Cloud Computing Insider

Public cloud reliability sucks, and the industry has spent years pretending otherwise. The sales pitch is always the same: better uptime, better resilience, less operational pain. But when public cloud fails, it fails big, and everyone pays for it. These platforms are supposed to reduce risk, yet they often concentrate it. One outage in a major provider can cripple applications, break authentication, disrupt storage, kill APIs, and leave entire businesses frozen. That is not resilience. That is shared fragility at a massive scale. The real problem is that public cloud providers have become too central to too much of the economy. Companies move critical systems into environments they do not control, cannot fully inspect, and cannot quickly recover from when things go wrong. Providers talk endlessly about redundancy, but customers still end up exposed to regional failures, control plane issues, cascading dependencies, and platform-wide mistakes. Public cloud is sold as modern infrastructure, but too often it behaves like an outsourced vulnerability. When it works, everyone congratulates the model. When it breaks, customers discover how little power they actually have. Public cloud reliability does not just disappoint. It fails in exactly the ways businesses were told it would not.

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.

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.

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.

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.

David Linthicum returns with a follow-up to his RSA predictions video—this time to see what actually happened at last week's RSA Conference and which calls held up under real-world scrutiny. Before the event, David laid out his expectations for the biggest cybersecurity themes, vendor narratives, and industry shifts likely to dominate the conversation. Now that RSA is over, it's time to review the results, separate hype from substance, and look at where those predictions were right on target. In this video, David breaks down the major trends that emerged, compares them against his original forecast, and explains why certain themes gained traction while others fell flat. From AI security messaging to platform consolidation, cloud security strategy, and the ever-growing noise around cyber innovation, this is a candid scorecard on what RSA actually revealed. This is not a victory lap for the sake of it. It's a practical look at how to read industry events more clearly, spot patterns before they become obvious, and understand what really matters beyond flashy announcements and packed expo floors. If you want sharp analysis, honest reflection, and a no-nonsense take on RSA's biggest storylines, this follow-up delivers the receipts.

We're on the edge of a real shift: the "cloud" may stop being purely a terrestrial phenomenon and become a layered network that includes orbit. The strongest case isn't that your favorite web app moves to space, but that space systems start acting like their own cloud region—compute, storage, and networking placed near satellites that generate massive amounts of data. If that happens, the first "clouds in space" won't look like hyperscale campuses; they'll look like compact, rugged orbital nodes that do AI inference, preprocessing, and caching, then beam results to Earth through high-throughput links. The big question is pace: in the near term, expect experiments and niche deployments for Earth observation, communications, and national security; mainstream adoption will require lower-cost launches, improved power and thermal designs, reliable optical crosslinks, and a clear cost advantage for specific workloads. Regulation and risk will shape it too—who owns the infrastructure, where the data "resides," and how you secure something you can't physically touch. So yes, we're likely to see "clouds in space," but as an extension of cloud architecture (edge + backbone), not a replacement for Earth regions—at least for a long while.

OpenAI helped kick off the AI revolution, but behind the hype the numbers tell a much darker story. In this video, we break down how a company that once looked untouchable is now burning staggering amounts of cash, losing ground to faster, leaner rivals, and scrambling to bolt ads onto its flagship product just to keep the lights on. We'll look at leaks and estimates that suggest OpenAI could lose tens of billions of dollars, with compute and hardware costs that swallow a huge chunk of every dollar it makes. We'll show how Anthropic has quietly overtaken OpenAI in enterprise LLM market share, why Microsoft and Nvidia now effectively hold OpenAI's fate in their hands, and how the GPU arms race they helped create has driven up prices for everyone else. This isn't a hit piece; it's a reality check. If you care about where AI is really heading, you need to understand why OpenAI's early lead may not last—and why the next decade of AI power is likely to belong to players with deeper pockets, better margins, and a more sustainable plan.

Neoclouds are a new wave of GPU-first cloud providers built specifically for AI training and inference, offering a focused alternative to hyperscalers like AWS, Azure, and Google Cloud. Instead of optimizing for thousands of general-purpose services, neoclouds optimize for what modern AI teams actually bottleneck on: high-availability NVIDIA-class GPUs, fast provisioning, bare‑metal performance, and low-latency networking for distributed workloads. That specialization can translate into lower effective cost per training run, higher sustained utilization, and faster iteration cycles—especially when hyperscaler capacity is tight or pricing is unpredictable. Providers such as CoreWeave, Lambda, Crusoe Cloud, Nebius, and Vultr market themselves on speed-to-GPU, simplified scaling, and infrastructure tuned for HPC-style jobs, from fine-tuning foundation models to high-throughput inference. For startups, labs, and enterprise AI teams, neoclouds can reduce the friction of getting from experiment to production by removing layers of platform complexity and prioritizing raw compute. The tradeoff is that hyperscalers still lead in global footprint, compliance breadth, and deep managed service ecosystems, so many teams adopt a hybrid approach—using neoclouds for heavy GPU workloads while keeping core data and platform services on a hyperscaler.

A surge in fraudulent cloud mining schemes is pulling in unsuspecting investors with promises of guaranteed, sky-high returns—claims that regulators stress are not just too good to be true, but technically impossible. These scams mimic the model of legitimate cloud mining, where users lease real hashing power from authentic data centers, but instead operate like Ponzi schemes: returns are paid with the money of new recruits rather than genuine crypto mining profits. Hallmarks of these frauds include fixed daily payouts, multi-level marketing ploys, and cloned websites that present a façade of sophistication. Meanwhile, global regulators are stepping up enforcement, with the SEC recently securing a $46 million judgment against a major scam operator and exchanges like Binance actively freezing assets tied to these illicit activities. At the same time, transparency-focused regulations such as the EU's Digital Operational Resilience Act are setting standards only legitimate providers can meet, making it easier to spot and stop fakes. This video exposes the red flags every investor should know, explains how authentic cloud mining really works—including what "hashing power" actually means—and arms viewers with tools to separate financial opportunity from crypto fakery.

RSAC 2026 is shaping up to be the year cybersecurity stops talking about "using AI" and starts obsessing over securing it. In this video, we break down the top conference trends emerging from early session themes and Innovation Sandbox signals: the Securing AI pivot (agent governance, inference-time protection, prompt injection, supply-chain integrity, and data leakage), Identity as the new perimeter (machine identities/NHIs eclipsing human users, phishing-resistant authentication, PKI at IoT scale), and the rise of Shadow AI as a board-level risk (discovering, inventorying, and controlling unauthorized AI apps and agents). We'll also unpack why "vibe coding" accelerates delivery while amplifying software supply chain exposure—and what actionable security inside CI/CD actually looks like in 2026, from SBOM programs and license compliance to automated dependency updates and build integrity. Finally, we connect the dots to operational resilience: when breach times can be measured in seconds, microsegmentation, lateral-movement controls, and real-time quarantine matter as much as prevention. If you're planning your RSAC agenda—or your 2026 roadmap—this is your fast, practical briefing, plus a shortlist of Innovation Sandbox finalists to watch. Subscribe for weekly security strategy takeaways, and drop a comment with the tool or trend you want us to analyze next in depth. Jo's LinkedIn: https://www.linkedin.com/in/jopeterson1/ Jo's email: Jo@clarify360.com Dave's LinkedIn: https://www.linkedin.com/in/davidlinthicum/ Dave's email: david@linthicumresearch.com Top 10 Innovation Sandbox Finalists (RSAC 2026) to Watch: @Charm Security: Agentic AI Workforce to prevent scams. @Clearly AI: AI-powered code reviews. @Crash Override: CI/CD build security. @Fig Security: SecOps resilience. @Geordie AI: Security and governance for AI agents. @Glide Identity: Next-gen authentication. @Humanix: Stopping social engineering via behavioral AI. @Realm Labs: Monitoring AI agent behavior. @Token Security: Managing non-human identity (NHI).

For years, big consulting firms have been selling "cloud and AI strategy" as if it were a product you can just buy off the shelf. In 2026, the jig is up. This video is your no‑nonsense buyer's guide to how large consulting firms should be engaged for cloud and AI work—what to let them do, what to keep in‑house, and where you're most likely to get burned. We'll walk through the real incentives behind those "preferred partner" AI cloud recommendations, how vendor alliances quietly shape your architecture, and why so many roadmaps end up looking like a sales deck instead of an operating model. You'll learn how to structure engagements so you get industrial‑scale execution—landing zones, governance, migrations—without handing over your strategy, your data, or your future. We'll cover the critical questions to ask before you sign, the red flags in proposals and SOWs, and how to demand deliverables that leave you stronger and more independent, not permanently dependent on the same firm. If you're a CIO, CTO, architect, or tech leader about to bring in a big consultancy for cloud or AI, watch this first. It might change how you buy—and what you're willing to buy.

In this crucial video, cloud security leader David Linthicum exposes a troubling statistic: 61 percent of cloud security incidents are completely preventable. Drawing on the latest security research, David reveals the most common—and avoidable—mistakes that leave enterprises vulnerable to cyberattacks, data leaks, and compliance violations. He breaks down the top culprits, from misconfigured cloud environments and weak access controls to lapses in ongoing monitoring and lack of employee training. David explains why organizations often falter on basic security hygiene and shows how these oversights create easy targets for attackers. Importantly, he translates these findings into actionable solutions. Viewers will learn why regular audits, continuous education, and automated security tools are critical for reducing risk, and how building a security-first culture can close the door on costly incidents. Packed with practical advice, this video gives IT leaders, security professionals, and business executives a blueprint for dramatically improving their organization's cloud security. Don't let your company become another statistic—discover how to avoid the 61 percent of incidents that should never happen, and transform the cloud from a weak spot into your enterprise's strongest line of defense.