Cloud Computing Insider

Try InMotion Cloud free for up to 30 days: https://inmotioncloud.com/david-linthicum Sponsored by InMotion Cloud. AI is reshaping the economics of cloud computing. It is not just adding more workloads to the cloud — it is changing how infrastructure is consumed, scaled, governed, and paid for. In this episode, David Linthicum explains why AI applications, agents, RAG systems, automated pipelines, testing environments, storage, networking, APIs, and AIOps-driven automation are creating new pressure on cloud budgets. The issue is no longer simply whether public cloud is "too expensive." The real question is whether each workload is running in the right place, under the right operating model, with the right cost structure.

Some AI workloads still benefit from public-cloud elasticity and rapid scaling. Others are more predictable, persistent, and infrastructure-heavy, making them better candidates for managed cloud or managed private cloud models. The goal is not to choose public cloud or private cloud as a philosophy. The goal is to match workloads to the infrastructure model that delivers the best balance of performance, governance, flexibility, and cost predictability. Thanks to InMotion Cloud for sponsoring this video. As you plan your AI strategy, take time to consider the true costs of AI — not just compute, but storage, networking, data movement, automation, operations, and long-term infrastructure control. The best platform is the one that fits the workload, the economics, and the business outcome. If you are evaluating where your AI workloads should run, check out InMotion Cloud.

The AI infrastructure story was always bigger than GPUs, bigger than cloud announcements, and bigger than vendor promises. The real issue was power. For the last few years, hyperscalers, analysts, and the broader market pushed a narrative that massive AI data center capacity was right around the corner. But anyone who understands infrastructure, utilities, and grid expansion should have known the math did not work. You cannot announce enormous data center growth and assume the power grid will magically keep up.

In this video, I break down why the AI data center capacity crunch was completely predictable, why so many enterprises made strategic decisions based on unrealistic promises, and why many are now scrambling to secure the compute they thought would already be available. I explain the disconnect between market hype and physical infrastructure, the dangerous habit of treating vendor optimism as operational fact, and why enterprises need to do their own analysis before betting growth plans on future capacity.

This is not just about data centers. It is about how hype repeatedly outruns reality in enterprise technology. If your organization is planning AI growth, cloud expansion, or major infrastructure investments, this is the reality check you need before making expensive mistakes.

After decades of working in cloud computing, enterprise architecture, AI, and digital transformation, I've seen one thing clearly: technical success is not just about who knows the newest platform, tool, or framework. It is about who gets ownership, who is trusted with hard problems, who is visible when major decisions are made, and who can connect technology to measurable business value. For women in IT, that means building deep technical expertise while also being deliberate about career strategy. Too many women are asked to do the work that keeps teams functioning without being given the credit, authority, budget exposure, or architectural ownership that leads to advancement.

That has to change. Women should be leading cloud migrations, AI governance efforts, cybersecurity strategies, platform modernization, data architecture, and cost optimization programs. They should be speaking up in design reviews, claiming the results they deliver, building strong sponsors, and choosing environments where their technical judgment is respected. The future of IT will belong to people who understand both technology and business outcomes. Women should not be waiting for permission to lead that future; they should be building the skills, visibility, and authority to own it.

Edge computing does not always need cloud-based back-ends, and pretending otherwise is one of the most expensive architectural mistakes in enterprise IT. Too many teams still assume that every edge device, every local decision, and every stream of operational data must be routed back to centralized cloud services before anything useful can happen. That assumption is not strategy; it is inertia dressed up as modernization. The truth is simpler: if a workload can be processed locally with better speed, lower bandwidth consumption, stronger privacy, higher resilience, and acceptable cost, then forcing a cloud dependency into the design is unnecessary. The cloud still matters, but its role should be selective, not automatic. It is excellent for aggregation, fleet management, historical analysis, large-scale coordination, and model training, but those strengths do not mean it belongs in every execution path. Edge computing earns its value by putting intelligence close to the event, the device, or the user. When architects understand that distinction, systems become faster, cheaper, and harder to break. When they do not, they build fragile, overconnected, overengineered platforms that confuse centralization with sophistication. That is the real debate, and it is long overdue. And the market is finally starting to notice.

In this episode of Cloud Computing Insider, Dave Linthicum talks with longtime technology analyst and broadcaster Eric Kavanagh about AI, agentic systems, automation, enterprise governance, and the realities behind today's technology hype. Eric shares his background in media, data, and technology analysis, including the evolution of DM Radio and Inside Analysis, and explains why independent, candid conversations are essential in an industry often dominated by vendor messaging.

The discussion centers on agentic AI: systems that can take action, pursue goals, and automate workflows. Dave and Eric explore the shift from manual work to automation, and now toward "automated automation," where AI agents may be delegated more responsibility. They stress that AI is not a sentient monster, but a powerful tool that can create serious risks when poorly implemented.

Using examples like the Hugging Face agent incident, enterprise security failures, workflow automation, and AI governance challenges, they argue that organizations must focus on guardrails, observability, control points, permissions, and responsible deployment. Eric also previews the Agentic Roadshow in Pittsburgh, focused on safe AI adoption. The episode closes with practical advice: identify friction points, understand business architecture, and deploy AI agents carefully, one workflow at a time.

For the past decade, enterprises have treated multicloud as the logical next step in cloud computing evolution. The promise was simple: more flexibility, less vendor lock-in, better resilience, and the freedom to place workloads wherever they fit best. In practice, however, that promise has often collapsed under the weight of operational reality. Instead of agility, many organizations got fragmentation. Instead of leverage, they got duplicated tooling, inconsistent governance, rising costs, and teams that could barely keep up with the complexity they created.

That is the real story of multicloud, and it is one too few people are willing to tell honestly. In this video, I want to break down why so many enterprises keep failing at multicloud, even after years of experience, millions in spending, and access to the best technology in the world. We'll look at the hidden problems around architecture, integration, security, observability, cost management, and talent, and why these issues keep showing up again and again. Multicloud is not failing because the technology is broken. It is failing because most enterprises still do not understand what it truly takes to design, deploy, and operate it well. And that is exactly what we are going to unpack today.

This video looks back at how cloud computing costs were discussed roughly 10 to 15 years ago, during the period when public cloud platforms such as Amazon Web Services, Microsoft Azure, and Google App Engine were becoming serious alternatives to traditional data centers. At the time, cloud computing was often promoted as a more cost-effective way to buy computing power, storage, and infrastructure because organizations no longer had to purchase servers, build data centers, or guess how much capacity they would need years in advance.

A major theme in these early discussions was the shift from capital expense to operating expense. Instead of spending large amounts of money upfront on hardware, companies could use public cloud services on a pay-as-you-go basis. Speakers also emphasized elasticity: businesses could scale resources up during busy periods and scale them down when demand dropped, reducing the cost of unused capacity.

However, these videos also show that cloud cost savings were not automatic. Providers framed cloud as cheaper when used efficiently, but they also discussed pricing models, reserved capacity, optimization, and total cost of ownership. Looking back, these talks reveal the early promise of cloud computing: lower upfront costs, faster deployment, and more flexible infrastructure.

If you think quitting a job is a simple matter of giving notice and moving on, this story may change the way you see work entirely. Across the tech and cloud computing world, more employees are finding out that leaving their employer can come with a shocking price tag. Buried in contracts and onboarding paperwork are repayment clauses that demand workers pay back thousands — sometimes tens of thousands — of dollars for training, certifications, or required job preparation if they leave too soon. These agreements are often presented as standard policy, but in practice they can function like a financial penalty for trying to change jobs.

In this video, we're looking at the growing use of training repayment agreements, sometimes called TRAPs, and why critics say they are being used to trap workers in jobs they no longer want. We'll break down how these clauses work, why they are especially relevant in tech, IT, and cloud computing, and what happens when employers claim the right to bill workers for required training. We'll also look at real legal cases, government action, and the broader trend of employers trying to turn ordinary job mobility into debt. If workers have to pay to leave, then the real question becomes: how free are they, really?

The cloud job market looks broken — but it's actually just hidden. In this video, we break down the real reasons qualified people aren't landing cloud computing jobs in 2026, and most of it isn't you. Roughly one in three job listings is a "ghost job" posted with no real intent to hire, and tech roles have some of the highest ghost rates of any sector. Meanwhile, hiring managers have stopped trusting resumes entirely — AI-generated applications flooded the market, and now even strong candidates get filtered out by pattern alone. The market is also split in two: recruiters say they can't find enough cloud infrastructure and distributed systems specialists, while generalists barely get interviews. Then there's the channel problem — cold applications convert at just 0.1–2%, while referrals fill 30–50% of all hires, yet most seekers spend their energy on the channel that barely works. Add disappearing entry-level roles and the new expectation that you understand cloud costs and ROI, not just the tech, and the picture becomes clear. This isn't a talent shortage — it's a signal and channel problem. We'll show you exactly what the market is actually rewarding, and how to position yourself so you stop getting filtered out.

The large technology analyst firms are in trouble, and not because the tech market suddenly became less important. They are struggling because the old model that made them powerful no longer works the way it used to. For years, firms like Gartner, Forrester, and IDC built enormous influence by controlling access to research, rankings, market narratives, and executive trust. If you wanted to understand a market, shortlist vendors, or justify a major enterprise bet, you often had to go through them. That created a business built on scarcity: scarce information, scarce access, and scarce authority.

But that scarcity is gone. Today, buyers can get market data faster, compare vendors more easily, and pressure-test claims with independent experts, operators, and AI tools in real time. At the same time, many of the big firms look slower, more expensive, and more tied to vendor relationships than ever. Their reports often feel polished but predictable, broad instead of sharp, and safe instead of honest. In a market moving at AI speed, that is a serious weakness. The result is a growing credibility problem: when companies can get faster, cheaper, and more practical insight elsewhere, the old analyst giants stop looking essential and start looking replaceable.