When Microsoft released Windows 11 five years ago, it felt like an operating system that was still being renovated, a work in progress. You'd think by now that those renovations would be complete, with Microsoft turning its attention to Windows 12. Instead, Windows 11 feels like it's here to stay, forming the foundation for Microsoft's next big ambition: transforming Windows into an agentic OS.
At a Windows and Surface event this week, Microsoft laid out its plan to bring AI agents to Windows, including hybrid intelligence that lets Windows take advantage of free local models instead of expensive cloud ones. "I think today is the day we'll …
President Donald Trump has a knack for turning words against his enemies. His first successful presidential run was built on monikers like "Little Marco" and "Crooked Hillary"; he changed "fake news" from a phrase describing scammy media outlets to a derogatory term for the press at large. Over the past few weeks, he's clearly decided he can work the same magic to promote artificial intelligence - branding a technology he wants to accelerate "super", while turning "artificial" into his latest go-to pejorative and declaring resisters "THE ENEMY." Some of the biggest names in AI are going along with him. But he's picked a tough linguistic batt …
GitHub COO Kyle Daigle, who now also leads developer marketing for all of Microsoft, speaks at GitHub Universe last year. (Microsoft Photo)
GeekWire is profiling over the next few weeks some of the people and teams that are shaping the evolution of Microsoft in what we’re calling its “Microsoft 2.5” era.
A delicate dance: In August 2025, Microsoft moved to integrate GitHub into the mothership, under its CoreAI division, after having allowed it to run as a quasi-independent entity since acquiring it in 2018. At the same time, GitHub CEO Thomas Dohmke announced he would leave the company at the end of the year.
A number of developers feared the worst: GitHub would become just another arm of Microsoft and lose the community-mindedness that had made it attractive to developers of all stripes, and especially open-source ones.
GitHub Chief Operating Officer Kyle Daigle — a 13-plus-year GitHub veteran — had been one of the main champions of the need to “keep GitHub GitHub.” He maintained that stance even after he added the “Chief Marketing Officer of Developer” title for all of Microsoft to his COO role in January 2026.
So, how has Microsoft done this past year with managing GitHub? In an interview with GeekWire, Daigle acknowledged that GitHub’s relationship with Microsoft has changed since the end of the standalone CEO era. But the goal is no longer just “don’t break GitHub.” It’s to export GitHub’s community, developer-first ethos and learnings across Microsoft.
GitHub has influence beyond just the GitHub product set now, Daigle said. And the fact that Microsoft opted to consolidate its developer-facing messaging and community outreach under Daigle, someone who came from GitHub, not from Microsoft’s traditional DevDiv organization, gives weight to his claim.
“Bringing teams together — engineering teams and marketing teams and everyone that wasn’t talking to each other before — has been a really big part of the work,” Daigle said. “I see all these opportunities for GitHub to directly help and impact the overall mission of Microsoft versus keeping it cloistered in a way that isn’t helping either GitHub’s mission or Microsoft’s mission.”
These days, developer teams across Microsoft and GitHub are sharing foundations and the GitHub Copilot software development kit across organizations “in a way that would have seemed improbable before,” he said.
As Microsoft historians know, “Microsoft’s challenge isn’t creating products. It’s connecting them,” Daigle said.
Growing pains: At the same time, the rise of AI agents has strained GitHub’s infrastructure. Over the past few months, GitHub has experienced some significant outages, including one in August that lasted nearly eight hours.
GitHub officials have said they are migrating from their own datacenters to Azure to try to ease some of the capacity issues. Daigle said it’s not a simple lift-and-shift process; rather, it’s a major re-architecture for the platform.
GitHub is roughly 60% through its Azure migration as of early October, he said.
It’s working with Azure storage teams on separating compute and storage for the Git distributed version control system to help alleviate bottlenecks caused by developers and agents working concurrently in the same repositories. Developers and agents made 7.38 billion commits on GitHub in September alone, according to the company.
GitHub’s recent growth would not have been manageable without Microsoft scaling expertise and Azure support, Daigle said. GitHub is in a unique position of being able to request major additional capacity, such as millions of CPUs, and work with Microsoft teams to provision it, he said.
The GitHub app and agent store: GitHub got its start as a platform for source control and collaboration. But its role is evolving as part of its Microsoft integration. In addition to developing and supporting Microsoft’s most successful Copilot, GitHub Copilot, GitHub has an important place in Microsoft’s agent-centric strategy.
GitHub is becoming “the store for anything that needs to be coded and needs some verification,” Daigle said. “We’ll connect you to whatever the right tool inside of Microsoft is for you to run that or whatever tool you’re using, not just (from) the Microsoft product suite.”
In the new world order, GitHub isn’t just the store for apps. Increasingly, it’s also the store for agents, which means GitHub is acting as the developer layer for Microsoft’s “agent factory” strategy.
(Microsoft co-founder Bill Gates envisioned Microsoft as a “software factory” that could produce software at scale. These days, CEO Satya Nadella and team talk about Microsoft as an “agent factory,” helping customers create agents at scale.)
GitHub currently serves more than 200 million developers, plus an unknown but quickly growing number of agents.
Developers need to be thinking in new ways when it comes to agents, Daigle said. They need to consider ideas such as scaling application programming interfaces (APIs) for agents separately from humans, creating APIs designed specifically for agents, rewriting documentation and tooling so agents can consume GitHub efficiently, and treating agent access as a fundamentally different workload from human access.
In the longer term, Daigle said he expects AI to make software economically viable for smaller audiences, such as a family, an individual or a single team. The app-store model may change substantially in the coming years, as people create highly specialized applications. GitHub could help like-minded users discover software for specific interests, such as a narrowly focused household or community use case.
GitHub historically has considered anyone who has seen or touched code to be part of the developer community. Daigle’s vision is for GitHub and Microsoft to let both professional developers and newer builders create and run software and agents without needing to manage token costs, uptime, monitoring or operational overhead.
GitHub will have more to say about its plans at the GitHub Universe conference in San Francisco, Oct. 28-29. The event won’t focus on code generation alone; it will include information on tools covering the full lifecycle, proactive security approaches and how to determine whether software is working well after deployment.
“We’re looking to solve the core parts of the platform, like Git, like Actions, in a way that I hope will make people excited and feel like they can absolutely trust GitHub for the next decade to come,” Daigle said.
Someone bursts through the door and announces, “There’s pizza!” Nearby, a team has duct tape and cardboard holding its prototype together. Another is debugging a model that won’t detect their movements.
This is a common scene during hackathons, and for a lot of people they’re the place to learn to build.
During several recent hackathons, we followed dozens of participants from the badge line to final demos and asked them what keeps them coming back.
Two days of intense focus
Teams come up with ideas for robots, apps, websites, hardware, and more, then try to get prototypes working before time runs out.
These aren’t small things, these are big endeavors, and they’re going to try to do it in basically two days.
Kyle, hackathon participant
Yana describes it as two days of intense focus on one small experimental project that moves the needle forward. For Nithya, it’s a rare chance to work as hard as you can on something you’re proud of.
It is basically an invention marathon.
Mike, hackathon participant
You don’t need a computer science degree
For most of computing history, building software required years of specialized training. Not anymore: with AI-powered tools like GitHub Copilot, natural language is becoming a universal programming language, and anyone with an idea can start turning it into working code.
Lance’s team hit a problem at the very last minute. They needed a news API so people could fact-check the information they were getting.
GitHub Copilot was able to not only create a frontend in 30 minutes but also connects with the actual API itself and integrates it all together.
Lance, hackathon participant
When they’re paired up with someone that’s a CS major, or when they just go and use AI to start a prototype and convince others to join in on their idea, it’s giving this autonomy for people to do what they’re most passionate about.
Kyle
The pizza is free, so are the new skills
Call it a learning event, Jon says, and people might not want to go. But tell them there’s pizza, and they’ll show up anyway, and they’ll still end up learning.
Jon calls it the best educational experience you can get as a technologist. Sarvesh puts it more simply: it’s a better way to learn.
Eric calls hackathons a huge grind of continuous work and continuous debugging. Vaishnavi’s team spent part of the time fighting a model that couldn’t detect their movements. “Like, nothing is working,” Vaishnavi said at one point. Mayank’s team got there through trial and error, with duct tape and cardboard.
Hackathons are a safe third place. It’s okay to fail and experiment. You’re not going to get an F, you’re not going to get fired over it.
Mike
Friendships of practice
Niels once searched Google to find out who came up with the term “hackathon,” and his own name came up. He doesn’t remember coining it, but he does remember setting laptops down wherever there was space, sleeping on the floor in sleeping bags, and going straight back to hacking after waking up.
If you feel you have to do it and you don’t like the people around you, then you’re missing the point.
Niels, hackathon participant
Akankshya found everyone open to collaborating and never felt pushed to quit or leave. Shehmeer could turn to the person on either side and ask how to do something. If they didn’t know, they usually knew someone there who did.
My first hackathon, I met two guys that are now my best friends.
Dev, hackathon participant
Zach has a name for it:
When you meet someone who you bond with over a shared interest, that’s a friendship of practice.
Zach, hackathon participant
Impact that changes lives
Mike’s first hackathon changed the course of his career:
At my first hackathon, I learned more in one weekend than I had in my entire university degree up to that point.
Mike
The next day, Mike looked in the mirror and told himself to forget being a lawyer and become a hacker.
Lee has seen hackathons act as a bridge between the spark of an idea and something rolled out into production. Shehmeer says organizers are trying to show a new generation that building isn’t out of reach.
For Yana, seeing the work happen pulls more people in, because there’s now a bit more of an open door.
You can solve problems yourself, you don’t have to ask someone else for permission.
Zach
Get started today
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AI systems are gaining more autonomy while governments, companies, and researchers are still working out how much oversight they need. On the latest episode of This Week in AI, we covered the US debate over AI governance, new frontier models, persistent agents, world models, and practical uses for AI in healthcare and disaster response.
AI oversight is moving beyond company promises
The Trump administration announced a voluntary agreement with major AI companies that calls for internal safety monitoring, external audits, and independent board reviews. Because the agreement carries no legal enforcement, it raises a familiar question about how far self-regulation can go when companies are developing increasingly powerful systems.
Government agencies are also testing what existing law can do. The Federal Trade Commission launched an investigation into OpenAI, Anthropic, and other AI companies focused on potential consumer risks. Cases like these could help establish whether current consumer protection laws are enough or whether AI will require a more specialized regulatory framework.
For technical leaders, regulation can influence how organizations evaluate models, document risks, manage access, and choose vendors. As AI moves deeper into business processes, teams will need governance practices that can withstand outside review.
AI systems are taking on longer, more autonomous work
OpenAI just released Dots, its “proactive assistant” designed to retain context, work across applications, pursue multiple goals, and act independently. To do that work without waiting for a prompt, Dots needs standing access to the apps and data it works across. That also increases the amount of personal data an agent can reach and raises the cost of mistakes or misuse.
Google is also extending how long a model can work on a task. The company says Gemini 4 Argon can generate up to a million output tokens in a single response, far beyond the typical output limits of current frontier models. The goal is to let a model stay with long multistep work such as extensive coding or financial and legal analysis. Longer-running models and persistent agents aren’t the same thing, but both let AI finish more work without handing control back to a person.
As agents act more on their own, they need to anticipate the consequences of their actions. That becomes even more important when AI moves beyond software and begins acting in the physical world. World models aim to teach AI how these environments work, including cause and effect, which is why many researchers see them as building blocks for robotics and physical AI. World Labs (recently acquired by AMD) is developing spatial intelligence models for interactive 3D environments, while British startup Worldmodeldata has licensed nearly 1 million hours of video game data paired with player actions. Researchers from NVIDIA, MIT, and Oxford also introduced Physis-Lang, which uses descriptions of physical causes and effects to help video models learn why events happen. Researchers still don’t know which training approach will work best, so they’re testing several kinds of data and model design.
Together, these examples show how AI can be used for good by helping people work through complex information faster and, ultimately, save lives, while keeping qualified experts responsible for the final decisions.
What’s next
As AI takes on more work and enters higher-stakes settings, organizations will need clearer answers about access and accountability. They’ll also need to decide where human judgment remains necessary as systems become more capable.
Join us again next Monday for another episode of This Week in AI, when we’ll dive into more of the news, issues, and key developments shaping the AI era. And check back each Friday for the latest episode, or watch on YouTube, Spotify, Apple, or wherever you get your podcasts.
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