A New York legislator wants to pick up the pieces of the dead California AI bill

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A New York legislator wants to pick up the pieces of the dead California AI bill

The first Democrat in New York history with a computer science background wants to revive some of the ideas behind the failed California AI safety bill, SB 1047, with a new version in his state that would regulate the most advanced AI models. It’s called the RAISE Act, an acronym for “Responsible AI Safety and Education.” Assembly member Alex Bores hopes his bill, currently an unpublished draft—subject to change—that MIT Technology Review has seen, will address many of the concerns that blocked SB 1047 from passing into law. SB 1047 was, at first, thought to be a fairly modest bill that would pass without much fanfare. In fact, it flew through the California statehouse with huge margins and received significant public support. However, before it even landed on Governor Gavin Newsom’s desk for signature in September, it sparked an intense national fight. Google, Meta, and OpenAI came out against the bill, alongside top congressional Democrats like Nancy Pelosi and Zoe Lofgren. Even Hollywood celebrities got involved, with Jane Fonda and Mark Hamill expressing support for the bill. Ultimately, Newsom vetoed SB 1047, effectively killing regulation of so-called frontier AI models not just in California but, with the lack of laws on the national level, anywhere in the US, where the most powerful systems are developed. Now Bores hopes to revive the battle. The main provisions in the RAISE Act include requiring AI companies to develop safety plans for the development and deployment of their models. The bill also provides protections for whistleblowers at AI companies. It forbids retaliation against an employee who shares information about an AI model in the belief that it may cause “critical harm”; such whistleblowers can report the information to the New York attorney general. One way the bill defines critical harm is the use of an AI model to create a chemical, biological, radiological, or nuclear weapon that results in the death or serious injury of 100 or more people. Alternatively, a critical harm could be a use of the AI model that results in 100 or more deaths or at least $1 billion in damages in an act with limited human oversight that if committed by a human would constitute a crime requiring intent, recklessness, or gross negligence. The safety plans would ensure that a company has cybersecurity protections in place to prevent unauthorized access to a model. The plan would also require testing of models to assess risks before and after training, as well as detailed descriptions of procedures to assess the risks associated with post-training modifications. For example, some current AI systems have safeguards that can be easily and cheaply removed by a malicious actor. A safety plan would have to address how the company plans to mitigate these actions. The safety plans would then be audited by a third party, like a nonprofit with technical expertise that currently tests AI models. And if violations are found, the bill empowers the attorney general of New York to issue fines and, if necessary, go to the courts to determine whether to halt unsafe development. A different flavour of bill The safety plans and external audits were elements of SB 1047, but Bores aims to differentiate his bill from the California one. “We focused a lot on what the feedback was for 1047,” he says. “Parts of the criticism were in good faith and could make improvements. And so we’ve made a lot of changes.” The RAISE Act diverges from SB 1047 in a few ways. For one, SB 1047 would have created the Board of Frontier Models, tasked with approving updates to the definitions and regulations around these AI models, but the proposed act would not create a new government body. The New York bill also doesn’t create a public cloud computing cluster, which SB 1047 would have done. The cluster was intended to support projects to develop AI for the public good. The RAISE Act doesn’t have SB 1047’s requirement that companies be able to halt all operations of their model, a capability sometimes referred to as a “kill switch.” Some critics alleged that the shutdown provision of SB 1047 would harm open-source models, since developers can’t shut down a model someone else may now possess (even though SB 1047 had an exemption for open-source models). The RAISE Act avoids the fight entirely. SB 1047 referred to an “advanced persistent threat” associated with bad actors trying to steal information during model training. The RAISE Act does away with that definition, sticking to addressing critical harms from covered models. Focusing on the wrong issues? Bores’ bill is very specific with its definitions in an effort to clearly delineate what this bill is and isn’t about. The RAISE Act doesn’t address some of the current risks from AI models, like bias, discrimination, and job displacement. Like SB 1047, it is very focused on catastrophic risks from frontier AI models. Some in the AI community believe this focus is misguided. “We’re broadly supportive of any efforts to hold large models accountable,” says Kate Brennan, associate director of the AI Now Institute, which conducts AI policy research. “But defining critical harms only in terms of the most catastrophic harms from the most advanced models overlooks the material risks that AI poses, whether it’s workers subject to surveillance mechanisms, prone to workplace injuries because of algorithmically managed speed rates, climate impacts of large-scale AI systems, data centers exerting massive pressure on local power grids, or data center construction sidestepping key environmental protections,” she says. Bores has worked on other bills addressing current harms posed by AI systems, like discrimination and lack of transparency. That said, Bores is clear that this new bill is aimed at mitigating catastrophic risks from more advanced models. “We’re not talking about any model that exists right now,” he says. “We are talking about truly frontier models, those on the edge of what we can build and what we understand, and there is risk in that.” The bill would cover only models that pass a certain threshold for how many computations their training required, typically measured in FLOPs (floating-point operations). In the bill, a covered model is one that requires more than 1026 FLOPs in its training and costs over $100 million. For reference, GPT-4 is estimated to have required 1025 FLOPs. This approach may draw scrutiny from industry forces. “While we can’t comment specifically on legislation that isn’t public yet, we believe effective regulation should focus on specific applications rather than broad model categories,” says a spokesperson at Hugging Face, a company that opposed SB 1047. Early days The bill is in its nascent stages, so it’s subject to many edits in the future, and no opposition has yet formed. There may already be lessons to be learned from the battle over SB 1047, however. “There’s significant disagreement in the space, but I think debate around future legislation would benefit from more clarity around the severity, the likelihood, and the imminence of harms,” says Scott Kohler, a scholar at the Carnegie Endowment for International Peace, who tracked the development of SB 1047. When asked about the idea of mandated safety plans for AI companies, assembly member Edward Ra, a Republican who hasn’t yet seen a draft of the new bill yet, said: “I don’t have any general problem with the idea of doing that. We expect businesses to be good corporate citizens, but sometimes you do have to put some of that into writing.” Ra and Bores co chair the New York Future Caucus, which aims to bring together lawmakers 45 and under to tackle pressing issues that affect future generations. Scott Wiener, a California state senator who sponsored SB 1047, is happy to see that his initial bill, even though it failed, is inspiring further legislation and discourse. “The bill triggered a conversation about whether we should just trust the AI labs to make good decisions, which some will, but we know from past experience, some won’t make good decisions, and that’s why a level of basic regulation for incredibly powerful technology is important,” he says. He has his own plans to reignite the fight: “We’re not done in California. There will be continued work in California, including for next year. I’m optimistic that California is gonna be able to get some good things done.” And some believe the RAISE Act will highlight a notable contradiction: Many of the industry’s players insist that they want regulation, but when any regulation is proposed, they fight against it. “SB 1047 became a referendum on whether AI should be regulated at all,” says Brennan. “There are a lot of things we saw with 1047 that we can expect to see replay in New York if this bill is introduced. We should be prepared to see a massive lobbying reaction that industry is going to bring to even the lightest-touch regulation.” Wiener and Bores both wish to see regulation at a national level, but in the absence of such legislation, they’ve taken the battle upon themselves. At first it may seem odd for states to take up such important reforms, but California houses the headquarters of the top AI companies, and New York, which has the third-largest state economy in the US, is home to offices for OpenAI and other AI companies. The two states may be well positioned to lead the conversation around regulation. “There is uncertainty at the direction of federal policy with the transition upcoming and around the role of Congress,” says Kohler. “It is likely that states will continue to step up in this area.” Wiener’s advice for New York legislators entering the arena of AI regulation? “Buckle up and get ready.”

Assembly member Alex Bores hopes his bill, currently an unpublished draft—subject to change—that MIT Technology Review has seen, will address many of the concerns that blocked SB 1047 from passing into law.

SB 1047 was, at first, thought to be a fairly modest bill that would pass without much fanfare. In fact, it flew through the California statehouse with huge margins and received significant public support.

However, before it even landed on Governor Gavin Newsom’s desk for signature in September, it sparked an intense national fight. Google, Meta, and OpenAI came out against the bill, alongside top congressional Democrats like Nancy Pelosi and Zoe Lofgren. Even Hollywood celebrities got involved, with Jane Fonda and Mark Hamill expressing support for the bill.

Ultimately, Newsom vetoed SB 1047, effectively killing regulation of so-called frontier AI models not just in California but, with the lack of laws on the national level, anywhere in the US, where the most powerful systems are developed.

Now Bores hopes to revive the battle. The main provisions in the RAISE Act include requiring AI companies to develop safety plans for the development and deployment of their models.

The bill also provides protections for whistleblowers at AI companies. It forbids retaliation against an employee who shares information about an AI model in the belief that it may cause “critical harm”; such whistleblowers can report the information to the New York attorney general. One way the bill defines critical harm is the use of an AI model to create a chemical, biological, radiological, or nuclear weapon that results in the death or serious injury of 100 or more people.

Alternatively, a critical harm could be a use of the AI model that results in 100 or more deaths or at least $1 billion in damages in an act with limited human oversight that if committed by a human would constitute a crime requiring intent, recklessness, or gross negligence.

The safety plans would ensure that a company has cybersecurity protections in place to prevent unauthorized access to a model. The plan would also require testing of models to assess risks before and after training, as well as detailed descriptions of procedures to assess the risks associated with post-training modifications. For example, some current AI systems have safeguards that can be easily and cheaply removed by a malicious actor. A safety plan would have to address how the company plans to mitigate these actions.

The safety plans would then be audited by a third party, like a nonprofit with technical expertise that currently tests AI models. And if violations are found, the bill empowers the attorney general of New York to issue fines and, if necessary, go to the courts to determine whether to halt unsafe development.

A different flavour of bill

The safety plans and external audits were elements of SB 1047, but Bores aims to differentiate his bill from the California one. “We focused a lot on what the feedback was for 1047,” he says. “Parts of the criticism were in good faith and could make improvements. And so we’ve made a lot of changes.”

The RAISE Act diverges from SB 1047 in a few ways. For one, SB 1047 would have created the Board of Frontier Models, tasked with approving updates to the definitions and regulations around these AI models, but the proposed act would not create a new government body. The New York bill also doesn’t create a public cloud computing cluster, which SB 1047 would have done. The cluster was intended to support projects to develop AI for the public good.

The RAISE Act doesn’t have SB 1047’s requirement that companies be able to halt all operations of their model, a capability sometimes referred to as a “kill switch.” Some critics alleged that the shutdown provision of SB 1047 would harm open-source models, since developers can’t shut down a model someone else may now possess (even though SB 1047 had an exemption for open-source models).

The RAISE Act avoids the fight entirely. SB 1047 referred to an “advanced persistent threat” associated with bad actors trying to steal information during model training. The RAISE Act does away with that definition, sticking to addressing critical harms from covered models.

Focusing on the wrong issues?

Bores’ bill is very specific with its definitions in an effort to clearly delineate what this bill is and isn’t about. The RAISE Act doesn’t address some of the current risks from AI models, like bias, discrimination, and job displacement. Like SB 1047, it is very focused on catastrophic risks from frontier AI models.

Some in the AI community believe this focus is misguided. “We’re broadly supportive of any efforts to hold large models accountable,” says Kate Brennan, associate director of the AI Now Institute, which conducts AI policy research.

“But defining critical harms only in terms of the most catastrophic harms from the most advanced models overlooks the material risks that AI poses, whether it’s workers subject to surveillance mechanisms, prone to workplace injuries because of algorithmically managed speed rates, climate impacts of large-scale AI systems, data centers exerting massive pressure on local power grids, or data center construction sidestepping key environmental protections,” she says.

Bores has worked on other bills addressing current harms posed by AI systems, like discrimination and lack of transparency. That said, Bores is clear that this new bill is aimed at mitigating catastrophic risks from more advanced models. “We’re not talking about any model that exists right now,” he says. “We are talking about truly frontier models, those on the edge of what we can build and what we understand, and there is risk in that.”

The bill would cover only models that pass a certain threshold for how many computations their training required, typically measured in FLOPs (floating-point operations). In the bill, a covered model is one that requires more than 10²⁶ FLOPs in its training and costs over $100 million. For reference, GPT-4 is estimated to have required 10²⁵ FLOPs.

This approach may draw scrutiny from industry forces. “While we can’t comment specifically on legislation that isn’t public yet, we believe effective regulation should focus on specific applications rather than broad model categories,” says a spokesperson at Hugging Face, a company that opposed SB 1047.

Early days

The bill is in its nascent stages, so it’s subject to many edits in the future, and no opposition has yet formed. There may already be lessons to be learned from the battle over SB 1047, however. “There’s significant disagreement in the space, but I think debate around future legislation would benefit from more clarity around the severity, the likelihood, and the imminence of harms,” says Scott Kohler, a scholar at the Carnegie Endowment for International Peace, who tracked the development of SB 1047.

When asked about the idea of mandated safety plans for AI companies, assembly member Edward Ra, a Republican who hasn’t yet seen a draft of the new bill yet, said: “I don’t have any general problem with the idea of doing that. We expect businesses to be good corporate citizens, but sometimes you do have to put some of that into writing.”

Ra and Bores co chair the New York Future Caucus, which aims to bring together lawmakers 45 and under to tackle pressing issues that affect future generations.

Scott Wiener, a California state senator who sponsored SB 1047, is happy to see that his initial bill, even though it failed, is inspiring further legislation and discourse. “The bill triggered a conversation about whether we should just trust the AI labs to make good decisions, which some will, but we know from past experience, some won’t make good decisions, and that’s why a level of basic regulation for incredibly powerful technology is important,” he says.

He has his own plans to reignite the fight: “We’re not done in California. There will be continued work in California, including for next year. I’m optimistic that California is gonna be able to get some good things done.”

And some believe the RAISE Act will highlight a notable contradiction: Many of the industry’s players insist that they want regulation, but when any regulation is proposed, they fight against it. “SB 1047 became a referendum on whether AI should be regulated at all,” says Brennan. “There are a lot of things we saw with 1047 that we can expect to see replay in New York if this bill is introduced. We should be prepared to see a massive lobbying reaction that industry is going to bring to even the lightest-touch regulation.”

Wiener and Bores both wish to see regulation at a national level, but in the absence of such legislation, they’ve taken the battle upon themselves. At first it may seem odd for states to take up such important reforms, but California houses the headquarters of the top AI companies, and New York, which has the third-largest state economy in the US, is home to offices for OpenAI and other AI companies. The two states may be well positioned to lead the conversation around regulation.

“There is uncertainty at the direction of federal policy with the transition upcoming and around the role of Congress,” says Kohler. “It is likely that states will continue to step up in this area.”

Wiener’s advice for New York legislators entering the arena of AI regulation? “Buckle up and get ready.”

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SK Hynix reported during its October earnings call that its HBM, DRAM, and NAND capacity is “essentially sold out” for 2026, while Micron recently exited the consumer memory market entirely to focus on enterprise and AI customers. Enterprise hardware costs surge The supply constraints have translated directly into sharp price increases across enterprise hardware. Samsung raised prices for 32GB DDR5 modules to $239 from $149 in September, a 60% increase, while contract pricing for DDR5 has surged more than 100%, reaching $19.50 per unit compared to around $7 earlier in 2025. DRAM prices have already risen approximately 50% year to date and are expected to climb another 30% in Q4 2025, followed by an additional 20% in early 2026, according to Counterpoint Research. The firm projected that DDR5 64GB RDIMM modules, widely used in enterprise data centers, could cost twice as much by the end of 2026 as they did in early 2025. Gartner forecast DRAM prices to increase by 47% in 2026 due to significant undersupply in both traditional and legacy DRAM markets, Chauhan said. Procurement leverage shifts to hyperscalers The pricing pressures and supply constraints are reshaping the power dynamics in enterprise procurement. For enterprise procurement, supplier size no longer guarantees stability. “As supply becomes more contested in 2026, procurement leverage will hinge less on volume and more on strategic alignment,” Rawat said. Hyperscale cloud providers secure supply through long-term commitments, capacity reservations, and direct fab investments, obtaining lower costs and assured availability. Mid-market firms rely on shorter contracts and spot sourcing, competing for residual capacity after large buyers claim priority supply.

Eight Trends That Will Shape the Data Center Industry in 2026

For much of the past decade, the data center industry has been able to speak in broad strokes. Growth was strong. Demand was durable. Power was assumed to arrive eventually. And “the data center” could still be discussed as a single, increasingly important, but largely invisible, piece of digital infrastructure. That era is ending. As the industry heads into 2026, the dominant forces shaping data center development are no longer additive. They are interlocking and increasingly unforgiving. AI drives density. Density drives cooling. Cooling and density drive power. Power drives site selection, timelines, capital structure, and public response. And once those forces converge, they pull the industry into places it has not always had to operate comfortably: utility planning rooms, regulatory hearings, capital committee debates, and community negotiations. The throughline of this year’s forecast is clarity: Clarity about workload classes. Clarity about physics. Clarity about risk. And clarity about where the industry’s assumptions may no longer hold. One of the most important shifts entering 2026 is that it may increasingly no longer be accurate, or useful, to talk about “data centers” as a single category. What public discourse often lumps together now conceals two very different realities: AI factories built around sustained, power-dense GPU utilization, and general-purpose data centers supporting a far more elastic mix of cloud, enterprise, storage, and interconnection workloads. That distinction is no longer academic. It is shaping how projects are financed, how power is delivered, how facilities are cooled, and how communities respond. It’s also worth qualifying a line we’ve used before, and still stand by in spirit: that every data center is becoming an AI data center. In 2026, we feel that statement is best understood more as a trajectory, and less a design brief. AI is now embedded across the data center stack: in

Data Center Jobs: Engineering, Construction, Commissioning, Sales, Field Service and Facility Tech Jobs Available in Major Data Center Hotspots

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist Data Center Facility Technician (All Shifts Available)Impact, TX This position is also available in: Ashburn, VA; Abilene, TX; Needham, MA; Lyndhurst, NJ; Philadelphia, PA; Atlantic City, NJ or New York, NY. Navy Nuke / Military Vets leaving service accepted! This opportunity is working with a leading mission-critical data center provider. This firm provides data center solutions custom-fit to the requirements of their client’s mission-critical operational facilities. They provide reliability of mission-critical facilities for many of the world’s largest organizations facilities supporting enterprise clients, colo providers and hyperscale companies. This opportunity provides a career-growth minded role with exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning EngineerAshburn, VA This traveling position is also available in: New York, NY; White Plains, NY; Richmond, VA; Montvale, NJ; Charlotte, NC; Atlanta, GA; Hampton, GA; New Albany, OH; Cedar Rapids, IA; Phoenix, AZ; Salt Lake City, UT; Dallas, TX or Chicago, IL. *** ALSO looking for a LEAD EE and ME CxA Agents and CxA PMs *** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as

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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