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Want to understand the current state of AI? Check out these charts.

EXECUTIVE SUMMARY If you’re following AI news, you’re probably getting whiplash. AI is a gold rush. AI is a bubble. AI is taking your job. AI can’t even read a clock. The 2026 AI Index from Stanford University’s Institute for Human-Centered Artificial Intelligence, AI’s annual report card, comes out today and cuts through some of that noise. Despite predictions that AI development may hit a wall, the report says that the top models just keep getting better. People are adopting AI faster than they picked up the personal computer or the internet. AI companies are generating revenue faster than companies in any previous technology boom, but they’re also spending hundreds of billions of dollars on data centers and chips. The benchmarks designed to measure AI, the policies meant to govern it, and the job market are struggling to keep up. AI is sprinting, and the rest of us are trying to find our shoes. All that speed comes at a cost. AI data centers around the world can now draw 29.6 gigawatts of power, enough to run the entire state of New York at peak demand. Annual water use from running OpenAI’s GPT-4o alone may exceed the drinking water needs of 12 million people. At the same time, the supply chain for chips is alarmingly fragile. The US hosts most of the world’s AI data centers, and one company in Taiwan, TSMC, fabricates almost every leading AI chip. The data reveals a technology evolving faster than we can manage. Here’s a look at some of the key points from this year’s report. The US and China are nearly tied In a long, heated race with immense geopolitical stakes, the US and China are almost neck and neck on AI model performance, according to Arena, a community-driven ranking platform that allows users to compare the outputs of large language models on identical prompts. In early 2023, OpenAI had a lead with ChatGPT, but this gap narrowed in 2024 as Google and Anthropic released their own models. In February 2025, R1, an AI model built by the Chinese lab DeepSeek, briefly matched the top US model, ChatGPT. As of March 2026, Anthropic leads, trailed closely by xAI, Google, and OpenAI. Chinese models like DeepSeek and Alibaba lag only modestly. With the best AI models separated in the rankings by razor-thin margins, they’re now competing on cost, reliability, and real-world usefulness. The index notes that the US and China have different AI advantages. While the US has more powerful AI models, more capital, and an estimated 5,427 data centers (more than 10 times as many as any other country), China leads in AI research publications, patents, and robotics. As competition intensifies, companies like OpenAI, Anthropic, and Google no longer disclose their training code, parameter counts, or data-set sizes. “We don’t know a lot of things about predicting model behaviors,” says Yolanda Gil, a computer scientist at the University of Southern California who coauthored the report. This lack of transparency makes it difficult for independent researchers to study how to make AI models safer, she says. AI models are advancing super fast Despite predictions that development will plateau, AI models keep getting better and better. By some measures, they now meet or exceed the performance of human experts on tests that aim to measure PhD-level science, math, and language understanding. SWE-bench Verified, a software engineering benchmark for AI models, saw top scores jump from around 60% in 2024 to almost 100% in 2025. In 2025, an AI system produced a weather forecast on its own. “I am stunned that this technology continues to improve, and it’s just not plateauing in any way,” says Gil. However, AI still struggles in plenty of other areas. Because the models learn by processing enormous amounts of text and images rather than by experiencing the physical world, AI exhibits “jagged intelligence.” Robots are still in their early days and succeed in only 12% of household tasks. Self-driving cars are farther along: Waymos are now roaming across five US cities, and Baidu’s Apollo Go vehicles are shuttling riders around in China. AI is also expanding into professional domains like law and finance, but no model dominates the field yet. But the way we test AI is broken These reports of progress should be taken with a grain of salt. The benchmarks designed to track AI progress are struggling to keep up as models quickly blow past their ceilings, the Stanford report says. Some are poorly constructed—a popular benchmark that tests a model’s math abilities has a 42% error rate. Others can be gamed: when models are trained on benchmark test data, for example, they can learn to score well without getting smarter. Because AI is rarely used the same way it’s tested, strong benchmark performance doesn’t always translate to real-world performance. And for complex, interactive technologies such as AI agents and robots, benchmarks barely exist yet. AI companies are also sharing less about how their models are trained, and independent testing sometimes tells a different story from what they report. “A lot of companies are not releasing how their models do in certain benchmarks, particularly the responsible-AI benchmarks,” says Gil. “The absence of how your model is doing on a benchmark maybe says something.” AI is starting to affect jobs Within three years of going mainstream, AI is now used by more than half of people around the world, a rate of adoption faster than the personal computer or the internet. An estimated 88% of organizations now use AI, and four in five university students use it. It’s early days for deployment, and AI’s impact on jobs is hard to measure. Still, some studies suggest AI is beginning to affect young workers in certain professions. According to a 2025 study by economists at Stanford, employment for software developers aged 22 to 25 has fallen nearly 20% since 2022. The decline might not be pinned on AI alone, as broader macroeconomic conditions could be to blame, but AI appears to be playing a part. Employers say that hiring may continue to tighten. According to a 2025 survey conducted by McKinsey & Company, a third of organizations expect AI to shrink their workforce in the coming year, particularly in service and supply chain operations and software engineering. AI is boosting productivity by 14% in customer service and 26% in software development, according to research cited by the index, but such gains are not seen in tasks requiring more judgment. Overall, it’s still too early to understand the bigger economic impact of AI. People have complicated feelings about AI Around the world, people feel both optimistic and anxious about AI: 59% of people think that it will provide more benefits than drawbacks, while 52% say that it makes them nervous, according to an Ipsos survey cited in the index. Notably, experts and the public see the future of AI very differently, according to a Pew survey. The biggest gap is around the future of work: While 73% of experts think that AI will have a positive impact on how people do their jobs, only 23% of the American public thinks so. Experts are also more optimistic than the public about AI’s impact on education and medical care, but they agree that AI will hurt elections and personal relationships. Among all countries surveyed, Americans trust their government least to regulate AI appropriately, according to another Ipsos survey. More Americans worry federal AI regulation won’t go far enough than worry it will go too far. Governments are struggling to regulate AI Governments around the world are struggling to regulate AI, but there were some minor successes last year. The EU AI Act’s first prohibitions, which ban the use of AI in predictive policing and emotion recognition, took effect. Japan, South Korea, and Italy also passed national AI laws. Meanwhile, the US federal government moved toward deregulation, with President Trump issuing an executive order seeking to handcuff states from regulating AI. Despite this federal action, state legislatures in the US passed a record 150 AI-related bills. California enacted landmark legislation, including SB 53, which mandates safety disclosures and whistleblower protections for developers of AI models. New York passed the RAISE Act, requiring AI companies to publish safety protocols and report critical safety incidents. But for all the legislative activity, Gil says, regulation is running behind the technology because we don’t really understand how it works. “Governments are cautious to regulate AI because … we don’t understand many things very well,” she says. “We don’t have a good handle on those systems.”

If you’re following AI news, you’re probably getting whiplash. AI is a gold rush. AI is a bubble. AI is taking your job. AI can’t even read a clock. The 2026 AI Index from Stanford University’s Institute for Human-Centered Artificial Intelligence, AI’s annual report card, comes out today and cuts through some of that noise.

Despite predictions that AI development may hit a wall, the report says that the top models just keep getting better. People are adopting AI faster than they picked up the personal computer or the internet. AI companies are generating revenue faster than companies in any previous technology boom, but they’re also spending hundreds of billions of dollars on data centers and chips. The benchmarks designed to measure AI, the policies meant to govern it, and the job market are struggling to keep up. AI is sprinting, and the rest of us are trying to find our shoes.

All that speed comes at a cost. AI data centers around the world can now draw 29.6 gigawatts of power, enough to run the entire state of New York at peak demand. Annual water use from running OpenAI’s GPT-4o alone may exceed the drinking water needs of 12 million people. At the same time, the supply chain for chips is alarmingly fragile. The US hosts most of the world’s AI data centers, and one company in Taiwan, TSMC, fabricates almost every leading AI chip.

The data reveals a technology evolving faster than we can manage. Here’s a look at some of the key points from this year’s report.

The US and China are nearly tied

In a long, heated race with immense geopolitical stakes, the US and China are almost neck and neck on AI model performance, according to Arena, a community-driven ranking platform that allows users to compare the outputs of large language models on identical prompts. In early 2023, OpenAI had a lead with ChatGPT, but this gap narrowed in 2024 as Google and Anthropic released their own models. In February 2025, R1, an AI model built by the Chinese lab DeepSeek, briefly matched the top US model, ChatGPT. As of March 2026, Anthropic leads, trailed closely by xAI, Google, and OpenAI. Chinese models like DeepSeek and Alibaba lag only modestly. With the best AI models separated in the rankings by razor-thin margins, they’re now competing on cost, reliability, and real-world usefulness.

Chart of the performance of top models on the Arena by select providers, showing the Arena score from May 2023 to Jan 2026 with the models all trending upward. The scores are tightly packed by US based Anthropic, xAI, Google and OpenAI lead Alibaba, DeepSeek and Mistral (in that order.) Meta trails the pack.

The index notes that the US and China have different AI advantages. While the US has more powerful AI models, more capital, and an estimated 5,427 data centers (more than 10 times as many as any other country), China leads in AI research publications, patents, and robotics.

As competition intensifies, companies like OpenAI, Anthropic, and Google no longer disclose their training code, parameter counts, or data-set sizes. “We don’t know a lot of things about predicting model behaviors,” says Yolanda Gil, a computer scientist at the University of Southern California who coauthored the report. This lack of transparency makes it difficult for independent researchers to study how to make AI models safer, she says.

AI models are advancing super fast

Despite predictions that development will plateau, AI models keep getting better and better. By some measures, they now meet or exceed the performance of human experts on tests that aim to measure PhD-level science, math, and language understanding. SWE-bench Verified, a software engineering benchmark for AI models, saw top scores jump from around 60% in 2024 to almost 100% in 2025. In 2025, an AI system produced a weather forecast on its own.

“I am stunned that this technology continues to improve, and it’s just not plateauing in any way,” says Gil.

line chart of Select AI Index technical performance benchmarks vs human performance, showing that skills such as image classification, English language understanding, multitask language understanding, visual reasoning, medium level reading comprehension, multimodal understanding and reasoning have surpassed the human baseline at or before 2025, with autonomous software engineering, mathmatical reasoning and agent multimodal computer use trending towards meeting the human baseline by 2026.

However, AI still struggles in plenty of other areas. Because the models learn by processing enormous amounts of text and images rather than by experiencing the physical world, AI exhibits “jagged intelligence.” Robots are still in their early days and succeed in only 12% of household tasks. Self-driving cars are farther along: Waymos are now roaming across five US cities, and Baidu’s Apollo Go vehicles are shuttling riders around in China. AI is also expanding into professional domains like law and finance, but no model dominates the field yet.

But the way we test AI is broken

These reports of progress should be taken with a grain of salt. The benchmarks designed to track AI progress are struggling to keep up as models quickly blow past their ceilings, the Stanford report says. Some are poorly constructed—a popular benchmark that tests a model’s math abilities has a 42% error rate. Others can be gamed: when models are trained on benchmark test data, for example, they can learn to score well without getting smarter.

Because AI is rarely used the same way it’s tested, strong benchmark performance doesn’t always translate to real-world performance. And for complex, interactive technologies such as AI agents and robots, benchmarks barely exist yet.

AI companies are also sharing less about how their models are trained, and independent testing sometimes tells a different story from what they report. “A lot of companies are not releasing how their models do in certain benchmarks, particularly the responsible-AI benchmarks,” says Gil. “The absence of how your model is doing on a benchmark maybe says something.”

AI is starting to affect jobs

Within three years of going mainstream, AI is now used by more than half of people around the world, a rate of adoption faster than the personal computer or the internet. An estimated 88% of organizations now use AI, and four in five university students use it.

It’s early days for deployment, and AI’s impact on jobs is hard to measure. Still, some studies suggest AI is beginning to affect young workers in certain professions. According to a 2025 study by economists at Stanford, employment for software developers aged 22 to 25 has fallen nearly 20% since 2022. The decline might not be pinned on AI alone, as broader macroeconomic conditions could be to blame, but AI appears to be playing a part.

two line charts showing the normalized headcount trends by age group from 2021 through 2025. On the left for software developers the early career (age 22-25) cohort drops rapidly after a peak in September 2022, with other ages still rising albeit less steeply. On the right, customer support agents see a similar trend, although the decline for the early career group is less steep than for software developers.

Employers say that hiring may continue to tighten. According to a 2025 survey conducted by McKinsey & Company, a third of organizations expect AI to shrink their workforce in the coming year, particularly in service and supply chain operations and software engineering. AI is boosting productivity by 14% in customer service and 26% in software development, according to research cited by the index, but such gains are not seen in tasks requiring more judgment. Overall, it’s still too early to understand the bigger economic impact of AI.

People have complicated feelings about AI

Around the world, people feel both optimistic and anxious about AI: 59% of people think that it will provide more benefits than drawbacks, while 52% say that it makes them nervous, according to an Ipsos survey cited in the index.

Notably, experts and the public see the future of AI very differently, according to a Pew survey. The biggest gap is around the future of work: While 73% of experts think that AI will have a positive impact on how people do their jobs, only 23% of the American public thinks so. Experts are also more optimistic than the public about AI’s impact on education and medical care, but they agree that AI will hurt elections and personal relationships.

Bar chart of US perceptions of AI's societal impact contrasting US adults with AI experts, with the percentage of AI experts saying that AI will have a positive impact in the next 20 years is 2-3 times higher than the US adults. The most optimistic AI experts are in the field of medical care with 84% predicting a positive outcome (versus 44% of US adults.) The greatest difference is for jobs with experts polling at 73% and US adults polling at 23%. Both groups have a similar (11% for experts and 9% of adults.) expectation for a positive outcome for AI in elections.

Among all countries surveyed, Americans trust their government least to regulate AI appropriately, according to another Ipsos survey. More Americans worry federal AI regulation won’t go far enough than worry it will go too far.

Governments are struggling to regulate AI

Governments around the world are struggling to regulate AI, but there were some minor successes last year. The EU AI Act’s first prohibitions, which ban the use of AI in predictive policing and emotion recognition, took effect. Japan, South Korea, and Italy also passed national AI laws. Meanwhile, the US federal government moved toward deregulation, with President Trump issuing an executive order seeking to handcuff states from regulating AI.

Despite this federal action, state legislatures in the US passed a record 150 AI-related bills. California enacted landmark legislation, including SB 53, which mandates safety disclosures and whistleblower protections for developers of AI models. New York passed the RAISE Act, requiring AI companies to publish safety protocols and report critical safety incidents.

line chart showing the number of AI-related bills passed into law by all US states from 2016-2025, which increases sharply in 2023 and peaks with 150 bills in 2025.

But for all the legislative activity, Gil says, regulation is running behind the technology because we don’t really understand how it works. “Governments are cautious to regulate AI because … we don’t understand many things very well,” she says. “We don’t have a good handle on those systems.”

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SATORP halts processing activities at Jubail refinery

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AI demand is so high, AWS customers are trying to buy out its entire capacity

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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

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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

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

Global oil markets have entered a period of acute volatility, with prices expected to surge into second-quarter 2026 as war-driven supply disruptions in the Middle East constrain flows through the Strait of Hormuz, according to the US Energy Information Administration (EIA)’s April Short-Term Energy Outlook. The agency estimates that Brent crude averaged $103/bbl in March and will climb further to a quarterly peak of about $115/bbl in second-quarter 2026, reflecting a sharp tightening in global supply following widespread production shut-ins across key Gulf producers. The disruption stems from the effective closure of the Strait of Hormuz, a critical chokepoint that typically carries nearly 20% of global oil supply. The US-Iran war in the region has forced producers including Saudi Arabia, Iraq, Kuwait, and the UAE to curtail output significantly. EIA estimates that crude production shut-ins averaged 7.5 million b/d in March and will rise to a peak of 9.1 million b/d in April. In this outlook, EIA assumes the conflict does not persist past April and that traffic through the Strait of Hormuz gradually resumes. Under those assumptions, EIA expects production shut-ins will fall to 6.7 million b/d in May and return close to pre-conflict levels in late 2026. The scale of the outage has rapidly flipped the market from prior expectations of oversupply into a pronounced deficit, with global inventories drawing sharply during the second quarter. Despite an assumption that the conflict does not persist beyond April, the agency warns that supply chains will take months to normalize, keeping a geopolitical risk premium embedded in prices through late 2026. EIA forecasts the Brent crude oil price will fall below $90/bbl in fourth-quarter 2026 and average $76/bbl in 2027, about $23/bbl higher than in its February STEO forecast. This price forecast is highly dependent on EIA’s assumptions of both the

Hillwood, PowerHouse Advance $20B Joliet Data Campus as Midwest AI Buildout Accelerates

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AI is a Positive Catalyst for Grid Growth

Data centers, particularly those optimized for artificial intelligence workloads, are frequently characterized in public discourse as a disruptive threat to grid stability and ratepayer affordability. But behind-the-narrative as we are, the AI‑driven data center growth is simply illuminating pre‑existing systemic weaknesses in electric infrastructure that have accumulated over more than a decade of underinvestment in transmission, substations, and interconnection capacity. Over the same period, many utilities operated under planning assumptions shaped by slow demand growth and regulatory frameworks that incentivized incremental upgrades rather than large, anticipatory capital programs. As a result, the emergence of gigawatt‑scale computing campuses appears to be a sudden shock to a system that, in reality, was already misaligned with long‑term decarbonization, electrification, and digitalization objectives. Utilities have been asked to do more with aging grids, slow permitting, and chronically constrained capital, and now AI and cloud are finally putting real urgency — and real investment — behind modernizing that backbone. In that sense, large‑scale compute is not the problem; it is the catalyst that makes it impossible to ignore the problem any longer. We are at a moment when data centers, and especially AI data centers, are being blamed for exposing weaknesses that were already there, when in reality they are giving society a chance to fix a power system that has been underbuilt for more than a decade. Utilities have been asked to do more with aging grids, slow permitting, and limited investment, and now AI and cloud are finally putting real urgency — and real capital — behind modernizing that backbone. In that sense, data centers aren’t the problem; they are the catalyst that makes it impossible to ignore the problem any longer. AI Demand Provided a Long‑Overdue Stress Test The nature of AI workloads intensified this dynamic. High‑performance computing clusters concentrate substantial power

From Land Grab to Structured Scale: Kirkland & Ellis Explains How Capital, Power, and Deal Complexity Are Defining the AI Data Center Boom

The AI data center market is no longer defined by speed alone. For much of the past three years, capital moved aggressively into digital infrastructure, chasing land, power, and platform scale as generative AI workloads began to reshape demand curves. But as Melissa Kalka, M&A and private equity partner, and Kimberly McGrath, real estate partner at Kirkland & Ellis, explain on the latest episode of the Data Center Frontier Show, the industry is now entering a more complex and more consequential phase. The land grab is over. Execution has begun. Capital remains abundant, but it is no longer forgiving. From Capital Rush to Capital Discipline As noted by Kalka and McGrath, the period from roughly 2022 through 2025 marked a rapid acceleration in AI infrastructure investment. Take-private deals involving CyrusOne, QTS, and Switch signaled a structural shift, while hyperscale demand scaled from tens of megawatts to hundreds, and now toward gigawatt-class campuses. But the current phase is not defined by a pullback in capital. Instead, it reflects an expansion of investment pathways and a corresponding increase in scrutiny. “There’s actually more deal flow now,” Kalka notes, pointing to the growing range of entry points across the capital stack, including development vehicles, yield-oriented structures, and private credit. With more capital chasing larger and more complex opportunities, investors are evaluating not just platforms, but the full lifecycle of assets from early-stage development through stabilization and long-term hold. That shift has pulled capital earlier into the process, where risk is higher and less defined. Power availability, permitting, and execution timelines are now central to underwriting decisions. What Defines a “Bankable” Platform In this environment, the definition of a bankable data center platform has tightened. Execution history remains foundational. Investors are looking for consistent delivery, operational reliability, and clean contractual performance. But those factors alone

CoreWeave and Bell Canada Reset AI Data Center Scale

From GPU Cloud to AI Factory Operator In sum, CoreWeave is moving beyond its origins as a fast-scaling GPU cloud built on scarcity. The company is increasingly positioning itself as an AI infrastructure operator, where competitive advantage comes from integration across hardware, networking, cooling, platform software, workload orchestration, and early access to NVIDIA’s latest systems. That positioning has been reinforced by NVIDIA itself. In January, NVIDIA outlined a deeper alignment with CoreWeave focused on building AI factories, accelerating the procurement of land, power, and shell, and validating CoreWeave’s AI-native software and reference architecture. The partnership also includes deployment of multiple generations of NVIDIA infrastructure across CoreWeave’s platform, including Rubin systems, Vera CPUs, and BlueField data processing units, alongside a $2 billion equity investment. No simple vendor relationship, this is co-development around physical AI infrastructure. Bell Canada and the Rise of Sovereign AI Capacity Viewed through that lens, Bell Canada’s Saskatchewan announcement can be seen as part of the same structural shift. On March 16, Bell and the Government of Saskatchewan unveiled plans for a 300 MW AI Fabric data center in the Rural Municipality of Sherwood, outside Regina. CoreWeave is expected to anchor the site’s NVIDIA-based GPU infrastructure, extending its AI-native platform into a sovereign, hyperscale, power-dense environment. BCE described the project as its largest-ever investment in the province and said it is expected to become Canada’s largest purpose-built AI data center campus. Bell projects up to $12 billion (CDN) in long-term economic impact, along with at least 800 construction jobs and a minimum of 80 permanent roles once the site is operational. More importantly, Bell is explicitly framing the development as a foundation for domestic compute capacity, positioning AI infrastructure as a national asset tied to economic growth and technological sovereignty. That project extends Bell’s broader sovereign AI strategy.

From Reactor Designs to Real Projects: SMRs Enter the Execution Era as AI Power Demand Accelerates

The pattern emerging is clear. The SMR story is no longer about reactor design. Recent announcements are centered on permits, fuel, supply chains, financing, and customer traction, i.e. the factors that determine whether SMRs become a viable market or remain a technology narrative. The conversation has transitioned from technically compelling reactor concepts to the harder problem of industrial execution. Through the first quarter of 2026, and especially in March, vendors moved beyond partnership announcements to concrete progress in licensing, fuel access, supply-chain development, control systems, customer alignment, and capital formation. The distinction now is between companies building credible deployment pathways and those still positioned around long-dated opportunity. At a high level, these developments fall into three categories. First, regulatory progress: the most difficult and time-consuming milestone. Second, efforts to establish manufacturing and fuel ecosystems that can support repeatable, fleet-scale deployment. Third, a broad repositioning toward power-intensive industrial users, utilities, and increasingly data center–driven load growth. The result is an SMR market that looks less like a single competitive race and more like a set of parallel business models converging on the same objective: dispatchable, carbon-free power that can be financed and deployed with greater predictability than traditional gigawatt-scale nuclear. X-energy: Building a Commercial Path to Scale X-energy has emerged as one of the more credible commercialization stories in the SMR market, with recent moves spanning capital markets, customer development, and supply-chain expansion. Reuters reported on March 20 that the company has confidentially filed for an IPO, aiming to capitalize on renewed investor interest in nuclear and rising electricity demand tied to AI infrastructure. That filing followed closely on an agreement with Talen Energy to evaluate multiple four-unit Xe-100 deployments across U.S. power markets, as well as a MOU with Japan’s IHI to expand U.S.-Japan supply chain capabilities for the reactor.

DCF Poll: Data Centers and the Public Trust Gap

Matt Vincent is Editor in Chief of Data Center Frontier, where he leads editorial strategy and coverage focused on the infrastructure powering cloud computing, artificial intelligence, and the digital economy. A veteran B2B technology journalist with more than two decades of experience, Vincent specializes in the intersection of data centers, power, cooling, and emerging AI-era infrastructure. Since assuming the EIC role in 2023, he has helped guide Data Center Frontier’s coverage of the industry’s transition into the gigawatt-scale AI era, with a focus on hyperscale development, behind-the-meter power strategies, liquid cooling architectures, and the evolving energy demands of high-density compute, while working closely with the Digital Infrastructure Group at Endeavor Business Media to expand the brand’s analytical and multimedia footprint. Vincent also hosts The Data Center Frontier Show podcast, where he interviews industry leaders across hyperscale, colocation, utilities, and the data center supply chain to examine the technologies and business models reshaping digital infrastructure. Since its inception he serves as Head of Content for the Data Center Frontier Trends Summit. Before becoming Editor in Chief, he served in multiple senior editorial roles across Endeavor Business Media’s digital infrastructure portfolio, with coverage spanning data centers and hyperscale infrastructure, structured cabling and networking, telecom and datacom, IP physical security, and wireless and Pro AV markets. He began his career in 2005 within PennWell’s Advanced Technology Division and later held senior editorial positions supporting brands such as Cabling Installation & Maintenance, Lightwave Online, Broadband Technology Report, and Smart Buildings Technology. Vincent is a frequent moderator, interviewer, and keynote speaker at industry events including the HPC Forum, where he delivers forward-looking analysis on how AI and high-performance computing are reshaping digital infrastructure. He graduated with honors from Indiana University Bloomington with a B.A. in English Literature and Creative Writing and lives in southern New Hampshire with

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

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

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

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

2025 playbook for enterprise AI success, from agents to evals

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

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

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

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