Cloud Computing Insider
Hosted by cloud computing pioneer David Linthicum, the Cloud Computing Insider podcast gets to the bottom of what cloud computing, and generative AI can bring to your enterprise. New content will focus on what's important to you as a user of cloud computing and generative AI, and the ability to find value the first time.
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Why So Much Tech Press Coverage Feels Fake
08/17/2026
Why So Much Tech Press Coverage Feels Fake
People trust tech media to explain the future, test new products, and tell the truth about the companies shaping our lives. But too often, that is not what we get. We get hype instead of scrutiny, access instead of independence, and headlines designed to drive traffic instead of inform the audience. The same outlets that are supposed to question powerful tech companies often depend on those same companies for advertising, interviews, event invitations, product samples, and insider access. That creates a system where being too honest can come at a cost. If a reporter or outlet pushes too hard, they risk losing the relationships that help keep the business running. On top of that, shrinking newsrooms, pressure to publish fast, and the constant chase for clicks make it even harder to slow down and tell the full story. The result is a media environment that often feels more like an extension of the tech industry than a check on it. This video is about why that happens, who benefits from it, and why audiences should be far more skeptical of the tech coverage they consume every day. Because when coverage gets compromised, the public loses the truth it actually needs most.
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How Cloud Vendors Lock You In Without You Knowing
08/10/2026
How Cloud Vendors Lock You In Without You Knowing
This video explores one of the biggest risks in cloud computing that many companies do not see until it is too late: vendor lock-in. Cloud providers rarely trap customers with one obvious move. Instead, lock-in builds quietly through convenience, proprietary services, data gravity, automation, skills, pricing models, and day-to-day operational habits. What looks like speed and simplicity at the beginning can become cost, rigidity, and lost negotiating power later. In this video, we break down the subtle ways cloud vendors make themselves hard to leave, even when the contract seems flexible. We look at how managed databases, AI platforms, integration services, observability tools, identity systems, and egress fees create dependencies that spread across the business. We also cover the hidden organizational side of lock-in, including retraining costs, process redesign, and architecture decisions that become difficult to reverse. Most importantly, this video explains how to spot lock-in early and what smart teams can do to preserve portability, leverage, and strategic options without slowing innovation. If you want to understand the real economics and power dynamics of cloud adoption, this is a video you should not miss. It is practical, direct, and built for leaders who want control before convenience turns into dependence.
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AI Is Becoming the Next Cloud Computing Disaster
08/03/2026
AI Is Becoming the Next Cloud Computing Disaster
David Linthicum breaks down a pattern too many enterprises are ignoring: we are making the same mistakes with AI that we made with cloud computing. Companies are rushing to adopt AI platforms, sign long-term contracts, and outsource critical capabilities before they understand the real economics behind those decisions. The result is familiar—unexpected costs, weak governance, deep technical dependency, and fewer strategic options later. David explains why AI enthusiasm is masking the hard architectural questions leaders should be asking right now. What will these systems actually cost at scale? How much control are we giving up when we build on proprietary models, APIs, and platforms? And what happens when the business needs to switch vendors, renegotiate pricing, or bring capabilities back in-house? If your organization is investing in AI without a clear view of total cost, lock-in risk, governance, and exit strategy, this conversation is essential. This is not an argument against AI. It is a warning against careless adoption. The companies that win with AI will not be the fastest to buy—it will be the smartest to architect, govern, and maintain flexibility. That discipline will separate sustainable transformation from another expensive, avoidable enterprise technology mistake for many organizations.
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The Future of AI-Powered Security Operations With Venkata Koppaka
07/29/2026
The Future of AI-Powered Security Operations With Venkata Koppaka
On this episode of The Cloud Computing Insider, we dive into AI-driven security operations with Venkata Koppaka of TENEX. As cyber threats accelerate and enterprise teams demand faster, smarter responses, this conversation explores what it really takes to build security operations that are both AI-powered and human-led. Venkata Koppaka is the CTO of TENEX, a company focused on fully-agentic, human-led security operations. TENEX was named the #1 fastest-growing cybersecurity company in the 2026 IT-Harvest Cyber 150 and is backed by a16z, Crosspoint Capital, Shield Capital, DTCP, and Deepwork Capital. The company serves enterprise and mid-market customers across the Google and Microsoft security ecosystems. In this discussion, we unpack how TENEX stands apart in a crowded MDR and SOC market, what customer adoption and validation signals say about platform maturity, and how anonymized customer feedback helps tell the broader market story. We also look at the significance of public validation from the Kansas City Chiefs and what it reveals about readiness at the enterprise level. Whether you lead security, cloud, or infrastructure teams, this episode offers a practical look at where the broader market is heading next.
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The Future of AI-Powered Security Operations With Venkata Koppaka
07/29/2026
The Future of AI-Powered Security Operations With Venkata Koppaka
On this episode of The Cloud Computing Insider, we dive into AI-driven security operations with Venkata Koppaka of TENEX. As cyber threats accelerate and enterprise teams demand faster, smarter responses, this conversation explores what it really takes to build security operations that are both AI-powered and human-led. Venkata Koppaka is the CTO of TENEX, a company focused on fully-agentic, human-led security operations. TENEX was named the #1 fastest-growing cybersecurity company in the 2026 IT-Harvest Cyber 150 and is backed by a16z, Crosspoint Capital, Shield Capital, DTCP, and Deepwork Capital. The company serves enterprise and mid-market customers across the Google and Microsoft security ecosystems. In this discussion, we unpack how TENEX stands apart in a crowded MDR and SOC market, what customer adoption and validation signals say about platform maturity, and how anonymized customer feedback helps tell the broader market story. We also look at the significance of public validation from the Kansas City Chiefs and what it reveals about readiness at the enterprise level. Whether you lead security, cloud, or infrastructure teams, this episode offers a practical look at where the broader market is heading next.
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Your Cloud Bill Is a Lie: FinOps in 2026 Gets Brutal
07/27/2026
Your Cloud Bill Is a Lie: FinOps in 2026 Gets Brutal
Cloud FinOps is no longer a side conversation for architects, CFOs, or cloud operations teams—it is the fight that will define who survives the next wave of enterprise transformation. In this video, we break down why 2026 is exposing the ugly truth: too many companies still have no idea where their cloud money is going, why AI workloads are detonating budgets, and why most FinOps programs are delivering far less value than promised. This is not about trimming a few wasted instances. It is about the brutal collision of cloud complexity, executive delusion, runaway SaaS spend, and AI-fueled infrastructure costs that are rewriting the economics of IT.
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Why Enterprise AI Is Leaving the Cloud
07/20/2026
Why Enterprise AI Is Leaving the Cloud
The AI industry sold the world on a simple story: intelligence lives in the cloud, giant models run remotely, and everyone rents access forever. That story is starting to crack. Enterprises are realizing that the real value in AI is not generic text generation, but private data, proprietary workflows, and tightly governed automation that cannot safely or cheaply live outside the company boundary. As local models improve, costs fall, and hardware gets better, the economics shift fast. Suddenly, paying endless inference fees to remote providers looks less like innovation and more like dependency. That is where the panic begins. Cloud-first AI vendors face margin pressure, platform lock-in gets challenged, and CIOs start asking why sensitive business knowledge is being shipped to third parties at all. In this video, we break down why the long-term future of enterprise AI may be private, local, and far less cloud-dependent than the market expected. We cover the cost curves, architecture changes, compliance drivers, and strategic risks that could reshape the AI stack. If this transition accelerates, the winners and losers in AI will look very different from what most investors, vendors, and analysts assume today over the next few years as local deployment scales globally.
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The True Cost of Cloud vs. On-Premises: A Comprehensive Guide
07/13/2026
The True Cost of Cloud vs. On-Premises: A Comprehensive Guide
In this insightful video, we explore the critical cost differences between public cloud and on-premises infrastructure for enterprises in 2026 and 2027. As organizations increasingly evaluate their IT strategies, understanding these financial implications is essential. We break down seven key workload types, including transaction-oriented applications, business analytics, AI, and customer-facing digital apps, highlighting how public cloud can often be significantly more expensive—typically ranging from 1.2x to 4.0x the cost of on-premises solutions. Viewers will gain clarity on when cloud options may be slightly cheaper and the circumstances that typically lead to higher costs. The video also features a detailed comparison table, providing a straightforward visual reference for decision-makers. By the end, you'll understand the strategic considerations necessary for optimizing your enterprise's infrastructure costs, ensuring you make informed choices that align with your business goals. Whether you're a seasoned IT professional or new to cloud computing, this video equips you with the knowledge needed to navigate the evolving landscape of enterprise technology.
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Cloud Negotiation Tricks They Don’t Want You to Know
07/06/2026
Cloud Negotiation Tricks They Don’t Want You to Know
This video breaks down how smart enterprises negotiate with cloud providers instead of simply accepting whatever pricing, terms, and service models are put in front of them. The focus is on how to approach AWS, Microsoft Azure, Google Cloud, and other providers with leverage, discipline, and a clear strategy. It explains why most companies lose money before the first workload even scales: they enter negotiations without usage data, cost controls, architectural clarity, or a realistic backup plan. The video covers how to push harder on pricing, multiyear commitments, SLAs, support terms, portability rights, consulting access, migration funding, training credits, and executive escalation paths. It also highlights a critical mistake many organizations make—letting procurement handle cloud deals without architects, finance leaders, and FinOps experts at the table. Viewers will learn that the strongest negotiation position comes from being informed, prepared, and able to walk away from bad terms. Rather than treating cloud contracts as fixed, this video shows how experienced buyers extract real value beyond headline discounts and protect themselves from lock-in, surprise costs, and weak accountability. If you want to stop overpaying, gain leverage, and work with cloud providers from a position of strength, this video gives you the mindset and talking points to do it.
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Serverless Cloud Hype Is Colliding with Reality
06/29/2026
Serverless Cloud Hype Is Colliding with Reality
Serverless computing was supposed to simplify cloud architecture, cut operational drag, and lower costs. Instead, what we got from AWS, Microsoft Azure, and Google Cloud is a far more complicated reality: more abstraction, more lock-in, more hidden costs, and more ways to build systems that look elegant in a slide deck but become painful in production. In this episode of Cloud Computing Insider, we take a hard look at the real state of serverless across the big three cloud providers. We break down AWS Lambda, Step Functions, and EventBridge, Azure Functions, Container Apps, and Logic Apps, and Google Cloud Run, Eventarc, and Workflows to expose where each platform delivers real value and where the marketing starts to fall apart. We also dig into the AI angle, where every hyperscaler is now trying to position serverless as the perfect foundation for inference, agents, and modern cloud-native automation. The problem is that “managed” does not mean “simple,” and “serverless” does not mean “cheap.” If you are an enterprise architect, cloud decision-maker, or developer tired of recycled hype and vendor messaging, this is the blunt, critical breakdown you need before making your next platform bet.
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The Consulting Collapse: Why Big Firms Are Failing in the AI Boom
06/25/2026
The Consulting Collapse: Why Big Firms Are Failing in the AI Boom
The consulting industry sold itself as the indispensable guide to enterprise transformation, but the results are getting harder to defend. While AI exploded, budgets shifted, and companies faced real pressure to modernize, many of the biggest firms still stumbled into the moment with bloated delivery models, weak differentiation, and armies of expensive talent built for a market that no longer exists. That is the contradiction at the center of this discussion: there has been no shortage of change, no shortage of enterprise disruption, and certainly no shortage of executive anxiety, yet many of the largest consulting firms have been cutting staff, missing growth expectations, and struggling to prove lasting value. The old playbook—sell fear, staff heavily, stretch timelines, and wrap everything in transformation language—is colliding with a much harsher reality. Clients want measurable outcomes, faster execution, smaller teams, and real technical depth, especially in AI. Instead, too many firms look slow, overpriced, and structurally dependent on inefficiency. This is not a temporary dip. It may be the early stage of a much deeper reckoning for Deloitte, PwC, EY, KPMG, Accenture, and the broader consulting establishment. The real question is no longer whether the model is under pressure; it is whether the model is breaking in plain sight.
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AI Features Sound Great — Until You See the Bill
06/22/2026
AI Features Sound Great — Until You See the Bill
This video breaks down the real cost difference between building a simple business inventory system with traditional software tools versus adding AI features on top. It shows how a standard inventory app usually covers core functions like stock tracking, reorder alerts, supplier records, reporting, and cloud hosting at a relatively predictable cost. Then it contrasts that with an AI-enabled version that adds natural-language search, smart reorder recommendations, anomaly detection, and assistant-style workflows. The video makes the case that AI does not just add a feature — it adds an entirely new cost layer. That includes model usage fees, prompt engineering, vector databases, better data preparation, extra quality testing, and ongoing monitoring. It also explains why monthly operating costs can become far less predictable when every query, recommendation, or automation runs through paid AI services. Using a small business inventory system as the example, the video gives viewers a practical way to think about ROI. If the business only needs accurate tracking and reporting, traditional development is often faster, cheaper, and cleaner. If the business truly benefits from automation and smarter decision support, AI can be worth it — but only when leaders understand the full build cost, operating cost, and maintenance burden before they commit.
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AI Killed the Green Cloud Fairy Tale
06/15/2026
AI Killed the Green Cloud Fairy Tale
The video argues that hyperscalers no longer deserve unquestioned credibility on green energy because the AI investment boom has exposed what their real priority has always been: growth. For years, the biggest cloud companies positioned themselves as champions of renewable power, carbon reduction, and sustainability leadership. But once AI created a massive new revenue opportunity, the narrative shifted. Now those same companies are racing to build enormous, power-hungry data centers across the world, even as they continue talking about long-term environmental commitments. That contradiction is the story. You cannot claim to be fully committed to green energy while dramatically increasing electricity demand at a scale that makes those promises harder and harder to honor. The video makes the case that this is not a sudden change in values, but a clearer view of how hyperscalers actually operate. They follow profitability horizons, investor sentiment, and market opportunity first. The messaging changes when the money changes. That is why trust is starting to erode. Stakeholders, customers, and the public are beginning to question whether the sustainability message was ever a true principle or simply a temporary positioning strategy. In the AI era, the mask is slipping: when forced to choose between green branding and AI profits, hyperscalers are showing what matters most
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Top 5 NeoClouds Explained: CoreWeave, Nebius, Crusoe, Lambda, Fluidstack
06/11/2026
Top 5 NeoClouds Explained: CoreWeave, Nebius, Crusoe, Lambda, Fluidstack
AI infrastructure is changing fast, and NeoClouds are becoming one of the most important categories in enterprise technology. In this video, I break down what a NeoCloud actually is, how it differs from traditional public cloud providers like AWS, Azure, and Google Cloud, and why so many enterprises are paying attention right now. The core idea is simple: NeoClouds are built specifically for AI workloads, especially GPU-heavy training and inference, rather than trying to be all things to all customers. I also walk through the most important features enterprises should evaluate before choosing a NeoCloud provider, including GPU scale, bare-metal performance, managed Kubernetes, orchestration, compliance readiness, private infrastructure options, and energy-aware design. From there, I compare five of the most talked-about NeoClouds in the market today: CoreWeave, Nebius, Crusoe, Lambda, and Fluidstack. If you are a CIO, CTO, infrastructure leader, AI engineer, investor, or founder trying to understand where AI cloud is heading, this overview will help you quickly grasp the landscape. The goal is to give you a practical, executive-level framework for evaluating the NeoCloud market and understanding which providers stand out for different enterprise needs.
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Cloud Wars End? Shocking Truth Behind Tech Giants Teaming Up!
06/08/2026
Cloud Wars End? Shocking Truth Behind Tech Giants Teaming Up!
The landscape of cloud computing is witnessing a seismic shift as major cloud providers, once fierce competitors, are now forging unexpected alliances to meet the demands of today’s connected digital world. Oracle’s recent partnership with Amazon Web Services (AWS) exemplifies this trend, delivering private managed connections that allow customers to move applications and data seamlessly between clouds. These moves are driven not only by the pursuit of technical innovation and access to new generative AI opportunities but also by mounting regulatory pressures, including the European Union’s Data Act, which mandates easier data movement and reduced transfer fees. Cloud titans like AWS, Google Cloud, Microsoft, and Oracle are responding by slashing costs and enhancing connectivity, often integrating advanced encryption protocols like MACsec for secure transport. This collaborative wave is also ushering in more sophisticated multicloud deployments, freeing customers from vendor lock-in and spurring innovations that unify infrastructure across platforms. As these hyperscalers tear down traditional barriers in pursuit of shared goals, the face of cloud computing is being completely reimagined. Are we heading towards a unified, hyper-connected cloud ecosystem, or is this just a strategic truce in an ever-evolving battle for dominance?
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The Enterprise AI Cloud Battle: AWS vs Microsoft vs Google
06/04/2026
The Enterprise AI Cloud Battle: AWS vs Microsoft vs Google
Cloud computing is entering a new chapter, and the biggest shift is happening around AI platforms. For years, enterprises compared AWS, Microsoft, and Google based on compute, storage, databases, and global infrastructure. Now the conversation is changing. The real question is which cloud provider gives businesses the best foundation for building, deploying, governing, and scaling AI applications in the real world. In this video, we are looking at that race through three specific products: Amazon Bedrock, Azure AI Foundry, and Google Vertex AI. These are not just feature bundles or branding exercises. They are becoming the control layers that enterprises use to access models, manage workflows, integrate data, and turn AI from experiments into production systems. We are going to break down where each platform is strongest, what kind of enterprise buyer each one is really built for, and how their strategies differ. AWS is leaning into flexibility and model choice, Microsoft is focusing on enterprise control and workflow integration, and Google is pushing a tightly connected stack built around Vertex AI, Gemini, and infrastructure depth. By the end, you should have a clearer view of which platform fits which type of AI application and why for enterprise success today.
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How “Cloud-First” Turned Into a Money Pit
06/01/2026
How “Cloud-First” Turned Into a Money Pit
For years, “cloud-first” was sold as the default path to modern business: faster deployment, more flexibility, less hardware, less hassle. And in some cases, that promise was real. But for a growing number of companies, cloud-first became a financial trap disguised as innovation. What starts as agility can quietly become dependency. What starts as convenience can become a recurring bill that never stops growing. This video looks at the moment cloud-first becomes clown-first — when enterprises stop making workload-by-workload decisions and start treating public cloud like a belief system. We look at companies like 37signals and Dropbox, which became high-profile examples of businesses realizing that public cloud was costing far more than expected at scale. Their stories raise a bigger question: how many firms adopted cloud-first because it was strategically right, and how many did it because everyone else did? This is not an anti-cloud rant. Public cloud can be powerful, fast, and absolutely the right choice in the right context. But when businesses push stable, predictable, long-term workloads into expensive rental infrastructure without serious cost discipline, the economics can turn ugly fast. At that point, cloud is not strategy. It is overhead with better branding.
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Why Enterprises Suck at AI
05/25/2026
Why Enterprises Suck at AI
Enterprises are struggling with AI for reasons that have less to do with the models and more to do with the way large companies operate. Many organizations rushed into AI because of hype, not because they had a clearly defined business problem worth solving. They bought tools before fixing messy data, broken workflows, disconnected systems, and weak governance. That means AI often gets dropped on top of chaos instead of improving a stable foundation. On top of that, leadership teams want fast results but resist the hard work: cleaning data, redesigning processes, training teams, and making decisions quickly. In many companies, AI projects get trapped in endless meetings, turf wars, compliance fear, and pilot purgatory. Another problem is obsession with large language models for their own sake, when the smarter move is to use whatever works, whether that is automation, analytics, smaller models, or traditional software. The result is predictable: lots of demos, lots of spending, and not enough production value. Enterprises do not fail at AI because AI is useless. They fail because they approach AI the same way they approach every trend: slowly, politically, and without enough operational discipline. That is why the promise keeps outrunning the payoff.
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Are Hyperscalers Turning Consulting Firms into Sales Channels?
05/21/2026
Are Hyperscalers Turning Consulting Firms into Sales Channels?
This commentary argues that Google Cloud’s “agentic enterprise” partner push is less an open ecosystem strategy than a carefully structured channel motion designed to increase platform dependency. Google is offering funding, early model access, embedded engineering help, and in-platform distribution, all of which make it harder for consulting firms to remain neutral when advising enterprise clients. The core concern is conflict of interest. Enterprises hire advisors to assess the full market across hyperscalers, SaaS providers, open-source models, on-prem architectures, and even non-AI alternatives, yet these incentives encourage partners to steer buyers toward Gemini Enterprise and adjacent Google services. The timing also matters. Even as AI budgets are rising sharply, actual agentic deployment remains immature and uneven, which suggests this ecosystem push is also about stimulating demand for technology that has not yet achieved broad organic adoption. From a David Linthicum perspective, the issue is not that Google should stop competing. It is that clients deserve transparent disclosure when supposedly independent recommendations may be shaped by partner funding, preferred access, and alliance economics. That is the difference between a healthy partner ecosystem and a vendor-led influence machine disguised as neutral transformation advice for enterprises making expensive long-term architectural and governance decisions.
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Why You Still Can’t Get a Cloud Job in 2026
05/18/2026
Why You Still Can’t Get a Cloud Job in 2026
If you are struggling to land a cloud computing job in 2026, this video breaks down the ugly truth nobody wants to say out loud. The market is not just competitive, it is distorted. Too many job seekers are wasting time on scam listings, ghost jobs, fake remote roles, and job descriptions written by people who clearly do not understand cloud work. On top of that, candidates are getting screened out by recruiters who cannot tell the difference between real ability and keyword stuffing. Employers say they want cloud talent, then post impossible wish lists, argue over which certifications matter, delay hiring because they think AI might replace part of the role, and refuse to pay what experienced cloud professionals are actually worth. This is not just a skills problem. It is a hiring market full of confusion, mixed signals, and bad incentives. In this video, I break down five reasons why qualified people still cannot get hired, why the cloud job search feels broken, and what these patterns say about the state of the tech industry right now. If you have been applying, getting ignored, and wondering whether the problem is you, this video will probably hit a little too close to home.
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The DRAM Squeeze Pushing Enterprises to Cloud
05/14/2026
The DRAM Squeeze Pushing Enterprises to Cloud
This video breaks down a troubling dynamic in the server market: the biggest cloud providers may be helping drive the DRAM shortage and then benefiting when enterprises are forced to react. According to The Register, AI infrastructure is absorbing huge amounts of memory, suppliers are prioritizing the largest buyers, and many enterprises are facing higher prices, slower deliveries, and less negotiating power when trying to expand or refresh their own hardware. That creates a harsh feedback loop. Hyperscalers secure scarce memory for massive AI buildouts, tighten supply for everyone else, and then sell cloud capacity to the same companies now struggling to afford on-prem infrastructure. The article does not prove illegal market manipulation, but it does describe a market structure that strongly favors hyperscalers through scale, supplier access, and the ability to capture demand when shortages hit enterprise buyers hardest. For enterprises, the real issue is not just rising DRAM prices. It is whether the economics of infrastructure are being reshaped in a way that leaves them with fewer real choices, more cloud dependency, and less control over their own AI and application strategy.
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Why Consultants Keep Recommending Bad Tech
05/11/2026
Why Consultants Keep Recommending Bad Tech
AI consulting is being sold as the next great enterprise revolution, but too much of the market looks more like a land grab than real transformation. In this video, I break down why so many consulting firms seem obsessed with partnerships, ecosystems, and “owning the stack,” while saying far less about measurable client value. The issue is not that AI lacks potential. It clearly has enormous potential. The issue is that many firms are treating vendor alignments and flashy positioning as if they were proof of capability. They are not. When consulting firms lead with partner logos, push agents before understanding the process, and recommend expensive technology stacks before proving the economics, clients often end up paying for complexity, hype, and experimentation instead of outcomes. That is where the AI consulting gold rush starts to look dangerous. This video explores the incentives driving the market, the gap between AI marketing and AI competence, and the warning signs enterprise buyers should watch for before signing large contracts. If consulting firms want to lead in AI, they need to prove they can create real business value, not just sell the latest version of fear of missing out to drive unnecessary enterprise spending today.
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Why Digital Transformation Stalls: Cloud, Culture, AI Governance, and Workforce Readiness
05/07/2026
Why Digital Transformation Stalls: Cloud, Culture, AI Governance, and Workforce Readiness
This session explores why digital transformation efforts often stall even after major cloud investments. The core issue is that infrastructure can be modernized faster than culture, governance, and workforce behavior. Rather than treating cloud as a migration exercise, leaders must use it to redesign operating models, decision-making, and value creation. The discussion highlights how the Capability Maturity Model can help organizations move from fragmented practices to continuous monitoring and a strong Cloud Center of Excellence. It also reframes the digital skills gap as a development challenge, showing why structured learning journeys, coaching, and social learning outperform one-off training or a search for rare AI talent. The session further examines why boards need greater digital fluency as AI adoption accelerates, and how governance weakens when leadership ambition outpaces oversight capability. It concludes with a practical look at modern AI governance, including dynamic controls for agentic systems, trust-centered assurance, and guardrails for democratized IT through approved tools, visibility, and risk monitoring. Attendees will leave with a clearer framework for aligning cloud, talent, governance, and trust to drive sustainable transformation. The focus is practical, executive-level, and aimed at organizations seeking to scale innovation without losing accountability, resilience, or strategic clarity across the enterprise today.
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Public Cloud Reliability Sucks. Let’s Stop Pretending
05/04/2026
Public Cloud Reliability Sucks. Let’s Stop Pretending
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.
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The Great Agentic Distraction: Why Cloud Innovation Just Slowed Down
04/30/2026
The Great Agentic Distraction: Why Cloud Innovation Just Slowed Down
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.
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5 Ways to Stay Relevant in Cloud Computing in 2026
04/27/2026
5 Ways to Stay Relevant in Cloud Computing in 2026
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.
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Agentic AI Meets Enterprise Reality
04/23/2026
Agentic AI Meets Enterprise Reality
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.
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Why I’ll Never Go Back to Big Consulting
04/20/2026
Why I’ll Never Go Back to Big Consulting
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.
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Did Cloud Providers Blacklist Me from Their Conferences?
04/16/2026
Did Cloud Providers Blacklist Me from Their Conferences?
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.
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Is AWS Losing Its Throne to Microsoft, Google, and Neo Clouds?
04/13/2026
Is AWS Losing Its Throne to Microsoft, Google, and Neo Clouds?
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.
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