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Aeroderivative Turbines Move to the Center of AI Data Center Power Strategy

From “Backup” to “Bridging” to Behind-the-Meter Power Plants The most important shift is conceptual: these systems are increasingly blurring the boundary between emergency backup and primary power supply. Traditionally, data center electrical architecture has been clearly tiered: UPS (seconds to minutes) to ride through utility disturbances and generator start. Diesel gensets (minutes to hours or […]

From “Backup” to “Bridging” to Behind-the-Meter Power Plants

The most important shift is conceptual: these systems are increasingly blurring the boundary between emergency backup and primary power supply.

Traditionally, data center electrical architecture has been clearly tiered:

  • UPS (seconds to minutes) to ride through utility disturbances and generator start.

  • Diesel gensets (minutes to hours or days) for extended outages.

  • Utility grid as the primary power source.

What’s changing is the rise of bridging power:  generation deployed to energize a site before the permanent grid connection is ready, or before sufficient utility capacity becomes available. Providers such as APR Energy now explicitly market turbine-based solutions to data centers seeking behind-the-meter capacity while awaiting utility build-out.

That framing matters because it fundamentally changes expected runtime. A generator that operates for a few hours per year is one regulatory category. A turbine that runs continuously for weeks or months while a campus ramps is something very different; and it is drawing increased scrutiny from regulators who are beginning to treat these installations as material generation assets rather than temporary backup systems.

The near-term driver is straightforward. AI workloads are arriving faster than grid infrastructure can keep pace. Data Center Frontier and other industry observers have documented the growing scramble for onsite generation as interconnection queues lengthen and critical equipment lead times expand.

Mainstream financial and business media have taken notice. The Financial Times has reported on data centers turning to aeroderivative turbines and diesel fleets to bypass multi-year power delays. Reuters has likewise covered large gas-turbine-centric strategies tied to hyperscale campuses, underscoring how quickly the co-located generation model is moving into the mainstream.

At the same time, demand pressure is tightening turbine supply chains. Industry reporting points to extended waits for new units, one reason repurposed engine cores and mobile aeroderivative packages are gaining attention.

The Packaging Revolution: Trailerized Megawatts

The most consequential innovation may not be the turbine core itself, but the packaging and systems integration that make these units viable for data center campus deployment.

Historically, aeroderivative turbines powered ships, offshore platforms, and heavy industrial facilities. What’s new in the data center context is the aggressive push toward:

  • Modular skid or trailer configurations

  • Integrated switchgear and controls

  • Accelerated commissioning playbooks

  • “Fleet” operating models using multiple identical units

Mitsubishi Power’s MOBILEPAC platform, for example, emphasizes modularity and shipping optimization for rapid deployment. The approach mirrors hyperscale construction philosophy: repeatable blocks, standardized interfaces, and parallelized build schedules.

If operators can add power blocks the same way they add data hall blocks, the entire project critical path begins to compress.

How These Turbines Fit Into Data Center Electrical Design

Even with rapid-start capability, aeroderivative turbines do not eliminate the need for core data center electrical infrastructure. A resilient architecture still requires:

  • UPS ride-through to ensure zero-interruption transfer

  • Switchgear, protection, and synchronization, particularly when paralleling multiple units

  • Power quality management, including harmonics control, voltage regulation, and transient response

  • Controls integration with generators, ATS/STS systems, and in some cases the utility interconnection

In bridging mode, turbines may function as the primary power source feeding campus distribution, with utility service either unavailable or capacity-limited. In traditional backup mode, the same systems may sit idle but fully ready to assume load during an outage.

One critical distinction: fast start does not mean instantaneous power. UPS systems remain essential; particularly for AI training clusters, where even a brief disturbance can trigger costly job interruptions or restarts. For that reason, most emerging architectures are best understood not as turbines replacing traditional resiliency layers, but as UPS plus aeroderivatives working in concert.

Emissions, Permitting, and the End of the “Temporary” Loophole

This is where the advantages of the jet-engine narrative meet regulatory reality: air permitting.

Once operators move from true “emergency generator” behavior to sustained or routine runtime, regulators begin asking a different set of questions focused on potential-to-emit calculations, New Source Review (NSR) thresholds, operating hour limits, and required controls for NOx, CO, VOCs, and particulate matter.

The EPA maintains extensive Clean Air Act guidance relevant to data center operators, including frameworks for determining potential emissions from onsite generation. The Congressional Research Service has likewise noted that facilities exceeding emissions thresholds typically trigger NSR permitting requirements, bringing additional scrutiny to large behind-the-meter deployments.

Recent controversy surrounding portable turbine fleets at AI data center sites has only intensified federal attention. Reporting in early 2026 indicated the EPA is tightening interpretations to prevent large mobile turbine installations from operating for extended periods without appropriate permits.

This shift has direct implications for the repurposed jet-engine model. Much of the economic value comes from the ability to run turbines long enough to bridge multi-year grid delays. If permitting regimes begin restricting runtime or requiring emissions controls that slow deployment, the schedule and cost advantages that make aeroderivatives attractive could narrow.

Economics: Why Pay for On-Site Turbines When the Grid Is Cheaper?

In most markets, utility power remains less expensive on a per-kilowatt-hour basis than self-generation. But for AI-centric developments, the binding constraint is often not the price of electricity; it is time to energized capacity.

When developers are committing billions of dollars to land, facilities, GPUs, and high-speed networking, every month spent waiting for power represents idle capital and deferred revenue. In that context, aeroderivative turbines function as a time-buying strategy:

  • Energize Phase 1 with onsite turbines

  • Begin revenue workloads earlier

  • Transition to utility supply or a dedicated plant later

There is also a longer-term hedging dimension. Onsite turbines can provide a measure of insulation against grid curtailments, transmission outages, or phased utility ramp schedules: risks that are becoming more material in regions facing large-load interconnection pressure.

The Tradeoffs Are Real

Aeroderivatives are not a universal solution. The model introduces a new set of operational and strategic considerations that developers must weigh carefully.

Fuel and Infrastructure Dependence

Gas-fired turbines require reliable fuel delivery with firm transportation rights, adequate pressure, and redundancy planning. Most deployments also maintain onsite liquid fuel backup. This can introduce new single points of failure compared with a purely utility-supplied architecture.

Noise and Community Impact

Aeroderivative units can generate significant acoustic output, and trailerized fleets add both visual and sound footprint. As recent projects have demonstrated, even in rural markets, continuous turbine operation can trigger strong community pushback, particularly as runtime extends beyond traditional emergency profiles.

Maintenance and Parts Availability

While aeroderivative platforms benefit from mature service ecosystems, growing demand and increased reliance on retired engine cores could create new bottlenecks, including hot-section components, qualified field technicians, and overhaul capacity.

Carbon Accounting and Reputational Risk

Even when positioned as temporary bridging assets, extended turbine runtime can collide with hyperscaler climate commitments and local air-quality concerns. As operating hours increase, the narrative around “backup” generation becomes harder to sustain.

Where the Jet-Engine Power Model Goes Next

So what does the future hold for aeroderivative turbines in data center power strategies? Three paths are beginning to emerge.

Phase 0 Becomes Standard Practice

One trajectory is normalization. Developers increasingly design campuses with an expected interim generation phase by using aeroderivative fleets to energize early capacity before transitioning to utility supply, long-term PPAs, or dedicated generation assets.

In this model, “bridging power” stops being an exception and becomes a standard Phase 0/Phase 1 tool in the AI infrastructure playbook.

Regulation Forces “Real Power Plant” Behavior

A second path depends heavily on regulatory interpretation. If federal and state agencies continue tightening rules around portable and temporary generation, many deployments may be pushed toward fully permitted onsite plants.

Those plants could still rely on gas turbines. But the rapid-deployment advantage that makes trailerized aeroderivatives so attractive today would narrow if projects must meet full stationary-source requirements from day one.

Hybrid Architectures Take Hold

A third path is technological convergence. As battery energy storage system (BESS) costs decline and grid services markets mature, more campuses are likely to pair aeroderivative turbines with substantial storage capacity.

In these hybrid designs, turbines increasingly provide firm capacity and long-duration support, while batteries handle ride-through, short transients, and peak shaving. Recent growth in BESS deployments suggests this shift is already underway across portions of the market.

Longer term, OEMs continue to promote hydrogen capability and other decarbonization pathways. But the near-term reality is more pragmatic: aeroderivative turbines are gaining traction because they solve the schedule problem better than any currently scalable alternative.

The Bottom Line

Jet engines are not replacing diesel gensets. But they are rapidly establishing themselves as the high-density, fast-deployment middle layer between UPS batteries and a grid that, in many regions, cannot keep pace with AI demand.

Aeroderivative turbines, particularly those built from retired aircraft cores, offer:

  • Fast-start, modular tens-of-megawatt power blocks.

  • Rapid deployment paths that bypass multi-year grid delays.

  • A practical bridge to permanent infrastructure.

The tradeoffs are equally clear. Greater fuel consumption, more complex permitting exposure, and rising community scrutiny will follow these systems as they move from emergency standby into sustained operation.

In the AI buildout race, aeroderivatives are winning on speed. Whether they can scale cleanly (i.e. operationally, environmentally, and politically) remains the industry’s next defining test.

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Equinor lets EPC contract for Gullfaks field

@import url(‘https://fonts.googleapis.com/css2?family=Inter:[email protected]&display=swap’); a { color: var(–color-primary-main); } .ebm-page__main h1, .ebm-page__main h2, .ebm-page__main h3, .ebm-page__main h4, .ebm-page__main h5, .ebm-page__main h6 { font-family: Inter; } body { line-height: 150%; letter-spacing: 0.025em; font-family: Inter; } button, .ebm-button-wrapper { font-family: Inter; } .label-style { text-transform: uppercase; color: var(–color-grey); font-weight: 600; font-size: 0.75rem; } .caption-style { font-size: 0.75rem; opacity: .6; } #onetrust-pc-sdk [id*=btn-handler], #onetrust-pc-sdk [class*=btn-handler] { background-color: #c19a06 !important; border-color: #c19a06 !important; } #onetrust-policy a, #onetrust-pc-sdk a, #ot-pc-content a { color: #c19a06 !important; } #onetrust-consent-sdk #onetrust-pc-sdk .ot-active-menu { border-color: #c19a06 !important; } #onetrust-consent-sdk #onetrust-accept-btn-handler, #onetrust-banner-sdk #onetrust-reject-all-handler, #onetrust-consent-sdk #onetrust-pc-btn-handler.cookie-setting-link { background-color: #c19a06 !important; border-color: #c19a06 !important; } #onetrust-consent-sdk .onetrust-pc-btn-handler { color: #c19a06 !important; border-color: #c19a06 !important; } Equinor Energy AS has let an engineering, procurement, and construction (EPC) contract to SLB to upgrade the subsea compression system for Gullfaks field in the Norwegian North Sea. Under the contract, SLB OneSubsea will deliver two next-generation compressor modules to replace the units originally supplied in 2015 as part of the world’s first multiphase subsea compression system. The upgraded modules will increase differential pressure and flow capacity, enhancing recovery and extending field life, SLB said, while installation within the existing subsea infrastructure will minimize downtime and reduce overall campaign costs, the company continued. Gullfaks field lies in block 34/10 in the northern part of the North Sea. Three large production platforms with concrete substructures make up the development solution for the main field.

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Oxy cutting oil-and-gas capex by $300 million, eyes 1% production growth

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Diamondback’s Van’t Hof growing ‘more confident about the macro’

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Ovintiv sets 2026 plan around Permian, Montney after declaring portfolio shift ‘complete’

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Interior trims environmental reviews to speed project development

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JLL: Hyperscale and AI Demand Push North American Data Centers Toward Industrial Scale

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7×24 Exchange’s Dennis Cronin on the Data Center Workforce Crisis: The Talent Cliff Is Already Here

The data center industry has spent the past two years obsessing over power constraints, AI density, and supply chain pressure. But according to longtime mission critical leader Dennis Cronin, the sector’s most consequential bottleneck may be far more human. In a recent episode of the Data Center Frontier Show Podcast, Cronin — a founding member of 7×24 Exchange International and board member of the Mission Critical Global Alliance (MCGA) — delivered a stark message: the workforce “talent cliff” the industry keeps discussing as a future risk is already impacting operations today. A Million-Job Gap Emerging Cronin’s assessment reframes the workforce conversation from a routine labor shortage to what he describes as a structural and demographic challenge. Based on recent analysis of open roles, he estimates the industry is currently short between 467,000 and 498,000 workers across core operational positions including facilities managers, operations engineers, electricians, generator technicians, and HVAC specialists. Layer in emerging roles tied to AI infrastructure, sustainability, and cyber-physical security, and the potential demand rises to roughly one million jobs. “The coming talent cliff is not coming,” Cronin said. “It’s here, here and now.” With data center capacity expanding at roughly 30% annually, the workforce pipeline is not keeping pace with physical buildout. The Five-Year Experience Trap One of the industry’s most persistent self-inflicted wounds, Cronin argues, is the widespread requirement for five years of experience in roles that are effectively entry level. The result is a closed-loop hiring dynamic: New workers can’t get hired without experience They can’t gain experience without being hired Operators end up poaching from each other Workers may benefit from the resulting 10–20% salary jumps, but the overall talent pool remains stagnant. “It’s not helping us grow the industry,” Cronin said. In a market defined by rapid expansion and increasing system complexity, that

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Aeroderivative Turbines Move to the Center of AI Data Center Power Strategy

From “Backup” to “Bridging” to Behind-the-Meter Power Plants The most important shift is conceptual: these systems are increasingly blurring the boundary between emergency backup and primary power supply. Traditionally, data center electrical architecture has been clearly tiered: UPS (seconds to minutes) to ride through utility disturbances and generator start. Diesel gensets (minutes to hours or days) for extended outages. Utility grid as the primary power source. What’s changing is the rise of bridging power:  generation deployed to energize a site before the permanent grid connection is ready, or before sufficient utility capacity becomes available. Providers such as APR Energy now explicitly market turbine-based solutions to data centers seeking behind-the-meter capacity while awaiting utility build-out. That framing matters because it fundamentally changes expected runtime. A generator that operates for a few hours per year is one regulatory category. A turbine that runs continuously for weeks or months while a campus ramps is something very different; and it is drawing increased scrutiny from regulators who are beginning to treat these installations as material generation assets rather than temporary backup systems. The near-term driver is straightforward. AI workloads are arriving faster than grid infrastructure can keep pace. Data Center Frontier and other industry observers have documented the growing scramble for onsite generation as interconnection queues lengthen and critical equipment lead times expand. Mainstream financial and business media have taken notice. The Financial Times has reported on data centers turning to aeroderivative turbines and diesel fleets to bypass multi-year power delays. Reuters has likewise covered large gas-turbine-centric strategies tied to hyperscale campuses, underscoring how quickly the co-located generation model is moving into the mainstream. At the same time, demand pressure is tightening turbine supply chains. Industry reporting points to extended waits for new units, one reason repurposed engine cores and mobile aeroderivative packages are gaining

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Cooling’s New Reality: It’s Not Air vs. Liquid Anymore. It’s Architecture.

By early 2026, the data center cooling conversation has started to sound less like a product catalog and more like a systems engineering summit. The old framing – air cooling versus liquid cooling – still matters, but it increasingly misses the point. AI-era facilities are being defined by thermal constraints that run from chip-level cold plates to facility heat rejection, with critical decisions now shaped by pumping power, fluid selection, reliability under ambient extremes, water availability, and manufacturing throughput. That full-stack shift is written all over a grab bag of recent cooling announcements. On one end of the spectrum we see a Department of Energy-funded breakthrough aimed directly at next-generation GPU heat flux. On the other, it’s OEM product launches built to withstand –20°F to 140°F operating conditions and recover full cooling capacity within minutes of a power interruption. In between we find a major acquisition move for advanced liquid cooling IP, a manufacturing expansion that more than doubles footprint, and the quiet rise of refrigerants and heat-transfer fluids as design-level considerations. What’s emerging is a new reality. Cooling is becoming one of the primary constraints on AI deployment technically, economically, and geographically. The winners will be the players that can integrate the whole stack and scale it. 1) The Chip-level Arms Race: Single-phase Fights for More Runway The most “pure engineering” signal in this news batch comes from HRL Laboratories, which on Feb. 24, 2026 unveiled details of a single-phase direct liquid cooling approach called Low-Chill™. HRL’s framing is pointed: the industry wants higher GPU and rack power densities, but many operators are wary of the cost and operational complexity of two-phase cooling. HRL says Low-Chill was developed under the U.S. Department of Energy’s ARPA-E COOLERCHIPS program, and claims a leap that goes straight at the bottleneck. It can increase

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Policy Shock: Big Tech Told to Power Its Own AI Buildout

The AI data center boom has been colliding with grid reality for more than two years. This week, the issue moved closer to the policy front lines. The White House is advancing a “ratepayer protection” framework that has gained visibility in recent days, aimed at ensuring large AI data center projects do not shift grid upgrade costs onto residential customers. It’s a signal widely interpreted by industry observers as encouraging hyperscalers to bring dedicated power solutions to the table. The Power Question Moves to Center Stage Washington now appears poised to push the industry toward a structural response to the data center power conundrum. The new federal impetus for major technology companies to shoulder the cost of their own power infrastructure is quickly emerging as one of the most consequential policy developments for the digital infrastructure sector in 2026. If formalized, the initiative would effectively codify a shift already underway which has found hyperscale and AI developers moving aggressively toward behind-the-meter generation and dedicated energy strategies. For an industry already grappling with interconnection delays, utility pushback, and mounting community scrutiny, the signal is unmistakable. The era of relying primarily on shared grid capacity for large AI campuses may be ending. From Market Trend to Policy Direction Large tech firms, including the biggest cloud and AI players, have been under increasing pressure from regulators and utilities concerned about ratepayer exposure and grid reliability. Policymakers are now signaling that future large-load approvals may hinge on whether developers can demonstrate energy self-sufficiency or dedicated supply. The logic is straightforward. AI campuses are arriving at hundreds of megawatts to gigawatt scale. Transmission upgrades are measured in multi-year timelines. Utilities face growing political pressure to protect residential customers. In that context, the emerging federal posture does not create a new trend so much as accelerate

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Enterprise Spotlight: Data Center Modernization

The demands for, and challenges of, deploying AI applications has ratcheted up the urgency to bring data centers into the AI age. It’s a strategic imperative and success requires partners across the infrastructure spectrum, from servers and storage to high-performance computing, networking, software, and security. IT leaders, intensely focused on data center modernization, need strategies, roadmaps, and products that will get them there. Download the March 2026 issue of the Enterprise Spotlight from the editors of CIO, Computerworld, CSO, InfoWorld, and Network World and learn how data center modernization is taking shape in 2026.

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

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

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

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

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2025 playbook for enterprise AI success, from agents to evals

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

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

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

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