What’s next for AI in 2025

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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

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Physical AI: A Reusable Robotics Stack for Data Center Operations This is where the recent collaboration between Multiply Labs and NVIDIA becomes relevant, even though the application is biomanufacturing rather than data centers. Multiply Labs has outlined a robotics approach built on three core elements: Digital twins using NVIDIA Isaac Sim to model hardware and validate changes in simulation before deployment. Foundation-model-based skill learning via NVIDIA Isaac GR00T, enabling robots to generalize tasks rather than rely on brittle, hard-coded behaviors. Perception pipelines including FoundationPose and FoundationStereo, that convert expert demonstrations into structured training data. Taken together, this represents a reusable blueprint for data center robotics. Applying the Lesson to Data Center Environments The same physical-AI techniques now being applied in lab and manufacturing environments map cleanly onto the realities of data center operations, particularly where safety, uptime, and variability intersect. Digital-twin-first deployment Before a robot ever enters a live data hall, it needs to be trained in simulation. That means modeling aisle geometry, obstacles, rack layouts, reflective surfaces, and lighting variation; along with “what if” scenarios such as blocked aisles, emergency egress conditions, ladders left in place, or spill events. Simulation-first workflows make it possible to validate behavior and edge cases before introducing any new system into a production environment. Skill learning beats hard-coded rules Data centers appear structured, but in practice they are full of variability: temporary cabling, staged parts, mixed-vendor racks, and countless human exceptions. Foundation-model approaches to manipulation are designed to generalize across that messiness far better than traditional rule-based automation, which tends to break when conditions drift even slightly from the expected state. Imitation learning captures tribal knowledge Many operational tasks rely on tacit expertise developed over years in the field, such as how to manage stiff patch cords, visually confirm latch engagement, or stage a

Applied Digital CEO Wes Cummins On the Hard Part of the AI Boom: Execution

Designing for What Comes After the Current AI Cycle Applied Digital’s design philosophy starts with a premise many developers still resist: today’s density assumptions may not hold. “We’re designing for maximum flexibility for the future—higher density power, lower density power, higher voltage delivery, and more floor space,” Cummins said. “It’s counterintuitive because densities are going up, but we don’t know what comes next.” That choice – to allocate more floor space even as rack densities climb – signals a long-view approach. Facilities are engineered to accommodate shifts in voltage, cooling topology, and customer requirements without forcing wholesale retrofits. Higher-voltage delivery, mixed cooling configurations, and adaptable data halls are baked in from the start. The goal is not to predict the future perfectly, Cummins stressed, but to avoid painting infrastructure into a corner. Supply Chain as Competitive Advantage If flexibility is the design thesis, supply chain control is the execution weapon. “It’s a huge advantage that we locked in our MEP supply chain 18 to 24 months ago,” Cummins said. “It’s a tight environment, and more timelines are going to get missed in 2026 because of it.” Applied Digital moved early to secure long-lead mechanical, electrical, and plumbing components; well before demand pressure fully rippled through transformers, switchgear, chillers, generators, and breakers. That foresight now underpins the company’s ability to make credible delivery commitments while competitors confront procurement bottlenecks. Cummins was blunt: many delays won’t stem from poor planning, but from simple unavailability. From 100 MW to 700 MW Without Losing Control The past year marked a structural pivot for Applied Digital. What began as a single, 100-megawatt “field of dreams” facility in North Dakota has become more than 700 MW under construction, with expansion still ahead. “A hundred megawatts used to be considered scale,” Cummins said. “Now we’re at 700

From Silicon to Cooling: Dell’Oro Maps the AI Data Center Buildout

For much of the past decade, data center growth could be measured in incremental gains: another efficiency point here, another capacity tranche there. That era is over. According to a cascade of recent research from Dell’Oro Group, the AI investment cycle has crossed into a new phase, one defined less by experimentation and more by industrial-scale execution. Across servers, networks, power, and cooling, Dell’Oro’s latest data points to a market being reshaped end-to-end by AI workloads which are pulling forward capital spending, redefining bill-of-material assumptions, and forcing architectural transitions that are rapidly becoming non-negotiable. Capex Becomes the Signal The clearest indicator of the shift is spending. Dell’Oro reported that worldwide data center capital expenditures rose 59 percent year-over-year in 3Q 2025, marking the eighth consecutive quarter of double-digit growth. Importantly, this is no longer a narrow, training-centric surge. “The Top 4 US cloud service providers—Amazon, Google, Meta, and Microsoft—continue to raise data center capex expectations for 2025, supported by increased investments in both AI and general-purpose infrastructure,” said Baron Fung, Senior Research Director at Dell’Oro Group. He added that Oracle is on track to double its data center capex as it expands capacity for the Stargate project. “What is notable this cycle is not just the pace of spending, but the expanding scope of investment,” Fung said. Hyperscalers are now scaling accelerated compute, general-purpose servers, and the supporting infrastructure required to deploy AI at production scale, while simultaneously applying tighter discipline around asset lifecycles and depreciation to preserve cash flow. The result is a capex environment that looks less speculative and more structural, with investment signals extending well into 2026. Accelerators Redefine the Hardware Stack At the component level, the AI effect is even more pronounced. Dell’Oro found that global data center server and storage component revenue jumped 40 percent

Rethinking Water in the AI Data Center Era

Finding Water by Eliminating Waste: Leakage as a Hidden Demand Driver ION Water and Meta frame leakage not as a marginal efficiency issue, but as one of the largest and least visible sources of water demand. According to the release, more than half of the water paid for at some properties can be lost to “invisible leaks,” including running toilets, aging water heaters, and faulty fixtures that go undetected for extended periods. ION’s platform is designed to surface that hidden demand. By monitoring water consumption at the unit level, the system flags anomalies in real time and directs maintenance teams to specific fixtures, rather than entire buildings. The company says this approach can reduce leak-driven water waste by as much as 60%. This represents an important evolution in how hyperscalers defend and contextualize their water footprints: Instead of focusing solely on their own direct WUE metrics, operators are investing in demand reduction within the same watershed where their data centers operate. That shift reframes the narrative from simply managing active water consumption to actively helping stabilize stressed local water systems. The Accounting Shift: Volumetric Water Benefits (VWB) The release explicitly positions the project as a model for Volumetric Water Benefits (VWB) initiatives, projects intended to deliver measurable environmental gains while also producing operational and financial benefits for underserved communities. This framing aligns with a broader stewardship accounting movement promoted by organizations such as the World Resources Institute, which has developed Volumetric Water Benefit Accounting (VWBA) as a standardized method for quantifying and valuing watershed-scale benefits. Meta is explicit that the project supports its water-positive commitment tied to its Temple, Texas data center community. The company has set a 2030 goal to restore more water than it consumes across its global operations and has increasingly emphasized “water stewardship in our data center

Microsoft and Meta’s Earnings Week Put the AI Data Center Cycle in Sharp Relief

If you’re trying to understand where the hyperscalers really are in the AI buildout, beyond the glossy campus renders and “superintelligence” rhetoric, this week’s earnings calls from Microsoft and Meta offered a more grounded view. Both companies are spending at a scale the data center industry has never had to absorb at once. Both are navigating the same hard constraints: power, capacity, supply chain, silicon allocation, and time-to-build. But the market’s reaction split decisively, and that divergence tells its own story about what investors will tolerate in 2026. To wit: Massive capex is acceptable when the return narrative is already visible in the P&L…and far less so when the payoff is still being described as “early innings.” Microsoft: AI Demand Is Real. So Is the Cost Microsoft’s fiscal Q2 2026 results reinforced the core fact that has been driving North American hyperscale development for two years: Cloud + AI growth is still accelerating, and Azure remains one of the primary runways. Microsoft said Q2 total revenue rose to $81.3 billion, while Microsoft Cloud revenue reached $51.5 billion, up 26% (constant currency 24%). Intelligent Cloud revenue hit $32.9 billion, up 29%, and Azure and other cloud services revenue grew 39%. That’s the demand signal. The supply signal is more complicated. On the call and in follow-on reporting, Microsoft’s leadership framed the moment as a deliberate capacity build into persistent AI adoption. Yet the bill for that build is now impossible to ignore: Reuters reported Microsoft’s capital spending totaled $37.5 billion in the quarter, up nearly 66% year-over-year, with roughly two-thirds going toward computing chips. That “chips first” allocation matters for the data center ecosystem. It implies a procurement and deployment reality that many developers and colo operators have been living: the short pole is not only power and buildings; it’s GPU

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

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

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

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

2025 playbook for enterprise AI success, from agents to evals

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

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

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

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