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Talent gap complicates cost-conscious cloud planning

The top strategy so far is what one enterprise calls the “Cloud Team.” You assemble all your people with cloud skills, and your own best software architect, and have the team examine current and proposed cloud applications, looking for a high-level approach that meets business goals. In this process, the team tries to avoid implementation […]

The top strategy so far is what one enterprise calls the “Cloud Team.” You assemble all your people with cloud skills, and your own best software architect, and have the team examine current and proposed cloud applications, looking for a high-level approach that meets business goals. In this process, the team tries to avoid implementation specifics, focusing instead on the notion that a hybrid application has an agile cloud side and a governance-and-sovereignty data center side, and what has to be done is push functionality into the right place.

The Cloud Team supporters say that an experienced application architect can deal with the cloud in abstract, without detailed knowledge of cloud tools and costs. For example, the architect can assess the value of using an event-driven versus transactional model without fixating on how either could be done. The idea is to first come up with approaches. Then, developers could work with cloud providers to map each approach to an implementation, and assess the costs, benefits, and risks.

Ok, I lied about this being the top strategy—sort of, at least. It’s the only strategy that’s making much sense. The enterprises all start their cloud-reassessment journey on a different tack, but they agree it doesn’t work.

The knee-jerk approach to cloud costs is to attack the implementation, not the design. What cloud features did you pick? Could you find ones that cost less? Could you perhaps shed all the special features and just host containers or VMs with no web services at all? Enterprises who try this, meaning almost all of them, report that they save less than 15% on cloud costs, a rate of savings that means roughly a five-year payback on the costs of making the application changes…if they can make them at all. Enterprises used to build all of their core software internally, but those I chat with say that more than two-thirds of their stuff is now off-the-shelf, and their development resources tune it to their needs. They can’t change the internals of what they get from third parties, and they don’t have the resources or the time to do it all themselves.

What can the Cloud Team accomplish, in comparison? Of 33 enterprises who used this approach in some form to redo applications to optimize cloud cost/benefit, the average savings reported was 55%, and the payback period on the implementation cost less than two years. Big difference, huh?

To enterprises who tried the Cloud Team, there’s also a deeper lesson. In fact, there are two. Remember the old “the cloud changes everything” claim? Well, it does, but not the way we thought, or at least not as simply and directly as we thought. The economic revolution of the cloud is selective, a set of benefits that has to be carefully fit to business problems in order to deliver the promised gains. Application development overall has to change, to emphasize a strategic-then-tactical flow that top-down design always called for but didn’t always deliver. That’s the first lesson. The second is that the kinds of applications that the cloud changes the most are applications we can’t move there, because they never got implemented anywhere else.

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IBM Cloud speeds AI workloads with Intel Gaudi 3 accelerators

For businesses that need more control over their AI development, IBM says they can deploy IBM watsonx.ai software with the Intel Gaudi 3-based virtual server on IBM Cloud VPC in Q2 2025. IBM watsonx.ai includes an end-to-end AI development studio, AI developer toolkit and full AI lifecycle management for developing AI services

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Russia Watchdog Halts Oil Loadings at One Berth in Novorossiysk

Russia’s transportation watchdog has ordered a 90-day halt to oil-loading operations at berth 8 of the Black Sea port of Novorossiysk, according to state infrastructure operator Transneft PJSC. The order came as security checks at the port identified some violations, Transneft, which controls the loading facilities, said in a statement on Wednesday. Tanker tracking by Bloomberg suggests that there will be no impact on crude oil flows and minimal impact on fuel flows from the step. The oil terminal has until June 30 to rectify the violations. The Novorossiysk port is a key export route for Russian crude oil and petroleum products and any significant disruptions to loadings may affect the nation’s production flows. However, last month, 23 crude tankers loaded there to carry Russian barrels abroad. All of them loaded at berths 1, 1a or 2, according to vessel tracking data compiled by Bloomberg and shipping industry data. The other oil berths in Novorossiysk are used for loading petroleum products, the data show. Since late February, no product tankers moored at berth 8 either, which suggests the facility isn’t used often. The suspension order comes just days after the transportation watchdog halted loadings at two moorings of the Caspian Pipeline Consortium located nearby. The authority identified security breaches there and ordered an indefinite halt until the violations are rectified. The order has left the CPC infrastructure with just one operational mooring.  As the CPC link is the single-largest export route for Kazakh crude, initially expected to load about 1.5 million barrels a day in April, the halt has more of an impact for Kazakhstan. With no options for rerouting all the barrels, the nation may need to cut oil production just as the Organization of Petroleum Exporting Countries nudges it to meet the production quota.  Apart from the Novorossiysk

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Oil Slumps, Gold Rallies as Traders Review Trump Tariff Hit

The long-awaited unveiling by President Donald Trump of sweeping reciprocal import tariffs triggered a slump in oil prices on Thursday, while gold rose to a fresh record.  There was also an important carve-out for commodities from Canada and Mexico, as well as for energy and energy products.  Here’s a round-up of the initial impact of Wednesday’s big announcement. Metals According to a White House fact sheet, steel, aluminum, gold and copper imports won’t be subject to reciprocal tariffs, providing at least some relief to domestic buyers who are already bearing the cost of Section 232 tariffs of 25% on all imports of some key metals. Gold, however, jumped as much as 1.1% to a record as investors sought safety following the tariff headlines. Bullion has climbed more than 20% this year after a ferocious run in 2024. Copper futures declined on concerns over the global economy.  Oil and Gasoline Crude immediately dropped as trading started in the Asian morning. Futures fell more than 2%, dipping below $70 a barrel, amid concerns about declining demand and the potential for a trade war. However, energy and energy products are exempt from the new levies, meaning oil and natural gas markets won’t be directly impacted.  Canada and Mexico — the two biggest foreign suppliers of oil to the US — were also left out of Wednesday’s cascade. Exports from the countries to the US that aren’t compliant with the North American trade agreement known as the USMCA will remain subject to an earlier levy of 25% generally (and 10% on Canadian energy), imposed by Trump earlier to counter illegal immigration and fentanyl smuggling. If that 25% tariff is later dropped for either country, the newly ordered reciprocal rate would apply, a senior administration official told reporters Wednesday. Agriculture  US farmers also benefit from the reprieve on USMCA goods.

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Largest US gas-fired power plant planned for data centers in Pennsylvania

Homer City Redevelopment, or HCR, and Kiewit Power Constructors intend to build up to 4.5 GW of gas-fired generation at a retired power plant in Pennsylvania to serve a planned data center campus, the companies said Wednesday. The $10 billion generating project is slated to be built at the site of the 1,884-MW coal-fired Homer City power plant, which was shuttered in mid-2023. The 3,200-acre site, about 50 miles east of Pittsburgh, includes interconnections to the PJM Interconnection and New York Independent System Operator grids. The site interconnects with FirstEnergy Pennsylvania Electric’s system in PJM. GE Vernova will supply the Homer City Energy Campus project with seven hydrogen-enabled, gas-fired turbines, with the first deliveries expected to begin in 2026, HCR and Kiewit said. The generating project is expected to start producing power by 2027, they said. The power plant would be the largest gas-fired power plant in the United States, according to Kiewit. Greenhouse gas emissions from the power plant would be about 60% less per MWh than from the previous coal-fired power plant, according to HCR and Kiewit. The planned power plant will be supplied with fuel from the Texas Eastern gas pipeline system. HCR received a $5 million state grant to support construction of an interconnection between the generating station and the pipeline, which is about five miles away. The planned power plant will also provide electricity to “thousands of homes on the local grid,” HCR and Kiewit said. The project is backed by Knighthead Capital Management, a New York City-based private equity firm that as of mid-September owned about 75% of Homer City Holdings, the owner of the Homer City power plant, according to a filing at the Federal Energy Regulatory Commission. GoldenTree Asset Management owns about 12% of the company. HCR didn’t respond to questions about the

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Russia Official Dmitriev to Meet Envoy Witkoff in Washington

US special envoy Steve Witkoff is expected to meet in Washington with senior Russian negotiator Kirill Dmitriev, according to a person familiar with the plans, an effort to smooth over tensions after President Donald Trump vented frustration with counterpart Vladimir Putin. Dmitriev on Thursday confirmed he was in Washington for meetings with representatives of the US administration. The meetings started on Wednesday and would continue Thursday, he said in a post on his Telegram channel that didn’t identify anyone with whom he’d meet. “Restoring dialogue is a difficult and gradual process,” he said. “A real understanding of the Russian position opens up new opportunities for constructive interaction, including in the investment and economic sphere.” Trump told NBC News over the weekend that he was “pissed off” with the Russian president and threatened secondary tariffs on buyers of Russian oil if Putin refused a ceasefire with Ukraine, rare public criticism of Moscow by the US president. Dmitriev, 49, runs Russia’s sovereign wealth fund and has played an important role in talks between the US and Russia. His presence in the US capital highlights the prospects of greater business cooperation between the two countries on potential projects, including in the Arctic and on liquefied natural gas. He’s a former Goldman Sachs Group Inc. investment banker who was educated at Stanford and Harvard Universities and has ties to Putin’s family.  The White House National Security Council declined to comment. CNN reported earlier on the plans for Witkoff and Dmitriev to meet.  US officials have become increasingly frustrated by Moscow’s slow-walking of negotiations. What appeared to be a breakthrough last week over a partial truce in the Black Sea quickly fell apart after Russian officials said it was contingent on sanctions relief. That assertion contradicted a US statement outlining the parameters of the agreement.  Russia and

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Phillips 66 Seals Acquisition of EPIC Midstream Assets

Phillips 66 has completed the acquisition of EPIC Y-Grade GP LLC and EPIC Y-Grade LP for about $2.2 billion, boosting its midstream footprint in the Permian Basin. The units, bought from EPIC Midstream Holdings LP, own natural gas liquids (NGL) pipelines, fractionation facilities and distribution systems. “This transaction strengthens our position as a leading integrated downstream energy provider”, Don Baldridge, Phillips 66 executive vice president for midstream and chemicals, said in a company statement. “We are evolving our portfolio and enhancing our ability to provide seamless and efficient delivery of energy products. “Phillips 66 will offer producers unparalleled flow assurance, while advancing a strategy that is expected to deliver attractive returns and create long-term value for our shareholders”. The acquired operations comprise two fractionators with a capacity of170,000 barrels per day (bpd) near Corpus Christi, Texas; purity distribution pipelines stretching about 350 miles; and an NGL pipeline around 885 miles long and with a capacity of 175,000 bpd. The NGL pipeline links the Delaware, Midland and Eagle Ford basins to the fractionation complexes and Phillips 66’s Sweeny Hub, which has facilities for crude distilling, naphtha reforming, fluid catalytic cracking, alkylation and hydrodesulfurization, as well as aromatics units, a vacuum distillation unit and a delayed coking unit. The pipeline capacity is being raised to 225,000 bpd, in a project expected to be completed in the second quarter. A further expansion has also been sanctioned to grow the capacity to 350,000 bpd; completion is expected 2026. EPIC has also put in place plans to raise the fractionation capacity to 280,000 bpd. “The acquired assets connect Permian production to Gulf Coast refiners, petrochemical companies and export markets, and are highly integrated with the Phillips 66 asset base”, Phillips 66 said. Announcing the agreement January 6, the company said, “Phillips 66 does not expect

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Octopus Energy takes 10% East Anglia One stake

Octopus Energy has taken a 10% stake in the 714MW East Anglia One offshore wind farm, based off the coast of Suffolk. Octopus acquired the stake from Macquarie Asset Management for an undisclosed sum on behalf of Vector, Octopus’ offshore wind fund aimed at investing in fixed and floating offshore wind projects. The deal marks Macquarie’s third sale of its stake in the project, having started holding 40% of the £2.5 billion project, with ScottishPower Renewables holding the rest. It sold 20% of the project to the Renewables Infrastructure Group (TRIG) in 2020 when the wind farm went into operations, followed by another 10% in 2024 to NTR on behalf of L&G NTR Clean Power and the Development Bank of Japan. The deal is also Octopus Energy’s fourth investment in a UK offshore wind farm and its seventh in Europe. In addition to East Anglia One, Octopus has stakes in the UK’s Hornsea One, Lincs and Walney Extension, along with Butendiek in Germany, and Borssele V and Borssele III & IV in the Netherlands. The East Anglia deal builds upon the company’s $2bn of total offshore wind investments made last year. The company has previously said it aims to invest £2bn in UK clean energy projects by 2030, with the East Anglia One deal contributing to this goal. Octopus added that it is looking at the French market as it plans to enter the country’s offshore wind tender and develop a brand-new offshore wind farm in partnership with Skyborn Renewables. Octopus Energy Generation CEO Zoisa North-Bond said: “Britain is blessed with strong winds and long coastlines – perfect conditions for offshore wind. “The sector has become a vital pillar of our energy system over the past years, and this investment will help to turbocharge this clean technology further, bringing cheaper,

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New MLCommons benchmarks to test AI infrastructure performance

The latest release also broadens its scope beyond chatbot benchmarks. A new graph neural network (GNN) test targets datacenter-class hardware and is designed for workloads like fraud detection, recommendation engines, and knowledge graphs. It uses the RGAT model based on a graph dataset containing over 547 million nodes and 5.8 billion edges. Judging performance Analysts suggest that these benchmarks will make it easier to judge the performance of various hardware chips and clusters based on documented models. “As every chipmaker seeks to prove that its hardware is good enough to support AI, we now have a standard benchmark that shows the quality of question support, math, and coding skills associated with hardware,” said Hyoun Park, CEO and Chief Analyst at Amalgam Insights.  Chipmakers can now compete not just on traditional speeds and feeds, but in mathematical skill and informational accuracy. This benchmark provides a rare opportunity to add new performance standards on cross-vendor hardware, Park added. “The latency in terms of how quickly tokens are delivered and the time for the user to see the response is the deciding factor,” said Neil Shah, partner and co-founder at Counterpoint Research. “This is where players such as NVIDIA, AMD, and Intel have to get the software right to help developers optimize the models and bring out the best compute performance.” Benchmarking and buying decisions Independent benchmarks like those from MLCommons play a key role in helping buyers evaluate system performance, but relying on them alone may not provide the full picture.

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Potential Nvidia chip shortage looms as Chinese customers rush to beat US sales ban

Will it lead to shortages? The US first placed export controls on chips sent to China in October 2022 as a means to slow the country’s technological advances. It blocked the sale of Nvidia’s A100 and H100 chips, leading the company to develop the less powerful A800 and H800 chips for the market; they were also subsequently banned. There was a surge in demand for the H20 following the arrival of Chinese startup DeepSeek’s ultra low-cost, open-source AI model in January. And while the H20 is reported to be 15 times slower than Nvidia’s newest Blackwell chips sold elsewhere in the world, it was designed specifically by Nvidia to comply with the further US export controls introduced in October 2023. It is being used by Chinese companies for training, although it’s billed as an inference chip, explained Matt Kimball, VP and principal analyst for datacenter compute and storage at Moor Insights & Strategy. Should Nvidia choose to focus its efforts on manufacturing more of the chips, Kimball said he doesn’t think it will impact supply in the US and Europe, as Blackwell is the main product sold in those markets and H20 is an N-1 Hopper architecture chip. “If you take this a step further and ask whether this large order slows down the production of chips destined for the US and Europe, I’d say the answer is no, as the Hopper family is built on a different process node than the Blackwell family,” he said. Still, Kimball noted, “supply chain management is difficult, especially for smaller organizations that are put to the back of the line as hyperscalers with multibillion dollar orders are first in line for the newest [chips].”

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European cloud group invests to create what it dubs “Trump-proof cloud services”

But analysts have questioned whether the Microsoft move truly addresses those European business concerns. Phil Brunkard, executive counselor at Info-Tech Research Group UK, said, commenting on last month’s announcement of the EU Data Boundary for the Microsoft Cloud,  “Microsoft says that customer data will remain stored and processed in the EU and EFTA, but doesn’t guarantee true data sovereignty.” And European companies are now rethinking what data sovereignty means to them. They are moving beyond having it refer to where the data sits to focusing on which vendors control it, and who controls them. Responding to the new Euro cloud plan, another analyst, IDC VP Dave McCarthy, saw the effort as “signaling a growing European push for data control and independence.” “US providers could face tougher competition from EU companies that leverage this tech to offer sovereignty-friendly alternatives. Although €1 million isn’t a game-changer on its own, it’s a clear sign Europe wants to build its own cloud ecosystem—potentially at the expense of US market share,” McCarthy said. “For US providers, this could mean investing in more EU-based data centers or reconfiguring systems to ensure European customers’ data stays within the region. This isn’t just a compliance checkbox. It’s a shift that could hike operational costs and complexity, especially for companies used to running centralized setups.” Adding to the potential bad news for US hyperscalers, McCarthy said that there was little reason to believe that this trend would be limited to Europe. “If Europe pulls this off, other regions might take note and push for similar sovereignty rules. US providers could find themselves adapting to a patchwork of regulations worldwide, forcing a rethink of their global strategies,” McCarthy said. “This isn’t just a European headache, it’s a preview of what could become a broader challenge.”

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Talent gap complicates cost-conscious cloud planning

The top strategy so far is what one enterprise calls the “Cloud Team.” You assemble all your people with cloud skills, and your own best software architect, and have the team examine current and proposed cloud applications, looking for a high-level approach that meets business goals. In this process, the team tries to avoid implementation specifics, focusing instead on the notion that a hybrid application has an agile cloud side and a governance-and-sovereignty data center side, and what has to be done is push functionality into the right place. The Cloud Team supporters say that an experienced application architect can deal with the cloud in abstract, without detailed knowledge of cloud tools and costs. For example, the architect can assess the value of using an event-driven versus transactional model without fixating on how either could be done. The idea is to first come up with approaches. Then, developers could work with cloud providers to map each approach to an implementation, and assess the costs, benefits, and risks. Ok, I lied about this being the top strategy—sort of, at least. It’s the only strategy that’s making much sense. The enterprises all start their cloud-reassessment journey on a different tack, but they agree it doesn’t work. The knee-jerk approach to cloud costs is to attack the implementation, not the design. What cloud features did you pick? Could you find ones that cost less? Could you perhaps shed all the special features and just host containers or VMs with no web services at all? Enterprises who try this, meaning almost all of them, report that they save less than 15% on cloud costs, a rate of savings that means roughly a five-year payback on the costs of making the application changes…if they can make them at all. Enterprises used to build all of

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Lightmatter launches photonic chips to eliminate GPU idle time in AI data centers

“Silicon photonics can transform HPC, data centers, and networking by providing greater scalability, better energy efficiency, and seamless integration with existing semiconductor manufacturing and packaging technologies,” Jagadeesan added. “Lightmatter’s recent announcement of the Passage L200 co-packaged optics and M1000 reference platform demonstrates an important step toward addressing the interconnect bandwidth and latency between accelerators in AI data centers.” The market timing appears strategic, as enterprises worldwide face increasing computational demands from AI workloads while simultaneously confronting the physical limitations of traditional semiconductor scaling. Silicon photonics offers a potential path forward as conventional approaches reach their limits. Practical applications For enterprise IT leaders, Lightmatter’s technology could impact several key areas of infrastructure planning. AI development teams could see significantly reduced training times for complex models, enabling faster iteration and deployment of AI solutions. Real-time AI applications could benefit from lower latency between processing units, improving responsiveness for time-sensitive operations. Data centers could potentially achieve higher computational density with fewer networking bottlenecks, allowing more efficient use of physical space and resources. Infrastructure costs might be optimized by more efficient utilization of expensive GPU resources, as processors spend less time waiting for data and more time computing. These benefits would be particularly valuable for financial services, healthcare, research institutions, and technology companies working with large-scale AI deployments. Organizations that rely on real-time analysis of large datasets or require rapid training and deployment of complex AI models stand to gain the most from the technology. “Silicon photonics will be a key technology for interconnects across accelerators, racks, and data center fabrics,” Jagadeesan pointed out. “Chiplets and advanced packaging will coexist and dominate intra-package communication. The key aspect is integration, that is companies who have the potential to combine photonics, chiplets, and packaging in a more efficient way will gain competitive advantage.”

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Silicon Motion rolls SSD kit to bolster AI workload performance

The kit utilizes the PCIe Dual Ported enterprise-grade SM8366 controller with support for PCIe Gen 5 x4 NVMe 2.0 and OCP 2.5 data center specifications. The 128TB SSD RDK also supports NVMe 2.0 Flexible Data Placement (FDP), a feature that allows advanced data management and improved SSD write efficiency and endurance. “Silicon Motion’s MonTitan SSD RDK offers a comprehensive solution for our customers, enabling them to rapidly develop and deploy enterprise-class SSDs tailored for AI data center and edge server applications.” said Alex Chou, senior vice president of the enterprise storage & display interface solution business at Silicon Motion. Silicon Motion doesn’t make drives, rather it makes reference design kits in different form factors that its customers use to build their own product. Its kits come in E1.S, E3.S, and U.2 form factors. The E1.S and U.2 forms mirror the M.2, which looks like a stick of gum and installs on the motherboard. There are PCI Express enclosures that hold four to six of those drives and plug into one card slot and appear to the system as a single drive.

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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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