts rather than casual consumers. Fast-forward 10 years, and smart glasses are on the verge of becoming—whisper it—cool. Meta’s smart glasses, made in partnership with Ray-Ban, are basically indistinguishable from the iconic Wayfarers Tom Cruise made famous in Risky Business. Meta also recently showed off its fashion-forward Orion augmented reality glasses prototype, while Snap unveiled its fifth-generation Spectacles, neither of which would look out of place in the trendiest district of a major city. In December, Google showed off its new unnamed Android XR prototype glasses, and rumors that Apple is still working on a long-anticipated glasses project continue to swirl. Elsewhere, Chinese tech giants Huawei, Alibaba, Xiaomi, and Baidu are also vying for a slice of the market. Sleeker designs are certainly making this new generation of glasses more appealing. But more importantly, smart glasses are finally on the verge of becoming useful, and it’s clear that Big Tech is betting that augmented specs will be the next big consumer device category. Here’s what to expect from smart glasses in 2025 and beyond. AI agents could finally make smart glasses truly useful Although mixed-reality devices have been around for decades, they have largely benefited specialized fields, including the medical, construction, and technical remote-assistance industries, where they are likely to continue being used, possibly in more specialized ways. Microsoft is the creator of the best-known of these devices, which layer virtual content over the wearer’s real-world environment, and marketed its HoloLens 2 smart goggles to corporations. The company recently confirmed it was ending production of that device. Instead, it is choosing to focus on building headsets for the US military in partnership with Oculus founder Palmer Luckey’s latest venture, Anduril. Now the general public may finally be getting access to devices they can use. The AI world is abuzz over agents, which augment large language models (LLMs) with the ability to carry out tasks by themselves. The past 12 months have seen huge leaps in AI multimodal LLMs’ abilities to handle video, images, and audio in addition to text, which opens up new applications for smart glasses that would not have been possible previously, says Louis Rosenberg, an AR researcher who worked on the first functional augmented-reality system at Stanford University in the 1990s. We already know Meta is definitely interested in AI agents. Although the company said in September that it has no plans to sell its Orion prototype glasses to the public, given their expense, Mark Zuckerberg raised expectations for its next generations of Meta’s smart glasses when he declared Orion the “most advanced pair of AR glasses ever made.” He’s also made it clear how deeply invested Meta is in bringing a “highly intelligent and personalized AI assistant” to as many users as possible and that he’s confident Meta’s glasses are the “perfect form factor for AI.” Although Meta is already making its Ray-Ban smart glasses’ AI more conversational—its new live AI feature responds to prompts about what its wearer is seeing and hearing via its camera and microphone—future agents will give these systems not only eyes and ears, but a contextual awareness of what’s around them, Rosenberg says. For example, agents running on smart glasses could hold unprompted interactive conversations with their wearers based on their environment, reminding them to buy orange juice when they walk past a store, for example, or telling them the name of a coworker who passes them on the sidewalk. We already know Google is deeply interested in this agent-first approach: The unnamed smart glasses it first showed off at Google I/O in May 2024 were powered by its Astra AI agent system. “Having worked on mixed reality for over 30 years, it’s the first time I can see an application that will really drive mass adoption,” Rosenberg says. Meta and Google will likely tussle to be the sector’s top dog It’s unclear how far we are from that level of mass adoption. During a recent Meta earnings call, Zuckerberg said 2025 would be a “defining year” for understanding the future of AI glasses and whether they explode in popularity or represent “a longer grind.” He has reason to be optimistic, though: Meta is currently ahead of its competition thanks to the success of the Ray-Ban Meta smart glasses—the company sold more than 1 million units last year. It also is preparing to roll out new styles thanks to a partnership with Oakley, which, like Ray-Ban, is under the EssilorLuxottica umbrella of brands. And while its current second-generation specs can’t show its wearer digital data and notifications, a third version complete with a small display is due for release this year, according to the Financial Times. The company is also reportedly working on a lighter, more advanced version of its Orion AR glasses, dubbed Artemis, that could go on sale as early as 2027, Bloomberg reports. Adding display capabilities will put the Ray-Ban Meta glasses on equal footing with Google’s unnamed Android XR glasses project, which sports an in-lens display (the company has not yet announced a definite release date). The prototype the company demoed to journalists in September featured a version of its AI chatbot Gemini, and much they way Google built its Android OS to run on smartphones made by third parties, its Android XR software will eventually run on smart glasses made by other companies as well as its own. These two major players are competing to bring face-mounted AI to the masses in a race that’s bound to intensify, adds Rosenberg—especially given that both Zuckerberg and Google cofounder Sergey Brin have called smart glasses the “perfect” hardware for AI. “Google and Meta are really the big tech companies that are furthest ahead in the AI space on their own. They’re very well positioned,” he says. “This is not just augmenting your world, it’s augmenting your brain.” It’s getting easier to make smart glasses—but it’s still hard to get them right When the AR gaming company Niantic’s Michael Miller walked around CES, the gigantic consumer electronics exhibition that takes over Las Vegas each January, he says he was struck by the number of smaller companies developing their own glasses and systems to run on them, including Chinese brands DreamSmart, Thunderbird, and Rokid. While it’s still not a cheap endeavor—a business would probably need a couple of million dollars in investment to get a prototype off the ground, he says—it demonstrates that the future of the sector won’t depend on Big Tech alone. “On a hardware and software level, the barrier to entry has become very low,” says Miller, the augmented reality hardware lead at Niantic, which has partnered with Meta, Snap, and Magic Leap, among others. “But turning it into a viable consumer product is still tough. Meta caught the biggest fish in this world, and so they benefit from the Ray-Ban brand. It’s hard to sell glasses when you’re an unknown brand.” That’s why it’s likely ambitious smart glasses makers in countries like Japan and China will increasingly partner with eyewear companies known locally for creating desirable frames, generating momentum in their home markets before expanding elsewhere, he suggests. More developers will start building for these devices These smaller players will also have an important role in creating new experiences for wearers of smart glasses. A big part of smart glasses’ usefulness hinges on their ability to send and receive information from a wearer’s smartphone—and third-party developers’ interest in building apps that run on them. The more the public can do with their glasses, the more likely they are to buy them. Developers are still waiting for Meta to release a software development kit (SDK) that would let them build new experiences for the Ray-Ban Meta glasses. While bigger brands are understandably wary about giving third parties access to smart glasses’ discreet cameras, it does limit the opportunities researchers and creatives have to push the envelope, says Paul Tennent, an associate professor in the Mixed Reality Laboratory at the University of Nottingham in the UK. “But historically, Google has been a little less afraid of this,” he adds. Elsewhere, Snap and smaller brands like Brilliant Labs, whose Frame glasses run multimodal AI models including Perplexity, ChatGPT, and Whisper, and Vuzix, which recently launched its AugmentOS universal operating system for smart glasses, have happily opened up their SDKs, to the delight of developers, says Patrick Chwalek, a student at the MIT Media Lab who worked on smart glasses platform Project Captivate as part of his PhD research. “Vuzix is getting pretty popular at various universities and companies because people can start building experiences on top of them,” he adds. “Most of these are related to navigation and real-time translation—I think we’re going to be seeing a lot of iterations of that over the next few years.”

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

For every technological gadget that becomes a household name, there are dozens that never catch on. This year marks a full decade since Google confirmed it was stopping production of Google Glass, and for a long time it appeared as though mixed-reality products—think of the kinds of face computers that don’t completely cover your field of view they way a virtual-reality headset does—would remain the preserve of enthusiasts rather than casual consumers.

Fast-forward 10 years, and smart glasses are on the verge of becoming—whisper it—cool. Meta’s smart glasses, made in partnership with Ray-Ban, are basically indistinguishable from the iconic Wayfarers Tom Cruise made famous in Risky Business. Meta also recently showed off its fashion-forward Orion augmented reality glasses prototype, while Snap unveiled its fifth-generation Spectacles, neither of which would look out of place in the trendiest district of a major city. In December, Google showed off its new unnamed Android XR prototype glasses, and rumors that Apple is still working on a long-anticipated glasses project continue to swirl. Elsewhere, Chinese tech giants Huawei, Alibaba, Xiaomi, and Baidu are also vying for a slice of the market.

Sleeker designs are certainly making this new generation of glasses more appealing. But more importantly, smart glasses are finally on the verge of becoming useful, and it’s clear that Big Tech is betting that augmented specs will be the next big consumer device category. Here’s what to expect from smart glasses in 2025 and beyond.

AI agents could finally make smart glasses truly useful

Although mixed-reality devices have been around for decades, they have largely benefited specialized fields, including the medical, construction, and technical remote-assistance industries, where they are likely to continue being used, possibly in more specialized ways. Microsoft is the creator of the best-known of these devices, which layer virtual content over the wearer’s real-world environment, and marketed its HoloLens 2 smart goggles to corporations. The company recently confirmed it was ending production of that device. Instead, it is choosing to focus on building headsets for the US military in partnership with Oculus founder Palmer Luckey’s latest venture, Anduril.

Now the general public may finally be getting access to devices they can use. The AI world is abuzz over agents, which augment large language models (LLMs) with the ability to carry out tasks by themselves. The past 12 months have seen huge leaps in AI multimodal LLMs’ abilities to handle video, images, and audio in addition to text, which opens up new applications for smart glasses that would not have been possible previously, says Louis Rosenberg, an AR researcher who worked on the first functional augmented-reality system at Stanford University in the 1990s.

We already know Meta is definitely interested in AI agents. Although the company said in September that it has no plans to sell its Orion prototype glasses to the public, given their expense, Mark Zuckerberg raised expectations for its next generations of Meta’s smart glasses when he declared Orion the “most advanced pair of AR glasses ever made.” He’s also made it clear how deeply invested Meta is in bringing a “highly intelligent and personalized AI assistant” to as many users as possible and that he’s confident Meta’s glasses are the “perfect form factor for AI.”

Although Meta is already making its Ray-Ban smart glasses’ AI more conversational—its new live AI feature responds to prompts about what its wearer is seeing and hearing via its camera and microphone—future agents will give these systems not only eyes and ears, but a contextual awareness of what’s around them, Rosenberg says. For example, agents running on smart glasses could hold unprompted interactive conversations with their wearers based on their environment, reminding them to buy orange juice when they walk past a store, for example, or telling them the name of a coworker who passes them on the sidewalk. We already know Google is deeply interested in this agent-first approach: The unnamed smart glasses it first showed off at Google I/O in May 2024 were powered by its Astra AI agent system.

“Having worked on mixed reality for over 30 years, it’s the first time I can see an application that will really drive mass adoption,” Rosenberg says.

Meta and Google will likely tussle to be the sector’s top dog

It’s unclear how far we are from that level of mass adoption. During a recent Meta earnings call, Zuckerberg said 2025 would be a “defining year” for understanding the future of AI glasses and whether they explode in popularity or represent “a longer grind.”

He has reason to be optimistic, though: Meta is currently ahead of its competition thanks to the success of the Ray-Ban Meta smart glasses—the company sold more than 1 million units last year. It also is preparing to roll out new styles thanks to a partnership with Oakley, which, like Ray-Ban, is under the EssilorLuxottica umbrella of brands. And while its current second-generation specs can’t show its wearer digital data and notifications, a third version complete with a small display is due for release this year, according to the Financial Times. The company is also reportedly working on a lighter, more advanced version of its Orion AR glasses, dubbed Artemis, that could go on sale as early as 2027, Bloomberg reports.

Adding display capabilities will put the Ray-Ban Meta glasses on equal footing with Google’s unnamed Android XR glasses project, which sports an in-lens display (the company has not yet announced a definite release date). The prototype the company demoed to journalists in September featured a version of its AI chatbot Gemini, and much they way Google built its Android OS to run on smartphones made by third parties, its Android XR software will eventually run on smart glasses made by other companies as well as its own.

These two major players are competing to bring face-mounted AI to the masses in a race that’s bound to intensify, adds Rosenberg—especially given that both Zuckerberg and Google cofounder Sergey Brin have called smart glasses the “perfect” hardware for AI. “Google and Meta are really the big tech companies that are furthest ahead in the AI space on their own. They’re very well positioned,” he says. “This is not just augmenting your world, it’s augmenting your brain.”

It’s getting easier to make smart glasses—but it’s still hard to get them right

When the AR gaming company Niantic’s Michael Miller walked around CES, the gigantic consumer electronics exhibition that takes over Las Vegas each January, he says he was struck by the number of smaller companies developing their own glasses and systems to run on them, including Chinese brands DreamSmart, Thunderbird, and Rokid. While it’s still not a cheap endeavor—a business would probably need a couple of million dollars in investment to get a prototype off the ground, he says—it demonstrates that the future of the sector won’t depend on Big Tech alone.

“On a hardware and software level, the barrier to entry has become very low,” says Miller, the augmented reality hardware lead at Niantic, which has partnered with Meta, Snap, and Magic Leap, among others. “But turning it into a viable consumer product is still tough. Meta caught the biggest fish in this world, and so they benefit from the Ray-Ban brand. It’s hard to sell glasses when you’re an unknown brand.”

That’s why it’s likely ambitious smart glasses makers in countries like Japan and China will increasingly partner with eyewear companies known locally for creating desirable frames, generating momentum in their home markets before expanding elsewhere, he suggests.

More developers will start building for these devices

These smaller players will also have an important role in creating new experiences for wearers of smart glasses. A big part of smart glasses’ usefulness hinges on their ability to send and receive information from a wearer’s smartphone—and third-party developers’ interest in building apps that run on them. The more the public can do with their glasses, the more likely they are to buy them.

Developers are still waiting for Meta to release a software development kit (SDK) that would let them build new experiences for the Ray-Ban Meta glasses. While bigger brands are understandably wary about giving third parties access to smart glasses’ discreet cameras, it does limit the opportunities researchers and creatives have to push the envelope, says Paul Tennent, an associate professor in the Mixed Reality Laboratory at the University of Nottingham in the UK. “But historically, Google has been a little less afraid of this,” he adds.

Elsewhere, Snap and smaller brands like Brilliant Labs, whose Frame glasses run multimodal AI models including Perplexity, ChatGPT, and Whisper, and Vuzix, which recently launched its AugmentOS universal operating system for smart glasses, have happily opened up their SDKs, to the delight of developers, says Patrick Chwalek, a student at the MIT Media Lab who worked on smart glasses platform Project Captivate as part of his PhD research. “Vuzix is getting pretty popular at various universities and companies because people can start building experiences on top of them,” he adds. “Most of these are related to navigation and real-time translation—I think we’re going to be seeing a lot of iterations of that over the next few years.”

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With the global demand for data centers continuing to surge ahead, fueled by the proliferation of artificial intelligence (AI), cloud computing, and digital services, it is unsurprising that we are seeing aggressive investment strategies, beyond those of the existing hyperscalers. One of the dynamic players in this market is Blue Owl Capital, a leading asset management firm that has made significant strides in the data center sector. Back in October 2024 we reported on its acquisition of IPI Partners, a digital infrastructure fund manager, for approximately $1 billion. This acquisition added over $11 billion to the assets Blue Owl manages and focused specifically on digital infrastructure initiatives. This acquisition was completed as of January 5, 2025 and IPI’s Managing Partner, Matt A’Hearn has been appointed Head of Blue Owl’s digital infrastructure strategy. A Key Player In Digital Infrastructure and Data Centers With multi-billion-dollar joint ventures and financing initiatives, Blue Owl is positioning itself as a key player in the digital infrastructure space. The company investments in data centers, the implications of its strategic moves, and the broader impact on the AI and digital economy highlights the importance of investment in the data center to the economy overall. With the rapid growth of the data center industry, it is unsurprising that aggressive investment fund management is seeing it as an opportunity. Analysts continue to emphasize that the global data center market is expected to grow at a compound annual growth rate (CAGR) of 10.2% from 2023 to 2030, reaching $517.17 billion by the end of the decade. In this rapidly evolving landscape, Blue Owl Capital has emerged as a significant contributor. The firm’s investments in data centers are not just about capitalizing on current trends but also about shaping the future of digital infrastructure. Spreading the Wealth In August 2024, Blue Owl

Global Data Center Operator Telehouse Launches Liquid Cooling Lab in the UK to Meet Ongoing AI and HPC Demand

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Flexential Partners with Lonestar to Support First Lunar Data Center

Flexential, a leading provider of secure and flexible data center solutions, this month announced that it has joined forces with Lonestar Data Holdings Inc. to support the upcoming launch of Freedom, Lonestar’s second lunar data center. Scheduled to launch aboard a SpaceX Falcon 9 rocket via Intuitive Machines, this mission is a critical step toward establishing a permanent data center on the Moon. Ground-Based Support for Lunar Data Storage Flexential’s Tampa data center will serve as the mission control platform for Lonestar’s lunar operations, providing colocation, interconnection, and professional services. The facility was chosen for its proximity to Florida’s Space Coast launch operations and its ability to deliver low-latency connectivity for critical functions. Flexential operates two data centers in Tampa and four in Florida as part of its FlexAnywhere® Platform, comprising more than 40 facilities across the U.S. “Flexential’s partnership with Lonestar represents our commitment to advancing data center capabilities beyond conventional boundaries,” said Jason Carolan, Chief Innovation Officer at Flexential. “By supporting Lonestar’s space-based data center initiative, we are helping to create new possibilities for data storage and disaster recovery. This project demonstrates how innovative data center expertise can help organizations prepare for a resilient future with off-world storage solutions.” A New Era of Space-Based Resiliency The growing demand for data center capacity, with U.S. power consumption expected to double from 17 GW in 2022 to 35 GW by 2030 (according to McKinsey & Company), is driving interest in space-based solutions. Storing data off-planet reduces reliance on terrestrial resources while enhancing security against natural disasters, warfare, and cyber threats. The Freedom data center will provide resiliency, disaster recovery, and edge processing services for government and enterprise customers requiring the highest levels of data protection. The solar-powered data center leverages Solid-State Drives (SSDs) and a Field Programmable Gate Array (FPGA) edge

Why DeepSeek Is Great for AI and HPC and Maybe No Big Deal for Data Centers

In the rapid and ever-evolving landscape of artificial intelligence (AI) and high-performance computing (HPC), the emergence of DeepSeek’s R1 model has sent ripples across industries. DeepSeek has been the data center industry’s topic of the week, for sure. The Chinese AI app surged to the top of US app store leaderboards last weekend, sparking a global selloff in technology shares Monday morning. But while some analysts predict a transformative impact within the industry, a closer examination suggests that, for data centers at large, the furor over DeepSeek might ultimately be much ado about nothing. DeepSeek’s Breakthrough in AI and HPC DeepSeek, a Chinese AI startup, this month unveiled its R1 model, claiming performance on par with, or even surpassing, leading models like OpenAI’s ChatGPT-4 and Anthropic’s Claude-3.5-Sonnet. Remarkably, DeepSeek developed this model at a fraction of the cost typically associated with such advancements, utilizing a cluster of 256 server nodes equipped with 2,048 GPUs. This efficiency has been attributed to innovative techniques and optimized resource utilization. AI researchers have been abuzz about the performance of the DeepSeek chatbot that produces results similar to ChatGPT, but is based on open-source models and reportedly trained on older GPU chips. Some researchers are skeptical of claims about DeepSeek’s development costs and means, but its performance appears to challenge common assumptions about the computing cost of developing AI applications. This efficiency has been attributed to innovative techniques and optimized resource utilization. Market Reactions and Data Center Implications The announcement of DeepSeek’s R1 model led to significant market reactions, with notable declines in tech stocks, including a substantial drop in Nvidia’s valuation. This downturn was driven by concerns that more efficient AI models could reduce the demand for high-end hardware and, by extension, the expansive data centers that house them. For now, investors are re-assessing the

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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