Mapping, modeling, and understanding nature with AI

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Mapping, modeling, and understanding nature with AI

AcknowledgementsThis research was co-developed by Google DeepMind and Google Research.Google DeepMind: Andrea Burns, Anton Raichuk, Arianna Manzini, Bart van Merrienboer, Burcu Karagol Ayan, Dominic Masters, Drew Purves, Jenny Hamer, Julia Haas, Keith Anderson, Matt Overlan, Maxim Neumann, Melanie Rey, Mustafa Chasmai, Petar Veličković, Ravi Rajakumar, Tom Denton, Vincent DumoulinGoogle Research and Google Partners: Ben Williams, Charlotte Stanton, Dan Morris, Elise Kleeman, Lauren Harrell, Michelangelo ConservaWe’d also like to thank our partners at UNEP-WCMC and QCIF, additional collaborators Aditee Kumthekar, Aparna Warrier, Artlind Kortoci, Burooj Ghani, Christine Kaeser-Chen, Grace Young, Kira Prabhu, Jamie McPike, Jane Labanowski, Jerome Massot, Kuan Lu, Mélisande Teng, Michal Kazmierski, Millie Chapman, Rishabh Baghel, Scott Riddle, Shelagh McLellan, Simon Guiroy, Stefan Kahl, Tim Coleman and Youngin Shin, as well as Peter Battaglia and Kat Chou for their support.

Acknowledgements

This research was co-developed by Google DeepMind and Google Research.

Google DeepMind: Andrea Burns, Anton Raichuk, Arianna Manzini, Bart van Merrienboer, Burcu Karagol Ayan, Dominic Masters, Drew Purves, Jenny Hamer, Julia Haas, Keith Anderson, Matt Overlan, Maxim Neumann, Melanie Rey, Mustafa Chasmai, Petar Veličković, Ravi Rajakumar, Tom Denton, Vincent Dumoulin

Google Research and Google Partners: Ben Williams, Charlotte Stanton, Dan Morris, Elise Kleeman, Lauren Harrell, Michelangelo Conserva

We’d also like to thank our partners at UNEP-WCMC and QCIF, additional collaborators Aditee Kumthekar, Aparna Warrier, Artlind Kortoci, Burooj Ghani, Christine Kaeser-Chen, Grace Young, Kira Prabhu, Jamie McPike, Jane Labanowski, Jerome Massot, Kuan Lu, Mélisande Teng, Michal Kazmierski, Millie Chapman, Rishabh Baghel, Scott Riddle, Shelagh McLellan, Simon Guiroy, Stefan Kahl, Tim Coleman and Youngin Shin, as well as Peter Battaglia and Kat Chou for their support.

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Akamai acquires Fermyon for edge computing as WebAssembly comes of age

Spin handles compilation from source to WebAssembly bytecode and manages execution on target platforms. The runtime abstracts the underlying technology while preserving WebAssembly’s performance and security characteristics. This bet on WebAssembly standards has paid off as the technology matured. WebAssembly has evolved significantly beyond its initial browser-focused design to support

Winners and losers in the latest Top500 supercomputer list

Winner: Slingshot-11 Slingshot-11 is a 200G proprietary interconnect developed by HPE and its Cray supercomputer subsidiary. As the number of Cray systems increases on the list, so goes the number of Slingshot-11 based systems. The total number of Slingshot-11 systems jumped from 37 and 2024 to 52 this year. Loser:

Chevron Joins TotalEnergies in New Nigerian Exploration Blocks

Chevron Corp has signed a deal to acquire 40 percent in Petroleum Prospecting License (PPL) 2000 and PPL 2001 offshore Nigeria from TotalEnergies SE. TotalEnergies will retain operatorship with a 40 percent interest. Local player South Atlantic Petroleum Ltd owns 20 percent. “This new joint venture aims at derisking and

Four things AWS needs to fix at re:Invent this week

When it comes to new AI analytics services from AWS, CIOs can expect more of the same, said David Linthicum, independent consultant and retired chief cloud strategy officer at Deloitte Consulting. “Realistically, they can expect AWS to keep integrating its existing services; the key test will be whether this shows

Energy Department Selects TVA and Holtec to Advance Deployment of U.S. Small Modular Reactors

WASHINGTON—The U.S. Department of Energy (DOE) today announced the selection of the Tennessee Valley Authority (TVA) and Holtec Government Services to support early deployments of advanced light-water small modular reactors (SMRs) in the United States. The project teams will receive up to $800 million in federal cost-shared funding to advance initial projects in Tennessee and Michigan and help expand the Nation’s capacity while facilitating additional follow-on projects and associated supply chains. The selections announced today will help deliver new nuclear generation in the early 2030s, strengthen domestic supply chains, and advance President Trump’s Executive Orders to usher in a nuclear renaissance and expand America’s Energy Dominance agenda. “President Trump has made clear that America is going to build more energy, not less, and nuclear is central to that mission,” said U.S. Secretary of Energy Chris Wright. “Advanced light-water SMRs will give our nation the reliable, round-the-clock power we need to fuel the President’s manufacturing boom, support data centers and AI growth, and reinforce a stronger, more secure electric grid. These awards ensure we can deploy these reactors as soon as possible.” With today’s announcement, DOE is supporting the following first-mover teams to develop and construct the first Gen III+ small modular reactor (Gen III+ SMR) plants in the United States: Tennessee Valley Authority (TVA) – $400,000,000 TVA plans to advance deployment of a GE Vernova Hitachi BWRX-300 at the Clinch River Nuclear site in Tennessee, as well as accelerate the deployment of additional units with Indiana Michigan Power and Elementl. Additionally, TVA plans to work with the domestic nuclear supply chain partners Scot Forge, North American Forgemasters, BWX Technologies, and Aecon. Other partners supporting the project include Duke Energy, Oak Ridge Associated Universities, and the Electric Power Research Institute. Holtec Government Services, LLC (Holtec) – $400,000,000 Holtec plans to deploy

Americans lost more power last year than any year in previous decade: EIA

Listen to the article 3 min This audio is auto-generated. Please let us know if you have feedback. U.S. electricity customers experienced an average of 11 hours of power outages in 2024, nearly twice as many as the annual average across the previous decade, according to a new report from the Energy Information Administration. Hurricanes accounted for 80% of those lost hours, with most of last year’s outages resulting from major weather events like hurricanes Beryl, Helene and Milton, EIA said in the report released Monday. “Interruptions attributed to major events averaged nearly nine hours in 2024, compared with an average of nearly four hours per year in 2014 through 2023,” EIA said. “Service interruptions that aren’t triggered by major events routinely average about two hours per year.” Optional Caption Courtesy of Energy Information Administration Customers in South Carolina were significant outliers in terms of outage duration, the report said, experiencing an average of 53 hours of outages in 2024. Much of this was due to last September’s Hurricane Helene, which left 1.2 million customers in South Carolina without electricity. The report appears to build on a growing body of evidence that extreme weather is taking a heavier toll on the electric power system in parts of the country. In October, JD Power released a report that found the average length of the longest outages are getting longer and concluded that disasters have become a “fact of life” for many utility customers. Helene, in particular, caused severe damage to utility systems in the U.S. Southeast and Mid-Atlantic. Duke Energy said after the hurricane that transmission infrastructure in upstate South Carolina “was severely damaged and, in many cases, destroyed” and would need to be entirely rebuilt. Three days after the hurricane struck, 900,000 Duke customers remained without power across North Carolina and South

Petrogas spuds exploration well onshore Indonesia

Petrogas (Basin) Ltd. spudded the Karim-1 exploration well in the Kepala Burung Production Sharing Contract (PSC), Southwest Papua, Indonesia. The well is being drilled onshore in a relatively under-explored area within Arar block and is about 23 km east of the Petrogas’ existing Arar production cluster. The well will be vertical and drilled to about 1,311 m TD. Drilling and completion is estimated to take 43 days. Karim-1 well is designed to assess the oil potential of the Miocene Kais reservoir within a structural closure located updip of the previous Klaifi-1 oil discovery, situated about 7 km northwest of Karim-1. The Miocene Kais formation is a carbonate sequence that forms a broad shallow marine platform with localized reefal complexes and is the main producing reservoir in the PSC. Following completion of Karim-1 well, the drilling rig will be deployed to drill the Northwest Klagagi-1 exploration well, which is about 15 km northeast of the Arar production cluster and about 12 km from the Karim-1 well site. The Karim-1 well and the Northwest Klagagi-1 well are part of exploration wells being drilled under the firm work commitment of the Kepala Burung PSC which began in 2020. Kepala Burung PSC covers an onshore area of 1,030 sq km within Salawati basin, which is one of the most prolific petroleum basins in Indonesia. Petrogas (Basin) Ltd. is a subsidiary of RH Petrogas Ltd. (82.65% owned). RH Petrogas is operator of the Kepala Burung PSC (70%) with Pertamina holding the remaining 30%.

NextDecade progresses Rio Grande LNG Train 6 plan with FERC pre-filing

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Then & Now: Global oil supply transformation

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bp Trinidad and Tobago completes Cypre drilling

@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; } bp Trinidad and Tobago (bpTT) completed the Cypre seven-well drilling program 78 km off the southeast coast of Trinidad within East Mayaro block, Columbus basin, in about 80 m of water. The wells are tied back into bpTT’s existing Juniper platform. Four wells were drilled and completed at end-2024 with the first gas delivered in April this year. The project team has now drilled, completed, and commissioned the remaining three wells. At peak, Cypre is projected to deliver about 45,000 boe/d. Cypre is bpTT’s third subsea development and is one of bp’s 10 major projects expected to start up worldwide between 2025 and 2027. Cypre is 100% owned by bpTT which is owned by bp (70%) and Repsol (30%).

What is co-packaged optics? A solution for surging capacity in AI data center networks

When it announced its CPO-capable switches, Nvidia said they would improve resiliency by 10 times at scale compared to previous switch generations. Several factors contribute to this claim, including the fact that the optical switches require four times fewer lasers, Shainer says. Whereas the laser source was previously part of the transceiver, the optical engine is now incorporated onto the ASIC, allowing multiple optical channels to share a single laser. Additionally, in Nvidia’s implementation, the laser source is located outside of the switch. “We want to keep the ability to replace a laser source in case it has failed and needs to be replaced,” he says. “They are completely hot-swappable, so you don’t need to shut down the switch.” Nonetheless, you may often hear that when something fails in a CPO box, you need to replace the entire box. That may be true if it’s the photonics engine embedded in silicon inside the box. “But they shouldn’t fail that often. There are not a lot of moving parts in there,” Wilkinson says. While he understands the argument around failures, he doesn’t expect it to pan out as CPO gets deployed. “It’s a fallacy,” he says. There’s also a simple workaround to the resiliency issue, which hyperscalers are already talking about, Karavalas says: overbuild. “Have 10% more ports than you need or 5%,” he says. “If you lose a port because the optic goes bad, you just move it and plug it in somewhere else.” Which vendors are backing co-packaged optics? In terms of vendors that have or plan to have CPO offerings, the list is not long, unless you include various component players like TSMC. But in terms of major switch vendors, here’s a rundown: Broadcom has been making steady progress on CPO since 2021. It is now shipping “to

Nvidia’s $2B Synopsys stake tests independence of open AI interconnect standard

But the concern for enterprise IT leaders is whether Nvidia’s financial stakes in UALink consortium members could influence the development of an open standard specifically designed to compete with Nvidia’s proprietary technology and to give enterprises more choices in the datacenter. Organizations planning major AI infrastructure investments view such open standards as critical to avoiding vendor lock-in and maintaining competitive pricing. “This does put more pressure on UALink since Intel is also a member and also took investment from Nvidia,” Sag said. UALink and Synopsys’s critical role UALink represents the industry’s most significant effort to prevent vendor lock-in for AI infrastructure. The consortium ratified its UALink 200G 1.0 Specification in April, defining an open standard for connecting up to 1,024 AI accelerators within computing pods at 200 Gbps per lane — directly competing with Nvidia’s NVLink for scale-up applications. Synopsys plays a critical role. The company joined UALink’s board in January and in December announced the industry’s first UALink design components, enabling chip designers to build UALink-compatible accelerators. Analysts flag governance concerns Gaurav Gupta, VP analyst at Gartner, acknowledged the tension. “The Nvidia-Synopsys deal does raise questions around the future of UALink as Synopsys is a key partner of the consortium and holds critical IP for UALink, which competes with Nvidia’s proprietary NVLink,” he said. Sanchit Vir Gogia, chief analyst at Greyhound Research, sees deeper structural concerns. “Synopsys is not a peripheral player in this standard; it is the primary supplier of UALink IP and a board member within the UALink Consortium,” he said. “Nvidia’s entry into Synopsys’ shareholder structure risks contaminating that neutrality.”

Cooling crisis at CME: A wakeup call for modern infrastructure governance

Organizations should reassess redundancy However, he pointed out, “the deeper concern is that CME had a secondary data center ready to take the load, yet the failover threshold was set too high, and the activation sequence remained manually gated. The decision to wait for the cooling issue to self-correct rather than trigger the backup site immediately revealed a governance model that had not evolved to keep pace with the operational tempo of modern markets.” Thermal failures, he said, “do not unfold on the timelines assumed in traditional disaster recovery playbooks. They escalate within minutes and demand automated responses that do not depend on human certainty about whether a facility will recover in time.” Matt Kimball, VP and principal analyst at Moor Insights & Strategy, said that to some degree what happened in Aurora highlights an issue that may arise on occasion: “the communications gap that can exist between IT executives and data center operators. Think of ‘rack in versus rack out’ mindsets.” Often, he said, the operational elements of that data center environment, such as cooling, power, fire hazards, physical security, and so forth, fall outside the realm of an IT executive focused on delivering IT services to the business. “And even if they don’t fall outside the realm, these elements are certainly not a primary focus,” he noted. “This was certainly true when I was living in the IT world.” Additionally, said Kimball, “this highlights the need for organizations to reassess redundancy and resilience in a new light. Again, in IT, we tend to focus on resilience and redundancy at the app, server, and workload layers. Maybe even cluster level. But as we continue to place more and more of a premium on data, and the terms ‘business critical’ or ‘mission critical’ have real relevance, we have to zoom out

Microsoft loses two senior AI infrastructure leaders as data center pressures mount

Microsoft did not immediately respond to a request for comment. Microsoft’s constraints Analysts say the twin departures mark a significant setback for Microsoft at a critical moment in the AI data center race, with pressure mounting from both OpenAI’s model demands and Google’s infrastructure scale. “Losing some of the best professionals working on this challenge could set Microsoft back,” said Neil Shah, partner and co-founder at Counterpoint Research. “Solving the energy wall is not trivial, and there may have been friction or strategic differences that contributed to their decision to move on, especially if they saw an opportunity to make a broader impact and do so more lucratively at a company like Nvidia.” Even so, Microsoft has the depth and ecosystem strength to continue doubling down on AI data centers, said Prabhu Ram, VP for industry research at Cybermedia Research. According to Sanchit Gogia, chief analyst at Greyhound Research, the departures come at a sensitive moment because Microsoft is trying to expand its AI infrastructure faster than physical constraints allow. “The executives who have left were central to GPU cluster design, data center engineering, energy procurement, and the experimental power and cooling approaches Microsoft has been pursuing to support dense AI workloads,” Gogia said. “Their exit coincides with pressures the company has already acknowledged publicly. GPUs are arriving faster than the company can energize the facilities that will house them, and power availability has overtaken chip availability as the real bottleneck.”

What is Edge AI? When the cloud isn’t close enough

Many edge devices can periodically send summarized or selected inference output data back to a central system for model retraining or refinement. That feedback loop helps the model improve over time while still keeping most decisions local. And to run efficiently on constrained edge hardware, the AI model is often pre-processed by techniques such as quantization (which reduces precision), pruning (which removes redundant parameters), or knowledge distillation (which trains a smaller model to mimic a larger one). These optimizations reduce the model’s memory, compute, and power demands so it can run more easily on an edge device. What technologies make edge AI possible? The concept of the “edge” always assumes that edge devices are less computationally powerful than data centers and cloud platforms. While that remains true, overall improvements in computational hardware have made today’s edge devices much more capable than those designed just a few years ago. In fact, a whole host of technological developments have come together to make edge AI a reality. Specialized hardware acceleration. Edge devices now ship with dedicated AI-accelerators (NPUs, TPUs, GPU cores) and system-on-chip units tailored for on-device inference. For example, companies like Arm have integrated AI-acceleration libraries into standard frameworks so models can run efficiently on Arm-based CPUs. Connectivity and data architecture. Edge AI often depends on durable, low-latency links (e.g., 5G, WiFi 6, LPWAN) and architectures that move compute closer to data. Merging edge nodes, gateways, and local servers means less reliance on distant clouds. And technologies like Kubernetes can provide a consistent management plane from the data center to remote locations. Deployment, orchestration, and model lifecycle tooling. Edge AI deployments must support model-update delivery, device and fleet monitoring, versioning, rollback and secure inference — especially when orchestrated across hundreds or thousands of locations. VMware, for instance, is offering traffic management

Networks, AI, and metaversing

Our first, conservative, view says that AI’s network impact is largely confined to the data center, to connect clusters of GPU servers and the data they use as they crunch large language models. It’s all “horizontal” traffic; one TikTok challenge would generate way more traffic in the wide area. WAN costs won’t rise for you as an enterprise, and if you’re a carrier you won’t be carrying much new, so you don’t have much service revenue upside. If you don’t host AI on premises, you can pretty much dismiss its impact on your network. Contrast that with the radical metaverse view, our third view. Metaverses and AR/VR transform AI missions, and network services, from transaction processing to event processing, because the real world is a bunch of events pushing on you. They also let you visualize the way that process control models (digital twins) relate to the real world, which is critical if the processes you’re modeling involve human workers who rely on their visual sense. Could it be that the reason Meta is willing to spend on AI, is that the most credible application of AI, and the most impactful for networks, is the metaverse concept? In any event, this model of AI, by driving the users’ experiences and activities directly, demands significant edge connectivity, so you could expect it to have a major impact on network requirements. In fact, just dipping your toes into a metaverse could require a major up-front network upgrade. Networks carry traffic. Traffic is messages. More messages, more traffic, more infrastructure, more service revenue…you get the picture. Door number one, to the AI giant future, leads to nothing much in terms of messages. Door number three, metaverses and AR/VR, leads to a message, traffic, and network revolution. I’ll bet that most enterprises would doubt

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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