The State of AI: Is China about to win the race?

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The State of AI: Is China about to win the race?

The State of AI is a collaboration between the Financial Times & MIT Technology Review examining the ways in which AI is reshaping global power. Every Monday for the next six weeks, writers from both publications will debate one aspect of the generative AI revolution reshaping global power. In this conversation, the FT’s tech columnist and Innovation Editor John Thornhill and MIT Technology Review’s Caiwei Chen consider the battle between Silicon Valley and Beijing for technological supremacy. John Thornhill writes: Viewed from abroad, it seems only a matter of time before China emerges as the AI superpower of the 21st century. Here in the West, our initial instinct is to focus on America’s significant lead in semiconductor expertise, its cutting-edge AI research, and its vast investments in data centers. The legendary investor Warren Buffett once warned: “Never bet against America.” He is right that for more than two centuries, no other “incubator for unleashing human potential” has matched the US. Today, however, China has the means, motive, and opportunity to commit the equivalent of technological murder. When it comes to mobilizing the whole-of-society resources needed to develop and deploy AI to maximum effect, it may be just as rash to bet against. The data highlights the trends. In AI publications and patents, China leads. By 2023, China accounted for 22.6% of all citations, compared with 20.9% from Europe and 13% from the US, according to Stanford University’s Artificial Intelligence Index Report 2025. As of 2023, China also accounted for 69.7% of all AI patents. True, the US maintains a strong lead in the top 100 most cited publications (50 versus 34 in 2023), but its share has been steadily declining. Similarly, the US outdoes China in top AI research talent, but the gap is narrowing. According to a report from the US Council of Economic Advisers, 59% of the world’s top AI researchers worked in the US in 2019, compared with 11% in China. But by 2022 those figures were 42% and 28%. The Trump administration’s tightening of restrictions for foreign H-1B visa holders may well lead more Chinese AI researchers in the US to return home. The talent ratio could move further in China’s favor. Regarding the technology itself, US-based institutions produced 40 of the world’s most notable AI models in 2024, compared with 15 from China. But Chinese researchers have learned to do more with less, and their strongest large language models—including the open-source DeepSeek-V3 and Alibaba’s Qwen 2.5-Max—surpass the best US models in terms of algorithmic efficiency. Where China is really likely to excel in future is in applying these open-source models. The latest report from Air Street Capital shows that China has now overtaken the US in terms of monthly downloads of AI models. In AI-enabled fintech, e-commerce, and logistics, China already outstrips the US. Perhaps the most intriguing—and potentially the most productive—applications of AI may yet come in hardware, particularly in drones and industrial robotics. With the research field evolving toward embodied AI, China’s advantage in advanced manufacturing will shine through. Dan Wang, the tech analyst and author of Breakneck, has rightly highlighted the strengths of China’s engineering state in developing manufacturing process knowledge—even if he has also shown the damaging effects of applying that engineering mentality in the social sphere. “China has been growing technologically stronger and economically more dynamic in all sorts of ways,” he told me. “But repression is very real. And it is getting worse in all sorts of ways as well.” I’d be fascinated to hear from you, Caiwei, about your take on the strengths and weaknesses of China’s AI dream. To what extent will China’s engineered social control hamper its technological ambitions? Caiwei Chen responds: Hi, John! You’re right that the US still holds a clear lead in frontier research and infrastructure. But “winning” AI can mean many different things. Jeffrey Ding, in his book Technology and the Rise of Great Powers, makes a counterintuitive point: For a general-purpose technology like AI, long-term advantage often comes down to how widely and deeply technologies spread across society. And China is in a good position to win that race (although “murder” might be pushing it a bit!). Chips will remain China’s biggest bottleneck. Export restrictions have throttled access to top GPUs, pushing buyers into gray markets and forcing labs to recycle or repair banned Nvidia stock. Even as domestic chip programs expand, the performance gap at the very top still stands. Yet those same constraints have pushed Chinese companies toward a different playbook: pooling compute, optimizing efficiency, and releasing open-weight models. DeepSeek-V3’s training run, for example, used just 2.6 million GPU-hours—far below the scale of US counterparts. But Alibaba’s Qwen models now rank among the most downloaded open-weights globally, and companies like Zhipu and MiniMax are building competitive multimodal and video models. China’s industrial policy means new models can move from lab to implementation fast. Local governments and major enterprises are already rolling out reasoning models in administration, logistics, and finance. Education is another advantage. Major Chinese universities are implementing AI literacy programs in their curricula, embedding skills before the labor market demands them. The Ministry of Education has also announced plans to integrate AI training for children of all school ages. I’m not sure the phrase “engineering state” fully captures China’s relationship with new technologies, but decades of infrastructure building and top-down coordination have made the system unusually effective at pushing large-scale adoption, often with far less social resistance than you’d see elsewhere. The use at scale, naturally, allows for faster iterative improvements. Meanwhile, Stanford HAI’s 2025 AI Index found Chinese respondents to be the most optimistic in the world about AI’s future—far more optimistic than populations in the US or the UK. It’s striking, given that China’s economy has slowed since the pandemic for the first time in over two decades. Many in government and industry now see AI as a much-needed spark. Optimism can be powerful fuel, but whether it can persist through slower growth is still an open question. Social control remains part of the picture, but a different kind of ambition is taking shape. The Chinese AI founders in this new generation are the most globally minded I’ve seen, moving fluidly between Silicon Valley hackathons and pitch meetings in Dubai. Many are fluent in English and in the rhythms of global venture capital. Having watched the last generation wrestle with the burden of a Chinese label, they now build companies that are quietly transnational from the start. The US may still lead in speed and experimentation, but China could shape how AI becomes part of daily life, both at home and abroad. Speed matters, but speed isn’t the same thing as supremacy. John Thornhill replies: You’re right, Caiwei, that speed is not the same as supremacy (and “murder” may be too strong a word). And you’re also right to amplify the point about China’s strength in open-weight models and the US preference for proprietary models. This is not just a struggle between two different countries’ economic models but also between two different ways of deploying technology. Even OpenAI’s chief executive, Sam Altman, admitted earlier this year: “We have been on the wrong side of history here and need to figure out a different open-source strategy.” That’s going to be a very interesting subplot to follow. Who’s called that one right? Further reading on the US-China competition There’s been a lot of talk about how people may be using generative AI in their daily lives. This story from the FT’s visual story team explores the reality From China, FT reporters ask how long Nvidia can maintain its dominance over Chinese rivals When it comes to real-world uses, toys and companions devices are a novel but emergent application of AI that is gaining traction in China—but is also heading to the US. This MIT Technology Review story explored it. The once-frantic data center buildout in China has hit walls, and as the sanctions and AI demands shift, this MIT Technology Review story took an on-the-ground look at how stakeholders are figuring it out.

In this conversation, the FT’s tech columnist and Innovation Editor John Thornhill and MIT Technology Review’s Caiwei Chen consider the battle between Silicon Valley and Beijing for technological supremacy.

John Thornhill writes:

Viewed from abroad, it seems only a matter of time before China emerges as the AI superpower of the 21st century.

Here in the West, our initial instinct is to focus on America’s significant lead in semiconductor expertise, its cutting-edge AI research, and its vast investments in data centers. The legendary investor Warren Buffett once warned: “Never bet against America.” He is right that for more than two centuries, no other “incubator for unleashing human potential” has matched the US.

Today, however, China has the means, motive, and opportunity to commit the equivalent of technological murder. When it comes to mobilizing the whole-of-society resources needed to develop and deploy AI to maximum effect, it may be just as rash to bet against.

The data highlights the trends. In AI publications and patents, China leads. By 2023, China accounted for 22.6% of all citations, compared with 20.9% from Europe and 13% from the US, according to Stanford University’s Artificial Intelligence Index Report 2025. As of 2023, China also accounted for 69.7% of all AI patents. True, the US maintains a strong lead in the top 100 most cited publications (50 versus 34 in 2023), but its share has been steadily declining.

Similarly, the US outdoes China in top AI research talent, but the gap is narrowing. According to a report from the US Council of Economic Advisers, 59% of the world’s top AI researchers worked in the US in 2019, compared with 11% in China. But by 2022 those figures were 42% and 28%.

The Trump administration’s tightening of restrictions for foreign H-1B visa holders may well lead more Chinese AI researchers in the US to return home. The talent ratio could move further in China’s favor.

Regarding the technology itself, US-based institutions produced 40 of the world’s most notable AI models in 2024, compared with 15 from China. But Chinese researchers have learned to do more with less, and their strongest large language models—including the open-source DeepSeek-V3 and Alibaba’s Qwen 2.5-Max—surpass the best US models in terms of algorithmic efficiency.

Where China is really likely to excel in future is in applying these open-source models. The latest report from Air Street Capital shows that China has now overtaken the US in terms of monthly downloads of AI models. In AI-enabled fintech, e-commerce, and logistics, China already outstrips the US.

Perhaps the most intriguing—and potentially the most productive—applications of AI may yet come in hardware, particularly in drones and industrial robotics. With the research field evolving toward embodied AI, China’s advantage in advanced manufacturing will shine through.

Dan Wang, the tech analyst and author of Breakneck, has rightly highlighted the strengths of China’s engineering state in developing manufacturing process knowledge—even if he has also shown the damaging effects of applying that engineering mentality in the social sphere. “China has been growing technologically stronger and economically more dynamic in all sorts of ways,” he told me. “But repression is very real. And it is getting worse in all sorts of ways as well.”

I’d be fascinated to hear from you, Caiwei, about your take on the strengths and weaknesses of China’s AI dream. To what extent will China’s engineered social control hamper its technological ambitions?

Caiwei Chen responds:

Hi, John!

You’re right that the US still holds a clear lead in frontier research and infrastructure. But “winning” AI can mean many different things. Jeffrey Ding, in his book Technology and the Rise of Great Powers, makes a counterintuitive point: For a general-purpose technology like AI, long-term advantage often comes down to how widely and deeply technologies spread across society. And China is in a good position to win that race (although “murder” might be pushing it a bit!).

Chips will remain China’s biggest bottleneck. Export restrictions have throttled access to top GPUs, pushing buyers into gray markets and forcing labs to recycle or repair banned Nvidia stock. Even as domestic chip programs expand, the performance gap at the very top still stands.

Yet those same constraints have pushed Chinese companies toward a different playbook: pooling compute, optimizing efficiency, and releasing open-weight models. DeepSeek-V3’s training run, for example, used just 2.6 million GPU-hours—far below the scale of US counterparts. But Alibaba’s Qwen models now rank among the most downloaded open-weights globally, and companies like Zhipu and MiniMax are building competitive multimodal and video models.

China’s industrial policy means new models can move from lab to implementation fast. Local governments and major enterprises are already rolling out reasoning models in administration, logistics, and finance.

Education is another advantage. Major Chinese universities are implementing AI literacy programs in their curricula, embedding skills before the labor market demands them. The Ministry of Education has also announced plans to integrate AI training for children of all school ages. I’m not sure the phrase “engineering state” fully captures China’s relationship with new technologies, but decades of infrastructure building and top-down coordination have made the system unusually effective at pushing large-scale adoption, often with far less social resistance than you’d see elsewhere. The use at scale, naturally, allows for faster iterative improvements.

Meanwhile, Stanford HAI’s 2025 AI Index found Chinese respondents to be the most optimistic in the world about AI’s future—far more optimistic than populations in the US or the UK. It’s striking, given that China’s economy has slowed since the pandemic for the first time in over two decades. Many in government and industry now see AI as a much-needed spark. Optimism can be powerful fuel, but whether it can persist through slower growth is still an open question.

Social control remains part of the picture, but a different kind of ambition is taking shape. The Chinese AI founders in this new generation are the most globally minded I’ve seen, moving fluidly between Silicon Valley hackathons and pitch meetings in Dubai. Many are fluent in English and in the rhythms of global venture capital. Having watched the last generation wrestle with the burden of a Chinese label, they now build companies that are quietly transnational from the start.

The US may still lead in speed and experimentation, but China could shape how AI becomes part of daily life, both at home and abroad. Speed matters, but speed isn’t the same thing as supremacy.

John Thornhill replies:

You’re right, Caiwei, that speed is not the same as supremacy (and “murder” may be too strong a word). And you’re also right to amplify the point about China’s strength in open-weight models and the US preference for proprietary models. This is not just a struggle between two different countries’ economic models but also between two different ways of deploying technology.

Even OpenAI’s chief executive, Sam Altman, admitted earlier this year: “We have been on the wrong side of history here and need to figure out a different open-source strategy.” That’s going to be a very interesting subplot to follow. Who’s called that one right?

Further reading on the US-China competition

There’s been a lot of talk about how people may be using generative AI in their daily lives. This story from the FT’s visual story team explores the reality

From China, FT reporters ask how long Nvidia can maintain its dominance over Chinese rivals

When it comes to real-world uses, toys and companions devices are a novel but emergent application of AI that is gaining traction in China—but is also heading to the US. This MIT Technology Review story explored it.

The once-frantic data center buildout in China has hit walls, and as the sanctions and AI demands shift, this MIT Technology Review story took an on-the-ground look at how stakeholders are figuring it out.

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HPE product barrage targets AI networks, agents, management

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APA inks $70-million Alaska acquisition

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Petronas, JERA sign 20-year LNG supply deal

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MRV lets EPCIC contract for Coral North FLNG project

Mozambique Rovuma Venture (MRV) SpA has let an engineering, procurement, construction, installation, and commissioning (EPCIC) contract to Technip Energies, in partnership with JGC and Samsung Heavy Industries, for the Coral North FLNG project offshore Mozambique. Under the contract, JGC France and Technip Energies, through their joint venture, will be primarily responsible for the engineering and procurement of the FLNG topside infrastructure as well as overall project management. Samsung Heavy Industries will undertake the engineering, procurement, and construction of the FLNG hull and the fabrication of the topside modules. The Coral North project includes construction of a new FLNG vessel with a production capacity of about 3.6 million tonnes/year (tpy). The vessel will be installed in Coral gas field, about 50 km offshore northern Mozambique. Coral North is designed as an enhanced replica of Coral Sul, the first development in Mozambique’s Area 4 offshore gas block, and is expected to double Coral hub’s capacity to 7 million tpy. Coral FLNG SA is a special-purpose entity incorporated by Area 4 partners Eni SPA (operator), China National Petroleum Corp. (CNPC), Empresa Nacional de Hidrocarbonetos (ENH), Galp Energia SGPS SA, and Korea Gas Corp. (KOGAS).

EIA forecasts prolonged oil market tightening amid Hormuz shipping disruptions

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Shell discovers oil in Namibia’s Orange basin

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Equinor to farm out 50% stake in Itaimbezinho block offshore Brazil

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ORLANDO, Fla. — For years, the data center industry optimized individual systems: power distribution, cooling, racks, UPS equipment, and mechanical infrastructure. In the AI era, according to Vertiv Distinguished Engineer and Vice President of Technical Business Development Peter Panfil, that approach is no longer sufficient. Speaking during Wednesday morning’s keynote at the 2026 7×24 Exchange Spring Conference, Panfil presented a vision in which the data center itself becomes a single, tightly orchestrated computing appliance—truly an “AI factory” whose success depends less on standalone components than on the seamless interaction between them. Throughout his presentation, titled “Scale at Speed: How Massively Parallel Compute GPUs Are Revolutionizing Data Center Design,” Panfil repeatedly returned to a single imperative: the AI infrastructure race is increasingly defined by execution velocity. “If you think you’re going big enough, go bigger,” he told attendees. “If you think you’re going fast enough, you’re going to have to go faster.” For an industry gathered under the conference’s overarching theme of future-proofing AI infrastructure, Panfil’s message suggested something subtly different. Rather than trying to predict the future, operators should build systems capable of adapting to it. “I would much rather be future ready,” he said, “than future proof.” Speed Becomes the New Competitive Metric One of the keynote’s recurring themes was that deployment speed has become an economic variable in its own right. Panfil argued that hyperscalers and AI providers increasingly view time-to-capacity as directly tied to business value, making delays in construction or commissioning far more expensive than traditional infrastructure inefficiencies. “The cost of speeding up has real benefits right now,” he observed. That urgency is changing the way facilities are assembled. Rather than coordinating numerous independent contractors and subsystem vendors on-site, Panfil described an emerging model built around highly standardized, factory-produced HAC [hot aisle containment] modules—or “hacks”—that arrive

Beyond the GPU: Cisco Says AI’s Biggest Challenge May Be the Network That Connects It All

For much of the AI boom, the industry’s attention has centered on GPUs, power availability, and liquid cooling. But according to Cisco Senior Business Development Manager Robin Olds, another critical constraint is rapidly moving to the forefront: the network itself. Speaking with Data Center Frontier on the show floor at Fiber Connect 2026, Olds argued that AI represents a once-in-a-generation shift comparable to the birth of the commercial internet, fundamentally changing traffic patterns and forcing service providers, data center operators, hyperscalers, and emerging neoclouds to rethink infrastructure design. “It’s really like the internet when it was created,” Olds said. “We’re at another intersection in time where we could really see things happening.” AI Is Rewriting the Bandwidth Equation The most significant change may not be compute density alone but the sustained demand AI places on transport networks. According to Olds, service providers are already seeing AI traffic account for roughly 30% of utilization on backbone infrastructure; a dramatic increase from less than 1% only two years ago. As AI workloads continue to proliferate, those utilization levels are expected to rise further. The next wave of agentic AI could amplify that trend. Unlike consumer chatbots, which generate bursty request patterns, autonomous AI agents continuously interact with applications and external services, creating more persistent traffic flows. “Everything’s about chatbots,” Olds observed. “It’s very spiky—up, down. Agentic AI is going to maintain utilization because now I have agents working on my behalf.” For data center developers, network operators, and cloud providers alike, that implies planning not just for peak demand but for elevated baseline utilization across metro and long-haul infrastructure. Compressing the Network Stack Cisco’s response centers on architectural simplification. Olds highlighted the company’s Agile Services Networking framework, which combines router and optical networking technologies with coherent optics to converge functions that historically

Emerging Power Strategies Transforming AI Data Center Development

This deal follows the industry trend shown in the Flex/EP² acquisition. One transaction targets engineered control and protection systems; the other targets high-power conductive components and value-added services around them. The clarity here is that data center growth is creating investable demand not just for chips and campuses, but for the industrial companies that make the electrical buildout possible. AZIO AI and EVTV: EV maker to AI infrastructure After multiple announcements of deals between the two companies, and the end of May, they decided a corporate merger was in order. The AZIO AI–EVTV merger turns Envirotech Vehicles Inc., from an electric-vehicle company into a prospective power-backed AI infrastructure and data center platform. The core impact is not just an increase in AI servers; it is the combination of compute hardware, controlled land, behind-the-meter power, fiber, modular deployment, and cooling validation building on the AI data center growth story. AZIO describes the deal as part of EVTV’s strategic transformation into an AI infrastructure and compute platform focused on domestic AI deployment, data center operations, and long-term compute capacity expansion. For data center ambitions, the most important element is power access. AZIO and EVTV say about 11 MW of power capacity has been identified at EVTV’s existing site, with hardware orders placed for an initial 6 MW deployment. They are also discussing long-term rights tied to as much as 500 MW of additional same-site capacity. That matters because AI data center development is increasingly constrained by power availability rather than just real estate or server supply. And with the modular infrastructure the company develops, having that power availability is the key to future site growth. EVTV later said it had expanded its controlled development footprint to more than 548 acres, secured dedicated fiber for current and future operations, and was advancing natural

Amazon claims its data centers are 7x more water-efficient than the industry average

“Amazon is on the leading edge, but it’s not a secret recipe,” he said. What sets the company apart is scale, execution, facility design, geographic mix, and its aggressive pursuit of energy goals. Others are doing the similar things, if through different avenues: Microsoft is investing in closed-loop cooling systems that dramatically reduce evaporative water loss. Google is heavily focused on reclaimed water and using AI to optimize data centers. Meta has long relied on outside-air cooling. And overall, the industry is moving toward liquid cooling for dense AI deployments, “which changes the water equation again,” said Kimball. One of the big variables is location: Climate influences water efficiency, so where a company builds its infrastructure is as important as its cooling methods. Further, power-consumptive AI changes the discussion, he emphasized; traditional enterprise workloads and dense AI training clusters create very different thermal profiles.

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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