Google plans to replenish more freshwater than its data centers consume by 2030 as it expands water stewardship efforts amid growing AI infrastructure scrutiny.
The post Google Bets Water Stewardship Can Ease AI Data Center Concerns appeared first on TechRepublic.
Google plans to replenish more freshwater than its data centers consume by 2030 as it expands water stewardship efforts amid growing AI infrastructure scrutiny.
The post Google Bets Water Stewardship Can Ease AI Data Center Concerns appeared first on TechRepublic.
Fresh off a $2 billion fundraising and $900 million line of credit, London-based data center startup Nscale is planning a big expansion at a new engineering office in Bellevue, Wash.
Nscale, one of the fastest-growing companies building AI computing infrastructure, recently inked a deal for nearly 24,000 square feet of space at The Eight office tower in downtown Bellevue.
The office is slated to open in January 2027. It will serve as Nscale’s primary engineering hub in the United States, a company spokesperson said. The company currently employs about 50 people in the Seattle area, and the new office will be able to accommodate up to 250 people.
The company earlier this year hired Nidhi Chappell, the former Microsoft corporate vice president who led Azure AI and high-performance computing infrastructure, including the supercomputers that power ChatGPT. As Nscale’s new president of AI infrastructure, based in the Seattle area, Chappell will oversee the company’s global engineering and data center operations.
“I’ve had a front-row seat to some of the biggest moments in AI over the past several years, but one thing has always stood out: the world remembers the breakthroughs, but it’s the people building the infrastructure behind the scenes who make them possible,” Chappell wrote in a LinkedIn post last week announcing the company’s first “onboarding” event in Seattle.
Nscale, which is also preparing to open an office in New York, said it selected Bellevue because of the Seattle region’s concentration of AI infrastructure talent and its proximity to major customers.
Microsoft is one example. Earlier this year, the companies announced an expanded collaboration to deploy Microsoft’s next-generation AI infrastructure across Europe, including large-scale installations of NVIDIA Vera Rubin GPUs in Norway, Portugal and other locations. Nscale said it would be among the first providers outside of Microsoft to deploy the Vera Rubin platform, supporting Microsoft’s growing AI cloud infrastructure.
The new office is the latest sign of Bellevue’s growing role in the AI economy. The Eastside has become a magnet for companies building AI applications and infrastructure, with xAI, OpenAI, Databricks, CoreWeave, Armada, Anduril and others establishing and expanding offices.
AI companies have been giving a boost to the regional office market overall. Claude maker Anthropic, for example, recently announced an expansion of its offices in Dexter Yard in Seattle.
Nscale was founded in 2024. Its $2 billion funding round earlier this year valued the company at $14.6 billion, believed to be the largest Series C financing ever raised by a European technology company. The capital is being used to expand Nscale’s AI cloud platform, GPU infrastructure and data center footprint across North America and Europe.
Its backers include Astra Capital Management, Citadel, Dell, Jane Street, Lenovo, Linden Advisors, Nokia, NVIDIA and Point72.
News of the Nscale office in Bellevue was first reported by the Puget Sound Business Journal.
New Zealand's AI infrastructure ambitions are running into growing local opposition over land, power, and water use, giving Samsung's planned floating data centers bigger relevance.
The post Samsung’s Floating Data Center Concept Finds a Fit in New Zealand appeared first on TechRepublic.
New Zealand's AI infrastructure ambitions are running into growing local opposition over land, power, and water use, giving Samsung's planned floating data centers bigger relevance.
The post Samsung’s Floating Data Center Concept Finds a Fit in New Zealand appeared first on TechRepublic.
JPMorgan Chase is building out a new AI software infrastructure team, anchored in Seattle, focused on running AI across its data centers and outside providers in a way that controls costs, protects its intellectual property, and avoids tying its fortunes to any one vendor.
Lori Beer, the bank’s global CIO, discussed the effort as part of a broader interview Tuesday during a stop in Seattle. She said the bank is being “careful about lock-in, strategic risk, financial risk, all those things.”
The move comes as business and tech leaders — including Microsoft CEO Satya Nadella and Palantir CEO Alex Karp — publicly warn about the risks of letting a small number of AI vendors accumulate control over costs, data, and the choice of which AI tools businesses can use.
Beer described the new group as an AI infrastructure team but said it works at the software level, separate from JPMorgan groups that build data centers or procure hardware.
She said the group will, for example, develop systems to determine when to route different types of AI workloads to JPMorgan’s own data centers, when to tap into public cloud providers, and when to use newer specialty computing suppliers.
AI agents are one example of where the bank is drawing a line.
Beer said JPMorgan will build and own the software that runs its agents, while treating the underlying AI models as interchangeable. The agentic layer is specific to JPMorgan’s business, whereas the underlying models are general-purpose, and JPMorgan wants to be able to switch among them as the market changes.
Cost is another focus. Given the option, Beer said, engineers naturally reach for the newest and most powerful model, even when a cheaper one works as well. Systems built by the new team will route specific workloads to different types of models.
The new AI infrastructure team will be spread across multiple JPMorgan locations, but Beer said the Seattle area offers a high concentration of the required skills, including engineers who built cloud infrastructure at Amazon, Microsoft, and other tech platforms before joining JPMorgan.
It’s part of a broader focus on AI at JPMorgan’s Seattle Tech Center, which has grown to about 400 people since opening in 2018, with a heavy emphasis on cybersecurity.
JPMorgan said this week that it has named Ture Armas, the bank’s CTO for Commercial Bank Lending Technology, to lead the Seattle Tech Center. Armas will continue in his existing role while adding oversight of the tech center’s strategy, talent, and community engagement. He replaces Mamtha Banerjee, who left in March.
The Seattle Tech Center is preparing to move next month into an expanded space at the JPMorganChase Center, the skyscraper that was renamed from the Russell Investments Center in January. The tech center is currently located in a smaller space in a nearby building. The move will put engineers closer to business teams, which Beer called critical as AI accelerates the pace of product development.
Beer, who started her career as a software engineer at a nuclear facility, joined JPMorgan in 2014 from health insurer WellPoint. In 2017, she became the first CIO to sit on the bank’s Operating Committee. She oversees a technology division of about 70,000 people, including 45,000 engineers, with a $20 billion annual budget.
JPMorgan reported record second-quarter results Tuesday morning, topping Wall Street expectations. On the earnings call, CEO Jamie Dimon said the bank has almost 1,000 AI use cases across the business, with about 50 he described as the most important, in areas including risk, fraud, marketing, note-taking, and document reading.
In what turned out to be a preview of Beer’s comments later in the day, CFO Jeremy Barnum described the bank’s AI priorities: “Use the right model for the right purpose, be smart about open source where appropriate, and ensure that you’re getting value out of it ultimately.”
As AI workloads drive soaring cloud bills, more companies are weighing whether to move computing out of public clouds and into their own data centers. But building and operating AI infrastructure is far more complicated than simply buying servers — networking has become one of the biggest technical hurdles.
That’s the opportunity Seattle startup Hedgehog is chasing.
Founded in 2022 by CEO Marc Austin, a Cisco networking veteran, Hedgehog develops open-source software designed to make private AI data centers operate more like hyperscale clouds. It has raised $11 million in seed funding, with plans to raise a series A financing round.
We caught up with Austin for the return of GeekWire’s Startup Spotlight to learn more about the 20-person company, the AI networking boom and what surprised him most about building a startup in one of tech’s fastest-moving markets.
In 50 words or less, give us your elevator pitch?
Hedgehog is open-source software that makes AI networking simple. AI clouds and enterprises use it to run GPU networks the way hyperscalers do — deployed in hours instead of months, operated by DevOps teams instead of armies of network engineers, on open hardware with no vendor lock-in.
What problem are you obsessed with solving?
Time to GPU value. A GPU cluster is the most expensive asset most companies will ever buy, and every day it sits idle waiting on the network is money burning. That wait is rarely the hardware — it’s the fabric: weeks or months of scarce network engineers hand-designing, cabling, tuning, and validating it across proprietary CLIs and locked-in vendor gear.
Meanwhile the people told to “own the network” usually aren’t network engineers at all — they’re platform and DevOps teams. We’re obsessed with collapsing that timeline: declare your network like intent in Kubernetes and go from racked GPUs to inference in hours instead of months — on open hardware, no lock-in, no room full of specialists. Cloud-grade networking without hyperscaler headcount.
What surprised you after talking to customers?
How rarely the buyer is a network engineer. It’s platform and DevOps teams, often at AI clouds who just took delivery of thousands of GPUs who are told “you own the network now.” They don’t want to learn BGP; they want a network that behaves like the rest of their cloud-native stack. The other surprise: they don’t just want to run the network, they want to sell it by carving up capacity for their own customers, like a cloud provider does.
How has AI changed the way you build your company?
Twice over.
Our product exists because AI broke traditional networking. Training and inference traffic melts networks designed for web apps.
And AI changed how we build: we use it heavily across engineering, testing, and go-to-market, which lets a small team continuously test every supported device and configuration in our lab and ship with hyperscaler-grade rigor. AI raised the bar for what a startup-sized team can deliver.
What’s one thing people misunderstand about your startup?
That “open source” means hobbyist. The opposite is true: openness is the enterprise feature. Our customers can audit every line of code that runs their fabric, extend it, and never get locked in. Nearly every competitor markets “open networking” while shipping a proprietary controller. Hedgehog is the only one that actually publishes the repo.
What’s the toughest decision you’ve made in the past year?
Betting entirely on Ethernet. We decided open, standards-based Ethernet would win AI networking and put everything behind it. Watching the industry’s largest AI operators now standardize on that same approach makes us feel good about the call — but saying no was hard.
What’s the one piece of advice you give to other entrepreneurs?
Pick the wave, not just the surfboard.
Product decisions are recoverable; betting against a structural industry shift isn’t. Find the standard, the architecture, or the buyer behavior that’s inevitable, align everything to it early, and be patient while the market catches up to your bet.
We’ll know our company has made it when…
Networking is boring again. When a platform engineer stands up a multi-tenant GPU cloud and the network is just a few lines of declared intent that nobody thinks twice about. When “network like a hyperscaler” describes every AI cloud, not just the giants running on Hedgehog, then we will have made it!
Apple’s failed car project reportedly shaped its Neural Engine, future M7 chips, and plans for more powerful AI server infrastructure.
The post Apple’s Failed Car Project Reportedly Shapes M7 AI Chips appeared first on TechRepublic.
Apple’s failed car project reportedly shaped its Neural Engine, future M7 chips, and plans for more powerful AI server infrastructure.
The post Apple’s Failed Car Project Reportedly Shapes M7 AI Chips appeared first on TechRepublic.
See what you missed in Daily Tech Insider from July 6–10.
The post AI Breakthroughs, Security Breaches, and Industry Shakeups Define the Week appeared first on TechRepublic.
See what you missed in Daily Tech Insider from July 6–10.
The post AI Breakthroughs, Security Breaches, and Industry Shakeups Define the Week appeared first on TechRepublic.
Meta reportedly plans to deploy its fourth custom AI chip by September as it looks to cut AI costs and reduce reliance on Nvidia and AMD.
The post Meta’s Custom AI Chip Timeline Just Got More Interesting appeared first on TechRepublic.
Amazon raised $25 billion through a bond sale as AI infrastructure spending accelerates, highlighting the growing cost of competing in the AI race.
The post Amazon Raises $25 Billion in Bond Sale as AI Spending Accelerates appeared first on TechRepublic.
Meta reportedly plans to deploy its fourth custom AI chip by September as it looks to cut AI costs and reduce reliance on Nvidia and AMD.
The post Meta’s Custom AI Chip Timeline Just Got More Interesting appeared first on TechRepublic.
Amazon raised $25 billion through a bond sale as AI infrastructure spending accelerates, highlighting the growing cost of competing in the AI race.
The post Amazon Raises $25 Billion in Bond Sale as AI Spending Accelerates appeared first on TechRepublic.
Samsung Heavy Industries plans to commercialize a floating AI data center by 2028 as power, land, and cooling constraints squeeze AI infrastructure.
The post Samsung Plans Floating Data Center Launch by 2028 appeared first on TechRepublic.
Samsung Heavy Industries plans to commercialize a floating AI data center by 2028 as power, land, and cooling constraints squeeze AI infrastructure.
The post Samsung Plans Floating Data Center Launch by 2028 appeared first on TechRepublic.
AI data centers are running into a network bottleneck as GPU clusters expand. For infrastructure teams, fabric design, congestion control, and interoperability now matter as much as power, cooling, and accelerator supply.
The post AI Data Centers Face a Networking Bottleneck as GPU Clusters Grow appeared first on TechRepublic.
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