Unlocking UK house-building with AI-accelerated planning

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Unlocking UK house-building with AI-accelerated planning

New UK government AI planning prototype built with Gemini aims to halve the time it takes to process homeowner applicationsAround the world, Governments are exploring how AI can deliver better public services, faster. The UK is working to build 1.5 million new homes by 2029, but local planning authorities are often slowed down by dense paperwork and administrative backlogs. To help get Britain building, we’re partnering with the UK government to help radically shorten the time it takes to process householder planning applications. Our goal is to help officers cut application decision times by 50%, freeing up time for planners so that more homes can be built. We’re excited to see how our National Partnerships for AI, which seek to support reimagining of public services to create more resilient societies, can help Britain build faster.Co-creating a new AI planning tool for faster decisionsFollowing the successful launch of Extract — a tool built with Gemini by the UK government’s Incubator for AI (i.AI) — to help councils turn old planning documents into clear, digital data — we’re partnering to develop a sophisticated new planning prototype for councils.Google DeepMind is working alongside the UK government, Google Cloud, Faculty, and local planning authorities in Barnet, Dorset and Camden, to co-develop an AI-powered prototype that acts as a highly skilled assistant for planning officers, handling the heavy lifting of data extraction and case analysis. Working in close partnership will enable us to build an optimized tool, suited to the unique needs and day-to-day challenges that planners contend with every day. Following early trials in Barnet, Camden and Dorset, the government plans for the new AI planning tool to be made available to all councils nationally from 2027.For a typical new planning application, officers spend hours cross-referencing policy documents, historical files and PDFs. This manual process creates a major bottleneck. It is especially challenging given householder applications account for nearly 70% of planning applications each year. By reducing the time spent on straightforward cases like loft conversions or extensions, the prototype being tested in Barnet, Camden and Dorset could help planning officers focus more on complex applications for public benefit.The AI planning tool will streamline routine tasks using AI, by:Consolidating data: Pre-processing backlogs, highlighting data gaps and extracting key site information so planners can review everything on one screen.Identifying local policies: Highlighting relevant national and local policies, pre-assessing compliance and providing exact citations for the officer to verify.Summarizing feedback: Reviewing individual consultation letters to identify key objections or precedents.Drafting assessments: Creating a first draft of the final report, including the rationale and proposed conditions.Crucially, the planning officer remains in full control as the final decision-maker. They review every line the tool generates, edit the reasoning and retain the authority to approve or reject an application. To ensure accountability, the prototype records its work at every step, creating a clear chain of thought and a robust audit trail for every decision to support the planning officer.

New UK government AI planning prototype built with Gemini aims to halve the time it takes to process homeowner applications

Around the world, Governments are exploring how AI can deliver better public services, faster. The UK is working to build 1.5 million new homes by 2029, but local planning authorities are often slowed down by dense paperwork and administrative backlogs. To help get Britain building, we’re partnering with the UK government to help radically shorten the time it takes to process householder planning applications. Our goal is to help officers cut application decision times by 50%, freeing up time for planners so that more homes can be built. We’re excited to see how our National Partnerships for AI, which seek to support reimagining of public services to create more resilient societies, can help Britain build faster.

Co-creating a new AI planning tool for faster decisions

Following the successful launch of Extract — a tool built with Gemini by the UK government’s Incubator for AI (i.AI) — to help councils turn old planning documents into clear, digital data — we’re partnering to develop a sophisticated new planning prototype for councils.

Google DeepMind is working alongside the UK government, Google Cloud, Faculty, and local planning authorities in Barnet, Dorset and Camden, to co-develop an AI-powered prototype that acts as a highly skilled assistant for planning officers, handling the heavy lifting of data extraction and case analysis. Working in close partnership will enable us to build an optimized tool, suited to the unique needs and day-to-day challenges that planners contend with every day. Following early trials in Barnet, Camden and Dorset, the government plans for the new AI planning tool to be made available to all councils nationally from 2027.

For a typical new planning application, officers spend hours cross-referencing policy documents, historical files and PDFs. This manual process creates a major bottleneck. It is especially challenging given householder applications account for nearly 70% of planning applications each year. By reducing the time spent on straightforward cases like loft conversions or extensions, the prototype being tested in Barnet, Camden and Dorset could help planning officers focus more on complex applications for public benefit.

The AI planning tool will streamline routine tasks using AI, by:

Consolidating data: Pre-processing backlogs, highlighting data gaps and extracting key site information so planners can review everything on one screen.
Identifying local policies: Highlighting relevant national and local policies, pre-assessing compliance and providing exact citations for the officer to verify.
Summarizing feedback: Reviewing individual consultation letters to identify key objections or precedents.
Drafting assessments: Creating a first draft of the final report, including the rationale and proposed conditions.

Crucially, the planning officer remains in full control as the final decision-maker. They review every line the tool generates, edit the reasoning and retain the authority to approve or reject an application. To ensure accountability, the prototype records its work at every step, creating a clear chain of thought and a robust audit trail for every decision to support the planning officer.

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HPE product barrage targets AI networks, agents, management

HPE Mist integration Continuing that integration theme, HPE said it will integrate Juniper’s natural language Mist AI into HPE Aruba Central and vice versa, all fed by its core AIOps Marvis AI engine. Marvis collects telemetry and user state data from Juniper’s routers, switches, access points, firewalls, and applications to detect and

APA inks $70-million Alaska acquisition

“As we continue to appraise and de-risk our resource base, ownership of this infrastructure provides greater flexibility and optionality in future development planning and represents a key step toward unlocking the potential of our position in Alaska,” John Christmann IV, chief executive officer of APA, said in a statement. Word

Petronas, JERA sign 20-year LNG supply deal

Cloud strategies have become more complicated than ever

CIOs need to ask themselves whether they have the expertise to handle that infrastructure, he adds. Ultimately, “you have to make the best of what you’ve got,” he says. Stay focused on fundamentals Organizations may want to chase the shiny object, which is agentic AI right now, but IT leaders

MRV lets EPCIC contract for Coral North FLNG project

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

EIA forecasts prolonged oil market tightening amid Hormuz shipping disruptions

In its June 2026 Short-Term Energy Outlook (STEO), the US Energy Information Administration (EIA) assumes the Strait of Hormuz will remain effectively closed in the near term, with oil shipments resuming in third–quarter 2026. The agency expects it will take until early 2027 for traffic through the waterway to return to pre-conflict levels. Some Middle East oil production is expected to remain disrupted beyond the forecast period. Global oil producers in the Middle East reduced crude oil production by more than 11 million b/d in May compared with pre-conflict levels because of limited shipping traffic through the strait. EIA estimates production shut-ins averaged 11.3 million b/d in May and forecasts disruptions of 11.34 million b/d in June before easing to 10.11 million b/d in the third quarter and 5.70 million b/d in the fourth quarter. Stay updated on oil price volatility, shipping disruptions, LNG market analysis, and production output at OGJ’s Iran war content hub. As a result, EIA expects global oil inventories to fall by an average of 6.3 million b/d in second-quarter 2026 and 7.6 million b/d in third-quarter 2026. OECD commercial inventories are forecast to fall to just under 2.3 billion bbl by December 2026, the lowest level since 2003. On a days-of-supply basis, OECD inventories are expected to fall to 50 days by yearend 2026. Brent crude oil averaged $107/bbl in May, down $10/bbl from April. EIA forecasts Brent prices will average about $105/bbl in June and July before declining to an average of $89/bbl in fourth-quarter 2026 as oil flows gradually resume. Brent is forecast to average $95/bbl in 2026 and $79/bbl in 2027. High fuel prices, reduced fuel availability, and government initiatives have lowered oil demand, EIA said. The agency now forecasts global oil demand will decline by 1.1 million b/d in 2026 compared

EnQuest looks to boost production by over 130% with Malaysian asset deals

EnQuest PLC subsidiary EnQuest Petroleum Production Malaysia Ltd. has agreed to acquire interests in producing upstream assets in Peninsular Malaysia and Sarawak. The company expects to leverage its integrated technical capabilities and experience in managing brownfield and late-life assets to support continued operations and redevelopment. Under three separate transaction packages with Petronas Carigali Sdn. Bhd. and E&P Malaysia Venture Sdn. Bhd., EnQuest will acquire interests in four offshore production sharing contracts (PSCs) for a maximum total consideration of up to $833 million. As part of the agreements, EnQuest Petroleum Production Malaysia will assume operatorship and participating interests in the Balingian PSC (Package 1, 90% participating interest), SK8 PSC (Package 1, 100% interest), and D35 PSC (Package 2, 50% interest), and will hold a nonoperated participating interest in the PM6/12 PSC (Package 3, 30% interest). The transaction also includes participation by Terengganu-based TI Exploration & Production Sdn. Bhd. (TI EP), which will hold a nonoperated interest in the PM6/12 PSC. TI EP is a joint venture between TI Petroleum Sdn. Bhd., a subsidiary of state-owned Terengganu Inc., and Ping Petroleum Ltd., an independent upstream company. On a 2025 net participating interest basis, the acquired interests are expected to add about 57,400 boe/d of production (47% liquids, 53% gas). This would increase EnQuest’s group production to more than 100,000 boe/d, representing a 134% increase compared with its 2025 production. The assets are expected to support production at around 100,000 boe/d through the end of the decade, the company said. EnQuest would also add 138 MMboe of 2P reserves and 208 MMboe of 2C resources (net WI). The acquisitions are expected to close by yearend, subject to customary conditions, including the waiver or expiry of applicable pre-emption rights associated with Package 2. Package 1, Package 2, and Package 3 are subject to separate acquisition

EIA: US crude inventories down 7.2 million bbl

US crude oil inventories for the week ended June 5, excluding the Strategic Petroleum Reserve, decreased by 7.2 million bbl from the previous week, according to data from the US Energy Information Administration (EIA). At 426.5 million bbl, US crude oil inventories are about 5% below the 5-year average for this time of year, the EIA report indicated. EIA said total motor gasoline inventories increased by 200,000 bbl from last week and are about 6% below the 5-year average for this time of year. Finished gasoline inventories increased while blending components inventories decreased last week. Distillate fuel inventories decreased by 200,000 bbl last week and are about 13% below the 5-year average for this time of year. Propane-propylene inventories increased by 1.1 million bbl from last week and are about 35% above the 5-year average for this time of year, EIA said. US crude oil refinery inputs averaged 17.0 million b/d for the week ended June 5, which was 80,000 b/d more than the previous week’s average. Refineries operated at 95.3% of capacity. Gasoline production increased, averaging 9.7 million b/d. Distillate fuel production increased, averaging 5.2 million b/d. US crude oil imports averaged 5.9 million b/d, down 500,000 b/d from the previous week. Over the last 4 weeks, crude oil imports averaged about 5.9 million b/d, 5.8% less than the same 4-week period last year. Total motor gasoline imports averaged 714,000 b/d. Distillate fuel imports averaged 130,000 b/d.

Shell discovers oil in Namibia’s Orange basin

Shell discovered oil at the Merlin-1X exploration well in Orange basin 250 km off the southern coast of Namibia. Merlin-1X, spudded on Apr. 8, 2026, is the tenth well drilled in Petroleum Exploration License 39 (PEL 0039). The well penetrated the Coniacian play and has delivered the most promising subsurface results to date in PEL 0039, indicating good reservoir quality with light oil and limited associated gas, the operator said in a release June 9. Shell said additional drilling late this year is under consideration as part of a broader exploratory appraisal program. PEL 0039 covers 12,000 sq km. Over the last 4 years, 10 wells have been drilled in the license: Graff-1X, La Rona-1X, Jonker-1X, Graff-1A, Lesedi-1X, Cullinan-1X, Jonker-1A, Jonker-2A, Enigma-1X, and Merlin-1X. Shell is operator of PEL 0039 with 45% working interest. Partners are QatarEnergy (45%) and the National Petroleum Corp. of Namibia (NAMCOR) (10%).

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

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

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

Marvell announces 102.4 Tbps switch silicon built for AI

Data movement has become an important concern in modern AI data centers. In the past, a cluster of a few servers could adequately handle back-office applications and databases. But with AI’s gigantic models, all sections of the data center need to move and receive data at high speeds. That requires a lot more power use than in the past. GPU- and XPU-based systems are approaching 120KW per rack, and switching and networking components consume approximately 15-25% of total rack power, making low-power switch silicon a strategic requirement. The Teralynx T100 delivers up to 25% lower power consumption than competitive solutions at a higher data rate. This enables AI infrastructures to deploy more accelerators within existing power envelopes without requiring additional power infrastructure. “As AI workloads evolve and scale exponentially, hyperscalers require network architectures that optimize latency, power and scalability simultaneously,” said Rishi Chugh, vice president and general manager of the data center switch business unit at Marvell, in a statement.

From the data center to the edge: How to build secure, effective enterprise AI infrastructure

While hyperscalers and neo-cloud providers may get the lion’s share of attention for providing AI infrastructure, many enterprises are taking a build-it-themselves approach to meet their specific AI requirements. The success of such projects is crucial to achieving business objectives, yet companies face significant challenges as they try to scale pilots to production. Organizations must keep up with the dynamic, ever-changing demands that AI applications place on compute and network infrastructure, from the data center to the edge. That means architecting systems to grow as demand warrants and to avoid performance bottlenecks. The architecture must also account for AI-driven security vulnerabilities and ensure appropriate defenses are in place. Yes, it’s a tall order. But here, in simplified form, is a three-step plan for meeting those objectives. Step one: Go modular Integrating all the required components in piecemeal fashion for an AI factory is complex, costly, and fraught with integration risk. Start with a modular design, based on proven NVIDIA reference architectures. A modular approach combines pre-validated accelerated computing hardware, AI software, and orchestration platforms, as well as networking and storage capabilities. A modular strategy speeds implementation and creates a faster time to value for your AI infrastructure. Using modules that combine compute, networking, and storage makes it easier to scale capacity as needed, whether in the data center or at edge facilities. In addition, the modular approach simplifies the job of addressing varying requirements, from inferencing engines at the edge to massive-scale model training in the data center, while staying within the same solution family. The same applies to easing integration processes, as modular platforms offer pre-validated software. The Cisco Secure AI Factory with NVIDIA approach, for example, includes hardware (Cisco AI PODS) that is pre-validated to work with NVIDIA AI Enterprise software; Cisco Security and Splunk Observability software; orchestration

OpenAI weighs Nvidia-backed lease for 10 GW Ohio data center campus

OpenAI would control the computing equipment under a 20-year lease and begin payments once the site starts operating, with the first phase expected in 2028. Nvidia is expected to supply the hardware and guarantee both OpenAI’s lease obligations and the developer’s financing, the report added. The reported structure highlights a broader shift in AI infrastructure strategy, where model developers, chip suppliers, and energy providers are forging increasingly long-term partnerships to secure compute capacity amid surging demand. “These types of symbiotic deals are becoming the norm as AI infrastructure rolls out,” said Neil Shah, vice president for research and partner at Counterpoint Research. “If a CIO picks OpenAI to be the base layer, they shouldn’t just accept whatever infrastructure comes with it. CIOs need to negotiate and demand that OpenAI uses a mix of capacity so all your eggs are not in one premium basket like Nvidia.” OpenAI and Nvidia did not immediately respond to requests for comment.

Arista unveils 1.6T rack-scale switch family for AI infrastructure

The new Arista family joins a growing ecosystem of vendors looking to tap into the 1.6T Ethernet world, which includes Cisco, Nvidia, Celestica and others. “Arista Network’s new 7060XE7 Series is a strong signal of where large-scale AI fabrics are heading: higher bandwidth, better power efficiency, and tighter integration between compute, optics, silicon, cooling, and network operating software,” wrote Sameh Boujelbene, vice president, data center switch and AI networks market research for Dell Oro, in a LinkedIn post. Among the features that stand out to her are “strong customer and ecosystem validation from Microsoft Azure, Oracle Cloud Infrastructure, Meta, AMD, and Broadcom.”

Water Emerges as a Critical Constraint for AI Data Centers

“There really has been a major shift within the last couple of years,” Bajpayee said. “I would even say within the last 12 months is where we have seen suddenly a rapid increase in the data center operators’ desire to control their water destiny.” For Gradiant, the MIT-born water technology company that built its reputation serving semiconductor manufacturers, pharmaceutical companies, and industrial customers worldwide, that shift has translated into a rapidly expanding pipeline of data center opportunities. More importantly, Bajpayee believes it signals a fundamental change in how the industry thinks about water itself. The conversation is no longer centered primarily on sustainability metrics or corporate environmental goals. Instead, operators increasingly view water as a business continuity issue. “We’re seeing operators themselves come to us and tell us that these are issues they are facing,” Bajpayee said. “They want to make sure they don’t get stalled, their permits don’t get pulled, their business doesn’t get stopped, and communities don’t push them out because they didn’t figure out a way to control their water.” From Water Treatment to Water Strategy That shift is occurring as Gradiant expands deployments of its recently announced HyperSolved platform, an end-to-end cooling water management system purpose-built for AI data centers. The company says HyperSolved is now being deployed with several of the world’s largest hyperscale operators across North America, Europe, and Asia, reflecting growing industry demand for integrated approaches to water infrastructure. While compute, networking, and power systems have evolved rapidly during the AI era, water management often remains fragmented, requiring operators to coordinate multiple vendors responsible for sourcing, treatment, cooling, wastewater management, reuse, discharge, and regulatory compliance. Gradiant’s approach seeks to consolidate those functions into a single integrated platform and operating model. The timing reflects the growing scale of the challenge. New AI data center

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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