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JLL: Hyperscale and AI Demand Push North American Data Centers Toward Industrial Scale

JLL’s North America Data Center Report Year-End 2025 makes a clear argument that the sector is no longer merely expanding but has shifted into a phase of industrial-scale acceleration driven by hyperscalers, AI platforms, and capital markets that increasingly treat digital infrastructure as core, bond-like collateral. The report’s central thesis is straightforward. Structural demand has […]

JLL’s North America Data Center Report Year-End 2025 makes a clear argument that the sector is no longer merely expanding but has shifted into a phase of industrial-scale acceleration driven by hyperscalers, AI platforms, and capital markets that increasingly treat digital infrastructure as core, bond-like collateral.

The report’s central thesis is straightforward. Structural demand has overwhelmed traditional real estate cycles. JLL supports that claim with a set of reinforcing signals:

  • Vacancy remains pinned near zero.

  • Most new supply is pre-leased years ahead.

  • Rents continue to climb.

  • Debt markets remain highly liquid.

  • Investors are engineering new financial structures to sustain growth.

Author Andrew Batson notes that JLL’s Data Center Solutions team significantly expanded its methodology for this edition, incorporating substantially more hyperscale and owner-occupied capacity along with more than 40 additional markets. The subtitle — “The data center sector shifts into hyperdrive” — serves as an apt one-line summary of the report’s posture.

The methodological change is not cosmetic. By incorporating hyper-owned infrastructure, total market size increases, vacancy compresses, and historical time series shift accordingly. JLL is explicit that these revisions reflect improved visibility into the market rather than a change in underlying fundamentals; and, if anything, suggest prior reports understated the sector’s true scale.

The Market in Three Words: Tight, Pre-Leased, Relentless

The report’s key highlights page serves as an executive brief for investors, offering a concise snapshot of market conditions that remain historically constrained.

Vacancy stands at just 1%, unchanged year over year, while 92% of capacity currently under construction is already pre-leased. At the same time, geographic diversification continues to accelerate, with 64% of new builds now occurring in so-called frontier markets. JLL also notes that Texas, when viewed as a unified market, could surpass Northern Virginia as the top data center market by 2030, even as capital availability and investor appetite remain notably strong.

Taken together, JLL argues these metrics undercut persistent bubble narratives and instead point to a market defined by durable structural demand. By year-end 2025, North America reached 39 GW of installed capacity, split almost evenly between 19 GW of leased colocation space and 20 GW of hyperscaler-owned inventory.

The pace of expansion remains striking. Nine gigawatts of new capacity were delivered in 2025 alone, with another 35 GW currently under construction, and the vast majority of that pipeline already spoken for.

While Northern Virginia, Dallas–Fort Worth, and the Pacific Northwest continue to anchor industry leadership, new and proposed development activity is now swelling rapidly across the Midwest and the South.

Bubble? What Bubble?

JLL directly addresses one of the loudest external critiques of the sector: vacancy risk. Despite record levels of new delivery, North American vacancy has held at just 1% for two consecutive years, underscoring the depth of current demand.

What little availability does exist tends to consist of small, fragmented blocks that are largely unsuitable for large-scale (particularly AI-scale) deployments. Most major tenants entering the market today are securing capacity for 2027–2028 delivery windows.

Crucially, with 92% of the development pipeline already precommitted, JLL suggests the risk of near-term oversupply remains limited.

The report does acknowledge pockets of risk tied to newer business models but estimates that exposure at less than 10% of future tenancy. In JLL’s view, the combination of roughly 99% sector occupancy and an investment-grade tenant base supports the case for continued structural durability.

Frontier Markets Are Taking Over

One of the report’s most consequential findings is geographic: of the 35 GW currently under construction in North America, fully 64% is located in frontier markets. Pressured by tightening constraints around land, water, and especially power in established hubs, the industry is increasingly pushing into territories that until recently were considered secondary.

Key beneficiaries include West Texas, Tennessee, Wisconsin, and Ohio:  markets where energy availability, land access, and permitting dynamics offer developers a clearer path to scale.

The underlying drivers are straightforward: power, land, and politics.

The report also documents a meaningful shift toward hyperscaler self-build. A decade ago, owner-occupied development represented roughly 20% of projects underway; today that figure has climbed to about 40%. At the same time, project scale continues to expand. JLL is now tracking more than ten campuses at or above the 1-gigawatt threshold, a level that would have been exceptional only a few years ago.

Against this backdrop, the report floats a provocative possibility: Texas, when treated as a unified market, could emerge as the world’s largest data center hub by 2030, supported by abundant energy resources and comparatively flexible development conditions.

Northern Virginia remains the industry’s center of gravity today. But the forward pipeline suggests momentum is steadily drifting southwest as developers confront mounting political friction, community pushback, and tightening resource constraints in legacy strongholds.

Demand Concentrates Around Hyperscalers and AI

Hyperscalers continue to dominate the demand landscape, accounting for roughly 65% of North American data center absorption, while enterprise verticals including finance, healthcare, and media have collectively slipped to about 27% as workloads continue migrating cloudward.

At the same time, two emerging buyer cohorts are beginning to register in a meaningful way. Neocloud providers were associated with roughly 1 GW of announced projects in 2025, often in nontraditional markets and with alternative development partners. Pure-play AI firms (most notably OpenAI and Anthropic) were linked to approximately 10 GW of project announcements, with deployments expected to roll out in phases.

The report’s most attention-grabbing signal, however, may be at the hyperscale level. JLL notes that the five largest hyperscalers have announced plans for approximately $710 billion in 2026 capital expenditures, a level theoretically sufficient to support about 35 GW of new or refreshed global capacity. The figure underscores how hyperscaler investment cycles now set the tempo not only for the data center sector, but increasingly for utilities, generation developers, and the broader digital infrastructure supply chain.

That demand pressure is arriving alongside continued pricing momentum. Average data center lease rates rose another 9% in 2025, bringing cumulative increases to roughly 60% since 2020. Larger capacity blocks – particularly those above 1 MW – saw the fastest growth, reflecting the premium attached to AI-scale deployments.

For tenants, the leasing environment remains firm. Annual escalators of 3% or more are now typical, concessions are limited, and renewal spreads remain meaningful. Notably, some frontier markets are now pricing near major metros, driven in part by perceived execution and delivery risk.

JLL nevertheless forecasts continued rent expansion, projecting a roughly 7% CAGR through 2030.

If vacancy is tight, power availability is tighter.

Grid interconnection timelines now average four years or longer across many regions. Developers able to bring flexibility to utility negotiations (through phased load strategies, bridge generation, or onsite power) are often able to accelerate their position in the queue.

Natural gas remains the dominant interim solution, with mobile turbines widely deployed to bridge near-term capacity gaps. Even so, most operators continue to view a permanent grid connection as the preferred long-term outcome.

At the same time, hyperscalers are increasingly pairing load growth with renewable procurement strategies. In parallel, battery energy storage systems (BESS) have surged into the multi-gigawatt range of announced deployments in 2025, evolving rapidly from optional reliability tools into core infrastructure components.

Capital Is Not the Constraint

JLL characterizes current financing conditions as an extraordinary vote of confidence in the sector. Data center debt origination surged from $27 billion in 2020 to $92 billion in 2025, underscoring the depth of institutional appetite. The year’s most eye-catching headline was the announced $40 billion consortium acquisition of Aligned Data Centers, expected to close in 2026 pending approvals.

While traditional asset sales totaled roughly $1.5 billion, the more important evolution is structural. Forward sales, joint ventures, and preferred equity structures are becoming increasingly common, highlighted by the $30 billion Blue Owl–Meta private capital joint venture. In effect, ownership models are becoming more programmable and capital stacks more engineered.

Securitization is also moving firmly into the mainstream. Asset-backed securities issuance exceeded $17 billion in 2025, nearly double the prior year, as lenders and investors grow more comfortable treating stabilized data center assets as durable income vehicles.

The appeal is straightforward: long lease terms, investment-grade tenants, and highly predictable cash flows. Increasingly, data center investments are being underwritten less like speculative real estate and more like infrastructure credit.

Stepping back across leasing fundamentals, geographic expansion, utility dynamics, and capital flows, JLL’s through-line is clear. Data centers have crossed the threshold from niche specialty into core global infrastructure. The tenant base includes some of the most profitable companies in the world. Capital providers are underwriting facilities with utility-like time horizons. And governments are actively competing to attract development.

Viewed holistically, the report advances five macro conclusions: demand appears structural rather than cyclical; most new supply is effectively pre-absorbed; growth is migrating toward energy-rich regions; pricing power remains with landlords; and capital markets show every sign of continuing to fund expansion.

If a limiting factor does emerge, JLL suggests it is far more likely to be power availability and permitting friction than a shortage of capital or customers.

A Sector Growing — and Being Re-Measured

One additional nuance in JLL’s latest edition deserves attention. By expanding its methodology to incorporate owner-occupied hyperscale capacity and more than 40 additional markets, the firm has effectively reset the statistical baseline for tracking the sector. Historical comparisons to prior reports should be viewed through this wider aperture as one that more fully captures the true scale of hyperscale-driven growth.

That scale is already approaching utility-level magnitude. JLL notes that the 35 GW currently under construction in North America alone is roughly equivalent to the annual electricity consumption of the United Kingdom or Italy; a striking illustration of how far the industry has moved beyond its traditional real estate framing.

At the same time, the report subtly underscores a new competitive reality. Developers that can bring flexible load profiles, bridge generation, or onsite solutions to utility negotiations are increasingly able to accelerate grid access. In an era of multi-year interconnection queues, power strategy is fast becoming one of the sector’s defining execution advantages.

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SATORP halts processing activities at Jubail refinery

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Intel secures Google cloud and AI infrastructure deal

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Broadcom strikes chip deals with Google, Anthropic

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BW Energy granted 25-year extension of license offshore Gabon

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Santos plans development of North Slope’s Quokka Unit

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Fluor, Axens secure contracts for US grassroots refinery project

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EIA: US crude inventories up 3.1 million bbl

US crude oil inventories for the week ended Apr. 3, excluding the Strategic Petroleum Reserve, increased by 3.1 million bbl from the previous week, according to data from the US Energy Information Administration (EIA). At 464.7 million bbl, US crude oil inventories are about 2% above the 5-year average for this time of year, the EIA report indicated. EIA said total motor gasoline inventories decreased by 1.6 million bbl from last week and are about 3% above the 5-year average for this time of year. Finished gasoline inventories increased while blending components inventories decreased last week. Distillate fuel inventories decreased by 3.1 million bbl last week and are about 5% below the 5-year average for this time of year. Propane-propylene inventories increased by 600,000 bbl from last week and are 71% above the 5-year average for this time of year, EIA said. US crude oil refinery inputs averaged 16.3 million b/d for the week ended Apr. 3, which was 129,000 b/d less than the previous week’s average. Refineries operated at 92% of capacity. Gasoline production decreased, averaging 9.4 million b/d. Distillate fuel production increased, averaging 5.0 million b/d. US crude oil imports averaged 6.3 million b/d, down 130,000 b/d from the previous week. Over the last 4 weeks, crude oil imports averaged about 6.6 million b/d, 9.1% more than the same 4-week period last year. Total motor gasoline imports averaged 571,000 b/d. Distillate fuel imports averaged 152,000 b/d.

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Oil prices plunge as Iran war tensions ease amid tentative Hormuz reopening

Crude oil prices plunged sharply on Apr. 7 after US President Donald Trump announced a conditional 2-week ceasefire agreement with Iran, contingent on reopening the Strait of Hormuz and restoring safe passage for energy shipments. Both Brent and WTI crude oil fell towards $95/bbl, marking their largest single-day decline since 2020. Under the agreement, Iran signaled willingness to halt attacks on shipping and allow transit through Hormuz while broader negotiations continue. The US also indicated it would assist in clearing a backlog of tankers and stabilizing maritime traffic. Benchmark crude prices initially surged above $110/bbl in early April amid fears of prolonged supply disruption after Iran effectively restricted traffic through the strait—a corridor responsible for roughly 20% of global oil flows. The blockade, triggered by escalating US-Iran hostilities, caused tanker traffic to collapse and stranded millions of barrels of crude and refined products in the region. Despite the price correction, analysts caution that supply disruptions and infrastructure damage will continue to constrain markets. The conflict has already impaired regional energy assets, including LNG infrastructure in Qatar, and forced producers across the Middle East to curtail output or delay exports. The US Energy Information Administration (EIA) warned that fuel prices may remain elevated for months even if flows normalize, citing logistical bottlenecks, depleted inventories, and continued geopolitical uncertainty. “In theory, the 10–13 million b/d of crude oil and product supply stranded behind the Strait should now be gradually released. Whether the pre-March status quo will be re-established depends entirely on whether the truce can be turned into a permanent peace during the negotiations in Pakistan,” said Tamas Varga, analyst, PVM Oil Associates. “What appears evident, at least for now, is that the current quarter, the April–June period, will be the tightest, as the scarcity of available oil, both crude and refined

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EIA: Brent crude to reach $115/bbl in second-quarter 2026

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OpenAI puts part of Stargate project on hold over runaway power costs

OpenAI has postponed plans to open one of the data centers central to its Stargate project. It announced its plan to open the data center in the UK with great fanfare last September, when it was regarded as a major boost for the country’s nascent AI industry, as well as proving a step up for OpenAI’s international credentials. At the time, Sam Altman, CEO of OpenAI, said, “The UK has been a longstanding pioneer of AI, and is now home to world-class researchers, millions of ChatGPT users, and a government that quickly recognized the potential of this technology.” All of that has been quietly forgotten. The plans for the data center in Northumberland, in the Northeast of England, have been put on hold, with the project ready to be revived when the conditions are ripe for major infrastructure investment, according to a report by the BBC.

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Neoclouds gain momentum in a supply-constrained world

And since they used the same hardware, both neoclouds and traditional cloud providers are subject to the same shortage problem. Component suppliers are reporting significant shortages due to demand for AI data centers and Synergy sees neoclouds also experiencing delays just like traditional cloud providers. “Demand is currently outstripping supply,” said Dinsmore. “It will take a while before that starts to come into more balance.” Among neoclouds, CoreWeave stands out as the most direct challenger to traditional hyperscale cloud providers. Meanwhile, OpenAI and Anthropic represent a distinct but increasingly important category, and that is platform-centric providers offering cloud-like access to foundational models and AI development environments. Synergy says that as demand for AI infrastructure accelerates, neoclouds are positioning themselves as focused alternatives to traditional hyperscale providers such as Amazon, Microsoft and Google.

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What is AI networking? How it adds intelligence to your infrastructure

The end goal is to make networks more reliable, efficient and performant. Enterprises are already seeing notable results when AI is applied to IT operations, including shorter deployment times, a decrease in trouble tickets, and faster time to resolution. With the help of AI, networks  will become more autonomous and self-healing (that is, able to address issues without the need for human intervention). In fact, Tier 1 and Tier 2 infrastructure is moving toward ‘no human in the loop,’ Nick Lippis, co-founder and co-chair of enterprise user community ONUG, recently told Network World. In time, humans will only need to step in for policy exceptions and high-risk decisions. “Layering in AI capabilities makes LAN management applications easier to use and more accessible across an organization,” Dell’Oro Group analyst Sian Morgan said. Gartner predicts that, by 2030, AI agents will drive most network activities, up from “minimal adoption” in 2025. The firm emphasizes that leaders who overlook the AI networking shift “risk higher MTTR [meantime to repair], rising costs, and growing security exposure.” The core components of AI networking It’s important to note that the use of AI and machine learning (ML) in network management is not new. AI for IT operations (AIOps), for instance, is a common practice that uses automation to improve broader IT operations. AI networking is specific to the network itself, covering domains including multi-cloud software, wired and wireless LAN, data center switching, SD-WAN and managed network services (MNS). The incorporation of generative AI, in particular, has brought AI networking to the fore, as enterprise leaders are rethinking every single aspect of their business, networking included.

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Aria Networks raises $125M and debuts its approach for AI-optimized networks

That embedded telemetry feeds adaptive tuning of Dynamic Load Balancing parameters, Data Center Quantized Congestion Notification (DCQCN) and failover logic without waiting for a threshold breach or a manual intervention. The platform architecture is layered. At the lowest levels, agents react in microseconds to link-level events such as transceiver flaps, rerouting leaf-spine traffic in milliseconds. At higher layers, agents make more strategic decisions about flow placement across the cluster. At the cloud layer, a large language model-based agent surfaces correlated insights to operators in natural language, allowing them to ask questions about specific jobs or alert conditions and receive context-aware responses. Karam argued that simply bolting an LLM onto an existing architecture does not deliver the same result. “If you ask it to do anything, it could hallucinate and bring down the network,” he said. “It doesn’t have any of the context or the data that’s required for this approach to be made safe.” Aria also exposes an MCP server, allowing external systems such as job schedulers and LLM routers to query network state directly and integrate it into their own decision-making. MFU and token efficiency as the target metrics Traditional networking is often evaluated in terms of bandwidth and latency. Aria is centering its platform around two metrics: Model FLOPS Utilization (MFU) and token efficiency. MFU is defined as the ratio of achieved FLOPS per accelerator to the theoretical peak. In practice, Karam said, MFU for training workloads typically runs between 33% and 45%, and inference often comes in below 30%. “The network has a major impact on the MFU, and therefore the token efficiency, because the network touches every aspect, every other component in your cluster,” Karam said.

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New v2 UALink specification aims to catch up to NVLink

But given there are no products currently available using UALink 1.0, UALink 2.0 might be viewed as a premature launch Need to play catch up David Harold, senior analyst with Jon Peddie Research, was guarded in his reaction. “While 2.0 is a significant step forward from 1.0, we need to bear in mind that even 1.0 solutions aren’t shipping yet – they aren’t due until later this year. So, Nvidia is way ahead of the open alternatives on connectivity, indeed ahead of the proprietary or Ethernet based solutions too,” he said. What this means, he added, is that non-Nvidia alternatives are currently lagging in the market. “They need to play catch up on several fronts, not just networking. … I can’t think of a single shipping product that meaningfully has advantages over a Nvidia solution,” he said. “Ultimately UALink remains desirable since it will enable heterogeneous, multi-vendor environments but it’s quite a way behind NVLink today. ” There are plenty of signs that organizations will find it hard to break free of the Nvidia dominance, however. A couple of months ago, RISC-V pioneer SiFive signed a deal with Nvidia to incorporate Nvidia NVLink Fusion into its data center products, a departure for RISC companies. According to Harold, other companies could be joining it. “Custom ASIC company MediaTek is an NVLink partner, and they told me last week that they are planning to integrate it directly into next-generation custom silicon for AI applications,” he said. “This will enable a wider range of companies to use NVLink as their high-speed interconnect.” Other options And, Harold noted, Nvidia is already looking at other options. “Nvidia is now shifting to look at the copper limit for networking speed, with an interest in using optical connectivity instead,” said Harold.

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Nvidia’s SchedMD acquisition puts open-source AI scheduling under scrutiny

Is the concern valid? Dr. Danish Faruqui, CEO of Fab Economics, a US-based AI hardware and datacenter advisory, said the risk was real. “The skepticism that Nvidia may prioritize its own hardware in future software updates, potentially delaying or under-optimizing support for rivals, is a feasible outcome,” he said. As the primary developer, Nvidia now controls Slurm’s official development roadmap and code review process, Faruqui said, “which could influence how quickly competing chips are integrated on new development or continuous improvement elements.” Owning the control plane alongside GPUs and networking infrastructure such as InfiniBand, he added, allows Nvidia to create a tightly vertically integrated stack that can lead to what he described as “shallow moats, where advanced features are only available or performant on Nvidia hardware.” One concrete test of that, industry observers say, will be how quickly Nvidia integrates support for AMD’s next-generation chips into Slurm’s codebase compared with how quickly it integrates its own forthcoming hardware and networking technologies, such as InfiniBand. Does the Bright Computing precedent hold? Analysts point to Nvidia’s 2022 acquisition of Bright Computing as a reference point, saying the software became optimized for Nvidia chips in ways that disadvantaged users of competing hardware. Nvidia disputed that characterization, saying Bright Computing supports “nearly any CPU or GPU-accelerated cluster.” Rawat said the comparison was instructive but imperfect. “Nvidia’s acquisition of Bright Computing highlights its preference for vertical integration, embedding Bright tightly into DGX and AI Factory stacks rather than maintaining a neutral, multi-vendor orchestration role,” he said. “This reflects a broader strategic pattern — Nvidia seeks to control the full-stack AI infrastructure experience.”

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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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Constellations

I. We had crash-landed on the planet. We were far from home. The spaceship could not be repaired, and the rescue beacon had failed. Besides

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