OnPath Energy targets £1b of UK clean energy investments

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OnPath Energy targets £1b of UK clean energy investments

Renewable energy developer OnPath Energy has announced plans to invest around £1 billion in clean energy projects across the UK over the next five years. The announcement came as the company opened its new Sunderland headquarters, at Chase House on Rainton Bridge Business Park, with Secretary of State for Education Bridget Phillipson attending. She said: […]

Renewable energy developer OnPath Energy has announced plans to invest around £1 billion in clean energy projects across the UK over the next five years.

The announcement came as the company opened its new Sunderland headquarters, at Chase House on Rainton Bridge Business Park, with Secretary of State for Education Bridget Phillipson attending.

She said: “Clean power will unlock billions of pounds in investment and reindustrialise Britain with thousands of skilled jobs across the country.

“Through our plan for change, this Labour government will build an energy system that can bring down bills for households and businesses for good.”

OnPath Energy owns and operates ten onshore wind farms across Scotland and northern England with construction across several more projects expected in 2025.

The company has created more than 30 jobs over the last 12 months, with more set to follow.

The Wearside company – formerly Banks Renewables – was acquired by Brookfield Asset Management in late 2023. The Toronto-headquartered fund, chaired by former Bank of England governor Mark Carney, has over $1trillion (£818bn) worth of assets under management.

OnPath Energy CEO Richard Dunkley said that the investment plan will “create and support hundreds of skilled, sustainable green jobs across the UK, including within the North East.

“Alongside this, we are working to bring communities with us on the energy transition by creating industry-leading commitments on community benefits and community shared ownership.

“We will achieve this by developing people, giving them the skills required to underpin the sector’s future success, and by operating in genuine partnership with stakeholders and the communities in which we’re working; we describe this as the OnPath Together approach to development.”

Dunkley added: “OnPath has ambitious plans to make further long-term capital investment in this high-quality renewable energy infrastructure, amplifying the difference we can make to a net zero future for the UK that will help to lower consumer bills, improve the UK’s energy security and deliver a just transition that is fair and inclusive for everyone.”

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Microsoft launches its second generation AI inference chip, Maia 200

“In practical terms, Maia 200 can effortlessly run today’s largest models, with plenty of headroom for even bigger models in the future,” Microsoft says. Maia feeds data to models differently, too, through what Microsoft refers to as a redesigned memory subsystem which features a specialized direct memory access (DMA) engine

Nvidia is still working with suppliers on RAM chips for Rubin

Nvidia changed its requirements for suppliers of the next generation of high-bandwidth memory, HBM4, but is close to certifying revised chips from Samsung Electronics for use in its AI systems, according to reports. Nvidia revised its specifications for memory chips for its Rubin platform in the third quarter of 2025,

Storage shortage may cause AI delays for enterprises

Higher prices ahead All indicators are showing a steep price increase for memory and storage in 2026. Brad Gastwirth, for example, says he met with many of the most important players in the market at CES earlier this month, and his analysis suggests there will be a 50% or more

Amazon layoffs expected to disproportionately hit AWS and tech talent

Jassy explained: “If you grow as fast as we did for several years, the size of businesses, the number of people, the number of locations, the types of businesses you’re in, you end up with a lot more people than what you had before, and you end up with a

Baker Hughes Sees Record Year for Industrial, Energy Tech Bookings

Baker Hughes Co has reported record orders of $14.87 billion from its industrial and energy technology (IET) business for 2025, including $4.02 billion for the fourth quarter. “IET achieved a record backlog of $32.4 billion at year-end, and book-to-bill exceeded 1x”, chair and chief executive Lorenzo Simonelli said in an online statement. “For the second consecutive year, non-LNG equipment orders represented approximately 85 percent of total IET orders, which highlights the end-market diversity and versatility of our IET portfolio”. IET delivered $3.81 billion in revenue for October-December 2025, up 13 percent from the prior quarter and nine percent year-on-year. “The increase was driven by gas technology equipment, up $189 million, or 11 percent year-over-year, [and] gas technology services, up $86 million, or 11 percent year-over-year”, Baker Hughes said. Q4 2025 IET orders totaled $4.02 billion, down three percent against the prior three-month period but up seven percent compared to Q4 2024. “The [year-over-year] increase was driven by continued strength in climate technology solutions, industrial technology, and gas technology services”, the Houston, Texas-based company said. Segment EBITDA came at $761 million, up 20 percent sequentially and 19 percent year-on-year. “The year-over-year increase in EBITDA was driven by productivity, volume, price and FX [foreign exchange], partially offset by inflation”, Baker Hughes said. Its other segment, oilfield services and equipment (OFSE), logged $3.57 billion in revenue for Q4 2025, down two percent quarter-on-quarter and eight percent year-on-year. That was driven by declines in its main markets, North America and the Middle East/Asia, with both regions registering quarter-on-quarter and year-on-year drops in revenue. OFSE orders in Q4 2025 totaled $3.86 billion, down five percent quarter-on-quarter but up three percent year-on-year. OFSE EBITDA landed at $647 million, down four percent quarter-on-quarter and 14 percent year-on-year. IET “more than offset continued macro‑driven softness in OFSE, where margins remained resilient

Analysts Explain Tuesday’s USA NatGas Price Drop

In separate exclusive interviews with Rigzone on Tuesday, Phil Flynn, a senior market analyst at the PRICE Futures Group, and Art Hogan, Chief Market Strategist at B. Riley Wealth, explained today’s U.S. natural gas price drop. “Natural gas is pulling back after the worst of the cold has passed,” Flynn told Rigzone. “We’ve lifted some of the winter storm warnings, and this should allow some of the freeze-offs in the basins to get production back up,” he added. “We saw [a] significant drop in production because of the cold weather and now some of that will be coming back online,” he continued. In his interview with Rigzone, Flynn warned that the weather is still going to be “key”. “Some forecasters are predicting a warm-up, but then after that another blast of the cold,” he said. “If that’s the case … these huge moves in natural gas may be far from over”, Flynn told Rigzone. He added, however, that, “at least in the short term, [a] return to more moderate temperatures from what we had experienced should allow for the market to recover as far as production goes, and exports”. When he was asked to explain the U.S. natural gas price drop today, Hogan told Rigzone that “trees don’t grow to the sky”. “U.S. natural gas prices dipped today amid profit-taking by traders, after soaring by over 117 percent in the five days to Monday,” he said. “The benchmark jumped by 30 percent on Monday alone. Last week, gas prices went up by as much as 70 percent amid frigid weather that apparently took gas traders by surprise,” he added. “This surprise led to frantic short-covering and position exits at a hefty loss. Currently, natural gas is trading at over $6.60 per million British thermal units [MMBtu], which is the highest in

In 2026, virtual power plants must scale or risk being left behind

Listen to the article 13 min This audio is auto-generated. Please let us know if you have feedback. Rising demand and new technologies are forcing utilities to coordinate distributed energy resources on an unprecedented scale, a trend likely to continue in 2026, analysts and stakeholders say. But intimidating demand forecasts from power-hungry data centers, coupled with aggressive policy shifts away from renewables and efficiency standards, are turning power providers toward large-scale generation like nuclear, geothermal, gas and coal — possibly to the detriment of aggregation and demand response programs, they say. “Utilities are shifting away from DER to focus on [utility-scale] wind and solar in the near term and then new natural gas, [extending the life of] aging coal, and [restarting] shuttered nuclear plants,” said Sally Jacquemin, vice president of power and utilities at AspenTech Digital Grid Management, Emerson. Investment in distribution system modernization is also growing, but DER “is a lower priority,” she added. But grid advocates and utility leaders say distributed resources could provide crucial benefits at a time of rising prices and accelerate the interconnection of large loads, which is a priority of the Trump administration. In order to do that, virtual power plants must evolve and scale more rapidly or skyrocketing electricity demand and costs will force attention to traditional resources, industry sources say. The value of DER to the system will be determined by policies set by states, grid operators, federal regulators and officials in the Trump administration. Allison Wannop, vice president of regulatory affairs and wholesale markets for Sparkfund, predicted that demand growth and affordability challenges will drive innovation to make the most of distribution system resources. “20th century solutions will not build a 21st century grid,” she said. ‘Visibility will be key to VPP proliferation’ 2025 was a good year for distributed energy

3D Energi Runs Out of Cash for Victoria Drill Campaign, Suspends Trading

3D Energi Ltd said Tuesday it has voluntarily halted trading on the Australian Securities Exchange (ASX), having defaulted on the payment of its share of costs in a ConocoPhillips-led exploration campaign in the Otway basin offshore Victoria. “Joint venture cash calls for the drilling program are higher than originally forecast and a balance of approximately $2.5 million remains outstanding by the company which it does not currently have”, Melbourne-based 3D Energi said in a stock filing. “A default notice has been issued by the joint venture operator to the company with a remedy period to 6th February. “Additional forecast company drilling program expenditure subject to cash calls due on 6th February is currently estimated at approximately $5.3 million, which if not paid by that date may well become the subject of an additional default notice and remedy period. “Consequently, the company is implementing a suspension of the trading of its shares on ASX while it addresses its funding position and the implications of payment default on the level of its ongoing interest in the permit”. 3D Energi plans to resume ASX trading in the first week of February. Earlier this month it announced the Charlemont-1 gas discovery, the joint venture’s second discovery under the VIC/P79 exploration after Essington-1. The newest well targeted the penultimate prospect in the Charlemont trend, which culminates with the La Bella discovery, according to 3D Energi. “Phase 1 of the Otway Exploration Drilling Program has identified important new natural gas resources close to existing offshore gas production and processing infrastructure in the Otway basin, supplying the Australian domestic gas market”, 3D Energi executive chair Noel Newell said in a statement January 14 announcing the second discovery. “This enhances the strategic significance of the discovery and supports future development optionality, subject to further technical and commercial evaluation,

EIA Sees NatGas Price Dropping in 2026 and Rising in 2027

The U.S. Energy Information Administration (EIA) projected that the U.S. natural gas Henry Hub spot price will drop this year and rise next year in its latest short term energy outlook (STEO). In the EIA’s January STEO, which was released on January 13 and completed its forecast on January 8, the EIA forecast that the commodity will average $3.46 per million British thermal units (MMBtu) in 2026 and $4.59 per MMBtu in 2027. The U.S. natural gas Henry Hub spot price averaged $3.53 per MMBtu in 2025, the EIA’s latest STEO showed. According to a quarterly breakdown included in its latest STEO, the EIA sees the U.S. natural gas Henry Hub spot price coming in at $3.38 per MMBtu in the first quarter of 2026, $2.75 per MMBtu in the second quarter, $3.42 per MMBtu in the third quarter, $4.28 per MMBtu in the fourth quarter, $4.78 per MMBtu in the first quarter of 2027, $4.30 per MMBtu in the second quarter, $4.43 per MMBtu in the third quarter, and $4.84 per MMBtu in the fourth quarter of next year. Last year, the Henry Hub spot price averaged $4.15 per MMBtu in the first quarter, $3.19 per MMBtu in the second quarter, $3.03 per MMBtu in the third quarter, and $3.75 per MMBtu in the fourth quarter, the EIA’s January STEO showed. “On an annual basis, U.S. natural gas prices are relatively flat in 2026 before rising in 2027 as market conditions tighten,” the EIA said in its latest STEO. “We expect the Henry Hub natural gas spot price will average just under $3.50 per million British thermal units (MMBtu) this year, a two percent decrease from 2025, and then rise by 33 percent in 2027 to an annual average of almost $4.60 per MMBtu,” it added. In its STEO,

Aramco Raises $4B in 1st Bond Sale of Year

(Update) January 26, 2026, 9:39 PM GMT: Article updated with pricing details in first three paragraphs. Saudi Aramco priced a $4 billion bond sale, its first note sale this year, as the world’s largest oil producer steps up borrowing to fund investment and dividends. The government-owned oil producer sold four bonds maturing in three to 30 years, according to a person familiar with the matter. The longest portion of the deal will pay 1.3 percentage point above Treasuries, said the person, who asked not to be identified because they are not authorized to speak publicly. That’s roughly a quarter-point less than initial pricing discussions. Overall, the sale attracted more than $22 billion of bids at the peak, with final books over $14 billion, the person said. Aramco is a key contributor to Saudi state finances, with large dividend payments supplementing royalties linked to crude sales. As oil prices have dipped and OPEC+ policy limited Saudi production, cash flows lagged payouts before a rebound in the third quarter. Aramco’s $17 billion in debt sales over the last two years helped support payouts. Saudi Arabia’s budget remains heavily dependent on oil revenue as the kingdom pursues an ambitious modernization drive. Crude prices remain well below levels needed to balance the state budget, forcing the government to project spending shortfalls for the coming years. Aramco has turned to debt to augment its cash flow and plans to invest more than $50 billion this year in oil and natural gas production, while maintaining its high base dividend of $21 billion. In November, the company reported a surprise jump in third-quarter profit as rising production outweighed lower crude prices. Earnings are set to slip for the full year, estimates compiled by Bloomberg show. While Brent crude has risen this year amid geopolitical tensions, including US attacks on

Gauging the real impact of AI agents

That creates the primary network issue for AI agents, which is dealing with implicit and creeping data. There’s a singular important difference between an AI agent component and an ordinary software component. Software is explicit in its use of data. The programming includes data identification. AI is implicit in its data use; the model was trained on data, and there may well be some API linkage to databases that aren’t obvious to the user of the model. It’s also often true that when an agentic component is used, it’s determined that additional data resources are needed. Are all these resources in the same place? Probably not. The enterprises with the most experience with AI agents say it would be smart to expect some data center network upgrades to link agents to databases, and if the agents are distributed away from the data center, it may be necessary to improve the agent sites’ connection to the corporate VPN. As agents evolve into real-time applications, this requires they also be proximate to the real-time system they support (a factory or warehouse), so the data center, the users, and any real-time process pieces all pull at the source of hosting to optimize latency. Obviously, they can’t all be moved into one place, so the network has to make a broad and efficient set of connections. That efficiency demands QoS guarantees on latency as well as on availability. It’s in the area of availability, with a secondary focus on QoS attributes like latency, that the most agent-experienced enterprises see potential new service opportunities. Right now, these tend to exist within a fairly small circle—a plant, a campus, perhaps a city or town—but over time, key enterprises say that their new-service interest could span a metro area. They point out that the real-time edge applications

Photonic chip vendor snags Gates investment

“Moore’s Law is slowing, but AI can’t afford to wait. Our breakthrough in photonics unlocks an entirely new dimension of scaling, by packing massive optical parallelism on a single chip,” said Patrick Bowen, CEO of Neurophos. “This physics-level shift means both efficiency and raw speed improve as we scale up, breaking free from the power walls that constrain traditional GPUs.” The new funding includes investments from Microsoft’s investment fund M12 that will help speed up delivery of Neurophos’ first integrated photonic compute system, including datacenter-ready OPU modules. Neurophos is not the only company exploring this field. Last April, Lightmatter announced the launch of photonic chips to tackle data center bottlenecks, And in 2024, IBM said its researchers were exploring optical chips and developing a prototype in this area.

Intel wrestling with CPU supply shortage

“We have important customers in the data center side. We have important OEM customers on both data center and client and that needs to be our priority to get the limited supply we have to those customers,” he added. CEO Lip-Bu Tan added that the continuing proliferation and diversification of AI workloads is placing significant capacity constraints on traditional and new hardware infrastructure, reinforcing the growing and essential role CPUs play in the AI era. Because of this, Intel decided to simplify its server road map, focusing resources on the 16-channel Diamond Rapids product and accelerate the introduction of Coral Rapids. Intel had removed multithreading from diamond Rapids, presumably to get rid of the performance bottlenecks. With each core running two threads, they often competed for resources. That’s why, for example, Ampere does not use threading but instead applies many more cores per CPU. With Coral Rapids, Intel is not only reintroducing multi-threading back into our data center road map but working closely with Nvidia to build a custom Xeon fully integrated with their NVLink technology to Build the tighter connection between Intel Xeon processors and Nvidia GPUs. Another aspect impacting supply has been yields or the new 18A process node. Tan said he was disappointed that the company could not fully meet the demand of the markets, and that while yields are in line with internal plans, “they’re still below where I want them to be,” Tan said. Tan said yields for 18A are improving month-over-month and Intel is targeting a 7% to 8% improvement each month.

Intel’s AI pivot could make lower-end PCs scarce in 2026

However, he noted, “CPUs are not being cannibalized by GPUs. Instead, they have become ‘chokepoints’ in AI infrastructure.” For instance, CPUs such as Granite Rapids are essential in GPU clusters, and for handling agentic AI workloads and orchestrating distributed inference. How pricing might increase for enterprises Ultimately, rapid demand for higher-end offerings resulted in foundry shortages of Intel 10/7 nodes, Bickley noted, which represent the bulk of the company’s production volume. He pointed out that it can take up to three quarters for new server wafers to move through the fab process, so Intel will be “under the gun” until at least Q2 2026, when it projects an increase in chip production. Meanwhile, manufacturing capacity for Xeon is currently sold out for 2026, with varying lead times by distributor, while custom silicon programs are seeing lead times of 6 to 8 months, with some orders rolling into 2027, Bickley said. In the data center, memory is the key bottleneck, with expected price increases of more than 65% year over year in 2026 and up to 25% for NAND Flash, he noted. Some specific products have already seen price inflation of over 1,000% since 2025, and new greenfield capacity for memory is not expected until 2027 or 2028. Moor’s Sag was a little more optimistic, forecasting that, on the client side, “memory prices will probably stabilize this year until more capacity comes online in 2027.” How enterprises can prepare Supplier diversification is the best solution for enterprises right now, Sag noted. While it might make things more complex, it also allows data center operators to better absorb price shocks because they can rebalance against suppliers who have either planned better or have more resilient supply chains.

Reports of SATA’s demise are overblown, but the technology is aging fast

The SATA 1.0 interface made its debut in 2003. It was developed by a consortium consisting of Intel, Dell, and storage vendors like Seagate and Maxtor. It quickly advanced to SATA III in 2009, but there never was a SATA IV. There was just nibbling around the edges with incremental updates as momentum and emphasis shifted to PCI Express and NVMe. So is there any life to be had in the venerable SATA interface? Surprisingly, yes, say the analysts. “At a high level, yes, SATA for consumer is pretty much a dead end, although if you’re storing TB of photos and videos, it is still the least expensive option,” said Bob O’Donnell, president and chief analyst with TECHnalysis Research. Similarly for enterprise, for massive storage demands, the 20 and 30 TB SATA drives from companies like Seagate and WD are apparently still in wide use in cloud data centers for things like cold storage. “In fact, both of those companies are seeing recording revenues based, in part, on the demand for these huge, high-capacity low-cost drives,” he said. “SATA doesn’t make much sense anymore. It underperforms NVMe significantly,” said Rob Enderle, principal analyst with The Enderle Group. “It really doesn’t make much sense to continue make it given Samsung allegedly makes three to four times more margin on NVMe.” And like O’Donnell, Enderle sees continued life for SATA-based high-capacity hard drives. “There will likely be legacy makers doing SATA for some time. IT doesn’t flip technology quickly and SATA drives do wear out, so there will likely be those producing legacy SATA products for some time,” he said.

DCN becoming the new WAN for AI-era applications

“DCN is increasingly treated as an end-to-end operating model that standardizes connectivity, security policy enforcement, and telemetry across users, the middle mile, and cloud/application edges,” Sanchez said. Dell’Oro defines DCN as platforms and services that deliver consistent connectivity, policy enforcement, and telemetry from users, across the WAN, to distributed cloud and application edges spanning branch sites, data centers and public clouds. The category is gaining relevance as hybrid architectures and AI-era traffic patterns increase the operational penalty for fragmented control planes. DCN buyers are moving beyond isolated upgrades and are prioritizing architectures that reduce operational seams across connectivity, security and telemetry so that incident response and change control can follow a single thread, according to Dell’Oro’s research. What makes DCN distinct is that it links user-to-application experience with where policy and visibility are enforced. This matters as application delivery paths become more dynamic and workloads shift between on-premises data centers, public cloud, and edge locations. The architectural requirement is eliminating handoffs between networking and security teams rather than optimizing individual network segments. Where DCN is growing the fastest Cloud/application edge is the fastest-growing DCN pillar. This segment deploys policy enforcement and telemetry collection points adjacent to workloads rather than backhauling traffic to centralized security stacks. “Multi-cloud remains a reality, but it is no longer the durable driver by itself,” Sanchez said. “Cloud/application edge is accelerating because enterprises are trying to make application paths predictable and secure across hybrid environments, and that requires pushing application-aware steering, policy enforcement, and unified telemetry closer to workloads.”

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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