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The State of AI: A vision of the world in 2030

Welcome back to The State of AI, a new collaboration between the Financial Times and MIT Technology Review. Every Monday, writers from both publications debate one aspect of the generative AI revolution reshaping global power. You can read the rest of the series here. In this final edition, MIT Technology Review’s senior AI editor Will Douglas Heaven talks with Tim Bradshaw, FT global tech correspondent, about where AI will go next, and what our world will look like in the next five years. (As part of this series, join MIT Technology Review’s editor in chief, Mat Honan, and editor at large, David Rotman, for an exclusive conversation with Financial Times columnist Richard Waters on how AI is reshaping the global economy. Live on Tuesday, December 9 at 1:00 p.m. ET. This is a subscriber-only event and you can sign up here.) Will Douglas Heaven writes:  Every time I’m asked what’s coming next, I get a Luke Haines song stuck in my head: “Please don’t ask me about the future / I am not a fortune teller.” But here goes. What will things be like in 2030? My answer: same but different.  There are huge gulfs of opinion when it comes to predicting the near-future impacts of generative AI. In one camp we have the AI Futures Project, a small donation-funded research outfit led by former OpenAI researcher Daniel Kokotajlo. The nonprofit made a big splash back in April with AI 2027, a speculative account of what the world will look like two years from now.  The story follows the runaway advances of an AI firm called OpenBrain (any similarities are coincidental, etc.) all the way to a choose-your-own-adventure-style boom or doom ending. Kokotajlo and his coauthors make no bones about their expectation that in the next decade the impact of AI will exceed that of the Industrial Revolution—a 150-year period of economic and social upheaval so great that we still live in the world it wrought. At the other end of the scale we have team Normal Technology: Arvind Narayanan and Sayash Kapoor, a pair of Princeton University researchers and coauthors of the book AI Snake Oil, who push back not only on most of AI 2027’s predictions but, more important, on its foundational worldview. That’s not how technology works, they argue. Advances at the cutting edge may come thick and fast, but change across the wider economy, and society as a whole, moves at human speed. Widespread adoption of new technologies can be slow; acceptance slower. AI will be no different.  What should we make of these extremes? ChatGPT came out three years ago last month, but it’s still not clear just how good the latest versions of this tech are at replacing lawyers or software developers or (gulp) journalists. And new updates no longer bring the step changes in capability that they once did.  And yet this radical technology is so new it would be foolish to write it off so soon. Just think: Nobody even knows exactly how this technology works—let alone what it’s really for.  As the rate of advance in the core technology slows down, applications of that tech will become the main differentiator between AI firms. (Witness the new browser wars and the chatbot pick-and-mix already on the market.) At the same time, high-end models are becoming cheaper to run and more accessible. Expect this to be where most of the action is: New ways to use existing models will keep them fresh and distract people waiting in line for what comes next.  Meanwhile, progress continues beyond LLMs. (Don’t forget—there was AI before ChatGPT, and there will be AI after it too.) Technologies such as reinforcement learning—the powerhouse behind AlphaGo, DeepMind’s board-game-playing AI that beat a Go grand master in 2016—is set to make a comeback. There’s also a lot of buzz around world models, a type of generative AI with a stronger grip on how the physical world fits together than LLMs display.  Ultimately, I agree with team Normal Technology that rapid technological advances do not translate to economic or societal ones straight away. There’s just too much messy human stuff in the middle.  But Tim, over to you. I’m curious to hear what your tea leaves are saying.  FT/MIT TECHNOLOGY REVIEW | ADOBE STOCK Tim Bradshaw responds” Will, I am more confident than you that the world will look quite different in 2030. In five years’ time, I expect the AI revolution to have proceeded apace. But who gets to benefit from those gains will create a world of AI haves and have-nots. It seems inevitable that the AI bubble will burst sometime before the end of the decade. Whether a venture capital funding shakeout comes in six months or two years (I feel the current frenzy still has some way to run), swathes of AI app developers will disappear overnight. Some will see their work absorbed by the models upon which they depend. Others will learn the hard way that you can’t sell services that cost $1 for 50 cents without a firehose of VC funding. How many of the foundation model companies survive is harder to call, but it already seems clear that OpenAI’s chain of interdependencies within Silicon Valley make it too big to fail. Still, a funding reckoning will force it to ratchet up pricing for its services. When OpenAI was created in 2015, it pledged to “advance digital intelligence in the way that is most likely to benefit humanity as a whole.” That seems increasingly untenable. Sooner or later, the investors who bought in at a $500 billion price tag will push for returns. Those data centers won’t pay for themselves. By that point, many companies and individuals will have come to depend on ChatGPT or other AI services for their everyday workflows. Those able to pay will reap the productivity benefits, scooping up the excess computing power as others are priced out of the market. Being able to layer several AI services on top of each other will provide a compounding effect. One example I heard on a recent trip to San Francisco: Ironing out the kinks in vibe coding is simply a matter of taking several passes at the same problem and then running a few more AI agents to look for bugs and security issues. That sounds incredibly GPU-intensive, implying that making AI really deliver on the current productivity promise will require customers to pay far more than most do today. The same holds true in physical AI. I fully expect robotaxis to be commonplace in every major city by the end of the decade, and I even expect to see humanoid robots in many homes. But while Waymo’s Uber-like prices in San Francisco and the kinds of low-cost robots produced by China’s Unitree give the impression today that these will soon be affordable for all, the compute cost involved in making them useful and ubiquitous seems destined to turn them into luxuries for the well-off, at least in the near term. The rest of us, meanwhile, will be left with an internet full of slop and unable to afford AI tools that actually work. Perhaps some breakthrough in computational efficiency will avert this fate. But the current AI boom means Silicon Valley’s AI companies lack the incentives to make leaner models or experiment with radically different kinds of chips. That only raises the likelihood that the next wave of AI innovation will come from outside the US, be that China, India, or somewhere even farther afield. Silicon Valley’s AI boom will surely end before 2030, but the race for global influence over the technology’s development—and the political arguments about how its benefits are distributed—seem set to continue well into the next decade.  Will replies:  I am with you that the cost of this technology is going to lead to a world of haves and have-nots. Even today, $200+ a month buys power users of ChatGPT or Gemini a very different experience from that of people on the free tier. That capability gap is certain to increase as model makers seek to recoup costs.  We’re going to see massive global disparities too. In the Global North, adoption has been off the charts. A recent report from Microsoft’s AI Economy Institute notes that AI is the fastest-spreading technology in human history: “In less than three years, more than 1.2 billion people have used AI tools, a rate of adoption faster than the internet, the personal computer, or even the smartphone.” And yet AI is useless without ready access to electricity and the internet; swathes of the world still have neither.  I still remain skeptical that we will see anything like the revolution that many insiders promise (and investors pray for) by 2030. When Microsoft talks about adoption here, it’s counting casual users rather than measuring long-term technological diffusion, which takes time. Meanwhile, casual users get bored and move on.  How about this: If I live with a domestic robot in five years’ time, you can send your laundry to my house in a robotaxi any day of the week.  JK! As if I could afford one.  Further reading  What is AI? It sounds like a stupid question, but it’s one that’s never been more urgent. In this deep dive, Will unpacks decades of spin and speculation to get to the heart of our collective technodream.  AGI—the idea that machines will be as smart as humans—has hijacked an entire industry (and possibly the US economy). For MIT Technology Review’s recent New Conspiracy Age package, Will takes a provocative look at how AGI is like a conspiracy.  The FT examined the economics of self-driving cars this summer, asking who will foot the multi-billion-dollar bill to buy enough robotaxis to serve a big city like London or New York.A plausible counter-argument to Tim’s thesis on AI inequalities is that freely available open-source (or more accurately, “open weight”) models will keep pulling down prices. The US may want frontier models to be built on US chips but it is already losing the global south to Chinese software.

Welcome back to The State of AI, a new collaboration between the Financial Times and MIT Technology Review. Every Monday, writers from both publications debate one aspect of the generative AI revolution reshaping global power. You can read the rest of the series here.

In this final edition, MIT Technology Review’s senior AI editor Will Douglas Heaven talks with Tim Bradshaw, FT global tech correspondent, about where AI will go next, and what our world will look like in the next five years.

(As part of this series, join MIT Technology Review’s editor in chief, Mat Honan, and editor at large, David Rotman, for an exclusive conversation with Financial Times columnist Richard Waters on how AI is reshaping the global economy. Live on Tuesday, December 9 at 1:00 p.m. ET. This is a subscriber-only event and you can sign up here.)

state of AI

Will Douglas Heaven writes: 

Every time I’m asked what’s coming next, I get a Luke Haines song stuck in my head: “Please don’t ask me about the future / I am not a fortune teller.” But here goes. What will things be like in 2030? My answer: same but different. 

There are huge gulfs of opinion when it comes to predicting the near-future impacts of generative AI. In one camp we have the AI Futures Project, a small donation-funded research outfit led by former OpenAI researcher Daniel Kokotajlo. The nonprofit made a big splash back in April with AI 2027, a speculative account of what the world will look like two years from now. 

The story follows the runaway advances of an AI firm called OpenBrain (any similarities are coincidental, etc.) all the way to a choose-your-own-adventure-style boom or doom ending. Kokotajlo and his coauthors make no bones about their expectation that in the next decade the impact of AI will exceed that of the Industrial Revolution—a 150-year period of economic and social upheaval so great that we still live in the world it wrought.

At the other end of the scale we have team Normal Technology: Arvind Narayanan and Sayash Kapoor, a pair of Princeton University researchers and coauthors of the book AI Snake Oil, who push back not only on most of AI 2027’s predictions but, more important, on its foundational worldview. That’s not how technology works, they argue.

Advances at the cutting edge may come thick and fast, but change across the wider economy, and society as a whole, moves at human speed. Widespread adoption of new technologies can be slow; acceptance slower. AI will be no different. 

What should we make of these extremes? ChatGPT came out three years ago last month, but it’s still not clear just how good the latest versions of this tech are at replacing lawyers or software developers or (gulp) journalists. And new updates no longer bring the step changes in capability that they once did. 

And yet this radical technology is so new it would be foolish to write it off so soon. Just think: Nobody even knows exactly how this technology works—let alone what it’s really for. 

As the rate of advance in the core technology slows down, applications of that tech will become the main differentiator between AI firms. (Witness the new browser wars and the chatbot pick-and-mix already on the market.) At the same time, high-end models are becoming cheaper to run and more accessible. Expect this to be where most of the action is: New ways to use existing models will keep them fresh and distract people waiting in line for what comes next. 

Meanwhile, progress continues beyond LLMs. (Don’t forget—there was AI before ChatGPT, and there will be AI after it too.) Technologies such as reinforcement learning—the powerhouse behind AlphaGo, DeepMind’s board-game-playing AI that beat a Go grand master in 2016—is set to make a comeback. There’s also a lot of buzz around world models, a type of generative AI with a stronger grip on how the physical world fits together than LLMs display. 

Ultimately, I agree with team Normal Technology that rapid technological advances do not translate to economic or societal ones straight away. There’s just too much messy human stuff in the middle. 

But Tim, over to you. I’m curious to hear what your tea leaves are saying. 

Tim Bradshaw and Will Douglas Heaven

FT/MIT TECHNOLOGY REVIEW | ADOBE STOCK

Tim Bradshaw responds“

Will, I am more confident than you that the world will look quite different in 2030. In five years’ time, I expect the AI revolution to have proceeded apace. But who gets to benefit from those gains will create a world of AI haves and have-nots.

It seems inevitable that the AI bubble will burst sometime before the end of the decade. Whether a venture capital funding shakeout comes in six months or two years (I feel the current frenzy still has some way to run), swathes of AI app developers will disappear overnight. Some will see their work absorbed by the models upon which they depend. Others will learn the hard way that you can’t sell services that cost $1 for 50 cents without a firehose of VC funding.

How many of the foundation model companies survive is harder to call, but it already seems clear that OpenAI’s chain of interdependencies within Silicon Valley make it too big to fail. Still, a funding reckoning will force it to ratchet up pricing for its services.

When OpenAI was created in 2015, it pledged to “advance digital intelligence in the way that is most likely to benefit humanity as a whole.” That seems increasingly untenable. Sooner or later, the investors who bought in at a $500 billion price tag will push for returns. Those data centers won’t pay for themselves. By that point, many companies and individuals will have come to depend on ChatGPT or other AI services for their everyday workflows. Those able to pay will reap the productivity benefits, scooping up the excess computing power as others are priced out of the market.

Being able to layer several AI services on top of each other will provide a compounding effect. One example I heard on a recent trip to San Francisco: Ironing out the kinks in vibe coding is simply a matter of taking several passes at the same problem and then running a few more AI agents to look for bugs and security issues. That sounds incredibly GPU-intensive, implying that making AI really deliver on the current productivity promise will require customers to pay far more than most do today.

The same holds true in physical AI. I fully expect robotaxis to be commonplace in every major city by the end of the decade, and I even expect to see humanoid robots in many homes. But while Waymo’s Uber-like prices in San Francisco and the kinds of low-cost robots produced by China’s Unitree give the impression today that these will soon be affordable for all, the compute cost involved in making them useful and ubiquitous seems destined to turn them into luxuries for the well-off, at least in the near term.

The rest of us, meanwhile, will be left with an internet full of slop and unable to afford AI tools that actually work.

Perhaps some breakthrough in computational efficiency will avert this fate. But the current AI boom means Silicon Valley’s AI companies lack the incentives to make leaner models or experiment with radically different kinds of chips. That only raises the likelihood that the next wave of AI innovation will come from outside the US, be that China, India, or somewhere even farther afield.

Silicon Valley’s AI boom will surely end before 2030, but the race for global influence over the technology’s development—and the political arguments about how its benefits are distributed—seem set to continue well into the next decade. 

Will replies: 

I am with you that the cost of this technology is going to lead to a world of haves and have-nots. Even today, $200+ a month buys power users of ChatGPT or Gemini a very different experience from that of people on the free tier. That capability gap is certain to increase as model makers seek to recoup costs. 

We’re going to see massive global disparities too. In the Global North, adoption has been off the charts. A recent report from Microsoft’s AI Economy Institute notes that AI is the fastest-spreading technology in human history: “In less than three years, more than 1.2 billion people have used AI tools, a rate of adoption faster than the internet, the personal computer, or even the smartphone.” And yet AI is useless without ready access to electricity and the internet; swathes of the world still have neither. 

I still remain skeptical that we will see anything like the revolution that many insiders promise (and investors pray for) by 2030. When Microsoft talks about adoption here, it’s counting casual users rather than measuring long-term technological diffusion, which takes time. Meanwhile, casual users get bored and move on. 

How about this: If I live with a domestic robot in five years’ time, you can send your laundry to my house in a robotaxi any day of the week. 

JK! As if I could afford one. 

Further reading 

What is AI? It sounds like a stupid question, but it’s one that’s never been more urgent. In this deep dive, Will unpacks decades of spin and speculation to get to the heart of our collective technodream. 

AGI—the idea that machines will be as smart as humans—has hijacked an entire industry (and possibly the US economy). For MIT Technology Review’s recent New Conspiracy Age package, Will takes a provocative look at how AGI is like a conspiracy. 

The FT examined the economics of self-driving cars this summer, asking who will foot the multi-billion-dollar bill to buy enough robotaxis to serve a big city like London or New York.
A plausible counter-argument to Tim’s thesis on AI inequalities is that freely available open-source (or more accurately, “open weight”) models will keep pulling down prices. The US may want frontier models to be built on US chips but it is already losing the global south to Chinese software.

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Why network bandwidth matters a lot

One interesting point about VPNs is raised by fully a third of capacity-hungry enterprises: SD-WAN is the cheapest and easiest way to increase capacity to remote sites. Yes, service reliability of broadband Internet access for these sites is highly variable, so enterprises say they need to pilot test in a target area to determine whether even business-broadband Internet is reliable enough, but if it is, high capacity is both available and cheap. Clearly data center networking is taking the prime position in enterprise network planning, even without any contribution from AI. Will AI contribute? Enterprises generally believe that self-hosted AI will indeed require more network bandwidth, but again think this will be largely confined to the data center. AI, they say, has a broader and less predictable appetite for data, and business applications involving the data that’s subject to governance, or that’s already data-center hosted, are likely to be hosted proximate to the data. That was true for traditional software, and it’s likely just as true for AI. Yes, but…today, three times as many enterprises say that they’d use AI needs simply to boost justification for capacity expansion as think they currently need it. AI hype has entered, and perhaps even dominates, capital network project justifications. These capacity trends don’t impact enterprises alone, they also reshape the equipment space. Only 9% of enterprises say they have invested in white-box devices to build capacity and data center configuration flexibility, but the number that say they would evaluate them in 2026 is double that. This may be what’s behind Cisco’s decision to push its new G300 chip. AI’s role in capital project justifications may also be why Cisco positions the G300 so aggressively as an AI facilitator. Make no mistake, though; this is really all about capacity and QoE, even for AI.

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JLL: Hyperscale and AI Demand Push North American Data Centers Toward Industrial Scale

JLL’s North America Data Center Report Year-End 2025 makes a clear argument that the sector is no longer merely expanding but has shifted into a phase of industrial-scale acceleration driven by hyperscalers, AI platforms, and capital markets that increasingly treat digital infrastructure as core, bond-like collateral. The report’s central thesis is straightforward. Structural demand has overwhelmed traditional real estate cycles. JLL supports that claim with a set of reinforcing signals: Vacancy remains pinned near zero. Most new supply is pre-leased years ahead. Rents continue to climb. Debt markets remain highly liquid. Investors are engineering new financial structures to sustain growth. Author Andrew Batson notes that JLL’s Data Center Solutions team significantly expanded its methodology for this edition, incorporating substantially more hyperscale and owner-occupied capacity along with more than 40 additional markets. The subtitle — “The data center sector shifts into hyperdrive” — serves as an apt one-line summary of the report’s posture. The methodological change is not cosmetic. By incorporating hyper-owned infrastructure, total market size increases, vacancy compresses, and historical time series shift accordingly. JLL is explicit that these revisions reflect improved visibility into the market rather than a change in underlying fundamentals; and, if anything, suggest prior reports understated the sector’s true scale. The Market in Three Words: Tight, Pre-Leased, Relentless The report’s key highlights page serves as an executive brief for investors, offering a concise snapshot of market conditions that remain historically constrained. Vacancy stands at just 1%, unchanged year over year, while 92% of capacity currently under construction is already pre-leased. At the same time, geographic diversification continues to accelerate, with 64% of new builds now occurring in so-called frontier markets. JLL also notes that Texas, when viewed as a unified market, could surpass Northern Virginia as the top data center market by 2030, even as capital

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7×24 Exchange’s Dennis Cronin on the Data Center Workforce Crisis: The Talent Cliff Is Already Here

The data center industry has spent the past two years obsessing over power constraints, AI density, and supply chain pressure. But according to longtime mission critical leader Dennis Cronin, the sector’s most consequential bottleneck may be far more human. In a recent episode of the Data Center Frontier Show Podcast, Cronin — a founding member of 7×24 Exchange International and board member of the Mission Critical Global Alliance (MCGA) — delivered a stark message: the workforce “talent cliff” the industry keeps discussing as a future risk is already impacting operations today. A Million-Job Gap Emerging Cronin’s assessment reframes the workforce conversation from a routine labor shortage to what he describes as a structural and demographic challenge. Based on recent analysis of open roles, he estimates the industry is currently short between 467,000 and 498,000 workers across core operational positions including facilities managers, operations engineers, electricians, generator technicians, and HVAC specialists. Layer in emerging roles tied to AI infrastructure, sustainability, and cyber-physical security, and the potential demand rises to roughly one million jobs. “The coming talent cliff is not coming,” Cronin said. “It’s here, here and now.” With data center capacity expanding at roughly 30% annually, the workforce pipeline is not keeping pace with physical buildout. The Five-Year Experience Trap One of the industry’s most persistent self-inflicted wounds, Cronin argues, is the widespread requirement for five years of experience in roles that are effectively entry level. The result is a closed-loop hiring dynamic: New workers can’t get hired without experience They can’t gain experience without being hired Operators end up poaching from each other Workers may benefit from the resulting 10–20% salary jumps, but the overall talent pool remains stagnant. “It’s not helping us grow the industry,” Cronin said. In a market defined by rapid expansion and increasing system complexity, that

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Aeroderivative Turbines Move to the Center of AI Data Center Power Strategy

From “Backup” to “Bridging” to Behind-the-Meter Power Plants The most important shift is conceptual: these systems are increasingly blurring the boundary between emergency backup and primary power supply. Traditionally, data center electrical architecture has been clearly tiered: UPS (seconds to minutes) to ride through utility disturbances and generator start. Diesel gensets (minutes to hours or days) for extended outages. Utility grid as the primary power source. What’s changing is the rise of bridging power:  generation deployed to energize a site before the permanent grid connection is ready, or before sufficient utility capacity becomes available. Providers such as APR Energy now explicitly market turbine-based solutions to data centers seeking behind-the-meter capacity while awaiting utility build-out. That framing matters because it fundamentally changes expected runtime. A generator that operates for a few hours per year is one regulatory category. A turbine that runs continuously for weeks or months while a campus ramps is something very different; and it is drawing increased scrutiny from regulators who are beginning to treat these installations as material generation assets rather than temporary backup systems. The near-term driver is straightforward. AI workloads are arriving faster than grid infrastructure can keep pace. Data Center Frontier and other industry observers have documented the growing scramble for onsite generation as interconnection queues lengthen and critical equipment lead times expand. Mainstream financial and business media have taken notice. The Financial Times has reported on data centers turning to aeroderivative turbines and diesel fleets to bypass multi-year power delays. Reuters has likewise covered large gas-turbine-centric strategies tied to hyperscale campuses, underscoring how quickly the co-located generation model is moving into the mainstream. At the same time, demand pressure is tightening turbine supply chains. Industry reporting points to extended waits for new units, one reason repurposed engine cores and mobile aeroderivative packages are gaining

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Cooling’s New Reality: It’s Not Air vs. Liquid Anymore. It’s Architecture.

By early 2026, the data center cooling conversation has started to sound less like a product catalog and more like a systems engineering summit. The old framing – air cooling versus liquid cooling – still matters, but it increasingly misses the point. AI-era facilities are being defined by thermal constraints that run from chip-level cold plates to facility heat rejection, with critical decisions now shaped by pumping power, fluid selection, reliability under ambient extremes, water availability, and manufacturing throughput. That full-stack shift is written all over a grab bag of recent cooling announcements. On one end of the spectrum we see a Department of Energy-funded breakthrough aimed directly at next-generation GPU heat flux. On the other, it’s OEM product launches built to withstand –20°F to 140°F operating conditions and recover full cooling capacity within minutes of a power interruption. In between we find a major acquisition move for advanced liquid cooling IP, a manufacturing expansion that more than doubles footprint, and the quiet rise of refrigerants and heat-transfer fluids as design-level considerations. What’s emerging is a new reality. Cooling is becoming one of the primary constraints on AI deployment technically, economically, and geographically. The winners will be the players that can integrate the whole stack and scale it. 1) The Chip-level Arms Race: Single-phase Fights for More Runway The most “pure engineering” signal in this news batch comes from HRL Laboratories, which on Feb. 24, 2026 unveiled details of a single-phase direct liquid cooling approach called Low-Chill™. HRL’s framing is pointed: the industry wants higher GPU and rack power densities, but many operators are wary of the cost and operational complexity of two-phase cooling. HRL says Low-Chill was developed under the U.S. Department of Energy’s ARPA-E COOLERCHIPS program, and claims a leap that goes straight at the bottleneck. It can increase

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Policy Shock: Big Tech Told to Power Its Own AI Buildout

The AI data center boom has been colliding with grid reality for more than two years. This week, the issue moved closer to the policy front lines. The White House is advancing a “ratepayer protection” framework that has gained visibility in recent days, aimed at ensuring large AI data center projects do not shift grid upgrade costs onto residential customers. It’s a signal widely interpreted by industry observers as encouraging hyperscalers to bring dedicated power solutions to the table. The Power Question Moves to Center Stage Washington now appears poised to push the industry toward a structural response to the data center power conundrum. The new federal impetus for major technology companies to shoulder the cost of their own power infrastructure is quickly emerging as one of the most consequential policy developments for the digital infrastructure sector in 2026. If formalized, the initiative would effectively codify a shift already underway which has found hyperscale and AI developers moving aggressively toward behind-the-meter generation and dedicated energy strategies. For an industry already grappling with interconnection delays, utility pushback, and mounting community scrutiny, the signal is unmistakable. The era of relying primarily on shared grid capacity for large AI campuses may be ending. From Market Trend to Policy Direction Large tech firms, including the biggest cloud and AI players, have been under increasing pressure from regulators and utilities concerned about ratepayer exposure and grid reliability. Policymakers are now signaling that future large-load approvals may hinge on whether developers can demonstrate energy self-sufficiency or dedicated supply. The logic is straightforward. AI campuses are arriving at hundreds of megawatts to gigawatt scale. Transmission upgrades are measured in multi-year timelines. Utilities face growing political pressure to protect residential customers. In that context, the emerging federal posture does not create a new trend so much as accelerate

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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.

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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

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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

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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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