Welcome to the Cloud Wars Minute â your daily cloud news and commentary show. Each episode provides insights and perspectives around the âreimagination machineâ that is the cloud.
In today’s Cloud Wars Minute, I highlight Oracle’s rise to #2 in the Cloud Wars Top 10 rankings, driven by AI-led innovation and rapid cloud growth.
Highlights
00:06 â Oracle has surged from #3 to #2 in the Cloud Wars Top 10, driven by its focus on data as the foundation for AI innovation. Co-CEOs Mike Sicilia and Clay Magouyrk are leading Oracle during a period of strong momentum, with the company leapfrogging Microsoft in cloud and AI influence.
01:27 â Oracle is embedding AI deeply across its entire product portfolio, from cloud infrastructure and applications to databases, rather than adding it on later. This approach is fueling rapid growth and helping customers adopt AI faster, more easily, and at lower cost.
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02:20 â Oracleâs co-CEOs, Mike Sicilia and Clay Magouyrk, face the unprecedented challenge of executing against a $523 billion backlog while continuing to drive innovation and strong customer engagement. Despite being smaller than major competitors, Oracleâs rapid AI-driven innovation has enabled it to surpass both AWS and Microsoft in cloud influence.
04:19 â The new co-CEOs are balancing rapid innovation with the challenge of executing a massive backlog, including but not limited to the $300 billion OpenAI deal. Drawing on deep internal and cloud infrastructure experience, Sicilia and Magouyrk are positioning Oracle for its next phase of growth.
Check out my full interview with Mike Sicilia here.
The following article originally appeared on Medium and is being republished here with the authorâs permission.
Open source has been evolving for half a century, but the last two decades have set the stage for what comes next. The 2000s were the âstar stageââwhen open source became mainstream, commercial, and visible. The 2010s decentralized it, breaking the hierarchy and making forking normal. Now, in the 2020s, itâs transforming again as generative AI enters the sceneâas a participant.
This decade isnât just faster. Itâs a different kind of speed. AI is starting to write, refactor, and remix code and open source projects at a scale no human maintainer can match. GitHub isnât just expanding; itâs mutating, filled with AI-generated derivatives of human work, on track to manage close to 1B repositories by the end of the decade.
If we want to understand whatâs happening to open source now, it helps to look back at how it evolved. The story of open source isnât a straight lineâitâs a series of turning points. Each decade changed not just the technology but also the culture around it: from rebellion in the 1990s to recognition in the 2000s to decentralization in the 2010s. Those shifts built the foundation for whatâs coming nextâan era where code isnât just written by developers but by the agents they are managing.
1990s: Setting the Stage
The late â80s and early â90s were defined by proprietary stacksâWindows, AIX, Solaris. By the mid-â90s, developers began to rebel. Open source wasnât just an ideal; it was how the web got built. Most sites ran Apache on the frontend but relied on commercial engines such as Dynamo and Oracle on the backend. The first web was open at the edges and closed at the core.
In universities and research labs, the same pattern emerged. GNU tools like Emacs, GCC, and gdb were everywhere, but they ran on proprietary systemsâSGI, Solaris, NeXT, AIX. Open source had taken root, even if the platforms werenât open. Mike Loukides and Andy Oramâs Programming with GNU Software (1996) captured that world perfectly: a maze of UNIX variants where every system broke your scripts in a new way. Anyone who learned command-line syntax on AIX in the early â90s still trips over it on macOS today.
That shiftâLinux and FreeBSD meeting the webâset the foundation for the next decade of open infrastructure. Clearly, Tim Berners-Leeâs work at CERN was the pivotal event that defined the next century, but I think the most tactical win from the 1990s was Linux. Even though Linux didnât become viable for large-scale use until 2.4 in the 2000s, it set the stage.
2000s: The Open Source Decade
The 2000s were when open source went mainstream. Companies that once sold closed systems started funding the foundations that challenged themâIBM, Sun, HP, Oracle, and even Microsoft. It wasnât altruism; it was strategy. Open source had become a competitive weapon, and being a committer had become a form of social capital. The communities around Apache, Eclipse, and Mozilla werenât just writing code; they built a sort of reputation game. âIâm a committerâ could fund a startup or land you a job.
As open source gained momentum, visibility became its own form of power. Being a committer was social capital, and fame within the community created hierarchy. The movement that had started as a rebellion against proprietary control began to build its own âhigh places.â Foundations became stages; conferences became politics. The centralized nature of CVS and Subversion reinforced this hierarchyâcontrol over a single master repository meant control over the project itself. Forking wasnât seen as collaboration; it was defiance. And so, even in a movement devoted to openness, authority began to concentrate.
By the end of the decade, open source had recreated the very structures it once tried to dismantle and there were power struggles around forking and controlâuntil Git arrived and quietly made forking not just normal but encouraged.
In 2006, Linus Torvalds quietly dropped something that would reshape it all: Git. It was controversial, messy, and deeply decentralizedâthe right tool at the right time.
2010s: The Great Decentralization
The 2010s decentralized everything. Git unseated Subversion and CVS, making forking normal. GitHub turned version control into a social network, and suddenly open source wasnât a handful of central projectsâit was thousands of competing experiments. Git made a fork cheap and local: Anyone could branch off instantly, hack in isolation, and later decide whether to merge back. That one idea changed the psychology of collaboration. Experimentation became normal, not subversive.
The effect was explosive. SourceForge, home to the CVS/SVN era, hosted about 240,000 projects by 2010. Ten years later, GitHub counted roughly 190 million repositories. Even if half were toy projects, thatâs a two-to-three-order-of-magnitude jump in project creation velocityâroughly one new repository every few seconds by the late 2010s. Git didnât just speed up commits; it changed how open source worked.
But the same friction that disappeared also removed filters. Because Git made experimentation effortless, âthrowaway projectsâ became viableâhalf-finished frameworks, prototypes, and personal experiments living side by side with production-grade code. By mid-decade, open source had entered its Cambrian phase: While the 2000s gave us five or six credible frontend frameworks, the 2010s produced 50 or 60. Git didnât just decentralize codeâit decentralized attention.
Now that weâre halfway through the 2020s, something new is happening. Generative AI has slipped quietly into the workflow, reshaping open source once againânot by killing it but by making forking even easier. It also forms one of the main training inputs for the output it generates.
Go back two decades to the 2000s, if a library didnât do what you needed, you joined the mailing list, earned trust, and maybe became a committer. That was slow, political, and occasionally productive. But for you or the companies you work for to be able to influence a project and commit code, weâre talking months or years of investment.
Today, if a project is 90 percent right, you fork it, describe the fix to an AI, and you move on 5 minutes later. No review queues. No debates about brace styles. The pull-request culture that once defined open source starts to feel optional because you arenât investing any time in it to begin with.
In fact, you might not even be aware that you forked and patched something. One of the 10 agents you launched in parallel to reimplement an API might have forked a library, patched it for your specific use case, and published it to your private GitHub npm repository while you were at lunch. And you might not even be paying attention to those details.
Trend prediction: Weâre going to have a nickname for developers who use GenAI and are unable to read the code it generated very soon because thatâs happening.
Is Open Source Done?
No. But itâs already changing. The big projects will continueâReact, Next.js, and DuckDB will keep growing because AI models already prefer them. And I do think there are still communities or developers who want to collaborate with other humans.
But there is a surge of AI-generated open source contributions and projects that will start to affect the ecosystem. Smaller, more focused libraries will start to see more forks. Thatâs my prediction, and it might get to the point where it doesnât make much sense anymore to track them.
Instead of half a dozen stable frameworks per category, weâll see hundreds of small, AI-tuned frameworks and forks, each solving one developerâs problem perfectly and then fading away. The social glue that once bound open sourceâmentorship, debate, shared maintenanceâgets thinner. Collaboration gives way to radical personalization. And I donât know if thatâs such a bad thing.
The Fork-It-and-Forget Decade
This is shaping up to be the âfork-it-and-forgetâ decade. Developersâand the agents they runâare moving at a new kind of velocity: forking, patching, and moving on. GitHub reports more than 420 million repositories as of early 2023, and itâs on pace to hit a billion by 2030.
We tore down the âhigh placesâ that defined the 2000s and replaced them with the frictionless innovation of the 2010s. Now the question is whether weâll even recognize open source by the end of this decade. I still pay attention to the libraries Iâm pulling in, but most developers using tools like Cursor to write complex code probably donâtâand maybe donât need to. The agent already forked it and moved on.
Maybe thatâs the new freedom: to fork, to forget, and to let the machines remember for us.
In this episode, we explore how to de-risk your career roadmap by identifying the hidden vulnerabilities that hold your decision-making hostage.
đ§ Episode Notes: De-risking Your Career By Understanding Your Vulnerabilities
True career growth requires gaining autonomy over your choices. This episode provides a framework for performing a "pre-mortem" of career failure by identifying the sources of power that currently influence your life and limiting their leverage over your future.
Identify Your Sources of Power: Perform an exercise to list the people, situations, and physical things (like money or debt) that drive your current decision-making and could shift your behavior if they changed.
Conduct a Career "Pre-mortem": Use this diagnostic approach to recognize what has the power to change your decisions, helping you prepare for potential failures before they occur.
Understand the "Hostage" Dynamic: Realize that while some leverage is aligned with your values, other factorsâlike large amounts of debtâcan hold your career hostage, forcing you to make decisions you otherwise wouldn't.
Balance Vulnerability and Autonomy: Distinguish between healthy vulnerability (such as in relationships with family) and unhealthy vulnerability (such as with creditors), and work to de-risk the latter to reclaim your agency.
The Link Between Debt and Career Risk: Learn how eliminating financial vulnerabilities, like credit card debt, increases your autonomy, potentially allowing you to take "principled" career risks you previously couldn't afford.
Re-evaluate Your Non-Negotiables: Use introspection to determine which parts of your job are truly essential and which "imagined" risks are preventing you from seeking better alignment with your personal purpose.
Shift Your Control Systems: Understand that growth often requires giving up control in one area (like spending habits) to gain control and autonomy in your professional path.
đ Today's Episode is Brought To you by: Wix Studio
Devs, if you think website builders mean limited controlâthink again. With Wix Studioâs developer-first ecosystem you can spend less time on tedious tasks and more on the functionalities that matter most: â Develop online in a VS Code-based IDE or locally via GitHub. â Extend and replace a suite of powerful business solutions. â And ship faster with Wix Studioâs AI code assistant. All of that, wrapped up in auto-maintained infrastructure for total peace of mind. Work in a developer-first ecosystem. Go to wixstudio.com.
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Cristina Cranga: Why Nice Teams Still Fail and the Power of Honest Conversations
Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.
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"Sometimes you can change people by only listening to them. Not giving adviceâdon't become an advice monster." - Cristina Cranga
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Cristina shares her experience of sensing that something was off with a team but being unable to pinpoint exactly what it was. Instead of jumping to conclusions, she paused, reflected, and created an intervention plan centered on one thing: starting honest conversations. Through one-on-one discussions with team members, she discovered that the problem wasn't performance or processâit was something deeper.Â
Expectations weren't aligned with reality, and frustration stemmed from a company culture that didn't offer psychological safety. Cristina introduces the concept of the "advice monster"âsomeone who constantly tells others what they should do rather than simply listening. She emphasizes that as Scrum Masters, we need to recognize the three layers of our influence: control, influence, and no control.Â
Even when we can't solve problems, being present and listening can create profound change. The key is self-awareness of our own vulnerability as humans and compassion for others who might be at 80% or 10% of their mental health and energy on any given day.
Self-reflection Question: How often do you enter conversations with the intention of truly understanding rather than solving, and what might you discover if you listened more and advised less?
Featured Book of the Week: The Fearless Organization by Amy Edmondson
Cristina chose The Fearless Organization by Amy Edmondson as her most influential book because it explains what Scrum Masters see every day but struggle to name. The book provides a mental model for why teams don't speak up and how to influence behavior without forcing it. As Cristina puts it: "She explains why nice teams still fail. Silence is not always alignment and politenessâmost of the time, it's distrust." The book repositions the Scrum Master role from someone focused on ceremonies to someone who creates the conditions for psychological safety. It also explains why process alone doesn't fix everything and helps Scrum Masters measure what really matters in a team.
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[The Scrum Master Toolbox Podcast Recommends]
đ„In the ruthless world of fintech, success isn't just about innovationâit's about coaching!đ„
Angela thought she was just there to coach a team. But now, she's caught in the middle of a corporate espionage drama that could make or break the future of digital banking. Can she help the team regain their mojo and outwit their rivals, or will the competition crush their ambitions? As alliances shift and the pressure builds, one thing becomes clear: this isn't just about the productâit's about the people.
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đš Will Angela's coaching be enough? Find out in Shift: From Product to Peopleâthe gripping story of high-stakes innovation and corporate intrigue.
Human, Innovation enthusiast with a curious mind always learning new things. Sometimes a dreamer and a restless soul. Her mission in life is helping People thrive. She has a background in psychology and an experience in supporting the implementation of multiple IT software projects in complex digital eco-systems with different technologies at international level. How these two worlds can shape a professional? Let's discover it ...
Thereâs a lot of talk right now about .NET 10, Visual Studio 2026, and something called Razor co-hosting. Itâs often just a bullet point on a slide, but what does it actually mean? And why should Blazor developers care?
A few days ago, I sat down with David Wengier, one of the engineers working on Razor co-hosting. We spent about an hour going through how Razor tooling used to work, why it was so complicated, and what has actually changed under the hood.
To understand why this matters, we first need to look at the old world: multiple language servers, lots of LSP messages flying back and forth, duplicated work, and two different ârealitiesâ when it comes to source code and line numbers. That complexity is the reason hot reload was flaky and why the editor sometimes felt⊠confused.
Razor co-hosting changes all of that. The Razor language server is no longer a separate process. Instead, Razor is now hosted inside the C# language server, sharing the same syntax tree and semantic model. Fewer round-trips, less translation, better performance, and much more reliable hot reload.
This video walks through: * How Razor tooling worked before * Why it was so hard to get right * What co-hosting actually means * Why this is a big deal for Blazor and Razor developers
Let me know in the comments what youâre seeing so far. Is the editor better? Is hot reload more reliable? This is just the first step, and thereâs more coming.
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