Cloud Computing Insider

Why did Microsoft stock drop even after a headline beat? In this episode, cloud analyst David Linthicum breaks down the market's "beat-and-drop" reaction to Microsoft's latest earnings and what it signals about Azure, AI, and hyperscaler spending. He explains how expectations for a clear cloud re-acceleration collided with guidance that sounded more like "we're investing ahead of demand," raising concerns about capital intensity and near-term margins. Linthicum walks through the optics around Azure growth, capacity build-outs for AI training and inference, and why investors are increasingly sensitive to capex without fast operating leverage.

The conversation also explores a structural shift in enterprise cloud choices—hybrid, private and sovereign cloud, colocation, and managed service providers—and how workload economics (egress, governance, and consumption creep) can make public cloud less attractive for steady-state compute. Finally, he balances the bear case with the bull case: Microsoft's distribution advantage across Microsoft 365, security, and developer tools, and the possibility that today's spend is strategic moat-building. If you want a clearer framework for reading cloud earnings, this video delivers. You'll learn what metrics to watch next quarter, how to interpret "capacity constraints" versus demand, and where AI monetization may show up first in pricing, usage, and margins.

The tech industry is brilliant at building new things—and terrible at admitting when it gets them wrong. In this video, I break down why our predictions about cloud, AI, big data, blockchain, metaverse, and more so often miss reality by a mile. From wildly optimistic analyst forecasts (including early cloud growth predictions that were way off) to vendor-driven hype cycles, we've built a system that rewards confidence, not accuracy.

I walk through concrete examples where the narrative sounded irresistible, the slideware looked perfect, and the pilots seemed promising—but large enterprises never followed at scale. The core problem isn't intelligence or innovation; it's confirmation bias, misaligned incentives, and a complete underestimation of real-world constraints: legacy systems, budgets, regulation, skills, and risk.

You'll learn how to recognize the telltale signs of hype, how to separate "cool demo" from "sustainable value," and how to ask the uncomfortable questions that cut through the noise. Whether you're a CIO, architect, engineer, or business leader, my goal is simple: help you stop getting pushed around by tech narratives—and start making grounded, reality-based decisions.

If you're tired of being sold "the future" that never quite arrives, this video is for you.

In this video, David Linthicum breaks down the sudden explosion of "agentic AI" playbooks, frameworks, and branded platforms now pouring out of the consulting industry. Every big firm wants to look like it owns the future of autonomous work, so the market is being flooded with glossy diagrams, maturity models, and "fast paths" that promise cheap, repeatable success—sometimes with language that feels close to a guarantee.

But agentic AI is not a plug-in. It's an architecture, and architecture only works when it matches your processes, data quality, controls, integration realities, and operating model. When frameworks lead with the platform instead of the problem, enterprises end up force‑fitting agents into brittle systems, over-building orchestration layers, and running old processes in parallel "just in case." The hidden costs show up later: governance overhead, constant tuning, fragile pilots, and disappointing ROI.

You'll learn the red flags to watch for, the questions to ask before funding an "agentic transformation," and how to pursue smaller, measurable wins without buying expensive theater. If you're a CIO, CTO, or business leader, this is your reality check before the next deck lands in your inbox. We'll also discuss when simpler automation beats agents—and when agents earn their keep.

Network-attached storage (NAS) is a dedicated, always‑on storage device that connects to your home or office network and lets multiple users and devices store, share, and back up data to a central box you physically own. In effect, it's your own private cloud: instead of renting space from iCloud, Google Drive, OneDrive, or AWS, you buy a NAS once and control the hardware, the capacity, and who can access it. This model is growing quickly; the global NAS market is already tens of billions of dollars in annual sales and is projected to roughly triple over the next decade, driven by exploding photo, video, and backup needs. Just as important as capacity is cost: a mid‑range NAS with several terabytes of usable storage often runs around 600 upfront, a figure that can undercut years of recurring cloud fees for 2–6 TB plans. Many consumers and small businesses are discovering that, at larger data sizes, NAS becomes cheaper over a three‑to‑five‑year horizon. And because the data lives on devices you own—often protected by encryption, redundancy, and local access controls—NAS is increasingly seen as a way to improve privacy, security, and peace of mind compared to relying solely on third‑party clouds.

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?

This video takes a hard look at the messy truth behind agentic AI in the enterprise — and why it feels like someone is lying to you. On one side, big tech vendors, cloud providers, and global consultancies are screaming that "2025 is the year of the AI agent," boasting about customers "deploying thousands of agents" across customer service, IT operations, and back‑office functions with massive efficiency gains. On the other side, independent analysts and people actually building this stuff say most organizations are still stuck in early pilots, that real value is limited to a narrow set of tightly scoped workflows, and that a lot of what's sold as "agents" is just old automation with an LLM slapped on the front.

In this video, I unpack those two conflicting narratives, show where each one is coming from, and explain why the hype machine has such a strong incentive to tell you the revolution is already here. We'll talk about agent washing, the lack of standards, and the very real risk of a trust crash between enterprises and their technology providers. If you're tired of being sold a future as if it's already reality, this one's for you.