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Meta defends Llama 4 release against ‘reports of mixed quality,’ blames bugs

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Meta’s new flagship AI language model Llama 4 came suddenly over the weekend, with the parent company of Facebook, Instagram, WhatsApp and Quest VR (among other services and products) revealing not one, not two, but three […]

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Meta’s new flagship AI language model Llama 4 came suddenly over the weekend, with the parent company of Facebook, Instagram, WhatsApp and Quest VR (among other services and products) revealing not one, not two, but three versions — all upgraded to be more powerful and performant using the popular “Mixture-of-Experts” architecture and a new training method involving fixed hyperparameters, known as MetaP.

Also, all three are equipped with massive context windows — the amount of information that an AI language model can handle in one input/output exchange with a user or tool.

But following the surprise announcement and public release of two of those models for download and usage — the lower-parameter Llama 4 Scout and mid-tier Llama 4 Maverick — on Saturday, the response from the AI community on social media has been less than adoring.

Llama 4 sparks confusion and criticism among AI users

An unverified post on the North American Chinese language community forum 1point3acres made its way over to the r/LocalLlama subreddit on Reddit alleging to be from a researcher at Meta’s GenAI organization who claimed that the model performed poorly on third-party benchmarks internally and that company leadership “suggested blending test sets from various benchmarks during the post-training process, aiming to meet the targets across various metrics and produce a ‘presentable’ result.”

The post was met with skepticism from the community in its authenticity, and a VentureBeat email to a Meta spokesperson has not yet received a reply.

But other users found reasons to doubt the benchmarks regardless.

“At this point, I highly suspect Meta bungled up something in the released weights … if not, they should lay off everyone who worked on this and then use money to acquire Nous,” commented @cto_junior on X, in reference to an independent user test showing Llama 4 Maverick’s poor performance (16%) on a benchmark known as aider polyglot, which runs a model through 225 coding tasks. That’s well below the performance of comparably sized, older models such as DeepSeek V3 and Claude 3.7 Sonnet.

Referencing the 10 million-token context window Meta boasted for Llama 4 Scout, AI PhD and author Andriy Burkov wrote on X in part that: “The declared 10M context is virtual because no model was trained on prompts longer than 256k tokens. This means that if you send more than 256k tokens to it, you will get low-quality output most of the time.”

Also on the r/LocalLlama subreddit, user Dr_Karminski wrote that “I’m incredibly disappointed with Llama-4,” and demonstrated its poor performance compared to DeepSeek’s non-reasoning V3 model on coding tasks such as simulating balls bouncing around a heptagon.

Former Meta researcher and current AI2 (Allen Institute for Artificial Intelligence) Senior Research Scientist Nathan Lambert took to his Interconnects Substack blog on Monday to point out that a benchmark comparison posted by Meta to its own Llama download site of Llama 4 Maverick to other models, based on cost-to-performance on the third-party head-to-head comparison tool LMArena ELO aka Chatbot Arena, actually used a different version of Llama 4 Maverick than the company itself had made publicly available — one “optimized for conversationality.”

As Lambert wrote: “Sneaky. The results below are fake, and it is a major slight to Meta’s community to not release the model they used to create their major marketing push. We’ve seen many open models that come around to maximize on ChatBotArena while destroying the model’s performance on important skills like math or code.”

Lambert went on to note that while this particular model on the arena was “tanking the technical reputation of the release because its character is juvenile,” including lots of emojis and frivolous emotive dialog, “The actual model on other hosting providers is quite smart and has a reasonable tone!”

In response to the torrent of criticism and accusations of benchmark cooking, Meta’s VP and Head of GenAI Ahmad Al-Dahle took to X to state:

“We’re glad to start getting Llama 4 in all your hands. We’re already hearing lots of great results people are getting with these models.

That said, we’re also hearing some reports of mixed quality across different services. Since we dropped the models as soon as they were ready, we expect it’ll take several days for all the public implementations to get dialed in. We’ll keep working through our bug fixes and onboarding partners.

We’ve also heard claims that we trained on test sets — that’s simply not true and we would never do that. Our best understanding is that the variable quality people are seeing is due to needing to stabilize implementations.

We believe the Llama 4 models are a significant advancement and we’re looking forward to working with the community to unlock their value.

Yet even that response was met with many complaints of poor performance and calls for further information, such as more technical documentation outlining the Llama 4 models and their training processes, as well as additional questions about why this release compared to all prior Llama releases was particularly riddled with issues.

It also comes on the heels of the number two at Meta’s VP of Research Joelle Pineau, who worked in the adjacent Meta Foundational Artificial Intelligence Research (FAIR) organization, announcing her departure from the company on LinkedIn last week with “nothing but admiration and deep gratitude for each of my managers.” Pineau, it should be noted also promoted the release of the Llama 4 model family this weekend.

Llama 4 continues to spread to other inference providers with mixed results, but it’s safe to say the initial release of the model family has not been a slam dunk with the AI community.

And the upcoming Meta LlamaCon on April 29, the first celebration and gathering for third-party developers of the model family, will likely have much fodder for discussion. We’ll be tracking it all, stay tuned.

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NetBox Labs embraces intersection of network management and AI

“NetBox is intent,” Beevers explained. “This is where network teams are documenting ‘Here is what the network and the infrastructure should look like.’ Think of intent as what is in NetBox.” With the general availability of NetBox Assurance announced this week, the platform now extends beyond documentation to address the

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9 steps to take to prepare for a quantum future

“If you’re in pharma or chemical industry, they’re using it already,” says Constellation’s Mueller. “You have to look into it,” Mueller warns. And quantum computers are already playing an important role in protein folding, he says. “Quantum qubits are taking over traditional architectures for protein folding and mapping,” he says.

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Russia on Alert as Its Key Oil Grade Nears $50

The Kremlin said it’s doing everything possible to minimize the impact of a global oil price rout on Russia’s economy as the nation’s key export grade plunges toward $50 a barrel for first time in 21 months. “We are very closely monitoring the situation, which is currently characterized as extremely turbulent, tense and emotionally overloaded,” the Interfax cited Kremlin spokesman Dimitri Peskov as saying Monday. Russian authorities are working to minimize “the consequences of this international economic storm for our economy.” Crude prices are critical for Russia’s federal budget, which relied on oil and gas for almost 30 percent of its proceeeds in January-February, according to government data.  As the nation’s spending in the first two months of the year accelerated due to the war in Ukraine, any decline in revenues could put pressure on the nation’s finances.  The country’s Urals grade, by far the country’s top export stream, slumped to $52.76 a barrel at the Baltic Sea port of Primorsk on Friday, data from Argus Media show. It was last below $50 in June 2023. Russia, which leads the OPEC+ producer alliance alongside Saudi Arabia, is closely monitoring the oil price decline, which Peskov said was driven by “the US decision to introduce tariffs for most countries in the world.”   Last month, Russia’s Finance Ministry said that expected the average oil price in 2025 to be closer to $60 a barrel instead of the $70 that the country had budgeted for the year, according to Prime newswire. In that scenario, it forecast the budget deficit would increase though by no more than 1 percent of gross domestic product. Headline oil prices have collapsed in the wake of wide-ranging tariffs that the US announced last week on the nation’s trading partners, clouding the global demand outlook. On top of that, OPEC+ group is

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ScottishPower brings in HSM Offshore Energy for East Anglia Two substation work

ScottishPower Renewables has awarded the contract to create the substation and jacket foundation for its East Anglia Two offshore wind farm to HSM Offshore Energy. Under the engineering, procurement, construction, installation and commissioning (EPCIC) contract. HSM will deliver the 5,100-tonne offshore high-voltage substation and its 3,700-tonne jacket foundation for the 960MW windfarm off the coast of Suffolk. Initial engineering works for the project got underway in summer 2024 under an early work agreement. Construction on the substation is expected to start in the fourth quarter of 2025, with the jacket ready for installation in the third quarter of 2027 and the topside later that year. HSM Offshore Energy CCO Hans Leerdam said: “This contract represents a significant milestone for HSM Offshore Energy and underscores our position as a trusted partner in the offshore wind industry. “The proactive approach taken by ScottishPower Renewables to reserve yard capacity early demonstrates the value of collaboration and forward-thinking in driving the energy transition. We are proud to contribute to the East Anglia Two project and to play a role in supporting the UK’s ambitious renewable energy targets.” The East Anglia Two offshore windfarm will be located in the southern North Sea approximately 33km from the Suffolk coast at its nearest point off Southwold and 37km to Lowestoft. ScottishPower previously signed up Siemens Gamesa to provide the project’s 64 turbines, with Cadeler contracted to transport and install them, along with their foundations. In addition, Seaway7 was brought into transport and install the project’s 64 inter-array cables. ScottishPower Renewables managing director for offshore Ross Ovens said: “It’s fantastic to be working with HSM Offshore Energy to bring our £4 billion East Anglia Two windfarm to life and produce more homegrown green generation for the UK. “This contract is a great example of the innovative and forward-thinking

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Hartlepool Nuclear power station moved to enhanced regulatory attention

The Office for Nuclear Regulation (ONR) has moved Hartlepool Nuclear power station into enhanced regulatory attention for safety. The plant, which is operated by EDF, was due to begin decommissioning in 2026, but had its lifespan extended again in December 2024. It’s due to continue operating until March 2027. The ONR says the decision for enhanced regulatory attention does not relate to the extension to the nuclear plant’s life, and was “based on evidence gained from ongoing targeted engagements at the site which have identified areas where improvements are required”. The regulator aims to influence improvements in areas including conventional health and safety, the number of site incidents and the production of nuclear safety cases. EDF has produced an improvement plan, which the ONR has accepted. © Supplied by EDFHartlepool nuclear power station on Teesside. Mike Webb, ONR’s superintending inspector for operating reactors, said: “When required, we place sites into enhanced attention to achieve improved performance in specific areas, which is a key part of our role as an independent regulator. “It’s encouraging that EDF has already submitted a performance improvement plan to us to address the areas we identified as requiring improvement at the site. “We have reviewed this plan and are confident it will address our outlined concerns. “With our enabling regulatory approach, we are committed to working with the licensee to ensure all these issues are addressed.” Mark Lees, Hartlepool power station director, said: “This power station has an excellent operating and safety record built up over more than 40 years of electricity generation and it is important to note that the ONR has clearly said it has confidence the site can continue to safely operate. Action plan “We have always worked with the ONR to address, and flag, technical issues as they emerge and to ensure

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Super-batteries’ planned under scheme to boost clean energy storage

Britain could be in line for a swathe of new “super-batteries” in the coming years, under plans to boost investment into renewable energy storage. Regulator Ofgem is launching a new funding scheme to make it less risky to develop and invest in large-scale storage projects, including so-called “water batteries”. The projects, officially called pumped storage hydropower, are a way of storing energy using reservoirs. The sites work by buying electricity when it is cheap – for example, when wind turbines are spinning – to pump water uphill from a river or lake, where it is then held at the top. When electricity is more expensive or at times of higher demand, the hydropower sites release the water back downhill to generate more power for the grid. The technology is not new and Britain has four such sites already in Scotland and Wales. One at Dinorwig in Snowdonia is nicknamed Electric Mountain. But no new sites have been built for 40 years because they are expensive to develop, even though operating costs are comparatively low. Several are in the works, including one at Loch Ness in the Highlands, but officials estimate Britain needs about five times more long duration energy storage capacity to hit net zero by 2050. Akshay Kaul, director-general for infrastructure at Ofgem, said: “Renewable energy is the key to securing Britain’s energy independence and driving down customer bills in the long term – so it’s vital that none of this precious resource goes to waste. “By creating the confidence for investors to support new projects such as super-batteries capable of storing the extra electricity created when the wind blows hard and the sun shines strong, we can reduce the need to turn to fossil-fuelled power as back-up when the weather changes.” Officials hope to make it easier to

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Which green hydrogen projects have secured UK government backing?

After the UK government announced its shortlist for the second hydrogen allocation round (HAR2), Energy Voice takes a look at the 27 green hydrogen projects. Altogether, the projects represent approximately 765 MW of green hydrogen production capacity, falling short of the 875 MW target the government set for HAR2. The shortlist includes eight projects in Scotland, three in Wales and 16 in England from a total of 80 applications from across the UK hydrogen sector. The UK government said the HAR2 projects will “create thousands of jobs in the UK’s industrial heartlands” and “unlock clean energy growth”. It comes after the first allocation round (HAR1) in 2023, which saw the government award £2 billion in funding to 11 green hydrogen projects. Following the announcement, the Department for Energy Security and Net Zero (DESNZ) said HAR2 developers will need to pass a rigorous due diligence stage to secure funding. Meanwhile, the UK’s offshore sector welcomed the government’s announcement of the HAR2 shortlist which it said has been “long overdue”. HAR1 winners continue success Among the successful HAR2 developers are several firms which secured funding as part of the HAR1 process. Carlton Power, Hygen, ScottishPower, EDF Renewables and MorGen Energy (formerly known as H2 Energy Europe) all backed up success in the first round with shortlisted projects in HAR2, as did Octopus Energy and RES Group joint venture Hyro. Carlton Power secured funding for three projects in HAR1, and the company will now add projects in Walsall and Hartlebury to its hydrogen development pipeline. Hygen’s Bardon Hill and Harper Lane hydrogen projects made the shortlist following on from the success of its Bradford Low Carbon Hydrogen in HAR1. Elsewhere, Hygen also saw success with the shortlisting of the Selms Muir Hydrogen project. Hygen is developing in partnership with Danish-owned European Energy UK

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Why EEC is the perfect gateway to export opportunities

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DARPA backs multiple quantum paths in benchmarking initiative

Nord Quantique plans to use the money to expand its team, says Julien Camirand Lemyre, the company’s president, CTO and co-founder. That’s an opportunity to accelerate the development of the technology, he says. “By extension, what this will mean for enterprise users is that quantum solutions to real-world business problems will be available sooner, due to that acceleration,” he says. “And so enterprise customers need to also accelerate how they are thinking about adoption because the advantages quantum will provide will be tangible.” Lemyre predicts that useful quantum computers will be available for enterprises before the end of the decade. “In fact, there has been tremendous progress across the entire quantum sector in recent years,” he says. “This means industry needs to begin thinking seriously about how they will integrate quantum computing into their operations over the medium term.” “We’re seeing, with the deployment of programs like the QBI in the US and investments of billions of dollars from  public and private investors globally, an increasing maturity of quantum technologies,” said Paul Terry, CEO at Photonic, which is betting on optically-linked silicon spin qubits.  “Our architecture has been designed from day one to build modular, scalable, fault-tolerant quantum systems able to be deployed in data centers,” he said. He’s not the only one to mention fault-tolerance. DARPA stressed fault-tolerance in its announcement, and its selections point to the importance of error correction for the future of quantum computing. The biggest problem with today’s quantum computers is that the number of errors increases faster than the number of qubits, making them impossible to scale up. Quantum companies are working on a variety of approaches to reduce the error rates low enough that quantum computers can get big enough to actually to real work.

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Zayo’s Fiber Bet: Scaling Long-Haul and Metro Networks for AI Data Centers

Zayo Group Holdings Inc. has emerged as one of the most aggressive fiber infrastructure players in North America, particularly in the context of AI-driven growth. With a $4 billion investment in AI-related long-haul fiber expansion, Zayo is positioning itself as a critical enabler of the AI and cloud computing boom. The company is aggressively expanding its long-haul fiber network, adding over 5,000 route miles to accommodate the anticipated 2-6X increase in AI-driven data center capacity by 2030. This initiative comes as AI workloads continue to push the limits of existing network infrastructure, particularly in long-haul connectivity. New Fiber Routes The new routes include critical connections between key AI data center hubs, such as Chicago-Columbus, Las Vegas-Reno, Atlanta-Ashburn, and Columbus-Indianapolis, among others. Additionally, Zayo is overbuilding seven existing routes to further enhance network performance, resiliency, and low-latency connectivity. This new development is a follow-on to 15 new long haul routes representing over 5300 route miles of new and expanded capacity deployed over the last five years. These route locations were selected based on expected data center growth, power availability, existing capacity constraints, and specific regional considerations. The AI Data Center Sector: A Significant Driver of Fiber Infrastructure The exponential growth of AI-driven data center demand means that the U.S. faces a potential bandwidth shortage. Zayo’s investments look to ensure that long-haul fiber capacity keeps pace with this growth, allowing AI data centers to efficiently transmit data between key markets. This is especially important as data center development locations are being driven more by land and power availability rather than proximity to market. Emerging AI data center markets get the high speed fiber they need, especially as they are moving away from expensive power regions (e.g., California, Virginia) to lower-cost locations (e.g., Ohio, Nevada, Midwest). Without the high-speed networking capabilities offered by

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Crusoe Adds 4.5 GW Natural Gas to Fuel AI, Expands Abilene Data Center to 1.2 GW

Crusoe and the Lancium Clean Campus: A New Model for Power-Optimized Compute Crusoe Energy’s 300-megawatt deployment at the Lancium Clean Campus in Abilene is a significant marker of how data center strategies are evolving to integrate more deeply with energy markets. By leveraging demand flexibility, stranded power, and renewable energy, Crusoe is following a path similar to some of the most forward-thinking projects in the data center industry. But it’s also pushing the model further—fusing AI and high-performance computing (HPC) with the next generation of power-responsive infrastructure. Here’s how Crusoe’s strategy compares to some of the industry’s most notable power-driven data center deployments: Google’s Oklahoma Data Center: Proximity to Renewable Growth A close parallel to Crusoe’s energy-centric site selection strategy is Google’s Mayes County data center in Oklahoma. Google sited its facility there to take advantage of abundant wind energy, aligning with the local power grid’s renewable capacity. Similarly, Crusoe is tapping into Texas’s deregulated energy market, optimizing for low-cost renewable power and the ability to flexibly scale compute operations in response to grid conditions. Google has also been an industry leader in time-matching workloads to renewable energy availability, something that Crusoe is enabling in real time through grid-responsive compute orchestration. Sabey Data Centers in Quincy: Low-Cost Power as a Foundation Another instructive comparison is Sabey Data Centers’ Quincy, Washington, campus, which was built around one of the most cost-effective power sources in the U.S.—abundant hydroelectric energy. Sabey’s long-term strategy has been to co-locate power-intensive compute infrastructure near predictable, low-cost energy sources. Crusoe’s project applies a similar logic but adapts it for a variable grid environment. Instead of relying on a fixed low-cost power source like hydro, Crusoe dynamically adjusts to real-time energy availability, a strategy that could become a model for future power-aware, AI-driven workloads. Compass and Aligned: Modular, Energy-Adaptive

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Executive Roundtable: Data Center Site Selection and Market Evolution in a Constrained Environment

For the third installment of our Executive Roundtable for the First Quarter of 2025, we asked our panel of seasoned industry experts about how the dynamics of data center site selection have never been more complex—or more critical to long-term success. In an industry where speed to market is paramount, operators must now navigate an increasingly constrained landscape in the age of AI, ultra cloud and hyperscale expansion, marked by fierce competition for land, tightening power availability, and evolving local regulations.  Traditional core markets such as Northern Virginia, Dallas, and Phoenix remain essential, but supply constraints and permitting challenges are prompting developers to rethink their approach. As hyperscalers and colocation providers push the boundaries of site selection strategy, secondary and edge markets are emerging as viable alternatives, driven by favorable energy economics, infrastructure investment, and shifting customer demand.  At the same time, power procurement is now reshaping the equation. With grid limitations and interconnection delays creating uncertainty in major hubs, operators are exploring new solutions, from direct utility partnerships to on-site generation with renewables, natural gas, and burgeoning modular nuclear concepts. The question now is not just where to build but how to ensure long-term operational resilience. As data center demand accelerates, operators face mounting challenges in securing suitable land, reliable power, and regulatory approvals in both established and emerging markets.  And so we asked our distinguished executive panel for the First Quarter of 2025, with grid capacity constraints, zoning complexities, and heightened competition shaping development decisions, how are companies refining their site selection strategies in Q1 2025 to balance speed to market, scalability, and sustainability? And, which North American regions are showing the greatest potential as the next wave of data center expansion takes shape?

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Podcast: iMasons CEO Santiago Suinaga on the Future of Sustainable AI Data Centers

For this episode of the DCF Show podcast, host Matt Vincent, Editor in Chief of Data Center Frontier, is joined by Santiago Suinaga, CEO of Infrastructure Masons (iMasons), to explore the urgent challenges of scaling data center construction while maintaining sustainability commitments, among other pertinent industry topics. The AI Race and Responsible Construction “Balancing scale and sustainability is key because the AI race is real,” Suinaga emphasizes. “Forecasted capacities have skyrocketed to meet AI demand. Hyperscale end users and data center developers are deploying high volumes to secure capacity in an increasingly constrained global market.” This surge in demand pressures the industry to build faster than ever before. Yet, as Suinaga notes, speed and sustainability must go hand in hand. “The industry must embrace a build fast, build smart mentality. Leveraging digital twin technology, AI-driven design optimization, and circular economy principles is critical.” Sustainability, he argues, should be embedded at every stage of new builds, from integrating low-carbon materials to optimizing energy efficiency from the outset. “We can’t afford to compromise sustainability for speed. Instead, we must integrate renewable energy sources and partner with local governments, utilities, and energy providers to accelerate responsible construction.” A key example of this thinking is peak shaving—using redundant infrastructure and idle capacities to power the grid when data center demand is low. “99.99% of the time, this excess capacity can support local communities, while ensuring the data center retains prioritized energy supply when needed.” Addressing Embodied Carbon and Supply Chain Accountability Decarbonization is a cornerstone of iMasons’ efforts, particularly through the iMasons Climate Accord. Suinaga highlights the importance of tackling embodied carbon—the emissions embedded in data center construction materials and IT hardware. “We need standardized reporting metrics and supplier accountability to drive meaningful change,” he says. “Greater transparency across the supply chain can be

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Executive Roundtable: The Changing Economics of Data Center Development

For the final installment of our Executive Roundtable for the First Quarter of 2025, we asked our panel of seasoned industry experts about how the economics of data center development are shifting as rising construction costs, supply chain volatility, and evolving enterprise workloads reshape the industry’s growth trajectory.  Operators are under pressure to deliver capacity at scale while managing higher capital expenditures, extended lead times for critical infrastructure, and increasing complexity in customer requirements. At the same time, demand remains strong, driven by AI workloads, cloud expansion, and heightened enterprise reliance on digital infrastructure. To remain competitive, data center providers must rethink everything from procurement strategies to financing models, balancing long-term investment with the need for speed and flexibility in deployment.  Energy procurement and real estate dynamics are also in flux. As power availability becomes a gating factor in core markets, operators and energy specialists are forging new partnerships to secure sustainable, cost-effective solutions. Meanwhile, the real estate landscape is evolving, with developers looking at creative approaches to land acquisition, modular construction, and even repurposing existing assets.  With construction costs escalating, supply chain disruptions extending project timelines, and enterprise IT requirements shifting toward AI and high-density workloads, data center operators, energy providers, and real estate firms are reevaluating their business models. And so we asked our expert panel: From innovative procurement strategies and new financing approaches to evolving power solutions and creative site selection methods, what adjustments are being made to sustain growth and profitability in 2025? And how are industry leaders balancing cost efficiency, scalability, and customer needs in an increasingly complex and competitive market?

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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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