Get ONMINE

Stay Ahead, Stay ONMINE

What’s next for AI in 2025

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here. For the last couple of years we’ve had a go at predicting what’s coming next in AI. A fool’s game given how fast this industry moves. But we’re on a roll, and we’re doing it again. How did we score last time round? Our four hot trends to watch out for in 2024 included what we called customized chatbots—interactive helper apps powered by multimodal large language models (check: we didn’t know it yet, but we were talking about what everyone now calls agents, the hottest thing in AI right now); generative video (check: few technologies have improved so fast in the last 12 months, with OpenAI and Google DeepMind releasing their flagship video generation models, Sora and Veo, within a week of each other this December); and more general-purpose robots that can do a wider range of tasks (check: the payoffs from large language models continue to trickle down to other parts of the tech industry, and robotics is top of the list).  We also said that AI-generated election disinformation would be everywhere, but here—happily—we got it wrong. There were many things to wring our hands over this year, but political deepfakes were thin on the ground.  So what’s coming in 2025? We’re going to ignore the obvious here: You can bet that agents and smaller, more efficient, language models will continue to shape the industry. Instead, here are five alternative picks from our AI team. 1. Generative virtual playgrounds  If 2023 was the year of generative images and 2024 was the year of generative video—what comes next? If you guessed generative virtual worlds (a.k.a. video games), high fives all round. We got a tiny glimpse of this technology in February, when Google DeepMind revealed a generative model called Genie that could take a still image and turn it into a side-scrolling 2D platform game that players could interact with. In December, the firm revealed Genie 2, a model that can spin a starter image into an entire virtual world. Other companies are building similar tech. In October, the AI startups Decart and Etched revealed an unofficial Minecraft hack in which every frame of the game gets generated on the fly as you play. And World Labs, a startup cofounded by Fei-Fei Li—creator of ImageNet, the vast data set of photos that kick-started the deep-learning boom—is building what it calls large world models, or LWMs. One obvious application is video games. There’s a playful tone to these early experiments, and generative 3D simulations could be used to explore design concepts for new games, turning a sketch into a playable environment on the fly. This could lead to entirely new types of games.  But they could also be used to train robots. World Labs wants to develop so-called spatial intelligence—the ability for machines to interpret and interact with the everyday world. But robotics researchers lack good data about real-world scenarios with which to train such technology. Spinning up countless virtual worlds and dropping virtual robots into them to learn by trial and error could help make up for that.    —Will Douglas Heaven 2. Large language models that “reason” The buzz was justified. When OpenAI revealed o1 in September, it introduced a new paradigm in how large language models work. Two months later, the firm pushed that paradigm forward in almost every way with o3—a model that just might reshape this technology for good. Most models, including OpenAI’s flagship GPT-4, spit out the first response they come up with. Sometimes it’s correct; sometimes it’s not. But the firm’s new models are trained to work through their answers step by step, breaking down tricky problems into a series of simpler ones. When one approach isn’t working, they try another. This technique, known as “reasoning” (yes—we know exactly how loaded that term is), can make this technology more accurate, especially for math, physics, and logic problems. It’s also crucial for agents. In December, Google DeepMind revealed an experimental new web-browsing agent called Mariner. In the middle of a preview demo that the company gave to MIT Technology Review, Mariner seemed to get stuck. Megha Goel, a product manager at the company, had asked the agent to find her a recipe for Christmas cookies that looked like the ones in a photo she’d given it. Mariner found a recipe on the web and started adding the ingredients to Goel’s online grocery basket. Then it stalled; it couldn’t figure out what type of flour to pick. Goel watched as Mariner explained its steps in a chat window: “It says, ‘I will use the browser’s Back button to return to the recipe.’” It was a remarkable moment. Instead of hitting a wall, the agent had broken the task down into separate actions and picked one that might resolve the problem. Figuring out you need to click the Back button may sound basic, but for a mindless bot it’s akin to rocket science. And it worked: Mariner went back to the recipe, confirmed the type of flour, and carried on filling Goel’s basket. Google DeepMind is also building an experimental version of Gemini 2.0, its latest large language model, that uses this step-by-step approach to problem solving, called Gemini 2.0 Flash Thinking. But OpenAI and Google are just the tip of the iceberg. Many companies are building large language models that use similar techniques, making them better at a whole range of tasks, from cooking to coding. Expect a lot more buzz about reasoning (we know, we know) this year. —Will Douglas Heaven 3. It’s boom time for AI in science  One of the most exciting uses for AI is speeding up discovery in the natural sciences. Perhaps the greatest vindication of AI’s potential on this front came last October, when the Royal Swedish Academy of Sciences awarded the Nobel Prize for chemistry to Demis Hassabis and John M. Jumper from Google DeepMind for building the AlphaFold tool, which can solve protein folding, and to David Baker for building tools to help design new proteins. Expect this trend to continue next year, and to see more data sets and models that are aimed specifically at scientific discovery. Proteins were the perfect target for AI, because the field had excellent existing data sets that AI models could be trained on.  The hunt is on to find the next big thing. One potential area is materials science. Meta has released massive data sets and models that could help scientists use AI to discover new materials much faster, and in December, Hugging Face, together with the startup Entalpic, launched LeMaterial, an open-source project that aims to simplify and accelerate materials research. Their first project is a data set that unifies, cleans, and standardizes the most prominent material data sets.  AI model makers are also keen to pitch their generative products as research tools for scientists. OpenAI let scientists test its latest o1 model and see how it might support them in research. The results were encouraging.  Having an AI tool that can operate in a similar way to a scientist is one of the fantasies of the tech sector. In a manifesto published in October last year, Anthropic founder Dario Amodei highlighted science, especially biology, as one of the key areas where powerful AI could help. Amodei speculates that in the future, AI could be not only a method of data analysis but a “virtual biologist who performs all the tasks biologists do.” We’re still a long way away from this scenario. But next year, we might see important steps toward it.  —Melissa Heikkilä 4. AI companies get cozier with national security There is a lot of money to be made by AI companies willing to lend their tools to border surveillance, intelligence gathering, and other national security tasks.  The US military has launched a number of initiatives that show it’s eager to adopt AI, from the Replicator program—which, inspired by the war in Ukraine, promises to spend $1 billion on small drones—to the Artificial Intelligence Rapid Capabilities Cell, a unit bringing AI into everything from battlefield decision-making to logistics. European militaries are under pressure to up their tech investment, triggered by concerns that Donald Trump’s administration will cut spending to Ukraine. Rising tensions between Taiwan and China weigh heavily on the minds of military planners, too.  In 2025, these trends will continue to be a boon for defense-tech companies like Palantir, Anduril, and others, which are now capitalizing on classified military data to train AI models.  The defense industry’s deep pockets will tempt mainstream AI companies into the fold too. OpenAI in December announced it is partnering with Anduril on a program to take down drones, completing a year-long pivot away from its policy of not working with the military. It joins the ranks of Microsoft, Amazon, and Google, which have worked with the Pentagon for years.  Other AI competitors, which are spending billions to train and develop new models, will face more pressure in 2025 to think seriously about revenue. It’s possible that they’ll find enough non-defense customers who will pay handsomely for AI agents that can handle complex tasks, or creative industries willing to spend on image and video generators.  But they’ll also be increasingly tempted to throw their hats in the ring for lucrative Pentagon contracts. Expect to see companies wrestle with whether working on defense projects will be seen as a contradiction to their values. OpenAI’s rationale for changing its stance was that “democracies should continue to take the lead in AI development,” the company wrote, reasoning that lending its models to the military would advance that goal. In 2025, we’ll be watching others follow its lead.  —James O’Donnell 5. Nvidia sees legitimate competition For much of the current AI boom, if you were a tech startup looking to try your hand at making an AI model, Jensen Huang was your man. As CEO of Nvidia, the world’s most valuable corporation, Huang helped the company become the undisputed leader of chips used both to train AI models and to ping a model when anyone uses it, called “inferencing.” A number of forces could change that in 2025. For one, behemoth competitors like Amazon, Broadcom, AMD, and others have been investing heavily in new chips, and there are early indications that these could compete closely with Nvidia’s—particularly for inference, where Nvidia’s lead is less solid.  A growing number of startups are also attacking Nvidia from a different angle. Rather than trying to marginally improve on Nvidia’s designs, startups like Groq are making riskier bets on entirely new chip architectures that, with enough time, promise to provide more efficient or effective training. In 2025 these experiments will still be in their early stages, but it’s possible that a standout competitor will change the assumption that top AI models rely exclusively on Nvidia chips. Underpinning this competition, the geopolitical chip war will continue. That war thus far has relied on two strategies. On one hand, the West seeks to limit exports to China of top chips and the technologies to make them. On the other, efforts like the US CHIPS Act aim to boost domestic production of semiconductors. Donald Trump may escalate those export controls and has promised massive tariffs on any goods imported from China. In 2025, such tariffs would put Taiwan—on which the US relies heavily because of the chip manufacturer TSMC—at the center of the trade wars. That’s because Taiwan has said it will help Chinese firms relocate to the island to help them avoid the proposed tariffs. That could draw further criticism from Trump, who has expressed frustration with US spending to defend Taiwan from China.  It’s unclear how these forces will play out, but it will only further incentivize chipmakers to reduce reliance on Taiwan, which is the entire purpose of the CHIPS Act. As spending from the bill begins to circulate, next year could bring the first evidence of whether it’s materially boosting domestic chip production.  —James O’Donnell

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

For the last couple of years we’ve had a go at predicting what’s coming next in AI. A fool’s game given how fast this industry moves. But we’re on a roll, and we’re doing it again.

How did we score last time round? Our four hot trends to watch out for in 2024 included what we called customized chatbots—interactive helper apps powered by multimodal large language models (check: we didn’t know it yet, but we were talking about what everyone now calls agents, the hottest thing in AI right now); generative video (check: few technologies have improved so fast in the last 12 months, with OpenAI and Google DeepMind releasing their flagship video generation models, Sora and Veo, within a week of each other this December); and more general-purpose robots that can do a wider range of tasks (check: the payoffs from large language models continue to trickle down to other parts of the tech industry, and robotics is top of the list). 

We also said that AI-generated election disinformation would be everywhere, but here—happily—we got it wrong. There were many things to wring our hands over this year, but political deepfakes were thin on the ground. 

So what’s coming in 2025? We’re going to ignore the obvious here: You can bet that agents and smaller, more efficient, language models will continue to shape the industry. Instead, here are five alternative picks from our AI team.

1. Generative virtual playgrounds 

If 2023 was the year of generative images and 2024 was the year of generative video—what comes next? If you guessed generative virtual worlds (a.k.a. video games), high fives all round.

We got a tiny glimpse of this technology in February, when Google DeepMind revealed a generative model called Genie that could take a still image and turn it into a side-scrolling 2D platform game that players could interact with. In December, the firm revealed Genie 2, a model that can spin a starter image into an entire virtual world.

Other companies are building similar tech. In October, the AI startups Decart and Etched revealed an unofficial Minecraft hack in which every frame of the game gets generated on the fly as you play. And World Labs, a startup cofounded by Fei-Fei Li—creator of ImageNet, the vast data set of photos that kick-started the deep-learning boom—is building what it calls large world models, or LWMs.

One obvious application is video games. There’s a playful tone to these early experiments, and generative 3D simulations could be used to explore design concepts for new games, turning a sketch into a playable environment on the fly. This could lead to entirely new types of games. 

But they could also be used to train robots. World Labs wants to develop so-called spatial intelligence—the ability for machines to interpret and interact with the everyday world. But robotics researchers lack good data about real-world scenarios with which to train such technology. Spinning up countless virtual worlds and dropping virtual robots into them to learn by trial and error could help make up for that.   

—Will Douglas Heaven

2. Large language models that “reason”

The buzz was justified. When OpenAI revealed o1 in September, it introduced a new paradigm in how large language models work. Two months later, the firm pushed that paradigm forward in almost every way with o3—a model that just might reshape this technology for good.

Most models, including OpenAI’s flagship GPT-4, spit out the first response they come up with. Sometimes it’s correct; sometimes it’s not. But the firm’s new models are trained to work through their answers step by step, breaking down tricky problems into a series of simpler ones. When one approach isn’t working, they try another. This technique, known as “reasoning” (yes—we know exactly how loaded that term is), can make this technology more accurate, especially for math, physics, and logic problems.

It’s also crucial for agents.

In December, Google DeepMind revealed an experimental new web-browsing agent called Mariner. In the middle of a preview demo that the company gave to MIT Technology Review, Mariner seemed to get stuck. Megha Goel, a product manager at the company, had asked the agent to find her a recipe for Christmas cookies that looked like the ones in a photo she’d given it. Mariner found a recipe on the web and started adding the ingredients to Goel’s online grocery basket.

Then it stalled; it couldn’t figure out what type of flour to pick. Goel watched as Mariner explained its steps in a chat window: “It says, ‘I will use the browser’s Back button to return to the recipe.’”

It was a remarkable moment. Instead of hitting a wall, the agent had broken the task down into separate actions and picked one that might resolve the problem. Figuring out you need to click the Back button may sound basic, but for a mindless bot it’s akin to rocket science. And it worked: Mariner went back to the recipe, confirmed the type of flour, and carried on filling Goel’s basket.

Google DeepMind is also building an experimental version of Gemini 2.0, its latest large language model, that uses this step-by-step approach to problem solving, called Gemini 2.0 Flash Thinking.

But OpenAI and Google are just the tip of the iceberg. Many companies are building large language models that use similar techniques, making them better at a whole range of tasks, from cooking to coding. Expect a lot more buzz about reasoning (we know, we know) this year.

—Will Douglas Heaven

3. It’s boom time for AI in science 

One of the most exciting uses for AI is speeding up discovery in the natural sciences. Perhaps the greatest vindication of AI’s potential on this front came last October, when the Royal Swedish Academy of Sciences awarded the Nobel Prize for chemistry to Demis Hassabis and John M. Jumper from Google DeepMind for building the AlphaFold tool, which can solve protein folding, and to David Baker for building tools to help design new proteins.

Expect this trend to continue next year, and to see more data sets and models that are aimed specifically at scientific discovery. Proteins were the perfect target for AI, because the field had excellent existing data sets that AI models could be trained on. 

The hunt is on to find the next big thing. One potential area is materials science. Meta has released massive data sets and models that could help scientists use AI to discover new materials much faster, and in December, Hugging Face, together with the startup Entalpic, launched LeMaterial, an open-source project that aims to simplify and accelerate materials research. Their first project is a data set that unifies, cleans, and standardizes the most prominent material data sets. 

AI model makers are also keen to pitch their generative products as research tools for scientists. OpenAI let scientists test its latest o1 model and see how it might support them in research. The results were encouraging. 

Having an AI tool that can operate in a similar way to a scientist is one of the fantasies of the tech sector. In a manifesto published in October last year, Anthropic founder Dario Amodei highlighted science, especially biology, as one of the key areas where powerful AI could help. Amodei speculates that in the future, AI could be not only a method of data analysis but a “virtual biologist who performs all the tasks biologists do.” We’re still a long way away from this scenario. But next year, we might see important steps toward it. 

—Melissa Heikkilä

4. AI companies get cozier with national security

There is a lot of money to be made by AI companies willing to lend their tools to border surveillance, intelligence gathering, and other national security tasks. 

The US military has launched a number of initiatives that show it’s eager to adopt AI, from the Replicator program—which, inspired by the war in Ukraine, promises to spend $1 billion on small drones—to the Artificial Intelligence Rapid Capabilities Cell, a unit bringing AI into everything from battlefield decision-making to logistics. European militaries are under pressure to up their tech investment, triggered by concerns that Donald Trump’s administration will cut spending to Ukraine. Rising tensions between Taiwan and China weigh heavily on the minds of military planners, too. 

In 2025, these trends will continue to be a boon for defense-tech companies like Palantir, Anduril, and others, which are now capitalizing on classified military data to train AI models. 

The defense industry’s deep pockets will tempt mainstream AI companies into the fold too. OpenAI in December announced it is partnering with Anduril on a program to take down drones, completing a year-long pivot away from its policy of not working with the military. It joins the ranks of Microsoft, Amazon, and Google, which have worked with the Pentagon for years. 

Other AI competitors, which are spending billions to train and develop new models, will face more pressure in 2025 to think seriously about revenue. It’s possible that they’ll find enough non-defense customers who will pay handsomely for AI agents that can handle complex tasks, or creative industries willing to spend on image and video generators. 

But they’ll also be increasingly tempted to throw their hats in the ring for lucrative Pentagon contracts. Expect to see companies wrestle with whether working on defense projects will be seen as a contradiction to their values. OpenAI’s rationale for changing its stance was that “democracies should continue to take the lead in AI development,” the company wrote, reasoning that lending its models to the military would advance that goal. In 2025, we’ll be watching others follow its lead. 

—James O’Donnell

5. Nvidia sees legitimate competition

For much of the current AI boom, if you were a tech startup looking to try your hand at making an AI model, Jensen Huang was your man. As CEO of Nvidia, the world’s most valuable corporation, Huang helped the company become the undisputed leader of chips used both to train AI models and to ping a model when anyone uses it, called “inferencing.”

A number of forces could change that in 2025. For one, behemoth competitors like Amazon, Broadcom, AMD, and others have been investing heavily in new chips, and there are early indications that these could compete closely with Nvidia’s—particularly for inference, where Nvidia’s lead is less solid. 

A growing number of startups are also attacking Nvidia from a different angle. Rather than trying to marginally improve on Nvidia’s designs, startups like Groq are making riskier bets on entirely new chip architectures that, with enough time, promise to provide more efficient or effective training. In 2025 these experiments will still be in their early stages, but it’s possible that a standout competitor will change the assumption that top AI models rely exclusively on Nvidia chips.

Underpinning this competition, the geopolitical chip war will continue. That war thus far has relied on two strategies. On one hand, the West seeks to limit exports to China of top chips and the technologies to make them. On the other, efforts like the US CHIPS Act aim to boost domestic production of semiconductors.

Donald Trump may escalate those export controls and has promised massive tariffs on any goods imported from China. In 2025, such tariffs would put Taiwan—on which the US relies heavily because of the chip manufacturer TSMC—at the center of the trade wars. That’s because Taiwan has said it will help Chinese firms relocate to the island to help them avoid the proposed tariffs. That could draw further criticism from Trump, who has expressed frustration with US spending to defend Taiwan from China. 

It’s unclear how these forces will play out, but it will only further incentivize chipmakers to reduce reliance on Taiwan, which is the entire purpose of the CHIPS Act. As spending from the bill begins to circulate, next year could bring the first evidence of whether it’s materially boosting domestic chip production. 

—James O’Donnell

Shape
Shape
Stay Ahead

Explore More Insights

More Articles

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

CompTIA launches SecAI+ certification

The certification covers how to secure AI platforms and functionality, how to use AI to improve processes such as incident response, security analytics, threat intelligence, and penetration testing. It also focuses on how AI can automate compliance and risk management procedures under human guidance, according to CompTIA. “CompTIA SecAI+ addresses

Read More »

Palo Alto to acquire Israeli startup Koi for agentic AI security

Prisma AIRS features AI model scanning, which lets enterprises safely adopt AI models by scanning them for vulnerabilities and secure the AI ecosystem against risks such as model tampering, malicious scripts, and deserialization attacks. Posture management provides enterprises with insight into their security posture as related to the AI ecosystem

Read More »

EBW Warned of Faltering Gas Demand Heading into Holiday Weekend

In a U.S. natural gas focused EBW Analytics Group report sent to Rigzone by the EBW team on Friday, Eli Rubin, an energy analyst at the company, warned of “faltering demand” heading into the President’s Day holiday weekend. “The March contract tested as high as $3.316 yesterday before selling off after a bearish EIA [U.S. Energy Information Administration] storage surprise, and ahead of deteriorating heating demand into President’s Day holiday weekend and an 11 billion cubic foot per day drop into next Wednesday,” Rubin said in Friday’s report. “The threat of cold air in Western Canada and the Pacific Northwest moving into the U.S. remains a primary source of support,” he added. “If the market returns from the holiday weekend without this threat materializing, however, sub-$3.00 per million British thermal units may be in play as the year over year storage deficit flips to a 170 billion cubic foot surplus by late February,” he continued. In the report, Rubin went on to state that “steep storage refill demand east of the Rockies and loose supply/demand fundamentals during recent Marches may offer some medium-term support”. He added, however, that “storage exiting March near 1,800 billion cubic feet, with gathering production tailwinds and decelerating year over year LNG growth into mid to late 2026, suggest a bearish outlook for NYMEX gas futures”. In its latest weekly natural gas storage report, which was released on February 12 and included data for the week ending February 6, the EIA revealed that, according to its estimates, working gas in storage was 2,214 billion cubic feet as of February 6. “This represents a net decrease of 249 billion cubic feet from the previous week,” the EIA highlighted in the report. “Stocks were 97 billion cubic feet less than last year at this time and 130 billion

Read More »

North America Drops 6 Rigs Week on Week

North America dropped six rigs week on week, according to Baker Hughes’ latest North America rotary rig count, which was published on February 13. The total U.S. rig count remained unchanged week on week and the total Canada rig count dropped by six during the same period, pushing the total North America rig count down to 773, comprising 551 rigs from the U.S. and 222 rigs from Canada, the count outlined. Of the total U.S. rig count of 551, 531 rigs are categorized as land rigs, 17 are categorized as offshore rigs, and three are categorized as inland water rigs. The total U.S. rig count is made up of 409 oil rigs, 133 gas rigs, and nine miscellaneous rigs, according to Baker Hughes’ count, which revealed that the U.S. total comprises 481 horizontal rigs, 57 directional rigs, and 13 vertical rigs. Week on week, the U.S. land rig count dropped by one, its offshore rig count rose by one, and its inland water rig count remained unchanged, Baker Hughes highlighted. The U.S. oil rig count decreased by three week on week, while its gas rig count increased by three and its miscellaneous rig count remained unchanged, the count showed. The U.S. horizontal rig count dropped by two week on week, its directional rig count rose by two week on week, and its vertical rig count remained flat during the same period, the count revealed. A major state variances subcategory included in the rig count showed that, week on week, Texas dropped three rigs, Oklahoma and North Dakota each dropped one rig, Louisiana added two rigs, and New Mexico, Pennsylvania, and Wyoming each added one rig. A major basin variances subcategory included in the rig count showed that, week on week, the Permian basin dropped three rigs, the Williston basin dropped

Read More »

Aramco Commits to 1 MMtpa for 20 Years from Commonwealth LNG

Saudi Arabian Oil Co (Aramco) has signed a 20-year agreement to buy one million metric tons per annum (MMtpa) of liquefied natural gas from the under-development Commonwealth LNG in Cameron Parish, Louisiana. “Commonwealth is advancing toward a final investment decision with line of sight to secure its remaining capacity”, said a joint statement by the offtake parties. “Aramco Trading joins Glencore, JERA, PETRONAS, Mercuria and EQT among international energy companies entering into long-term offtake contracts with the platform”. Early this month Commonwealth announced a 20-year deal to supply one MMtpa to Geneva, Switzerland-based energy and commodities trader Mercuria. Commonwealth LNG is a project of Kimmeridge Energy Management Co LLC and Mubadala Investment Co through their joint venture Caturus HoldCo LLC. Expected to start operation 2030, Commonwealth LNG is designed to produce up to 9.5 million metric tons a year of LNG. “This agreement highlights the strong international demand for U.S. LNG and underscores how our longstanding relationships and capabilities position Caturus to serve global markets”, said Caturus chief executive David Lawler. “Our contract with Aramco Trading underscores the differentiated value Caturus can bring through our global reach in offering wellhead to water services”, Lawler added. Mohammed K. Al Mulhim, Aramco Trading president and CEO, said, “This agreement reflects Aramco Trading’s efforts to secure a reliable, long-term energy supply for global markets while strengthening our presence in the LNG sector”. The Gulf Coast project is permitted to ship up to 9.5 MMtpa of LNG, equivalent to around 1.21 billion cubic feet per day of gas according to Kimmeridge. The United States Energy Department granted the project authorization to export to countries without a free trade agreement (FTA) with the U.S. in August 2025 and FTA authorization in April 2020. The developers expect the first phase of the project to generate around

Read More »

Enbridge Q4 Profit Up YoY

Enbridge Inc has reported CAD 1.95 billion ($1.43 billion) in earnings and CAD 1.92 billion in adjusted earnings for the fourth quarter of 2025, up from CAD 493 million and CAD 1.64 billion for the same three-month period in 2024 respectively. Q4 2025 income per share of CAD 0.88 ($0.63), adjusted for extraordinary items, beat the Zacks Consensus Estimate of $0.6. Calgary-based Enbridge, which operates oil and gas pipelines in Canada and the United States, earlier bumped up its quarterly dividend by three percent against the prior rate to CAD 0.97. The annualized rate for 2026 is CAD 3.88 per share. Q4 2025 adjusted EBITDA rose 1.62 percent year-on-year to CAD 5.21 billion “due primarily to favorable gas transmission contracting and Venice Extension entering service, colder weather and higher rates and customer growth at Enbridge Gas Ontario, partially offset by the absence in 2025 of equity earnings related to investment tax credits from our investment in Fox Squirrel Solar”, Enbridge said in an online statement. United States gas transmission contributed CAD 997 million to segment adjusted EBITDA, down from CAD 1 billion for Q4 2024. The U.S. figure benefited from the startup of the Venice Extension Project, which expands the Texas Eastern system’s capacity to deliver gas to Gulf Coast markets, and Enbridge’s acquisition of a stake in the Matterhorn Express Pipeline. Enbridge also recognized “favorable contracting and successful rate case settlements on our U.S. Gas Transmission assets”, partially offset by the timing of operating costs. Adjusted EBITDA from Canadian gas transmission increased from CAD 157 million for Q4 2024 to CAD 190 million for Q4 2025, helped by “higher revenues at Aitken Creek due to favorable storage spreads”. Liquid pipelines logged CAD 2.45 billion in adjusted EBITDA, up from CAD 2.4 billion for Q4 2024. The Mainline System, which carries

Read More »

Analyst Highlights Focus of IEW Event

Focus at the London International Energy Week (IEW) last week was the balancing of geopolitics versus assessed surplus of oil globally in 2026. That’s what Skandinaviska Enskilda Banken AB (SEB) Chief Commodities Analyst Bjarne Schieldrop noted in a SEB report sent to Rigzone on Monday morning, adding that one delegate at the event stated that “if OPEC doesn’t cut, we’ll have $45 per barrel in June”. “That may be true,” Schieldrop said in the report. “But OPEC+ is meeting every month, taking a measure of the state of the global oil market and then decides what to do on the back of that. The group has been very explicit that they may cut, increase, or keep production steady depending on their findings,” he added. “We believe they will and thus we do not buy into $45 per barrel by June because, if need-be, they will trim production as they say they will,” he continued, pointing out that OPEC+ is next scheduled to meet on March 1 “to discuss production for April”. Schieldrop highlighted in the report that, in its February oil market report, the International Energy Agency (IEA) “restated its view that the world will only need 25.7 million barrels per day of crude from OPEC in 2026 versus a recent production by the group of 28.8 million barrels per day”. “I.e. that to keep the market balanced the group will need to cut production by some three million barrels per day,” he said. “Though strategic stock building around the world needs to be deducted from that. And the appetite for such stock building could be solid given elevated geopolitical risks. Thus what will flow to commercial stocks in the end remains to be seen,” he stated. Schieldrop went on to note in the report that increased Iranian tension could drive Brent

Read More »

Hungary Asks Croatia to Allow Russian Crude Shipments

Hungary requested that Croatia allow the shipment of Russian crude via the Adriatic pipeline while a key route through Ukraine remains blocked. Hungarian Foreign Minister Peter Szijjarto and Slovak Economy Minister Denisa Sakova jointly wrote to the Croatian government in Zagreb with the request, Szijjarto said in a statement Sunday. Oil transit along the Druzhba pipeline via Ukraine has been halted since late last month amid large-scale Russian attacks on Ukraine’s energy infrastructure, with the governments in Budapest and Kyiv in a standoff over the fallout. Budapest relies on the Druzhba pipeline connecting Hungary with Russia through war-torn Ukraine for most of its oil flows. Hungarian Prime Minister Viktor Orban, who has remained committed to buying Russian energy sources for his landlocked country, has also frequently engaged in debate with neighboring Croatia over the capacity of the Adriatic pipeline.  Energy policy is also likely to feature in Orban’s talks in Budapest with US Secretary of State Marco Rubio on Monday. Orban has found an ally in Slovak counterpart Robert Fico, who on Sunday echoed his views that Ukraine was using the Druzhba pipeline for political leverage, which officials in Kyiv have denied. What do you think? We’d love to hear from you, join the conversation on the Rigzone Energy Network. The Rigzone Energy Network is a new social experience created for you and all energy professionals to Speak Up about our industry, share knowledge, connect with peers and industry insiders and engage in a professional community that will empower your career in energy.

Read More »

Adani bets $100 billion on AI data centers as India eyes global hub status

The sovereignty question Adani framed the investment as a matter of national digital sovereignty, saying it would reserve a significant portion of GPU capacity for Indian AI startups and research institutions. Analysts were not convinced the structure supported the claim. “I believe it is too distant from digital sovereignty if the majority of the projects are being built to serve leading MNC AI hyperscalers,” said Shah. “Equal investments have to happen for public AI infrastructure, and the data of billions of users — from commerce to content to health — must remain sovereign.” Gogia framed the gap in operational terms. “Ownership alone does not define sovereignty,” he said. “The practical determinants are who controls privileged access during incidents, where critical workloads fail over when grids are stressed, and what regulatory oversight mechanisms are contractually enforceable.” Those are questions Adani has not yet answered and the market, analysts say, will be watching for more than just construction progress. But Banerjee said the market would not wait nine years to judge the announcement. “In practice, I think the market will judge this on near-term proof points, grid capacity secured, power contracting in place, and anchor tenants signed, rather than the headline capex or long-dated targets,” he said.

Read More »

Arista laments ‘horrendous’ memory situation

Digging in on campus Arista has been clear about its plans to grow its presence campus networking environments. Last Fall, Ullal said she expects Arista’s campus and WAN business would grow from the current $750 million-$800 million run rate to $1.25 billion, representing a 60% growth opportunity for the company. “We are committed to our aggressive goal of $1.25 billion for ’26 for the cognitive campus and branch. We have also successfully deployed in many routing edge, core spine and peering use cases,” Ullal said. “In Q4 2025, Arista launched our flagship 7800 R4 spine for many routing use cases, including DCI, AI spines with that massive 460 terabits of capacity to meet the demanding needs of multiservice routing, AI workloads and switching use cases. The combined campus and routing adjacencies together contribute approximately 18% of revenue.” Ethernet leads the way “In terms of annual 2025 product lines, our core cloud, AI and data center products built upon our highly differentiated Arista EOS stack is successfully deployed across 10 gig to 800 gigabit Ethernet speeds with 1.6 terabit migration imminent,” Ullal said. “This includes our portfolio of EtherLink AI and our 7000 series platforms for best-in-class performance, power efficiency, high availability, automation, agility for both the front and back-end compute, storage and all of the interconnect zones.” Ullal said she expects Ethernet will get even more of a boost later this year when the multivendor Ethernet for Scale-Up Networking (ESUN) specification is released.  “We have consistently described that today’s configurations are mostly a combination of scale out and scale up were largely based on 800G and smaller ratings. Now that the ESUN specification is well underway, we need a good solid spec. Otherwise, we’ll be shipping proprietary products like some people in the world do today. And so we will tie our

Read More »

From NIMBY to YIMBY: A Playbook for Data Center Community Acceptance

Across many conversations at the start of this year, at PTC and other conferences alike, the word on everyone’s lips seems to be “community.” For the data center industry, that single word now captures a turning point from just a few short years ago: we are no longer a niche, back‑of‑house utility, but a front‑page presence in local politics, school board budgets, and town hall debates. That visibility is forcing a choice in how we tell our story—either accept a permanent NIMBY-reactive framework, or actively build a YIMBY narrative that portrays the real value digital infrastructure brings to the markets and surrounding communities that host it. Speaking regularly with Ilissa Miller, CEO of iMiller Public Relations about this topic, there is work to be done across the ecosystem to build communications. Miller recently reflected: “What we’re seeing in communities isn’t a rejection of digital infrastructure, it’s a rejection of uncertainty driven by anxiety and fear. Most local leaders have never been given a framework to evaluate digital infrastructure developments the way they evaluate roads, water systems, or industrial parks. When there’s no shared planning language, ‘no’ becomes the safest answer.” A Brief History of “No” Community pushback against data centers is no longer episodic; it has become organized, media‑savvy, and politically influential in key markets. In Northern Virginia, resident groups and environmental organizations have mobilized against large‑scale campuses, pressing counties like Loudoun and Prince William to tighten zoning, question incentives, and delay or reshape projects.1 Loudoun County’s move in 2025 to end by‑right approvals for new facilities, requiring public hearings and board votes, marked a watershed moment as the world’s densest data center market signaled that communities now expect more say over where and how these campuses are built. Prince William County’s decision to sharply increase its tax rate on

Read More »

Nomads at the Frontier: PTC 2026 Signals the Digital Infrastructure Industry’s Moment of Execution

Each January, the Pacific Telecommunications Council conference serves as a barometer for where digital infrastructure is headed next. And according to Nomad Futurist founders Nabeel Mahmood and Phillip Koblence, the message from PTC 2026 was unmistakable: The industry has moved beyond hype. The hard work has begun. In the latest episode of The DCF Show Podcast, part of our ongoing ‘Nomads at the Frontier’ series, Mahmood and Koblence joined Data Center Frontier to unpack the tone shift emerging across the AI and data center ecosystem. Attendance continues to grow year over year. Conversations remain energetic. But the character of those conversations has changed. As Mahmood put it: “The hype that the market started to see is actually resulting a bit more into actions now, and those conversations are resulting into some good progress.” The difference from prior years? Less speculation. More execution. From Data Center Cowboys to Real Deployments Koblence offered perhaps the sharpest contrast between PTC conversations in 2024 and those in 2026. Two years ago, many projects felt speculative. Today, developers are arriving with secured power, customers, and construction underway. “If 2024’s PTC was data center cowboys — sites that in someone’s mind could be a data center — this year was: show me the money, show me the power, give me accurate timelines.” In other words, the market is no longer rewarding hypothetical capacity. It is demanding delivered capacity. Operators now speak in terms of deployments already underway, not aspirational campuses still waiting on permits and power commitments. And behind nearly every conversation sits the same gating factor. Power. Power Has Become the Industry’s Defining Constraint Whether discussions centered on AI factories, investment capital, or campus expansion, Mahmood and Koblence noted that every conversation eventually returned to energy availability. “All of those questions are power,” Koblence said.

Read More »

Cooling Consolidation Hits AI Scale: LiquidStack, Submer, and the Future of Data Center Thermal Strategy

As AI infrastructure scales toward ever-higher rack densities and gigawatt-class campuses, cooling has moved from a technical subsystem to a defining strategic issue for the data center industry. A trio of announcements in early February highlights how rapidly the cooling and AI infrastructure stack is consolidating and evolving: Trane Technologies’ acquisition of LiquidStack; Submer’s acquisition of Radian Arc, extending its reach from core data centers into telco edge environments; and Submer’s partnership with Anant Raj to accelerate sovereign AI infrastructure deployment across India. Layered atop these developments is fresh guidance from Oracle Cloud Infrastructure explaining why closed-loop, direct-to-chip cooling is becoming central to next-generation facility design, particularly in regions where water use has become a flashpoint in community discussions around data center growth. Taken together, these developments show how the industry is moving beyond point solutions toward integrated, scalable AI infrastructure ecosystems, where cooling, compute, and deployment models must work together across hyperscale campuses and distributed edge environments alike. Trane Moves to Own the Cooling Stack The most consequential development comes from Trane Technologies, which on February 10 announced it has entered into a definitive agreement to acquire LiquidStack, one of the pioneers and leading innovators in data center liquid cooling. The acquisition significantly strengthens Trane’s ambition to become a full-service thermal partner for data center operators, extending its reach from plant-level systems all the way down to the chip itself. LiquidStack, headquartered in Carrollton, Texas, built its reputation on immersion cooling and advanced direct-to-chip liquid solutions supporting high-density deployments across hyperscale, enterprise, colocation, edge, and blockchain environments. Under Trane, those technologies will now be scaled globally and integrated into a broader thermal portfolio. In practical terms, Trane is positioning itself to deliver cooling across the full thermal chain, including: • Central plant equipment and chillers.• Heat rejection and controls

Read More »

Infrastructure Maturity Defines the Next Phase of AI Deployment

The State of Data Infrastructure Global Report 2025 from Hitachi Vantara arrives at a moment when the data center industry is undergoing one of the most profound structural shifts in its history. The transition from enterprise IT to AI-first infrastructure has moved from aspiration to inevitability, forcing operators, developers, and investors to confront uncomfortable truths about readiness, resilience, and risk. Although framed around “AI readiness,” the report ultimately tells an infrastructure story: one that maps directly onto how data centers are designed, operated, secured, and justified economically. Drawing on a global survey of more than 1,200 IT leaders, the report introduces a proprietary maturity model that evaluates organizations across six dimensions: scalability, reliability, security, governance, sovereignty, and sustainability. Respondents are then grouped into three categories—Emerging, Defined, and Optimized—revealing a stark conclusion: most organizations are not constrained by access to AI models or capital, but by the fragility of the infrastructure supporting their data pipelines. For the data center industry, the implications are immediate, shaping everything from availability design and automation strategies to sustainability planning and evolving customer expectations. In short, extracting value from AI now depends less on experimentation and more on the strength and resilience of the underlying infrastructure. The Focus of the Survey: Infrastructure, Not Algorithms Although the report is positioned as a study of AI readiness, its primary focus is not models, training approaches, or application development, but rather the infrastructure foundations required to operate AI reliably at scale. Drawing on responses from more than 1,200 organizations, Hitachi Vantara evaluates how enterprises are positioned to support production AI workloads across six dimensions as stated above: scalability, reliability, security, governance, sovereignty, and sustainability. These factors closely reflect the operational realities shaping modern data center design and management. The survey’s central argument is that AI success is no longer

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 »
More Articles