Stay Ahead, Stay ONMINE

The rise of browser-use agents: Why Convergence’s Proxy is beating OpenAI’s Operator

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A new wave of AI-powered browser-use agents is emerging, promising to transform how enterprises interact with the web. These agents can autonomously navigate websites, retrieve information, and even complete transactions – but early testing reveals significant […]

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More


A new wave of AI-powered browser-use agents is emerging, promising to transform how enterprises interact with the web. These agents can autonomously navigate websites, retrieve information, and even complete transactions – but early testing reveals significant gaps between promise and performance.

While consumer examples offered by OpenAI’s new browser-use agent Operator, like ordering pizza or buying game tickets, have grabbed headlines, the question is about where the main developer and enterprise use cases are. “The thing that we don’t know is what will be the killer app,” said Sam Witteveen, co-founder of Red Dragon, a company that develops AI agent applications. “My guess is it’s going to be things that just take time on the web that you don’t actually enjoy.” This includes things like going on the web and searching for the cheapest price of a product or booking the best hotel accommodations. More likely it will be used in combination with other tools like Deep Research, where companies can then do even more sophisticated research plus execution of tasks around the web.

Companies need to carefully evaluate the rapidly evolving landscape as established players and startups take different approaches to solving the autonomous browsing challenge.

Key players in the browser-use agent landscape

The field has quickly become crowded with both major tech companies and innovative startups:

Operator and Proxy are the most advanced, in terms of being consumer-friendly and out-of-the-box ready. Many of the others appear to be positioning themselves more for developer or enterprise usage. For example, Browser Use, a Y-Combinator startup that allows users to customize the models used with the agent. This gives you more control over how the agent works, including using a model from your local machine. But it’s definitely more involved.

The others listed above provide a varying degree of functionality and interaction with local machine resources. I decided not even to test ByteDance’s UI-TARS for now, because it requested lower level access to my machine’s security and privacy features (if I test it out, I’ll definitely use a secondary computer). 

Testing reveals reasoning challenges

So the easiest to test are OpenAI’s Operator and Convergence’s Proxy. In our testing, the results highlighted how reasoning capabilities can matter more than raw automation features. Operator, in particular, was more buggy.

For example, I asked the agents to find and summarize VentureBeat’s five most popular stories. It was an ambiguous task, because VentureBeat doesn’t have a “most popular” section per se. Operator struggled with this. It first fell into an infinite scrolling loop while searching for ‘most popular’ stories, requiring manual intervention. In another attempt, it found a three-year-old article titled “Top five stories of the week.” In contrast, Proxy demonstrated better reasoning by identifying the five most visible stories on the homepage as a practical proxy for popularity, and it gave accurate summaries.

The distinction became even clearer in real-world tasks. I asked the agents to book a reservation at a romantic restaurant for noon in Napa, California. Operator approached the task linearly — finding a romantic restaurant first, then checking availability at noon. When no tables were available, it reached a dead end. Proxy showed more sophisticated reasoning by starting with OpenTable to find restaurants that were both romantic and available at the desired time. It even came back with a slightly better rated restaurant.

Even seemingly simple tasks revealed important differences. When searching for a “YubiKey 5C NFC price” on Amazon, Proxy quickly found the item more easily than Operator. 

OpenAI hasn’t divulged much about technologies it uses for training its Operator agent, other than saying it has trained its model on browser-use tasks. Convergence, however, has provided more detail: Its agent uses something called Generative Tree Search to “leverage Web-World Models that predict the state of the web after a proposed action has been taken. These are generated recursively to produce a tree of possible futures that are searched over to select the next optimal action, as ranked by our value models. Our Web-World models can also be used to train agents in hypothetical situations without generating a lot of expensive data.” (More here).

Benchmarks may be useless for now

On paper, these tools appear closely matched. Convergence’s Proxy achieves 88% on the WebVoyager benchmark, which evaluates web agents across 643 real-world tasks on 15 popular websites like Amazon and Booking.com. OpenAI’s Operator scores 87%, while Browser-Use says it reaches 89% but only after changing the WebVoyager codebase slightly, it conceded, “according to our needs”.

These benchmark scores should really be taken with a grain of salt, though, as they can be gamed. The real test comes in practical usage for real-world cases. It’s very early, the space is so rapidly changing, and these products are changing almost on a daily basis. The results will depend more on the specific jobs you’re trying to do, and you may want to instead rely on the vibes you get while using the different products.

Enterprise implications

The implications for enterprise automation are significant. As Witteveen points out in our video podcast conversation about this, where we do a deep dive into this browser-use trend, many companies are currently paying for virtual assistants – operated by real people – to handle basic web research and data gathering tasks. These browser-use agents could dramatically change that equation.

“If AI takes this over,” Witteveen notes, “that’s going to be some of the first low hanging fruit of people losing their jobs. It’s going to show up in some of these kinds of things.”

This could feed into the robotic process automation (RPA) trend, where browser use is pulled in as just another tool for companies to automate more tasks. And as mentioned earlier, the more powerful uses cases will be when an agent combined browser use with other tools, including things like Deep Research, where an LLM-driven agent uses a search tool plus browser use to do more sophisticated jobs.

Cost dynamics driving innovation

Another key factor driving rapid development is the availability of powerful open-source reasoning models like DeepSeek-R1. This allows companies building these browser-use agents to compete effectively with larger players by leveraging these models rather than building their own.

The pricing pressure is already evident. While OpenAI requires a $200 monthly ChatGPT Pro subscription to access Operator, Convergence offers limited free use (up to five uses per day) and a $20/month unlimited plan. This competitive dynamic should accelerate enterprise adoption, though clear use cases are still emerging.

Security and integration challenges

Several hurdles remain before widespread enterprise adoption. Some websites actively block automated browsing, while others require CAPTCHA verification. While OpenAI and Convergence have tools that can get past CAPTCHAs, they let users take over the task to fill them out — instead of doing them directly, since the whole point of CAPTCHAs is to ensure a human is at the other end. Tools like ByteDance’s UI-TARS request deep system access, which raises security concerns for enterprise deployment.

Additionally, the approach to website cooperation varies. OpenAI has worked with specific partners like Instacart, Priceline, DoorDash and Etsy, while others attempt to navigate any website. This inconsistency could impact reliability for enterprise use cases. And of course, any time an agent hits a site requiring login details, that will slow things — as the agents will turn things over to you to fill in those details.

Looking ahead

For enterprises evaluating these tools, the focus should be on specific use cases where autonomous web interaction could provide clear value – whether in research, customer service, or process automation. The technology is progressing rapidly, but success will depend on matching capabilities to concrete business needs.

As this space evolves, expect to see more enterprise-focused features and potentially specialized agents for specific industries or tasks. The race between established players and innovative startups should drive both technical advancement and competitive pricing, making 2025 a crucial year for enterprise browser-use agent adoption.

For more detail on these trends and testing results, check out the full video conversation between Sam Witteveen and myself.

Shape
Shape
Stay Ahead

Explore More Insights

Stay ahead with more perspectives on cutting-edge power, infrastructure, energy,  bitcoin and AI solutions. Explore these articles to uncover strategies and insights shaping the future of industries.

Shape

Arista debuts unified SD-WAN edge platform

“Multi-vendor branch complexity creates the ultimate blind spot, and your adversaries are actively hiding in it,” wrote Brendan Gibbs, Arista’s vice president, AI, routing, and switching platforms, in a blog post about the new platform. Sprawling multi-vendor infrastructure creates operational headaches and increases security risks, according to Gibbs. “When you

Read More »

Helios marks AMD’s biggest AI infrastructure push yet

The architecture behind Helios The launch of Helios marks AMD’s latest attempt to strengthen its position in a market where Nvidia continues to dominate AI infrastructure. Unlike previous AMD AI offerings centred on individual accelerators, Helios is designed as a complete rack-scale system integrating compute, networking and software. According to

Read More »

United States and Saudi Arabia Reach Historic Nuclear Cooperation Agreement

WASHINGTON—U.S. Secretary of Energy Chris Wright and Saudi Minister of Energy His Royal Highness (HRH) Prince Abdulaziz bin Salman signed a peaceful nuclear cooperation agreement, commonly known as a 123 agreement, alongside an accompanying bilateral safeguards agreement. Together, these two agreements lay the legal foundation for a decades-long, multi-billion-dollar partnership that advances several priority economic and strategic objectives, including nuclear nonproliferation. The 123 agreement provides great access for American companies in the Saudi nuclear energy program, benefiting American industry, workers, and supply chains while helping to meet Saudi energy needs. The two agreements also advance U.S. and regional security by upholding high standards of nuclear safety, security, and nonproliferation and strengthening the United States’ competitive edge in civil nuclear technology. “These agreements reflect our two nations’ shared commitment to strengthening U.S.-Saudi commercial relations, delivering prosperity at home and security to our allies abroad,” said Secretary Wright. “Rest assured, these agreements uphold the highest standards of nuclear safety and nonproliferation, while relying on the world’s best nuclear technology and scientists, designed right here in the United States. Thanks to President Trump, the American nuclear renaissance is underway and will deliver long-term benefits to the American and Saudi people.” Under President Trump’s leadership, America is restoring its competitive edge in the global civil nuclear marketplace. This agreement builds on President Trump’s Executive Order, Deploying Advanced Nuclear Reactor Technologies for National Security, and specifically Section 8 on Promoting American Nuclear Exports, which supports an expansion of international partners for U.S. civil nuclear cooperation under Section 123 of the Atomic Energy Act of 1954, as amended. This partnership will: Expand American nuclear technology exports Create high-paying U.S. jobs and long-term economic growth Strengthen America’s energy and national security posture Reinforce global nonproliferation standards Deepen the strategic partnership between the United States and the Kingdom of

Read More »

Secretary of Energy Chris Wright Announces First Genesis Mission Projects Selected to Accelerate AI-Driven Scientific Discovery

WASHINGTON—The U.S. Department of Energy (DOE) today announced the first projects selected under the Genesis Mission Request for Applications (RFA) as part of President Trump’s historic Genesis Mission. The national portfolio of research teams will help develop and demonstrate AI-enabled scientific workflows designed to accelerate breakthroughs in energy, discovery science, and national security. Designed to double America’s scientific productivity, the Genesis Mission brings together DOE’s world-class scientific capabilities, advanced AI, high-performance computing, and the nation’s leading researchers to transform how scientific discovery is conducted and strengthen American leadership in science and technology. “America has no shortage of bold ideas or talented scientists, and the response to the Genesis Mission proves that,” said U.S. Secretary of Energy Chris Wright. “The 278 projects selected today represent the very best of our nation’s scientific enterprise. The remarkable number of high-quality proposals we received demonstrates that America’s innovation pipeline is strong, and it points to even greater opportunities for future investment and continued expansion of the Genesis Mission portfolio.” The Genesis Mission RFA generated the largest response to a funding opportunity in DOE history. Following a rigorous merit review process, the selected projects represent: 278 awards: 87 led by DOE and National Nuclear Security Administration (NNSA) National Laboratories, 168 led by universities, 19 led by companies, and 4 led by nonprofit organizations. 342 participating institutions: 16 DOE and NNSA National Laboratories, 142 universities, 157 companies, 13 nonprofit organizations, and 14 other institutions. These projects will address some of the nation’s most pressing energy, scientific, and engineering challenges, including in nuclear energy, critical mineral extraction, intelligent chip design, and commercial fusion energy.  Among the selected projects, the largest is a three-year, $60 million investment in nuclear energy that will harness AI to help deliver nuclear facilities faster and safer while cutting operating costs to provide Americans with affordable, reliable, and secure energy.

Read More »

DOE and DOL Partner to Advance Mining Innovation and Safety

WASHINGTON—The U.S. Department of Energy (DOE) and the U.S. Department of Labor (DOL) today signed a Memorandum of Understanding (MOU) establishing a framework to accelerate the deployment of artificial intelligence (AI), automation, advanced sensors, and other emerging technologies across the nation’s mining sector. The five-year agreement strengthens federal coordination to advance mining innovation while improving worker safety, increasing productivity, and supporting the secure domestic production of critical minerals. By combining DOE’s expertise in energy technologies and resource recovery with DOL’s longstanding leadership in mine safety, the partnership advances the Trump Administration’s commitment to strengthen critical mineral supply chains, support high-paying American jobs, and unleash American energy dominance “America’s security and economic future depend on developing a strong domestic mining sector,” said U.S. Secretary of Energy Chris Wright. “By pairing the Energy Department’s technical expertise with the Labor Department’s leadership on mine safety, we can support American miners, secure domestic supply chains, and put cutting-edge technology to work for the people who power our nation.” “Today’s agreement ensures that the Department of Labor and the Department of Energy will work side by side to prepare the mining workforce, advance mining technology, and support the safe production of the coal that powers America’s future,” said Acting Secretary of Labor Keith Sonderling. “It is our commitment to you that this MOU will further President Trump’s promise to restore coal as a key driver of America’s energy supply chain and American coal will again be the envy of the world for generations to come.” Under the agreement, DOE’s Hydrocarbons and Geothermal Energy Office (HGEO) and Office of Critical Minerals and Energy Innovation (CMEI) will collaborate closely with DOL’s Mine Safety and Health Administration (MSHA) to share non-proprietary data, research, and technical expertise that supports the deployment of next-generation mining technologies. The partnership will focus

Read More »

Energy Secretary Secures Grid Amid Period of Hot Weather

WASHINGTON—The U.S. Department of Energy (DOE) issued an emergency order to mitigate blackout risks and keep Americans powered during the region’s energy emergency brought on by hot weather conditions. The order directs the Southwest Power Pool, Inc. (SPP) to dispatch specified units and to order their operation as needed to maintain reliability. The order also authorizes SPP to direct backup generation resources to operate as a last resort before declaring an Energy Emergency Alert (EEA) 3 or during an EEA 3. The order was issued pursuant to a request from SPP. “The Trump Administration is tapping into an abundant supply of unused backup generation to maintain affordable, reliable, and secure power for hardworking American families and businesses,” said U.S. Secretary of Energy Chris Wright. “The previous administration’s energy subtraction policies weakened the grid, leaving Americans more vulnerable during emergency events. Thanks to President Trump’s leadership, we are reversing those failures and using every available tool to ensure Americans have continued access to affordable, reliable, and secure energy to power and cool their homes.”  DOE estimates more than 35 gigawatts (GW) of unused backup generation remains available nationwide.   On day one of his second term, President Trump declared a national energy emergency after the Biden administration’s energy subtraction agenda left behind a grid increasingly vulnerable to blackouts.   Power outages cost the American people $44 billion per year, according to data from DOE’s National Laboratories. This order mitigates the possibility of power outages in the region and highlights the common sense policies of the Trump Administration to ensure Americans have access to affordable, reliable, and secure power. The order was effective upon issuance on July 20, 2026, and shall expire at 11:59 PM ET on July 21, 2026. 

Read More »

S&P Global: Hormuz vessel transits fall amid heightened security risks

Vessel traffic through the Strait of Hormuz remained subdued July 10-12 as heightened regional security risks continued to weigh on movements through the strategic waterway, according to S&P Global MINT and S&P Global Commodities at Sea data. A total of 73 vessels transited the strait during the 3-day period, averaging fewer than 25 crossings/day. Transits fell to 11 on July 12, the lowest since June 14, after Iran declared the strait closed amid what the Persian Gulf Strait Authority described as “illegal movements” of US military forces in the region. No inbound crossings were recorded July 12, the first such occurrence since June 12. Six of the day’s 11 transits were assessed as compliant vessels. Total crossings were 32 on July 10 and 30 on July 11. The Joint Maritime Information Center (JMIC) said July 12 that the regional threat level remained severe. Despite Iran’s closure declaration, JMIC said the southern route remained available and had been expanded for two-way vessel traffic. Energy carriers—including oil, chemical, LPG, and LNG tankers—accounted for about 48% of transits July 10-12. About two-thirds of energy-carrier crossings involved compliant vessels, although only 10 compliant energy carriers entered the Persian Gulf, mostly without visible automatic identification system (AIS) signals. Inbound tanker capacity also softened. An average 6.5 million b/d of new oil and LPG tanker capacity entered the Gulf through Hormuz July 1-12, with VLCCs and Suezmaxes accounting for nearly 80%. Average inbound capacity fell to 6 million b/d July 10-12 from 8.5 million b/d in the first week of July. All compliant outbound energy carriers transiting Hormuz during the 3-day period did so without visible AIS signals, including ADNOC-operated LNG carrier AL HAMRA and several VLCC and product tankers. Iran-linked and US-sanctioned vessels accounted for nearly 60% of all crossings during the period.

Read More »

Beyond AI Pilots: Scaling AI-Enabled Decision Making in Energy

Date: Thursday, August 6, 2026Time: 11:00 AM (GMT-04:00) Eastern Time – New YorkDuration: 60 minutes Already registered? Click here to log in now. Artificial Intelligence is rapidly becoming a strategic priority across industrial organizations, yet many companies continue to struggle with fragmented data, disconnected workflows, and AI initiatives that never move beyond pilot projects. The challenge is not access to AI—it is creating the business context, governance, and lifecycle intelligence needed to transform AI insights into measurable operational outcomes. Join Siemens Digital Industries Software to learn how Intelligence Center X, part of the Siemens Xcelerator portfolio, helps organizations connect enterprise data, workflows, and AI capabilities into a single governed environment where people and AI work together to drive faster, more informed decisions. In this session, we’ll explore how organizations can: • Move beyond isolated AI experiments to enterprise-scale deployment • Connect engineering, manufacturing, operations, supply chain, and service data into a unified intelligence framework • Enable AI agents to operate within governed, human-in-the-loop business processes • Improve operational performance through AI-assisted decision-making • Accelerate issue resolution, reduce manual effort, and increase organizational agility Attendees will also learn how Intelligence Center X combines lifecycle intelligence, industrial data models, AI orchestration, and low-code application development to create production-ready AI solutions that deliver measurable business value. Real-world examples will demonstrate how organizations have achieved significant improvements, including reductions in manual effort, faster issue resolution, improved data quality, and enhanced decision-making capabilities. Whether you are responsible for digital transformation, operations, manufacturing, engineering, or executive strategy, this webinar will provide practical insight into building a scalable foundation for industrial AI and creating a future where people and AI work together to drive business outcomes.

Read More »

10 Reasons You Cannot Afford to Miss DCF Trends Summit 2026

The data center industry has no shortage of AI infrastructure ambition. What it lacks is certainty. Power is harder to secure. Designs are advancing faster than facilities can be built. Supply chains remain vulnerable. Liquid cooling is adding operational demands. Projects that look viable on paper can still stall on permitting, commissioning or community opposition. The question in 2026 is no longer how large the AI opportunity may become. It is what can actually be delivered, and who has learned how to deliver it. That question defines the 2026 Data Center Frontier Trends Summit, August 4–6 at the Hyatt Regency Reston. Across three days, the people building, powering, financing and operating next-generation infrastructure will examine what is working, where execution is failing and how the market is responding. This is not a conference about whether AI will create demand. It is about who will be able to meet it. The advantage will belong to those who join the conversation before its conclusions become market consensus. Here are 10 reasons to be in the room. 1. The industry has entered the execution era For several years, the market has been defined by projected demand, capacity, density and investment. The next phase will be defined by execution. AI data center announcements remain abundant. Energized, commissioned and operational capacity is harder to find. DCFTS begins with a live editorial calibration, followed by “The New Geography of AI,” featuring EdgeCore CEO Lee Kestler, Data Center Frontier founder Rich Miller and DCF Editor in Chief Matt Vincent. The focus: how power, entitled land, utility partnerships and execution speed are determining where AI capacity can be built—and who can deliver it. Demand creates opportunity. Execution determines who captures it. 2. Power will be treated as the foundation of AI strategy Power is no longer one workstream

Read More »

Time to Power: Sage Geosystems CEO Cindy Taff on Geothermal’s AI Infrastructure Moment

Three years ago, the data center industry’s energy conversation was largely framed around emissions. Hyperscale operators were setting carbon-free energy targets, signing renewable power agreements, and aligning their expanding infrastructure portfolios with corporate sustainability commitments. The arrival of generative AI has not eliminated those priorities. But it has reordered them. “Three years ago, data center energy, they were really focused on low emissions, no emissions,” said Cindy Taff, CEO of Sage Geosystems. “Now the primary challenge is just enough energy.” Speaking on the Data Center Frontier Show podcast, Taff described an energy market being reshaped by the speed and physical scale of AI infrastructure development. After decades of relatively flat U.S. electricity demand, AI has introduced a new class of concentrated, rapidly arriving industrial load. The result is a shift away from thinking only about how much generating capacity exists in aggregate and toward a harder question: Can usable power be delivered at a specific site, on a predictable schedule, in the quantities an AI campus requires? For hyperscalers, neocloud providers, data center developers, utilities, and energy companies, that distinction is becoming central to project execution. “I think time to power is the most precious metric right now versus cost or total capacity,” Taff said. Capacity on Paper Is Not Power at the Site Announcements of new generation can create the appearance of an energy system capable of meeting rising data center demand. But a megawatt located far from a planned campus, trapped behind a transmission constraint, or unavailable until the next decade has limited value to a developer trying to energize an AI facility within several years. “Aggregate capacity is not going to solve the problem if the power really isn’t where and when you need it,” Taff said. Data centers are large physical facilities tied to specific parcels,

Read More »

Tech Explainer: Data Center Cooling – Air, Evaporative, Liquid, and Hybrid Approaches

Data Center Cooling Glossary The following definitions reflect common terminology used in Department of Energy guidance, ASHRAE TC 9.9 materials, Berkeley Lab resources and Green Grid efficiency metrics. Adiabatic Cooling — A cooling process that uses water evaporation to lower the temperature of air before it reaches a heat exchanger or cooling coil. It can reduce compressor demand but consumes water when evaporative assistance is active. Air-Cooled Data Center — A facility in which heat is removed from IT equipment primarily by moving conditioned air through servers, even if that heat is later transferred to water or refrigerant elsewhere in the cooling system. Air Handler — Equipment that moves, filters and conditions air before delivering it to a data hall or other controlled space. Air-Side Economizer — A system that uses suitable outdoor air, either directly or mixed with return air, to reduce or avoid compressor-based refrigeration. Airflow Management — The practice of delivering conditioned air where it is needed while preventing hot exhaust air from recirculating into server inlets. Approach Temperature — The temperature difference between the two fluids leaving a heat exchanger at their closest thermal point. In a cooling tower, it commonly refers to the difference between leaving-water temperature and entering-air wet-bulb temperature. A smaller approach generally indicates more effective heat transfer. ASHRAE TC 9.9 — The ASHRAE technical committee focused on mission-critical facilities, data centers, technology spaces and electronic equipment. It is a major source of environmental and thermal guidance for data center operators and equipment manufacturers. Blanking Panel — A panel installed in unused rack spaces to prevent hot exhaust air from recirculating to server intakes. British Thermal Unit, or BTU — A unit of heat energy commonly used to express the heating or cooling capacity of equipment. Cabinet — An enclosure, also commonly called

Read More »

The AI Infrastructure Split Screen: Capital Rush Meets Community Resistance

It would be difficult to construct a more revealing snapshot of the AI infrastructure market than the one delivered in mid-July. In the same news cycle, Csquare completed a billion-dollar initial public offering, Switch was linked to a potential $10 billion IPO, and Databricks reached a reported valuation of $188 billion. At the project level, developers advanced or disclosed campuses measured not in tens or hundreds of megawatts, but in gigawatts—from Meta’s expanding Louisiana complex and Google’s reported Wyoming plans to new Crusoe, QTS, MARA and Tract developments. Yet the same week brought a state-level permitting pause in New York, a decisive project rejection in Palm Beach County, planned protests across more than 20 states, and fresh disputes over parkland, water availability and local control. This is the data center and AI landscape in 2026: capital is abundant but increasingly discriminating; power is more valuable than the underlying real estate; and community consent has become nearly as important as interconnection capacity. Public Markets Put Different Prices on the AI Stack The capital-market headlines illustrated how differently investors are valuing the various layers of AI infrastructure. Csquare priced 50 million shares at $21, raising approximately $1.05 billion and establishing an equity valuation of roughly $3.2 billion. The offering was substantial, but it priced below the proposed $23-to-$27 range, and the shares finished their first trading day slightly below the offer price. Brookfield retained approximately 67% of the company’s voting power following the transaction. That reception contrasts sharply with the valuation being discussed for Switch. The DigitalBridge-backed operator has reportedly engaged Goldman Sachs and JPMorgan for a potential IPO that could raise as much as $10 billion and value Switch near $80 billion, including debt. The transaction remains prospective, but the figure is striking when compared with the $11 billion take-private agreement

Read More »

New York State just hit pause on the AI data center boom

The moratorium could result in some “border-hopping,” with enterprises hosting local servers in adjacent states like Pennsylvania, Connecticut, or New Jersey, but that’s not likely to be widespread, Kimball noted. The realistic regional impact will be “more of a slow squeeze rather than a shock,” he said. This could result in tighter colocation availability and firmer pricing in the New York Metropolitan area over the next few years. Cloud providers may also steer new AI capacity to regions like Georgia, Ohio, Texas, and Utah, where power and permitting are more predictable. An inflection point, but more trickle-down than direct impact Indeed, noted Jeremy Roberts, senior director for research and content at Info-Tech Research Group, the moratorium is an “inflection point” and a “way to placate an increasingly angry public,”.

Read More »

TeraWulf’s $19B Anthropic Lease Puts Its Brownfield AI Strategy to the Test

He added that the company’s strategy is centered on owning and operating critical infrastructure, maintaining direct relationships with customers and controlling the long-term evolution of its campuses. This Model Differs Significantly from the Previous Abernathy JV TeraWulf and Fluidstack created the Abernathy venture in 2025 to develop a 168-MW critical IT load campus on approximately 120 acres near Abernathy, Texas. The project’s total utility requirement has been described as approximately 240 MW. Fluidstack committed to a 25-year lease at the campus, with Google providing approximately $1.3 billion of credit support for Fluidstack’s obligations. TeraWulf acquired a 50.1% interest in the joint venture through an investment of approximately $450 million. The project subsequently issued $1.3 billion in senior secured notes to support construction and related expenses. The Abernathy agreements were expected to produce approximately $9.5 billion in contracted revenue for the joint venture over the initial 25-year term. Construction has been advancing toward delivery during the second half of 2026. Following the sale, Fluidstack and the other purchasers will control the project. TeraWulf agreed to sell its Abernathy interest for approximately $530 million, compared with its $450 million investment in the joint venture. The consideration is scheduled to be paid in three installments through April 2027, with the proceeds expected to support investment in infrastructure opportunities that TeraWulf intends to own and operate directly. The decision does not necessarily indicate that TeraWulf has become less interested in partnerships with Fluidstack. Fluidstack remains an important tenant at TeraWulf’s Lake Mariner campus in New York, and the companies have built a substantial pipeline of AI infrastructure together. In infrastructure terms, TeraWulf is acting as both developer and capital allocator. It originated the Abernathy project, helped secure the customer and financing structure, advanced construction and is now monetizing its interest before the campus begins

Read More »

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.

Read More »

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

Read More »

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

Read More »

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

Read More »