Cloud Computing Insider

Big Tech says it's "backup," "sync," and "convenience"—but what happens when your computer quietly starts moving your personal files into the cloud by default? In this episode, David Linthicum breaks down a growing industry pattern: technology providers designing defaults that automatically capture your data, route it into their storage platforms, and make that choice feel inevitable. We start with the Microsoft Windows 11 upgrade experience, where many users discover Desktop, Documents, and Pictures being pushed into OneDrive through folder redirection and persistent prompts—often without a clear, informed decision at setup. From there, we connect the dots to Apple's iCloud, where "it just works" can also mean "it just uploads," and to Google's Drive-first ecosystem that normalizes cloud storage as the primary home for files. Finally, we revisit AWS and the long-running idea that computing is something you rent—not own—turning the PC into a subscription and your data into recurring revenue. This isn't an anti-cloud rant: cloud storage can be genuinely useful. The issue is default capture, confusing consent, lock-in economics, and the shrinking space for truly local-first computing. If your files are your property, why do vendors treat them like a product funnel?

Serverless is marketed as "no servers, no ops, just code"—but that convenience hides a deeper tradeoff: long-term freedom. In this video, I break down how platforms like AWS Lambda, Google Cloud Functions, and Firebase quietly lock you into a single provider, not through the language you write in. Still, through the glue you adopt: event formats, IAM models, triggers, logging, deployment pipelines, and tightly coupled managed services.   We'll look at where lock-in really lives architecturally, why leaning hard into proprietary auth, queues, databases, and logging can turn your system into a beautiful cage, and how to avoid that without giving up the speed that makes serverless attractive in the first place. You'll learn practical patterns like hexagonal/onion architecture, keeping business logic pure and side-effect-free, pushing cloud-specifics to the edges, and wrapping provider APIs behind your own interfaces for storage, messaging, and identity. I'll also cover strategies for keeping your data portable and planning for the day you might need to change clouds—or run on bare metal.   Serverless isn't the enemy. Blind trust is. Use the cloud's superpowers, but design as if you'll have to leave.

Cloud providers are quietly rebuilding their platforms around generative AI—and dragging you along for the ride. In this episode of Cloud Computing Insider, Dave breaks down how AWS, Azure, and Google Cloud are shifting from general‑purpose cloud to AI‑native cloud, where everything is optimized (and monetized) around GPUs, proprietary models, and tightly integrated AI services. We'll look at why this is happening now, how it shows up in your architecture and your bill, and why "AI‑ready" often really means "AI‑locked‑in." From exploding inference costs to agentic AI baked into workflows, you'll see how the defaults are being stacked in the providers' favor. But this isn't just a rant—we'll also explore your options. Do you lean into the hyperscalers' AI platforms, or start carving out room for AltClouds like private, sovereign, and MSP‑run clouds that aren't rebuilding everything around AI? How do you keep data, models, and architecture portable enough that you still have real choices in three years? If you care about cloud costs, control, and long‑term flexibility, this is the AI/cloud conversation you actually need to hear.

Cloud Centers of Excellence were supposed to save your cloud strategy—yet in most enterprises, they've become the single biggest bottleneck. In this video, David Linthicum takes a brutally honest look at why so many CCoEs have devolved into "Cloud Centers of No," strangling innovation while pretending to provide governance. We'll dissect how these committees burn time, money, and engineering talent with endless review boards, PDFs, and politics, all while claiming to be "best practice."   But this isn't just a rant; it's a blueprint. David lays out exactly how to blow up the gatekeeper model and rebuild your CCoE as a lean, product-focused cloud platform team that developers actually want to use. You'll learn how to replace manual approvals with automated guardrails, static standards with living golden paths, and ivory-tower architects with embedded, hands-on experts. If you suspect your CCoE is more theater than value, this video will give you the language, arguments, and patterns to force a reset—and turn cloud governance from a tax into a competitive advantage.  

In his "Cloud Computing Year in Review," David Linthicum offers a clear, opinionated look at how the cloud landscape has actually changed versus what was just hype. He situates these developments within the broader history of cloud, showing which "new" ideas are actually rediscoveries of long standing architectural principles. He walks through the major trends of the year – from the rise of multi cloud and FinOps to the deep integration of AI, data, and cloud native architectures – and explains what they mean in practical terms for enterprises. Rather than simply listing technologies, Linthicum focuses on business impact: cost optimization, complexity management, governance, and the ongoing struggle to modernize legacy systems. He highlights where cloud providers delivered real innovation, where they fell short, and how issues like security, resilience, and skills gaps shaped real world adoption. Throughout, his tone is pragmatic and slightly skeptical, cutting through marketing buzz to emphasize architecture, operations, and value realization. The review closes by outlining what IT leaders should carry into the coming year: a stronger focus on measurable outcomes, smarter use of multi cloud, tighter alignment between cloud strategy and data/AI strategy, and a renewed emphasis on talent and culture as key enablers of successful cloud transformation. Since you didn't specify a particular year, this is written to fit his typical annual "year in review" style. Would you like me to tailor this to a specific year or adjust the tone for an academic or professional audience?