Stay Ahead, Stay ONMINE

GitHub Copilot previews agent mode as market for agentic AI coding tools accelerates

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Agentic AI is all the rage today across multiple sectors, including application development and coding. Today at long last, GitHub has joined the agentic AI party with the launch of GitHub Copilot agent mode. The promise […]

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


Agentic AI is all the rage today across multiple sectors, including application development and coding.

Today at long last, GitHub has joined the agentic AI party with the launch of GitHub Copilot agent mode. The promise of agentic AI in development is about enabling developers to build more code with just a simple prompt. The new agent mode will enable Copilot to iterate on its own code and fix errors automatically. Looking forward, GitHub is also previewing a fully autonomous software engineering agent, Project Padawan, that can independently handle entire development tasks.

The new agentic AI features mark the latest step in the multi-year evolution of the AI-powered coding development space that GitHub helped to pioneer. The Microsoft-owned GitHub first previewed GitHub Copilot in 2021, with general availability coming in 2022. In the AI world, that’s a long time ago, before ChatGPT became a household name and most people had ever heard the term “generative AI.”

GitHub has been steadily iterating on Copilot. Initially, the service relied on the OpenAI Codex large language model (LLM). In October 2024, users gained the ability to choose from a variety of LLMs, including Anthropic’s Claude, Google’s Gemini 1.5 and OpenAI’s GPT4o. Alongside the agent mode launch, GitHub is now also adding support for Gemini 2.0 Flash and OpenAI’s o3-mini. Microsoft overall has been emphasizing agentic AI, assembling one of the largest AI agent ecosystems in the market.

AI that supports ‘peer programming’

The new GitHub Copilot agent mode service comes as a series of rivals, mostly led by startups, have shaken up the development landscape. Cursor, Replit, Bolt and Lovable are all chasing the growing market for AI-powered development that GitHub helped to create.

When GitHub Copilot first emerged, it was positioned as a pair programming tool, which pairs with a developer. Now, GitHub is leaning into the term peer programming as it embraces agentic AI.

“Developer teams will soon be joined by teams of intelligent, increasingly advanced AI agents that act as peer-programmers for everyday tasks,” said GitHub CEO Thomas Dohmke. “With today’s launch of GitHub Copilot agent mode, developers can generate, refactor and deploy code across the files of any organization’s codebase with a single prompt command.”

Technical breakdown: How GitHub’s new agent architecture works

Since its initial debut, GitHub Copilot has provided a series of core features. Among them is intelligent code completion, which is the ability to suggest code snippets to execute a given function. Copilot also functions as an assistant, allowing developers to input natural language queries to generate code, or get answers about a specific code base. The system, while intelligent, still requires a non-trivial amount of human interaction.

Agent mode goes beyond that. According to GitHub, the platform enables Copilot to iterate on its own output, as well as the results of that output. This can significantly improve results and code output.

Here’s a detailed breakdown of agent mode operation.

Task understanding and planning:

  • When given a prompt, agent mode doesn’t just generate code — it analyzes complete task requirements;
  • According to GitHub, the system can “infer additional tasks that were not specified, but are also necessary for the primary request to work”. 

Iterative execution:

  • The agent iterates on both its own output and the result of that output;
  • It continues iteration until all subtasks are completed.

Self-healing capabilities:

  • Automatically recognizes errors in its output;
  • Can fix identified issues without developer intervention;
  • Analyzes runtime errors and implements corrections;
  • Suggests and executes necessary terminal commands.

Project Padawan brings the ‘force’ to development

While agent mode certainly is more powerful than the basic GitHub Copilot operation, it’s still not quite a fully automated experience.

To get to that full experience, GitHub is previewing Project Padawan. In popular culture, a ‘Padawan’ is a reference to a Jedi apprentice from the Star Wars science fiction franchise. 

Project Padawan builds on the agent mode and extends it with more automation. In a blog post, Dohmke noted that Padawan will allow users to assign an issue to GitHub Copilot, and the agentic AI system will handle the entire task. That task can include code development, setting up a repository and assigning humans to review the final code.

“In a sense, it will be like onboarding Copilot as a contributor to every repository on GitHub,” Dohmke said.

Comparing GitHub’s agent to other agentic AI coding options

GitHub in some respects is a late entrant to the agentic AI coding race.

Cursor AI and Bolt AI debuted their first AI agents in 2023, while Replit released its agent in 2024. Those tools have had over a year to iterate, gain a following and develop brand loyalty.

I personally have been experimenting with Replit agents for the last several months. Just this week, the company brought the technology to its mobile app — which you wouldn’t think is a big deal, but it is. The ability to use a simple prompt, without the need for a full desktop setup to build software, is powerful. Replit’s agent also provides AI prompt tuning to help generate the best possible code. The Replit system runs entirely in the cloud and users like me don’t need to download anything. 

Bolt doesn’t have a mobile app, but it does have a really nice web interface that makes it easy for beginners to get started. Cursor is a bit more bulky in that it involves a download, but it is a powerful tool for professional developers.

So how does GitHub Copilot agent mode compare? GitHub is the de facto standard for code repositories on the internet today. More than 150 million developers, including more than 90% of the Fortune 100 companies, use GitHub. According to the company, more than 77,000 organizations have adopted GitHub Copilot. That makes the technology very sticky. Those organizations already relying heavily on GitHub and Copilot are not going to move away from the technology easily.

In comparison to Replit and Bolt, GitHub Copilot agent mode is not a web-based feature, at least not today. Its preview is currently only available with GitHub Copilot in VS code. That creates a small barrier to entry for absolute newbies for sure, but the reality is also that VS code is arguably the most popular and widely used integrated development environment (IDE).

Developers are a picky bunch. That’s why there are so many different programming languages and frameworks (there seems to be a new JavaScript framework emerging every other month). The bottom line is about comfort and workflow. For existing GitHub Copilot and VS code users, the new agent mode brings a much needed feature that will help improve productivity. For those that aren’t stuck in the GitHub Copilot world, agent mode could very well help bring Github Copilot back into the conversation about which agentic AI-driven coding tool to use.

GitHub Copilot agent mode is currently available in preview and requires VS code insiders, which is intended for early adopters. GitHub has not yet provided any pricing details or a date for general availability.

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

IP Fabric 7.9 boosts visibility across hybrid environments

Multicloud and hybrid network viability has also been extended to include IPv6 path analysis, helping teams reason about connectivity in dual-stack and hybrid environments. This capability addresses a practical challenge for enterprises deploying IPv6 alongside existing IPv4 infrastructure. Network teams can now validate that applications can reach IPv6 endpoints and

Read More »

Crude Settles Higher After Volatile Week

Oil edged higher at the end of a volatile week, as traders weighed tensions in Iran and positive sentiment in wider markets. West Texas Intermediate settled near $60 a barrel after plunging 4.6% on Thursday, the most since June. President Donald Trump said in a social media post that he “greatly” respects Iran’s decision to cancel scheduled hangings of protesters. His rhetoric over recent days has reduced expectations of an immediate US response to violent protests in the Islamic Republic, which could have led to disruptions to the country’s roughly 3.3 million barrel-per-day oil production, as well as shipping. Nevertheless, Washington is boosting its military presence in the Middle East. At least one aircraft carrier is moving into the region and other military assets are expected to be shifted there in the coming days and weeks, Fox News reported, citing military sources. Traders have in the past covered bearish wagers ahead of the weekend in periods of heightened geopolitical risks. “While the risk of imminent intervention from the US against Iran has subsided, it’s pretty clear that the risk is still present, which should keep the market on its toes in the short term,” said Warren Patterson, head of commodities strategy at ING Groep NV. “However, the longer this goes on without a US response, the risk premium will continue to evaporate, allowing more bearish fundamentals to take center stage.” Disruption to Kazakh exports from the Black Sea, short-term tightness in the North Sea and a host of financial flows from options markets to commodity index rebalancing have also helped lift an oil market coming off its biggest drop since 2020 on rising supplies. In a sign that lower prices are starting to bite, Harold Hamm, the billionaire wildcatter who helped kick off the US shale revolution, said his firm

Read More »

U.S. Energy Secretary and Slovakia’s Prime Minister Sign Agreement to Advance U.S.-Slovakia Civil Nuclear Program

WASHINGTON—U.S. Secretary of Energy Chris Wright and Slovak Prime Minister Robert Fico today signed an Intergovernmental Agreement (IGA) to advance cooperation on Slovakia’s civil nuclear power program. This landmark agreement includes the development of a new, state-owned American 1,200 MWe nuclear unit at the Jaslovské Bohunice Nuclear Power Plant, deepening the U.S.-Slovakia strategic partnership and strengthening European energy security. The agreement builds on President Trump’s commitment to advancing American energy leadership. A project of this scale is expected to create thousands of American jobs across engineering, advanced manufacturing, construction, nuclear fuel services, and project management, while reinforcing U.S. supply chains and expanding access to global markets for American-made nuclear technology. These efforts lay the foundation for sustained U.S. engagement in Slovakia’s nuclear energy program and support future civil nuclear projects across the region. It also supports Slovakia’s efforts to diversify its energy supply, strengthen long-term energy security, and integrate advanced American nuclear technology into Central Europe’s energy infrastructure. “The United States is proud to partner with Slovakia as a trusted ally as we expand cooperation across the energy sector,” said Energy Secretary Chris Wright. “Today’s civil nuclear agreement reflects our shared commitment to strengthening European energy security and sovereignty for decades to come. By deploying America’s leading nuclear technology, we are creating thousands of good-paying American jobs, expanding global markets for U.S. nuclear companies, and driving economic growth at home”. “I see this moment as a significant milestone in our bilateral relations, but also as a clear signal that Slovakia and the United States are united by a common strategic thinking about the future of energy – about its safety, sustainability, and technological maturity,” said the Prime Minister of the Slovak Republic Robert Fico. The planned nuclear unit represents a multibillion-dollar energy infrastructure investment and one of the largest in

Read More »

Valero to Cut 200+ Jobs as California Refinery Closes

Valero Energy Corp. plans to let go of 237 employees at its Benicia refinery as it winds down operations at one of California’s few remaining fuel-making plants. Valero expects the shutdown to be permanent and 237 jobs will be cut March 15 to July 1, the company said in a letter to California’s employment regulator and local officials. Those losing jobs are not represented by a union and represent the bulk of the plant’s 348-person staff.  “We do not plan to coordinate services with the local workforce development board or any other entity,” refinery manager Lauren Bird, whose position is being eliminated, said in the letter. The Texas-based oil company announced in 2025 plans to close the plant and last-ditch efforts by Governor Gavin Newsom, regulators and local officials to keep the gates open were unsuccessful. Multiple California refineries have closed or converted to making biofuels in recent years, dwindling fuel supply in a state where drivers regularly pay the highest gasoline prices in the nation. Last week, Newsom praised plans by Valero to continue supplying the state with gasoline amid the shutdown, saying the decision to import fuel to the region was a constructive development from an earlier possibility of a full-on exit. WHAT DO YOU THINK? Generated by readers, the comments included herein do not reflect the views and opinions of Rigzone. All comments are subject to editorial review. Off-topic, inappropriate or insulting comments will be removed.

Read More »

Trump Administration Calls for Emergency Power Auction to Build Big Power Plants Again

WASHINGTON—U.S. Secretary of Energy Chris Wright and Secretary of the Interior Doug Burgum, vice-chair and chair of the National Energy Dominance Council (NEDC) respectively, today joined Mid-Atlantic governors urging PJM Interconnection, L.L.C. (PJM) to temporarily overhaul its market rules to strengthen grid reliability and reduce electricity costs for American families and businesses by building more than $15 billion of reliable baseload power generation.  The initiative calls on PJM to conduct an emergency procurement auction to address escalating electricity prices and growing reliability risks across the mid-Atlantic region of the United States. The action follows a series of PJM policies over the years that have weakened the electric grid, including the premature shutdown of reliable power generation.  President Trump declared a National Energy Emergency on his first day in office, warning that the previous administrations energy subtraction agenda left the country vulnerable to blackouts and soaring electricity prices. During the Biden administration, PJM forced nearly 17 gigawatts of reliable baseload power generation offline. For the first time in history, PJM’s capacity auction failed to secure enough generation resources to meet basic reliability requirements. If not fixed, it will lead to further rising prices and blackouts.  “High electricity prices are a choice,” said Energy Secretary Chris Wright. “The Biden administration’s forceful closures of coal and natural gas plants without reliable replacements left the United States in an energy emergency. Perhaps no region in America is more at risk than in PJM. That’s why President Trump asked governors across the Mid-Atlantic to come together and call upon PJM to allow America to build big reliable power plants again. Our directives will restore affordable and reliable electricity so American families thrive and America’s manufacturing industries once again boom. President Trump promised to unleash American energy and put the American people first. This plan keeps

Read More »

Russian Oil and Gas Revenue Falls to Lowest in 5 Years

Russia’s revenues from its oil and gas industry, vital to financing its war in Ukraine, dropped to a five-year low in 2025 as crude prices slumped and gas exports declined. The nation’s budget received a total of 8.48 trillion rubles ($108 billion) in oil and gas taxes last year, Finance Ministry said on Thursday. That’s 24 percent less than in 2024 and the lowest level since the start of the decade, historic figures show.  Russia, a top-three global oil producer and home to the world’s largest gas reserves, heavily relies on tax revenues from the two industries to fill its state coffers. The decline, mainly driven by a combination of weaker global oil prices, stronger ruble and energy sanctions against Russia, comes as the Kremlin has boosted military spending significantly above what it planned to fund the war, which is about to enter a fifth year. To bridge the widening gap between revenues and spending, the government in Moscow has eaten into more than half of the country’s National Wellbeing Fund – a buffer against economic shocks – and turned to expensive borrowings that will take years to pay back.   Oil revenues dropped more than 22 percent year on year to 7.13 trillion rubles, reaching the lowest level since 2023, Bloomberg calculations show. Concerns about an oversupply in the global crude market, and discounts for Russian barrels in particular due to western sanctions, hit the flow of money into state coffers. The official data show that the average price of Urals, Russia’s main oil-export blend, for tax purposes was $57.65 a barrel in 2025, a 15 percent drop from a year earlier.   Starting from November, when the US blacklisted two major oil producers Rosneft PJSC and Lukoil PJSC, the discount of Urals to the Brent benchmark widened to about $27 a barrel at

Read More »

Hamm to Halt Drilling in Bakken Shale

(Update) January 16, 2026, 3:39 PM GMT: Article updated with context throughout. Harold Hamm, the billionaire wildcatter who helped kick off the US shale oil revolution, said he’s about to shut down his company’s drilling in North Dakota’s Bakken for the first time in decades because of low crude prices. “This will be the first time in over 30 years that Harold Hamm has not had an operation with drilling rigs in North Dakota,” Hamm, the founder of shale driller Continental Resources Inc., said in a telephone interview Thursday. “There’s no need to drill it when margins are basically gone.” It’s a significant milestone for the Bakken. The shale patch in North Dakota is where Hamm, 80, first proved that fracking and horizontal drilling techniques could be successfully applied to previously untouchable oil reserves. The fracking revolution ushered in a new growth era in US oil and the country went on to become the world’s top producer. Operators in the US shale patch, once the world’s leader in oil production growth, are now closely watching commodity prices as they hover near the level that makes drilling profitable for producers. If prices drop into the low $50-per-barrel range for several months, companies are expected to make more drastic cuts to drilling and fracking.  While each shale basin has different cost levels, the Bakken in particular is seen as a bellwether for the direction of US crude output. The average well in the Bakken requires a minimum of $58 a barrel to cover costs and generate a small profit, according to a report from BloombergNEF. That’s up almost 4% from a year earlier, largely due to rising drilling expenses. Meanwhile global oil prices have steadily declined in the past several months on expectations of a glut. West Texas Intermediate, the US benchmark, has fallen

Read More »

NVIDIA’s Rubin Redefines the AI Factory

The Architecture Shift: From “GPU Server” to “Rack-Scale Supercomputer” NVIDIA’s Rubin architecture is built around a single design thesis: “extreme co-design.” In practice, that means GPUs, CPUs, networking, security, software, power delivery, and cooling are architected together; treating the data center as the compute unit, not the individual server. That logic shows up most clearly in the NVL72 system. NVLink 6 serves as the scale-up spine, designed to let 72 GPUs communicate all-to-all with predictable latency, something NVIDIA argues is essential for mixture-of-experts routing and synchronization-heavy inference paths. NVIDIA is not vague about what this requires. Its technical materials describe the Rubin GPU as delivering 50 PFLOPS of NVFP4 inference and 35 PFLOPS of NVFP4 training, with 22 TB/s of HBM4 bandwidth and 3.6 TB/s of NVLink bandwidth per GPU. The point of that bandwidth is not headline-chasing. It is to prevent a rack from behaving like 72 loosely connected accelerators that stall on communication. NVIDIA wants the rack to function as a single engine because that is what it will take to drive down cost per token at scale. The New Idea NVIDIA Is Elevating: Inference Context Memory as Infrastructure If there is one genuinely new concept in the Rubin announcements, it is the elevation of context memory, and the admission that GPU memory alone will not carry the next wave of inference. NVIDIA describes a new tier called NVIDIA Inference Context Memory Storage, powered by BlueField-4, designed to persist and share inference state (such as KV caches) across requests and nodes for long-context and agentic workloads. NVIDIA says this AI-native context tier can boost tokens per second by up to 5× and improve power efficiency by up to 5× compared with traditional storage approaches. The implication is clear: the path to cheaper inference is not just faster GPUs.

Read More »

Power shortages, carbon capture, and AI automation: What’s ahead for data centers in 2026

“Despite a broader use of AI tools in enterprises and by consumers, that does not mean that AI compute, AI infrastructure in general, will be more evenly spread out,” said Daniel Bizo, research director at Uptime Institute, during the webinar. “The concentration of AI compute infrastructure is only increasing in the coming years.” For enterprises, the infrastructure investment remains relatively modest, Uptime Institute found. Enterprises will limit investment to inference and only some training, and inference workloads don’t require dramatic capacity increases. “Our prediction, our observation, was that the concentration of AI compute infrastructure is only increasing in the coming years by a couple of points. By the end of this year, 2026, we are projecting that around 10 gigawatts of new IT load will have been added to the global data center world, specifically to run generative AI workloads and adjacent workloads, but definitely centered on generative AI,” Bizo said. “This means these 10 gigawatts or so load, we are talking about anywhere between 13 to 15 million GPUs and accelerators deployed globally. We are anticipating that a majority of these are and will be deployed in supercomputing style.” 2. Developers will not outrun the power shortage The most pressing challenge facing the industry, according to Uptime, is that data centers can be built in less than three years, but power generation takes much longer. “It takes three to six years to deploy a solar or wind farm, around six years for a combined-cycle gas turbine plant, and even optimistically, it probably takes more than 10 years to deploy a conventional nuclear power plant,” said Max Smolaks, research analyst at Uptime Institute. This mismatch was manageable when data centers were smaller and growth was predictable, the report notes. But with projects now measured in tens and sometimes hundreds of

Read More »

Google warns transmission delays are now the biggest threat to data center expansion

The delays stem from aging transmission infrastructure unable to handle concentrated power demands. Building regional transmission lines currently takes seven to eleven years just for permitting, Hanna told the gathering. Southwest Power Pool has projected 115 days of potential loss of load if transmission infrastructure isn’t built to match demand growth, he added. These systemic delays are forcing enterprises to reconsider fundamental assumptions about cloud capacity. Regions including Northern Virginia and Santa Clara that were prime locations for hyperscale builds are running out of power capacity. The infrastructure constraints are also reshaping cloud competition around power access rather than technical capabilities. “This is no longer about who gets to market with the most GPU instances,” Gogia said. “It’s about who gets to the grid first.” Co-location emerges as a faster alternative to grid delays Unable to wait years for traditional grid connections, hyperscalers are pursuing co-location arrangements that place data centers directly adjacent to power plants, bypassing the transmission system entirely. Pricing for these arrangements has jumped 20% in power-constrained markets as demand outstrips availability, with costs flowing through to cloud customers via regional pricing differences, Gogia said. Google is exploring such arrangements, though Hanna said the company’s “strong preference is grid-connected load.” “This is a speed to power play for us,” he said, noting Google wants facilities to remain “front of the meter” to serve the broader grid rather than operating as isolated power sources. Other hyperscalers are negotiating directly with utilities, acquiring land near power plants, and exploring ownership stakes in power infrastructure from batteries to small modular nuclear reactors, Hanna said.

Read More »

OpenAI turns to Cerebras in a mega deal to scale AI inference infrastructure

Analysts expect AI workloads to grow more varied and more demanding in the coming years, driving the need for architectures tuned for inference performance and putting added pressure on data center networks. “This is prompting hyperscalers to diversify their computing systems, using Nvidia GPUs for general-purpose AI workloads, in-house AI accelerators for highly optimized tasks, and systems such as Cerebras for specialized low-latency workloads,” said Neil Shah, vice president for research at Counterpoint Research. As a result, AI platforms operating at hyperscale are pushing infrastructure providers away from monolithic, general-purpose clusters toward more tiered and heterogeneous infrastructure strategies. “OpenAI’s move toward Cerebras inference capacity reflects a broader shift in how AI data centers are being designed,” said Prabhu Ram, VP of the industry research group at Cybermedia Research. “This move is less about replacing Nvidia and more about diversification as inference scales.” At this level, infrastructure begins to resemble an AI factory, where city-scale power delivery, dense east–west networking, and low-latency interconnects matter more than peak FLOPS, Ram added. “At this magnitude, conventional rack density, cooling models, and hierarchical networks become impractical,” said Manish Rawat, semiconductor analyst at TechInsights. “Inference workloads generate continuous, latency-sensitive traffic rather than episodic training bursts, pushing architectures toward flatter network topologies, higher-radix switching, and tighter integration of compute, memory, and interconnect.”

Read More »

Cisco’s 2026 agenda prioritizes AI-ready infrastructure, connectivity

While most of the demand for AI data center capacity today comes from hyperscalers and neocloud providers, that will change as enterprise customers delve more into the AI networking world. “The other ecosystem members and enterprises themselves are becoming responsible for an increasing proportion of the AI infrastructure buildout as inferencing and agentic AI, sovereign cloud, and edge AI become more mainstream,” Katz wrote. More enterprises will move to host AI on premises via the introduction of AI agents that are designed to inject intelligent insight into applications and help improve operations. That’s where the AI impact on enterprise network traffic will appear, suggests Nolle. “Enterprises need to host AI to create AI network impact. Just accessing it doesn’t do much to traffic. Having cloud agents access local data center resources (RAG etc.) creates a governance issue for most corporate data, so that won’t go too far either,” Nolle said.  “Enterprises are looking at AI agents, not the way hyperscalers tout agentic AI, but agents running on small models, often open-source, and are locally hosted. This is where real AI traffic will develop, and Cisco could be vulnerable if they don’t understand this point and at least raise it in dialogs where AI hosting comes up,” Nolle said. “I don’t expect they’d go too far, because the real market for enterprise AI networking is probably a couple years out.” Meanwhile, observers expect Cisco to continue bolstering AI networking capabilities for enterprise branch, campus and data centers as well as hyperscalers, including through optical support and other gear.

Read More »

Microsoft tells communities it will ‘pay its way’ as AI data center resource usage sparks backlash

It will work with utilities and public commissions to set the rates it pays high enough to cover data center electricity costs (including build-outs, additions, and active use). “Our goal is straightforward: To ensure that the electricity cost of serving our data centers is not passed on to residential customers,” Smith emphasized. For example, the company is supporting a new rate structure Wisconsin that would charge a class of “very large customers,” including data centers, the true cost of the electricity required to serve them. It will collaborate “early, closely, and transparently” with local utilities to add electricity and supporting infrastructure to existing grids when needed. For instance, Microsoft has contracted with the Midcontinent Independent System Operator (MISO) to add 7.9GW of new electricity generation to the grid, “more than double our current consumption,” Smith noted. It will pursue ways to make data centers more efficient. For example, it is already experimenting with AI to improve planning, extract more electricity from existing infrastructure, improve system resilience, and speed development of new infrastructure and technologies (like nuclear energy). It will advocate for state and national public policies that ensure electricity access that is affordable, reliable, and sustainable in neighboring communities. Microsoft previously established priorities for electricity policy advocacy, Smith noted, but “progress has been uneven. This needs to change.” Microsoft is similarly committed when it comes to data center water use, promising four actions: Reducing the overall amount of water its data centers use, initially improving it by 40% by 2030. The company is exploring innovations in cooling, including closed-loop systems that recirculate cooling liquids. It will collaborate with local utilities to map out water, wastewater, and pressure needs, and will “fully fund” infrastructure required for growth. For instance, in Quincy, Washington, Microsoft helped construct a water reuse utility that recirculates

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 »