In this episode, Scott Hanselman and Mark Russinovich pull back the curtain on Microsoft's Build conference with a look at the cutting room floor of Mark's Inside Azure Innovations breakout session. They explore fascinating technologies that didn't make the final presentation, including AI-powered network repair agents, advanced data center cooling systems, supply chain security innovations, prompt injection defenses, and groundbreaking approaches to cloud infrastructure. Along the way, they discuss how major technology presentations are assembled, the challenge of choosing what to showcase, and the incredible engineering work happening behind the scenes at Microsoft.

See the innovations that did make the final cut in the recording of Mark’s Inside Azure Innovations talk at Microsoft Build 2026: Inside Azure Innovations with Mark Russinovich.

Takeaways:

• How Mark decides what innovations make it into major conference presentations
• New technologies and tools being used to manage cloud infrastructure at scale
• Why some of the most interesting innovations never make it onto the conference stage

Who are they?

View Scott Hanselman on LinkedIn

View Mark Russinovich on LinkedIn

Watch Scott and Mark Learn on YouTube

Listen to other episodes at scottandmarklearn.to

Discover and follow other Microsoft podcasts at microsoft.com/podcasts

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In this episode, Scott Hanselman and Mark Russinovich reflect on conversations from Microsoft Build before diving into a thoughtful debate about AI's impact on software engineering. They explore why coding is ultimately about clear specifications, testing, and evaluation, not just generating code and discuss whether AI will truly replace developers or simply change how software is built. Along the way, they compare AI-generated software to IKEA furniture versus custom craftsmanship, examine the future of junior engineers, and share their perspectives on quality, responsibility, and what it really means to ship software in an AI-driven world.

Takeaways:

• How experienced engineers evaluate AI-generated code
• The critical role of testing, evaluation, and human oversight in AI-assisted development
• Why clear specifications are still the foundation of successful software projects.

Who are they?

View Scott Hanselman on LinkedIn

View Mark Russinovich on LinkedIn

Watch Scott and Mark Learn on YouTube

Listen to other episodes at scottandmarklearn.to

Discover and follow other Microsoft podcasts at microsoft.com/podcasts

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In this episode, Scott Hanselman and Mark Russinovich dive into a growing issue in AI-driven research: hallucinated references in academic papers. After scanning thousands of conference submissions, Mark uncovers widespread citation inaccuracies, sparking a broader conversation about accountability, cognitive surrender, and the risks of over-relying on AI tools. They explore where AI adds value versus where it erodes critical thinking, from academic writing to everyday coding and content creation. The conversation shifts into the realities of vibe coding, the tradeoffs of speed versus quality, and what it means to maintain taste and expertise in an AI-accelerated world, ultimately asking: if AI lowers the barrier to create, does it also lower the standard?

LLM Writing in Tech: Blogs and Articles

Takeaways:

• AI hallucinations are already impacting academic research quality
• The future challenge isn’t using AI; it’s maintaining standards while using it
• There’s a growing tension between efficiency and craftsmanship

Who are they?

View Scott Hanselman on LinkedIn

View Mark Russinovich on LinkedIn

Watch Scott and Mark Learn on YouTube

Listen to other episodes at scottandmarklearn.to

Discover and follow other Microsoft podcasts at microsoft.com/podcasts

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In this episode, Scott Hanselman and Mark Russinovich unpack the tension between subjective preference and objective usability, arguing that strong product instincts come from years of exposure, experience, and pattern recognition rather than innate talent. Through examples ranging from UI design to AI-assisted coding, they highlight how good decision-making requires both a holistic systems view and attention to detail. The conversation also examines the limits of accelerating expertise, the role of education in building foundational thinking, and why human judgment remains critical even as AI tools become more capable.

Takeaways:

• Without clear intent and strong taste, outputs can drift or degrade
• Strong design decisions come from balancing small details
• Good product instincts come from repeated exposure to patterns, tools, and decisions over time

Who are they?

View Scott Hanselman on LinkedIn

View Mark Russinovich on LinkedIn

Watch Scott and Mark Learn on YouTube

Listen to other episodes at scottandmarklearn.to

Discover and follow other Microsoft podcasts at microsoft.com/podcasts

Hosted on Acast. See acast.com/privacy for more information.

In this episode, Scott Hanselman and Mark Russinovich ​​dive into the evolution of ZoomIt, exploring new features like panoramic screen capture, webcam overlays, and lightweight video editing tools. They discuss the technical challenges behind building these capabilities, especially stitching images across any application and how AI-assisted coding is accelerating development while introducing new edge cases. Along the way, the conversation blends deep technical insight with candid, behind-the-scenes moments, highlighting both the complexity of modern software development and the value of experimentation and iteration.

Takeaways:

• ZoomIt is evolving into a more advanced, all-in-one screen tool
• Some of the most valuable moments in development and content come from failures
• AI-assisted coding can dramatically speed up development, but still requires close human oversight

Who are they?

View Scott Hanselman on LinkedIn

View Mark Russinovich on LinkedIn

Watch Scott and Mark Learn on YouTube

Listen to other episodes at scottandmarklearn.to

Discover and follow other Microsoft podcasts at microsoft.com/podcasts

Hosted on Acast. See acast.com/privacy for more information.

In this episode, Scott Hanselman and Mark Russinovich ​​explore how software development is evolving in the age of AI, challenging the idea that everything should start with a fully defined spec. They highlight a more iterative, sculpting approach to building, where continuous refinement, testing, and human judgment are essential and discuss the realities of AI-assisted coding, including edge cases, maintenance, and the limits of productivity gains.

Takeaways:

• AI-assisted coding works best as an iterative process, not a one-shot, fully spec’d solution
• Edge cases and real-world usage quickly expose gaps that initial builds
• AI can accelerate development, but human review, testing, and bottlenecks still limit true productivity gains

Who are they?

View Scott Hanselman on LinkedIn

View Mark Russinovich on LinkedIn

Watch Scott and Mark Learn on YouTube

Listen to other episodes at scottandmarklearn.to

Discover and follow other Microsoft podcasts at microsoft.com/podcasts

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In this episode, Scott Hanselman and Mark Russinovich ​​unpack how AI systems actually behave beneath the surface, pushing past hype into the messy reality of how models are trained, aligned, and deployed.

They explore whether AI systems are inherently benevolent or simply shaped by incentives, training data, and reinforcement learning, and why behaviors like deception can emerge under certain conditions. The conversation moves from philosophical questions about human nature versus machine behavior into the practical mechanics of large language models, including how reinforcement learning with human feedback shapes outputs and why alignment is far from perfect.

Along the way, they ground the discussion in a real engineering challenge, stitching a scrolling panorama from screen captures, to show how complex systems come together through heuristics, edge cases, and iteration.

Takeaways:

• AI behavior is shaped by training and incentives, not built-in intent or morality
• AI can accelerate coding, but testing, edge cases, and reliability require human oversight
• Reinforcement learning pushes models to be helpful and agreeable, sometimes at the cost of accuracy

Who are they?

View Scott Hanselman on LinkedIn

View Mark Russinovich on LinkedIn

Watch Scott and Mark Learn on YouTube

Listen to other episodes at scottandmarklearn.to

Discover and follow other Microsoft podcasts at microsoft.com/podcasts

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In this episode, Scott Hanselman and Mark Russinovich dive into the realities of building complex software with AI coding agents. Mark shares his experience using modern models to implement a shared-memory transport for gRPC across Go and .NET, explaining how AI dramatically accelerated development while still requiring constant oversight. They discuss the surprising strengths and limitations of AI coding tools, to the massive productivity gains that make the frustration worthwhile. The conversation also explores the challenges of solving hard engineering problems, including an attempt to build a scrolling screenshot stitcher, and wraps with thoughts on the future of developer tooling and a potential live episode of the show.

Takeaways:

• AI coding agents can speed up complex development but still require human oversight
• Developers often need to guide and correct the model throughout the process
• Even with challenges, AI can reduce months of work to days

Who are they?

View Scott Hanselman on LinkedIn

View Mark Russinovich on LinkedIn

Watch Scott and Mark Learn on YouTube

Listen to other episodes at scottandmarklearn.to

Discover and follow other Microsoft podcasts at microsoft.com/podcasts

Hosted on Acast. See acast.com/privacy for more information.