Nearly 20 years ago (!), in 2007, I published my first and only book: Microsoft 2.0. It focused on changes I expected at the company in the “Post-Gates” era. What would remain the same and what likely would be different once co-founder and CEO Bill Gates had left the building?

CEO Satya Nadella has not exited the company (yet). But there’s no question that Microsoft and its mission have morphed considerably in the past year or two. I’m not quite ready to christen this the Microsoft 3.0 era, even though Nadella handed the reins of Microsoft’s dominant commercial business to Judson Althoff nearly a year ago.

That decision resulted in Nadella moving into more of a “founder mode” role, allowing him to focus less on the day-to-day work of running the business. (Microsoft historians may recall that Gates made a somewhat similar move back in 2000 when he became Microsoft’s chief software architect.)

While it might not yet be time for Microsoft 3.0, we arguably could be in the “Microsoft 2.5” era. Windows and Office are still around and still play a big role. Microsoft still builds and sells developer tools and databases. But there’s no question that the cloud and all things AI are at the top of the pecking order now.

I’m embarking on a series here at GeekWire that will focus on what matters to Microsoft and, by extension, to its customers, partners, investors, and employees these days. Who are some of the people shaping and leading the company? What are their opportunities and challenges right now?

Over the next few weeks, I will be profiling various Microsoft execs working on plans for Microsoft’s ongoing evolution. Some are company veterans; some are newcomers. I’ll be talking with top execs from Microsoft’s Security, Copilot, Windows + Devices, Xbox, GitHub, and more.

I’m interested in their strategies for Microsoft’s key products and technologies and how they plan to try to turn Microsoft’s ambitious vision into reality. What are their teams building? What do they see as their biggest challenges and opportunities? And where do they see the technologies in their respective areas heading?

I feel like many of us who’ve been keeping track of the biggest tech companies (myself included) have fallen into the trap of blaming or attributing everything a company does to AI. Layoffs? AI is the culprit. Price increases? It’s all thanks to AI. Changing sales strategies? Chalk it up to AI …

But upon further reflection, I believe Microsoft’s strategy is more nuanced than “AI or bust.” There’s no question that Microsoft’s AI ambitions are shaping its goals and tactics. But Microsoft, as a heavily enterprise-focused entity, can’t simply stop supporting products that aren’t built from the ground up with AI (as much as it might like to do so). Nor can it just leave behind customers who aren’t 100% onboard with its AI moves.

Couple those enterprise hurdles with some not-so-popular consumer decisions, like axing 3,200 people in the gaming unit, and Microsoft’s approach to turning the ship looks a lot trickier.

Our Microsoft 2.5 series kicks off Thursday. Stay tuned.

An AWS software bug showed some customers billing estimates in the billions and trillions. Here is what failed, why invoices were unaffected, and what IT teams should know.

The post AWS Billion-Dollar Software Bug Explained appeared first on TechRepublic.

One of Amazon’s top cloud leaders will be joining Meta as the Facebook parent company considers turning its massive AI buildout into a cloud business of its own.

That’s the report from the Wall Street Journal overnight, quoting anonymous sources saying that Dave Brown, the senior Amazon executive who led AWS compute and AI services, will join Meta in the coming weeks to work on its data center build-out.

Meta hasn’t committed to becoming a cloud provider, but CEO Mark Zuckerberg has said the idea is on the table. He told shareholders in May that companies were regularly approaching Meta to pay for access to its AI models or spare computing capacity — a business that would put Meta in competition with cloud providers it now relies on, including AWS.

At Meta, Brown will report to infrastructure chief Santosh Janardhan, according to the WSJ report. Janardhan co-leads Meta Compute, an initiative Zuckerberg launched in January to plan the company’s data center buildout. Meta has said it expects to spend $125 billion to $145 billion on capital expenditures this year, much of it tied to AI data centers.

Amazon isn’t commenting on the report. We’ve contacted Meta for confirmation and details.

Brown’s departure from AWS was announced on Wednesday, with a warmly worded message from AWS CEO Matt Garman giving no indication that Amazon would try to challenge or restrict his new role on competitive grounds.

AWS has gone to court before to enforce noncompete agreements against departing executives, suing two AWS leaders who left for Google Cloud in 2019 and 2020, respectively. But such agreements have grown harder to enforce. California bars them almost entirely, and Washington — Amazon’s home state — enacted a near-total ban this year, though it doesn’t take effect until mid-2027.

Garman’s message said Brown had decided to take “a new role outside of the company” but did not say where he was going. He’s remaining at AWS through the end of July to help with the transition.

At AWS, Brown will be succeeded by Dave Treadwell, a longtime Amazon executive who has run the technology behind the company’s retail operations and spent 27 years at Microsoft before joining Amazon in 2016. He takes over AWS Compute and ML Services on Aug. 1.

[Update, Friday, July 17: Brown is joining Meta, the WSJ reported. More here.]

Dave Brown, who joined Amazon Web Services as one of its earliest EC2 engineers and rose to lead its compute, AI and machine learning services, is leaving after nearly 19 years. 

AWS CEO Matt Garman told employees in a memo posted publicly Wednesday that Brown will depart at the end of July for an unspecified “new role outside of the company.” Amazon exec Dave Treadwell, who joined the company in 2016 after 27 years at Microsoft, will take over the group Aug. 1.

Brown’s exit comes about three months after Amazon promoted him to senior vice president. Brown had been on the company’s senior leadership team since 2023.

His tenure stretched back to the early days of the cloud. He joined AWS in 2007 in Cape Town, South Africa, where Amazon based part of its early EC2 engineering, before relocating to the Seattle area.

In an interview with GeekWire earlier this year, as the company marked the AWS 20th anniversary, Brown recalled Amazon CEO Andy Jassy, then the company’s top cloud executive, gathering the small Cape Town team in those days and telling them the business could one day be worth a billion dollars.

Brown said he could barely grasp the figure at a time when the service was bringing in tens of dollars a day: “I couldn’t even imagine how much a billion dollars was. It sounded like a lot of money.”

AWS today runs at roughly $150 billion in annualized revenue, and grew 28% in its most recent quarter — its fastest pace in nearly four years.

Brown’s role grew with the business. After starting as an engineer on EC2, or Elastic Compute Cloud, he went on to lead its broader compute organization, including close collaborations with the executives running Amazon’s custom silicon business. His purview also expanded to include the machine learning and AI services now central to AWS, such as the Bedrock and SageMaker platforms.

Treadwell has run Amazon’s eCommerce Foundation, the technical backbone of the company’s online retail operations, since joining in 2016. Before that he spent 27 years at Microsoft, where as a corporate vice president he worked on Windows, Xbox, and the .NET software framework.

In his memo, Garman described Treadwell — known internally as “Tread” — as one of AWS’s largest and most vocal internal customers, someone who pushed the cloud group to innovate and will now lead it.

Brown will remain through the end of July to help with the transition. In his own farewell note, he said it felt like the right time to begin a new chapter. “I’ll be cheering you all on from the sidelines,” he wrote.

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!

Microsoft is preparing to cut thousands of jobs next week, continuing to rein in operating costs as the company pours unprecedented sums into AI infrastructure. 

Business Insider broke the news Tuesday afternoon, saying that the cuts will impact less than 2.5% of the company’s global workforce of about 220,000 people. It includes not just Xbox, where cuts have been signaled for weeks, but also layoffs in sales and consulting. 

GeekWire confirmed the details of the report with a person familiar with the company’s plan. Microsoft isn’t commenting on the report.

The timing follows a familiar pattern. Microsoft often restructures its operations around the close of its fiscal year on June 30, and the cuts would come just as the new year begins. 

The reductions were bigger last year. Microsoft laid off more than 15,000 people in two rounds of cuts a few weeks apart: about 6,000 in May 2025, then around 9,000 (roughly 4% of the company at the time) in early July 2025.

One difference this year: Microsoft’s first-ever voluntary retirement program. About a third of the approximately 8,750 eligible U.S. employees took the buyout, reportedly allowing the company to cut a smaller share of its workforce through layoffs than a year ago. 

The company is on pace to spend more than $100 billion building AI and cloud infrastructure in the fiscal year that just ended — up from $88.7 billion the year before — with about two-thirds going to the chips that power AI. 

Microsoft shares closed Tuesday at $373.02, down 19% over the past month and near a 52-week low, as Wall Street questions whether its heavy AI spending will pay off.

The layoffs come amid a broader wave of restructuring across the tech industry, which has shed more jobs than any other sector this year. U.S. tech companies have announced 123,653 cuts so far in 2026, up 66% from the same stretch of 2025, according to a report from outplacement firm Challenger, Gray & Christmas. 

Across all sectors, not just tech, AI was the most commonly cited reason for job cuts in May — the third straight month it has led the list. The 38,579 cuts attributed to AI were the most in any month since Challenger began tracking the cause in 2023. For the year, AI has been linked to 87,714 cuts, already surpassing the 54,836 attributed to it in all of 2025.