In this video, I break down what I call the Great Agentic Distraction: the growing tendency of cloud providers to pour talent, capital, and roadmap attention into agentic AI while slowing improvement in the core cloud services most enterprises still depend on. For more than a decade, cloud computing won enterprise trust through steady gains in reliability, security, governance, observability, scalability, and cost efficiency. That kind of progress mattered because it improved real-world operations for nearly everyone.

Now the market narrative has shifted. Providers are racing to retool platforms for agentic AI, autonomous workflows, orchestration layers, and AI-centric architectures. But while that future may be promising, most enterprises are not there yet. They still need better performance, lower complexity, stronger controls, predictable costs, and simpler operations.

Using a clear visual timeline, I explain how core-service innovation rose steadily, then flattened as the agentic AI push began. The key issue is not whether agentic AI is valuable. It is whether the industry is starving today's enterprise priorities to finance tomorrow's vision. If that imbalance continues, many customers may see agentic AI not as progress, but as a distraction. And that should worry every CIO planning the next five years of investment.

Cloud careers in 2026 are being shaped by more than technical knowledge alone. Employers still want strong capability in platforms like AWS, Azure, and Google Cloud, but they are increasingly looking for people who can connect infrastructure work to security, automation, cost control, AI readiness, and business outcomes. That means career growth now depends on building a solid foundation, adding a specialty, and proving your skills through hands-on work that others can see and trust.

Certifications can open doors, but projects, architecture writeups, GitHub repositories, and real operational experience help keep those doors open. Just as important, professionals need to stay visible by keeping LinkedIn and X profiles current, sharing ideas, posting lessons learned, and pointing people toward the work they have done.

Networking matters too, especially through live events such as meetups, conferences, and community groups, where many opportunities begin through conversation rather than formal applications. The overall message from today's cloud career advice is simple: learn continuously, build publicly, communicate clearly, and make sure both your skills and your professional presence show that you are ready for what comes next. Professionals who combine curiosity, consistency, and credibility will be best positioned to grow, earn trust, and lead future initiatives.

Agentic AI is one of the most talked-about trends in enterprise AI, AI architecture, and digital transformation, but how much of it is real and how much is hype? In this video, David Linthicum explains what agentic AI actually is, how AI agents work, and why so many companies are overestimating the business value of autonomous AI systems.

You'll learn where agentic AI can deliver real results, especially in complex enterprise environments that require distributed decision-making, autonomous workflows, and dynamic system orchestration. David also breaks down why agentic AI is often overhyped, overused, and more expensive than expected once you factor in AI governance, enterprise architecture, operational risk, observability, and the true cost of deploying AI at scale.

This video also covers four critical attributes of successful agentic AI use cases that many architects and technology leaders miss, including dynamic environments, bounded action spaces, edge-based decision value, and recovery and control in production systems. If you are evaluating enterprise AI strategy, AI automation, AI agents in business, or the future of autonomous enterprise systems, this discussion provides a practical and grounded perspective.

Watch if you want a realistic view of agentic AI in the enterprise—where it works, where it fails, and what most experts are getting wrong.

This video is a blunt critique of why I will never return to a big consulting firm. After years inside the machine, I came to see how far the industry has drifted from real client service and honest advisory work. What gets marketed as strategy and transformation is often a sales engine driven by partner incentives, internal politics, and quarterly revenue pressure. The result is predictable: clients are treated like accounts to mine, not businesses to serve; leadership rewards politics over competence; and chaos gets mistaken for innovation.

I break down the five reasons I walked away: consulting has become a glorified sales job, partner-led tech selling feels like legalized fraud, the operating model is disorganized, the wrong people are in charge, and the client is no longer the first-class citizen they should be. This is not a polite industry overview. It is a direct, first-hand view of what big consulting has become and why I believe the model is broken at its core. If you have ever questioned the value, incentives, or integrity of large consulting firms, this conversation will probably hit home. It names the incentives, the culture, and the leadership failures most insiders are afraid to say.

In this video, we take a hard look at the growing tension between independent cloud analysts and the hyperscaler machine. Why would one of the industry's most outspoken voices, David Linthicum, seem absent from the biggest cloud conferences and marquee vendor stages? Is it a coincidence, a branding mismatch, or something deeper about how Big Cloud handles criticism?

David Linthicum has built his reputation on blunt analysis, not polished vendor talking points. He has consistently challenged cloud cost narratives, called out poor architecture decisions, and questioned the gap between marketing hype and enterprise reality. That kind of honesty may be valuable to buyers, but it can also make powerful companies uncomfortable.

This video explores the possibility that being independent, candid, and analytically tough comes with a price in an industry driven by sponsorships, messaging control, and carefully managed narratives. We break down the incentives, the politics, and the unspoken rules behind major cloud events.

If you care about cloud, enterprise tech, and who gets a microphone in this industry, this conversation matters. Watch to hear the argument, weigh the evidence, and decide for yourself whether this is industry politics, reputation management, or simply the cost of telling the truth publicly.

The cloud infrastructure market is buzzing with new developments. According to fresh data from Synergy Research Group, Amazon Web Services (AWS) is still the biggest player, but its dominance is slipping as rivals surge ahead. AWS's hold on the industry has softened to just under 30%, down from above 32% in 2021, as Microsoft and Google continue to capture more ground—now holding 20% and 13% market share, respectively.

While "the Big Three" still account for 63% of global cloud spending, the real shake-up is coming from below: Oracle and a new generation of so-called "Neo Clouds" like CoreWeave, Crusoe, Nebius, and Lambda. These agile contenders are rapidly eating into the market, riding the explosive growth of cloud adoption worldwide—Q3 revenues topped $107 billion. Country-specific surges in India, Ireland, Mexico, and others signal global momentum, and in the US, the market expanded by 28% alone. While the top providers remain far ahead, with Google nearly four times bigger than Alibaba, the pace of change is accelerating. A new era of cloud competition is unfolding, and AWS can't afford to rest easy as both old foes and disruptive newcomers battle for a bigger piece of the growing pie.

In this compelling exposé, we pull back the curtain on the grand narratives spun by today's AI leaders—and reveal the dramatic gap between their promises and reality. With names like Sam Altman (OpenAI), Elon Musk (Tesla, SpaceX), and Dario Amodei (Anthropic) at the forefront, bold claims about Artificial General Intelligence (AGI), world-changing productivity, and society-shifting job automation have gripped the media and investors alike. But how much of this hype stands up to scrutiny?

We break down the flashy headlines, scrutinize the data, and show how many AI initiatives have failed to deliver real returns or transformative outcomes. From OpenAI's pivot to ad-based revenue to the shifting definitions of AGI used to secure investments, it's clear: the AI boom is fueled as much by marketing and financial necessity as by technical progress. Academics, economists, and internal reports challenge the myth of imminent AI dominance and expose the real motivations behind these public statements.

Everyone's talking about AGI—the idea that we're on the verge of creating an AI that can do anything a human can do, only faster and better. Tech billionaires are hyping it, headlines are breathless, and the race between the world's biggest companies seems unstoppable. But is the reality actually matching the hype? Not quite. Beneath the impressive demos and viral moments, today's AI still has some serious, stubborn problems that don't get talked about enough. It breaks under pressure, makes things up with total confidence, loses the plot on anything complicated, and doesn't truly understand the world the way even a child does. And simply throwing more money, more data, and more computing power at it may not fix any of that. In this video, we break down five fundamental reasons why AGI—true, all-purpose machine intelligence—is not coming anytime soon, in plain language anyone can understand. This isn't about being anti-AI or dismissing real progress. It's about cutting through the noise, being honest about where the technology actually stands, and understanding why the gap between "impressive AI" and "general intelligence" is still very wide.