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Meta-Corning fiber deal signals a new bottleneck in AI infrastructure

The role of fiber in AI scaling As AI systems scale, limits in networking are increasingly holding back performance, leaving costly GPUs underused and reducing the payoff from large infrastructure investments. Manish Rawat, a semiconductor analyst at TechInsights, pointed out that optical fiber is now emerging as the next structural constraint on AI scaling with […]

The role of fiber in AI scaling

As AI systems scale, limits in networking are increasingly holding back performance, leaving costly GPUs underused and reducing the payoff from large infrastructure investments.

Manish Rawat, a semiconductor analyst at TechInsights, pointed out that optical fiber is now emerging as the next structural constraint on AI scaling with potentially long-term implications.

“Fiber is the silent dependency that scales non-linearly with AI growth,” Rawat said. “AI workloads generate massive east-west traffic, requiring tight synchronization across thousands of GPUs, which sharply increases intra-data-center and inter-campus optical demand.”

But the so-called networking wall is not a single bottleneck, according to Sanchit Vir Gogia, chief analyst at Greyhound Research.

“It’s an overlapping set of constraints that surface when AI workloads hit scale, spanning fiber availability, switching density, optical transceiver limits, and architectural inefficiencies,” Gogia said.

The combined stress of AI scale and concurrent government broadband rollouts has snapped the historical assumption that fiber is abundant and cheap, Gogia added.

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Amazon confirms 16,000 job cuts, including to AWS

Amazon is cutting about 16,000 jobs across the company, SVP of People Experience and Technology Beth Galetti wrote in an email to employees Wednesday. The cuts were widely expected — and although Galetti’s email did not mention Amazon Web Services, the cuts came as no surprise to AWS staff, some

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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,

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SC Expects Market Sentiment to Gradually Turn More Positive

In a report sent to Rigzone recently, Standard Chartered Bank Energy Research Head Emily Ashford outlined that Standard Chartered expects oil market sentiment “to gradually turn more positive”. “We expect [crude oil] market sentiment to gradually turn more positive as the bearish oversupply narrative so prevalent in H2-2025 weakens, and traders turn their attention to a more positive H2-2026,” Ashford stated in the report. “We expect the supply glut estimates of the main market commentators to be revised towards more seasonal norms, although prices are likely to remain in the low to mid $60s per barrel,” Ashford added. Ashford also outlined in the report that Standard Chartered expects “an uptick in volatility and increasing focus on risks to both supply and demand”. The Standard Chartered Bank Energy Research Head went on to note in the report that “low prices have begun to quash U.S. shale output growth” and added that, “if OPEC+’s gradual return of barrels recommences in Q2-2026, this would start to highlight the tightness and geographic concentration of spare capacity, which should be price supportive in the medium term”. In the U.S. Energy Information Administration’s (EIA) latest short term energy outlook, which was released earlier this month, the EIA projected that Lower 48 States crude oil production, including lease condensate and excluding the Gulf of America, will drop from 11.28 million barrels per day in 2025 to 11.11 million barrels per day this year. Total U.S. crude oil output, including lease condensate, is forecast in the STEO to drop from 13.61 million barrels per day in 2025 to 13.59 million barrels per day in 2026. A statement posted on OPEC’s website on January 4 revealed that, in a meeting held that day, Saudi Arabia, Russia, Iraq, UAE, Kuwait, Kazakhstan, Algeria, and Oman “reaffirmed their decision on 2 November

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Woodside Expects Lower Production This Year

Woodside Energy Group Ltd’s output in 2025 exceeded guidance and set a company record but it expects a slowdown in 2026 due to downtime from preparations to start up the Scarborough Energy Project. The Perth-based liquefied natural gas (LNG)-focused producer on Wednesday reported 198.8 million barrels of oil equivalent (MMboe) in production last year, beating the company’s forecast of 192-197 MMboe. “This performance was driven by sustained plateau production at Sangomar [offshore Senegal] through late October and Pluto LNG operating at 100 percent reliability for the second half of the year”, acting chief executive Liz Westcott said in the company’s quarterly statement of results. This year Woodside expects to produce 172-186 MMboe. The projected year-on-year drop “reflects planned downtime at Pluto as we prepare the facility to begin processing Scarborough gas and for first LNG cargo in Q4 2026”, Westcott said. Westcott was referring to the Scarborough Energy Project, which includes the development of the Scarborough field off the coast of Karratha, the construction of a second gas processing train for Pluto LNG with a capacity of five million metric tons per annum (MMtpa) and modifications to Pluto Train 1, according to Woodside. Woodside expects the project to produce up to eight MMtpa of LNG and supply 225 terajoules per day of gas to the Western Australian market. In the fourth quarter of 2025 Woodside produced 48.9 MMboe, down four percent from the prior three-month period and five percent against Q4 2024. Q4 2025 production consisted of 1.71 billion cubic feet a day (Bcfd) of natural gas and 232,000 barrels per day (bpd) of liquids. The decrease was “driven by seasonal weather impacts and lower Australian east-coast demand”, Woodside said. The decline also follows Woodside’s sale of producing oil and gas assets in Greater Angostura in Trinidad and Tobago to Perenco,

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Eni in Talks with Mercuria on Trading Partnership

Italian energy major Eni SpA has held talks with Mercuria Energy Group over a potential partnership in commodity trading, according to people familiar with the matter. The talks follow a period of bumper profitability for the energy trading industry, as major producers look to increase margins on oil and gas sales. Spokespeople for Eni and Mercuria declined to comment. The negotiations are still ongoing and a partnership agreement may not be finalized, according to the people, who asked not to be named to discuss private information. It wouldn’t be the only trading partnership under discussion in the industry. Bloomberg reported last year that Indian Oil Corp. was planning to form a trading joint venture and had held talks with Vitol Group about it. Meanwhile, Mercuria itself has a metals partnership with the Democratic Republic of Congo’s state miner Gecamines. Geneva-headquartered Mercuria is one of a group of major energy trading houses that made record profits as oil, gas and power markets fractured following Russia’s full-scale invasion of Ukraine. The company has paid out $5.5 billion in dividends over the last three years to shareholders, with the largest recipients being former Goldman Sachs Group Inc. traders Marco Dunand and Daniel Jaeggi. Profits totaled $1.31 billion in its last financial year through September, according to accounts seen by Bloomberg News. Eni has a sizable trading operation around its portfolio of gas, liquefied gas, oil and power producing assets. London is a key hub for the trading unit, which also has regional desks in Singapore and Houston. What do you think? We’d love to hear from you, join the conversation on the Rigzone Energy Network. The Rigzone Energy Network is a new social experience created for you and all energy professionals to Speak Up about our industry, share knowledge, connect with peers and

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Libya Extends Waha Concessions to 2050

ConocoPhillips, TotalEnergies SE and Libya’s National Oil Corp (NOC) have signed an agreement extending the onshore Waha concessions to December 2050. “This agreement sets new fiscal terms allowing to increase the production of these concessions that are currently producing around 370,000 barrels of oil equivalent per day (boed)”, French energy giant TotalEnergies said in an online statement. “Therefore, it paves the way for a new phase of investments, including the development of the North Gialo field, which is expected to add 100,000 boed of production”. Patrick Pouyanné, TotalEnergies chair and chief executive, said, “Extending the Waha concession, with its low-cost and low-emission giant resources offering many opportunities to grow production, fits perfectly with our strategy”. The North African country, where TotalEnergies has been present since 1956, accounted for 113,000 boed of the company’s 2025 production, the statement noted. These came from the Waha concessions, where TotalEnergies owns a 20.42 percent stake; the offshore Al Jurf field, where TotalEnergies holds 37.5 percent; the onshore El Sharara field, which straddles the former Block NC 115, owned 15 percent by TotalEnergies, and the former Block NC 186, where TotalEnergies has a 12 percent ownership; and the onshore Mabruk field, where TotalEnergies owns 37.5 percent. NOC operates the Waha concessions with a 59.16 percent through Waha Oil Co. Houston, Texas-based ConocoPhillips owns 20.42 percent. The new concession deal was signed during the Libya Energy and Economy Summit in Tripoli. NOC said in a press release November 3, 2025 the partners plan to drill seven exploration wells in the concessions in 2026. Plans for Waha this year also include a seismic survey and the processing and reprocessing of existing seismic data according to the release. In 2025 NOC announced two production well startups and one production well restart. On September 12, 2025 it said it had

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EU Warns Against Over-Reliance on USA Gas

The European Union’s competition chief has warned against relying “too much” on US liquefied natural gas imports as the bloc looks to diversify its energy basket.  “We know that we cannot rely on Russian gas, and that we should pay attention not to rely too much on American gas,” Teresa Ribera told RTE Radio in an interview on Tuesday.  Following Russia’s invasion of Ukraine, Europe has replaced some of its lost Russian gas volumes with US LNG, and has been under pressure to bump up those purchases. A trade deal with Washington last year included a commitment to purchase $750 billion in US energy by 2028.  Ribera’s comments come as US President Donald Trump’s stance on Greenland has the continent on edge. Although he has vowed not to use force to take over the territory, his ‘framework’ deal with NATO involves stationing US missiles and mining rights aimed at keeping Chinese interests out. It also hinges on the US leader standing by his promise not to impose tariffs against European nations, Bloomberg reported. The EU agreed last year to accelerate a phaseout of Russian gas and sever ties with its once-primary supplier of the fuel. Energy companies have closely monitored the bloc’s strategy to seek alternative sources and also speed up its renewables rollout as it aims to zero-out emissions by 2050. If Europe fulfills all its supply deals for US LNG and its gas demand reduction efforts falter, as much as 80% of its imports could come from the US in 2030, up from 57% in 2025, the Institute for Energy Economics and Financial Analysis said in a report earlier this month. The EU still receives about 15% of its LNG supplies from Moscow, making Russia the second-largest provider of the fuel to Europe after the US. As more US

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Crude Gains as Geopolitical Risks Grow

Oil rose to the highest since October as US President Donald Trump touted a growing US military presence near Iran, while traders monitored the fallout from a sweeping winter storm and a weaker dollar boosted the appeal of commodities. West Texas Intermediate gained 2.9% to settle above $62, while a gauge of the greenback fell to its lowest level in four years amid investor caution about US policymaking, making commodities priced in the currency more attractive. Oil has rebounded this year, despite widespread expectations of a glut, as setbacks to Kazakh exports tightened the European market and US threats against Iran injected a risk premium into prices. Trump reiterated on Tuesday that a “flotilla” was headed to the Middle East, adding that he’d “rather not see anything happen.” In the options market, a bullish call skew in US futures has stayed intact for nearly two weeks, marking the longest stretch since October 2024. Investors frequently turn to options as a relatively inexpensive hedge against uncertain geopolitical outcomes. In the US, freezing conditions have disrupted a number of refineries on the Gulf Coast and a smattering of domestic output, though the impact is unlikely to be prolonged. US natural gas futures retreated on Tuesday following a powerful rally due to the cold snap, as did diesel futures in New York. Concerns about Kazakh supplies, meanwhile, have eased as a key Black Sea terminal came back online, while the country’s largest producer is preparing to resume output at the giant Tengiz field. Still, Tengiz is set to restore less than half of its normal production by Feb. 7, according to Reuters, limiting downward pressure on prices. Elsewhere, Chevron Corp. is working to bring more Venezuelan crude to a market that’s mostly well supplied. US officials are working to issue a general license

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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

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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.

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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.

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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.

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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.

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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.”

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

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

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

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

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2025 playbook for enterprise AI success, from agents to evals

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

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

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

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