The UK government announced it has injected £36 million to increase the power of one of the UK’s leading supercomputing centres sixfold. This includes backing a new National Computational Resource supercomputer ....
The post UK Invests £36M in AMD-Powered Supercomputer at the Univ. of Cambridge appeared first on Inside HPC & AI News | High-Performance Computing & Artificial Intelligence.
The market researchers at Gartner have extended their forecast out to 2027 and dropped 2024 from the view since it is now more than a year past. …
Gartner Takes Another Stab At Forecasting AI Spending was written by Timothy Prickett Morgan at The Next Platform.
Everyone is jumpy about how much capital expenses Microsoft has on the books in 2025 and what it expects to spend on datacenters and their hardware in 2026. …
Microsoft Is More Dependent On OpenAI Than The Converse was written by Timothy Prickett Morgan at The Next Platform.
If you want to know the state of the art in GenAI model development, you watch what the Super 8 hyperscalers and cloud builders are doing and you also keep an eye on the major model builders outside of these companies – mainly, OpenAI, Anthropic, and xAI as well as a few players in China like DeepSeek. …
Big Blue Poised To Peddle Lots Of On Premises GenAI was written by Timothy Prickett Morgan at The Next Platform.
Microsoft is not just the world’s biggest consumer of OpenAI models, but also still the largest partner providing compute, networking, and storage to OpenAI as it builds its latest GPT models. …
Microsoft Takes On Other Clouds With “Braga” Maia 200 AI Compute Engines was written by Timothy Prickett Morgan at The Next Platform.
Intel has been pushing its two-core server CPU strategy for so long, in one form or another, that we have become accustomed to differentiating products the way Intel does and then try to figure out what workloads these chips might be useful for. …
Intel Is Still Struggling In The Datacenter, But It Could Get Better was written by Timothy Prickett Morgan at The Next Platform.
Meta Platforms is not happy just being the social network for the world anymore. …
Meta Platforms Metamorphizing Into An AI Cloud For Sovereigns was written by Timothy Prickett Morgan at The Next Platform.
No, we did not miss the fact that Nvidia did an “acquihire” of AI accelerator and system startup and rival Groq on Christmas Eve. …
Is Nvidia Assembling The Parts For Its Next Inference Platform? was written by Timothy Prickett Morgan at The Next Platform.
If the GenAI expansion runs out of gas, Taiwan Semiconductor Manufacturing Co, the world’s most important foundry for advanced chippery, will be the first to know. …
TSMC Has No Choice But To Trust The Sunny AI Forecasts Of Its Customers was written by Timothy Prickett Morgan at The Next Platform.
If GenAI is going to go mainstream and not just be a bubble that helps prop up the global economy for a couple of years, AI inference is going to have to come down in price – and do so faster than it has done thus far. …
Cerebras Inks Transformative $10 Billion Inference Deal With OpenAI was written by Timothy Prickett Morgan at The Next Platform.
As the year came to an end, we tore apart IDC’s assessments for server spending, including the huge jump in accelerated supercomputers for running GenAI and more traditional machine learning workloads and as this year got started, we did forensic analysis and modeling based on the company’s reckoning of Ethernet switching and routing revenues. …
By Decade’s End, AI Will Drive More Than Half Of All Chip Sales was written by Timothy Prickett Morgan at The Next Platform.
AI is having a seismic impact on processes across all industries, speeding things up and driving costs down. …
Startup Quantum Elements Brings AI, Digital Twins To Quantum Computing was written by Jeffrey Burt at The Next Platform.
In the early days of D-Wave’s history, the company made a decision to pursue annealing as its first technology to build a quantum computer because it promised to offer the fastest path to commercial quantum computing. …
D-Wave Makes Gate-Model Power Move With Quantum Circuits Buy was written by Jeffrey Burt at The Next Platform.
Building realistic 3D environments for robotics simulation has traditionally been a labor-intensive process, often requiring weeks of manual modeling and setup....Building realistic 3D environments for robotics simulation has traditionally been a labor-intensive process, often requiring weeks of manual modeling and setup. Now, with generative world models, you can go from a text prompt to a photorealistic, simulation-ready world in a fraction of time. By combining NVIDIA Isaac Sim, an open source robotics reference framework, with generative models such as…
Building powerful physical AI models requires diverse, controllable, and physically-grounded data at scale. Collecting large-scale, diverse real-world datasets...Building powerful physical AI models requires diverse, controllable, and physically-grounded data at scale. Collecting large-scale, diverse real-world datasets for training can be expensive, time-intensive, and dangerous. NVIDIA Cosmos open world foundation models (WFMs) address these challenges by enabling scalable, high-fidelity synthetic data generation for physical AI and the augmentation of…
Building autonomous robots requires robust, low-latency visual perception for depth, obstacle recognition, localization, and navigation in dynamic environments....
A Vision for the Next Era of Compute from Structure Research’s Jabez Tan
Framing the Future of AI Infrastructure
At the infra/STRUCTURE Summit 2025, held October 15–16 at the Wynn Las Vegas, Jabez Tan, Head of Research at Structure Research, opened the event with a forward-looking keynote titled “Where Is AI Taking Data Centers?” His presentation provided a data-driven perspective on how artificial intelligence (AI) is reshaping digital infrastructure, redefining scale, design, and economics across the global data center ecosystem.
Tan’s session served as both a retrospective on how far the industry has come and a roadmap for where it’s heading. With AI accelerating demand beyond traditional cloud models, his insights set the tone for two days of deep discussion among the sector’s leading operators, investors, and technology providers.
From the Edge to the Core – A Redefinition of Scale
Tan began by looking back just a few years to what he called “the 2022 era of edge obsession.” At that time, much of the industry believed the future of cloud would depend on thousands of small, distributed edge data centers. “We thought the next iteration of cloud would be hundreds of sites at the base of cell towers,” Tan recalled. “But that didn’t really happen.”
Instead, the reality has inverted. “The edge has become the new core,” he said. “Rather than hundreds of small facilities, we’re now building gigawatts of capacity in centralized regions where power and land are available.”
That pivot, Tan emphasized, is fundamentally tied to economics, where cost, energy, and accessibility converge. It reflects how hyperscalers and AI developers are chasing efficiency and scale over proximity, redefining where and how the industry grows.
The AI Acceleration – Demand Without Precedent
Tan then unpacked the explosive demand for compute since late 2022, when AI adoption began its steep ascent following the launch of ChatGPT. He described the industry’s trajectory as a “roller coaster” marked by alternating waves of panic and optimism—but one with undeniable momentum.
The numbers he shared were striking. NVIDIA’s GPU shipments, for instance, have skyrocketed: from 1.3 million H100 Hopper GPUs in 2024 to 3.6 million Blackwell GPUs sold in just the first three months of 2025, a threefold increase in supply and demand. “That translates to an increase from under one gigawatt of GPU-driven demand to over four gigawatts in a single year,” Tan noted.
Tan linked this trend to a broader shift: “AI isn’t just consuming capacity, it’s generating revenue.” Large language model (LLM) providers like OpenAI, Anthropic, and xAI are now producing billions in annual income directly tied to compute access, signaling a business model where infrastructure equals monetization.
Measuring in Compute, Not Megawatts
One of the most notable insights from Tan’s session was his argument that power is no longer the most accurate measure of data center capacity. “Historically, we measured in square footage, then in megawatts,” he said. “But with AI, the true metric is compute, the amount of processing power per facility.”
This evolution is forcing analysts and operators alike to rethink capacity modeling and investment forecasting. Structure Research, Tan explained, is now tracking data centers by compute density, a more precise reflection of AI-era workloads. “The way we define market share and value creation will increasingly depend on how much compute each facility delivers,” he said.
From Training to Inference – The Next Compute Shift
Tan projected that as AI matures, the balance between training and inference workloads will shift dramatically. “Today, roughly 60% of demand is tied to training,” he explained. “Within five years, 80% will be inference.”
That shift will reshape infrastructure needs, pushing more compute toward distributed yet interconnected environments optimized for real-time processing. Tan described a future where inference happens continuously across global networks, increasing utilization, efficiency, and energy demands simultaneously.
The Coming Capacity Crunch
Perhaps the most sobering takeaway from Tan’s talk was his projection of a looming data center capacity shortfall. Based on Structure Research’s modeling, global AI-related demand could grow from 13 gigawatts in 2025 to more than 120 gigawatts by 2030, far outpacing current build rates.
“If development doesn’t accelerate, we could face a 100-gigawatt gap by the end of the decade,” Tan cautioned. He noted that 81% of capacity under development in the U.S. today comes from credible, established providers, but even that won’t be enough to meet demand. “The solution,” he said, “requires the entire ecosystem, utilities, regulators, financiers, and developers to work in sync.”
Fungibility, Flexibility, and the AI Architecture of the Future
Tan also emphasized that AI architecture must become fungible, able to handle both inference and training workloads interchangeably. He explained how hyperscalers are now demanding that facilities support variable cooling and compute configurations, often shifting between air and liquid systems based on real-time needs.
“This isn’t just about designing for GPUs,” he said. “It’s about designing for fluidity, so workloads can move and scale without constraint.”
Tan illustrated this with real-world examples of AI inference deployments requiring hundreds of cross-connects for data exchange and instant access to multiple cloud platforms. “Operators are realizing that connectivity, not just capacity, is the new value driver,” he said.
Agentic AI – A Telescope for the Mind
To close, Tan explored the concept of agentic AI, systems that not only process human inputs but act autonomously across interconnected platforms. He compared its potential to the invention of the telescope.
“When Galileo introduced the telescope, it challenged humanity’s view of its place in the universe,” Tan said. “Large language models are doing something similar for intelligence. They make us feel small today, but they also open an entirely new frontier for discovery.”
He concluded with a powerful metaphor: “If traditional technologies were tools humans used, AI is the first technology that uses tools itself. It’s a telescope for the mind.”
A Market Transformed by Compute
Tan’s session underscored that AI is redefining not only how data centers are built but also how they are measured, financed, and valued. The industry is entering an era where compute density is the new currency, where inference will dominate workloads, and where collaboration across the entire ecosystem is essential to keep pace with demand.
Infra/STRUCTURE 2026: Save the Date
Want to tune in live, receive all presentations, gain access to C-level executives, investors and industry leading research? Then save the date for infra/STRUCTURE 2026 set for October 7-8, 2026 at The Wynn Las Vegas. Pre-Registration for the 2026 event is now open, and you can visit www.infrastructuresummit.io to learn more.
The post Where Is AI Taking Data Centers? appeared first on Data Center POST.
It takes the Earth hundreds of millions of years to create usable energy.
It takes us milliseconds to burn it.
That imbalance between nature’s patience and our speed has quietly become one of the defining forces of our time.
All the power that moves our civilization began as light. Every joule traces back to the Big Bang, carried forward by the sun, stored in plants, pressed into fuels, and now released again as electricity. The current that runs through a data center today began its journey billions of years ago…ancient energy returning to motion through modern machines.
And what do we do with it? We turn it into data.
Data has become the fastest-growing form of energy use in human history. We are creating it faster than we can process, understand, or store it. The speed of data now rivals the speed of light itself, and it far exceeds our ability to assign meaning to it.
The result is a civilization burning geological time to produce digital noise.
The Asymmetry of Time
A hyperscale data center can take three to five years to design, permit, and build. The GPUs inside it process information measured in trillionths of a second. That mismatch; years to construct, microseconds to consume, defines the modern paradox of progress. We are building slower than we burn.
Energy creation is slow. Data consumption is instantaneous. And between those two speeds lies a widening moral and physical gap.
When we run a model, render an image, or stream a video, we aren’t just using electricity. We’re releasing sunlight that’s been waiting since the dawn of life to be freed. The electrons are real, finite, and irreplaceable in any human timeframe — yet we treat data as limitless because its cost is invisible.
Less than two percent of all new data is retained after a year. Ninety-eight percent disappears — deleted, overwritten, or simply forgotten. Still, we build ever-larger servers to hold it. We cool them, power them, and replicate them endlessly. It’s as if we’ve confused movement with meaning.
The Age of the Cat-Video Factory
We’ve built cat-video factories on the same grid that could power breakthroughs in medicine, energy, and climate.
There’s nothing wrong with joy or humor. Those things are a beautiful part of being human. But we’ve industrialized the trivial. We’re spending ancient energy to create data that doesn’t last the length of a memory. The cost isn’t measured in dollars; it’s measured in sunlight.
Every byte carries a birth certificate of energy. It may have traveled billions of years to arrive in your device, only to vanish in seconds. We are burning time itself — and we’re getting faster at it every year.
When Compute Outruns Creation
AI’s rise has made this imbalance impossible to ignore. A one-gigawatt data campus, power consumption that once was allocated to the size of a national power plant, can now belong to a single company. Each facility may cost tens of billions of dollars and consume electricity on par with small nations. We’ve reached a world where the scarcity of electrons defines the frontier of innovation.
It’s no longer the code that limits us; it’s the current.
The technology sector celebrates speed: faster training, faster inference, faster deployment. But nature doesn’t share that sense of urgency. Energy obeys the laws of thermodynamics, not the ambitions of quarterly growth. What took the universe 18 billion years to refine (the conversion of matter into usable light) we now exhaust at a pace that makes geological patience seem quaint.
This isn’t an argument against technology. It’s a reminder that progress without proportion becomes entropy. Efficiency without stewardship turns intelligence into heat.
The Stewardship of Light
There’s a better lens for understanding this moment. One that blends physics with purpose.
If all usable power began in the Big Bang and continues as sunlight, then every act of computation is a continuation of that ancient light’s journey. To waste data is to interrupt that journey; to use it well is to extend it. Stewardship, then, isn’t just environmental — it’s existential.
In finance, CFOs use Return on Invested Power, ROIP to judge whether the energy they buy translates into profitable compute and operational output. But there’s a deeper layer worth considering: a moral ROIP. Beyond the dollars, what kind of intelligence are we generating from the power we consume? Are we creating breakthroughs in medicine, energy, and climate, or simply building larger cat-video factories?
Both forms of ROIP matter. One measures financial return on electrons; the other measures human return on enlightenment. Together, they remind us that every watt carries two ledgers: one economic, one ethical.
We can’t slow AI’s acceleration. But we can bring its metabolism back into proportion. That begins with awareness… the humility to see that our data has ancestry, that our machines are burning the oldest relics of the cosmos. Once you see that, every click, every model, every watt takes on new weight.
The Pause Before Progress
Perhaps our next revolution isn’t speed at all. Perhaps it’s stillness, the mere ability to pause and ask whether the next byte we create honors the journey of the photons that power it.
The call isn’t to stop. It’s to think proportionally.
To remember that while energy cannot be created or destroyed, meaning can.
And that the true measure of progress may not be how much faster we can turn power into data, but how much more wisely we can turn data into light again.
Sunlight is the power. Data is the shadow.
The question is whether our shadows are getting longer… or wiser.
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About the Author
Paul Quigley is President of Airsys Cooling Technologies. He writes about the intersection of power, data, and stewardship. Airsys focuses on groundbreaking technology with a conscience
The post The Speed of Burn appeared first on Data Center POST.
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