Cloud Computing Insider
- Episodes
- 177
- Released
- several times a week
- Latest episode
- Nov 12, 2026
About the podcast
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.
Episodes 177
Private Cloud Is Back — And Public Cloud Should Be Worried
Private cloud is back, but this is not a return to the old enterprise data center model of slow provisioning, ticket queues, static infrastructure, and underused hardware sitting in corporate facilities. In …
Show notes
Private cloud is back, but this is not a return to the old enterprise data center model of slow provisioning, ticket queues, static infrastructure, and underused hardware sitting in corporate facilities.
In this episode of Cloud Computing Insider, David Linthicum looks at why enterprises are reconsidering private cloud, hybrid cloud, and cloud-like infrastructure inside the business.
The drivers are very different from the private cloud conversation of 10 or 15 years ago. VMware uncertainty after the Broadcom acquisition has forced many enterprises to rethink their virtualization and private infrastructure strategies. OpenStack is getting renewed attention as companies look for more open and flexible private cloud options. AI workloads are pushing infrastructure closer to enterprise data because of latency, cost, GPU availability, data sovereignty, and compliance concerns. Edge computing is also forcing companies to run cloud-like services outside centralized public cloud regions.
This does not mean public cloud is going away. It means the old "cloud-first for everything" assumption is being replaced by a more mature workload-placement model.
The new private cloud is not about nostalgia. It is about API-driven infrastructure, automation, self-service, cloud-native operations, Kubernetes, platform engineering, on-prem AI, distributed edge environments, and better control over economics and risk.
The mistake would be thinking private cloud is just the old data center with a new label. The opportunity is building internal infrastructure that behaves like cloud where it matters, while avoiding the runaway cost, lock-in, latency, sovereignty, and operational tradeoffs that come with putting every workload in public cloud.
Major Cloud Providers Banned My Book. Here's What They Don't Want You to Know
I'm reacting to reports that major cloud providers have tried to keep Insider's Guide to Cloud Computing out of internal libraries, partner events, and industry conversations. If true, that reaction says more …
Show notes
I'm reacting to reports that major cloud providers have tried to keep Insider's Guide to Cloud Computing out of internal libraries, partner events, and industry conversations. If true, that reaction says more about the industry's discomfort with scrutiny than it says about my book.
This video is not an attack on cloud computing. I've spent decades building, teaching, and advising enterprises on cloud architecture. It is an attack on the propaganda that says cloud is always cheaper, automatically more secure, endlessly reliable, and right for every workload.
I break down the costs vendors too often minimize: egress fees, API consumption, managed-service sprawl, overprovisioning, and lock-in that can erode negotiating power. I also address the security and reliability risks that do not disappear when a workload moves to a hyperscaler.
The goal is simple: give enterprise leaders the facts they need to make cloud decisions based on business value, architecture, resilience, and transparent economics—not keynote promises.
If you are responsible for technology strategy, cloud budgets, security, or modernization, this conversation matters. Read the book, challenge the assumptions, and never let the company sending the invoice be your only source of architecture advice. The stakes are too high for blind trust alone.
Amazon Wants to Break Nvidia's Grip on AI—With Trainium
Nvidia GPUs have become the default engine of the AI boom, but AWS is making a very different bet: if customers are building and running large AI models inside Amazon's cloud, Amazon wants more of that workload …
Show notes
Nvidia GPUs have become the default engine of the AI boom, but AWS is making a very different bet: if customers are building and running large AI models inside Amazon's cloud, Amazon wants more of that workload running on Amazon silicon.
In this episode of Cloud Computing Insider, David Linthicum examines AWS Trainium, Amazon's purpose-built AI accelerator for model training and inference, and why it matters for enterprise cloud strategy.
AWS says Trainium is designed to deliver strong economics for high-performance AI training and inference at scale, while Trn2 instances offer 30–40% better price performance than comparable GPU-based EC2 P5e and P5en instances. That does not mean Nvidia is going away. AWS still offers a large portfolio of Nvidia-powered instances, and Nvidia remains deeply embedded in AI development through CUDA, GPUs, networking, software, and developer mindshare.
The real issue is control. If AWS can move more AI workloads onto Trainium, it can reduce dependence on scarce Nvidia GPUs, improve cloud margins, offer differentiated pricing, and keep customers deeper inside the AWS ecosystem.
For enterprises, Trainium is not just a chip story. It is a cloud architecture, cost management, vendor lock-in, performance portability, and AI infrastructure strategy story.
Why Independent Cloud Voices Are Being Pushed Out of Cloud Conferences
I've spent decades watching the cloud industry mature, and here's the uncomfortable truth: the biggest cloud conferences are not neutral industry events. They are controlled stages owned by the same …
Show notes
I've spent decades watching the cloud industry mature, and here's the uncomfortable truth: the biggest cloud conferences are not neutral industry events. They are controlled stages owned by the same hyperscalers that dominate the market: AWS, Microsoft, and Google. These companies decide who gets invited, who gets approved to speak, who gets to demo, and which messages can reach thousands of customers, partners, analysts, and enterprise decision-makers.
That matters because these events shape how the market thinks about cloud strategy, AI, modernization, security, and cost. When the agenda is controlled by the vendor, the story naturally favors the vendor. You hear the success stories, the polished demos, the customer wins, and the product launches. What you hear far less about are the cost overruns, failed migrations, lock-in, complexity, skills gaps, and architectural mistakes that real enterprises deal with every day. I'm not saying hyperscalers should not promote their platforms. Of course they will. But we should stop pretending these blockbuster conferences are open forums for independent debate. They are curated narratives, and if your perspective challenges that narrative, you may never get near the microphone.
The 5 Cloud Products That Changed Enterprise Computing Forever
Not every cloud product changed enterprise computing. Many were useful. Some were overhyped. A few were absorbed into larger platforms and forgotten. But a small number of cloud technologies fundamentally …
Show notes
Not every cloud product changed enterprise computing. Many were useful. Some were overhyped. A few were absorbed into larger platforms and forgotten. But a small number of cloud technologies fundamentally changed how enterprises buy, build, run, scale, and govern systems.
In this episode of Cloud Computing Insider, David Linthicum breaks down five cloud products and technology categories that actually mattered: Microsoft Azure, cloud data and analytics platforms, serverless computing, Kubernetes and managed container platforms, and the original AWS building blocks, EC2 and S3.
These technologies changed enterprise computing because they reduced real constraints. They helped companies avoid long hardware procurement cycles, standardize platforms, reduce server management, break down data silos, and modernize legacy integration patterns.
This is not a list of the newest or flashiest cloud services. It is a list of the cloud technologies that changed enterprise behavior. They reshaped IT operating models, architecture decisions, application deployment, data strategy, governance, and the economics of infrastructure.
The cloud products that mattered most were not just products. They became patterns that every major enterprise technology organization had to respond to.
The Cloud Capacity Crisis Is Here
The cloud was supposed to be infinite — but AI may have finally exposed its limits. In this video, we break down why Amazon Web Services is growing at a massive pace, yet still struggling to keep up with demand …
Show notes
The cloud was supposed to be infinite — but AI may have finally exposed its limits. In this video, we break down why Amazon Web Services is growing at a massive pace, yet still struggling to keep up with demand from AI companies and enterprise customers. AWS revenue is surging, backlog is exploding, and Amazon is planning enormous infrastructure spending, but even that may not be enough to satisfy demand through 2026 and 2027.
This story is bigger than one earnings report. It shows how artificial intelligence is changing the economics of cloud computing. The new bottlenecks are not just software or algorithms — they are power grids, data centers, GPUs, memory, cooling systems, networking gear, and construction timelines. Cloud capacity is becoming a scarce resource, and companies that reserve compute early may gain a serious advantage over competitors who wait too long.
We'll look at why AWS is still winning financially, why that success creates new pressure, and why the future of AI may depend less on smarter models and more on who can build enough physical infrastructure fast enough.
Why Moving Off AWS and Azure Is About to Become IMPOSSIBLE
For two decades, enterprise architects fought tooth and nail against proprietary APIs, databases, and multi-year cloud contracts to keep their systems portable. We thought containers, microservices, and …
Show notes
For two decades, enterprise architects fought tooth and nail against proprietary APIs, databases, and multi-year cloud contracts to keep their systems portable. We thought containers, microservices, and Kubernetes had finally solved the vendor lock-in problem for good. But while everyone was celebrating software portability, the major cloud providers quietly pulled off the ultimate architectural bait-and-switch: they moved the lock-in down into the physical silicon. In this video, I break down why custom cloud chips—like AWS Graviton and Trainium, Google TPUs and Axion, and Microsoft Azure's Maia and Cobalt—are rewriting the rules of enterprise IT. While these purpose-built processors offer unbeatable price-to-performance and massive runtime cost cuts on paper, they come with a hidden operational tax. Once your core AI models, compilation pipelines, and runtime frameworks are hyper-tuned to execute on a specific vendor's proprietary instruction set, you can't just containerize your workload and shift it elsewhere. The hardware moat is real, and it's turning standard cloud migrations into multi-million-dollar re-engineering nightmares. Watch this breakdown before you migrate your mission-critical applications to custom cloud hardware, and learn how to navigate performance gains without falling into the silicon trap.
Why Digital Transformation Failed to Make Companies More Productive
Technology has never been more powerful, more available, or more affordable to deploy. Cloud computing, SaaS, DevOps, automation, cybersecurity platforms, data tools, and now AI were all supposed to make IT …
Show notes
Technology has never been more powerful, more available, or more affordable to deploy. Cloud computing, SaaS, DevOps, automation, cybersecurity platforms, data tools, and now AI were all supposed to make IT faster, cheaper, and more valuable to the business. But the numbers tell a more uncomfortable story: organizations are spending more on technology than ever, while productivity gains and business outcomes are not keeping pace.
In this conversation, David Linthicum breaks down why IT may actually be getting worse at its core job: turning technology dollars into measurable business value. The issue is not that the tools are failing. The issue is that IT organizations have become buried in complexity, technical debt, poor architecture, bloated processes, cloud waste, and metrics that reward activity instead of outcomes.
If global IT spending keeps climbing while productivity growth slows, then leaders need to ask a harder question: are we really becoming more efficient, or are we just buying more technology to work around the problems we refuse to fix?
This episode challenges the conventional wisdom around digital transformation and asks whether modern IT has lost the plot.
$74,000 Just to Download Your Own Data?!
Moving a petabyte out of the public cloud isn't a technical problem — it's a money problem. Uploading is free on every major provider, but downloading that same data back to your own servers triggers egress …
Show notes
Moving a petabyte out of the public cloud isn't a technical problem — it's a money problem. Uploading is free on every major provider, but downloading that same data back to your own servers triggers egress fees that can run into the tens of thousands of dollars. In this video, I break down exactly what it costs to pull one petabyte from AWS, Azure, and Google Cloud back to on-premises systems, comparing the open internet against private circuits like Direct Connect, ExpressRoute, and Cloud Interconnect. I walk through the real math, the hidden fees nobody mentions, the time each path takes, and the trade-offs around security, governance, and bandwidth. If you're planning a cloud migration, a repatriation, or just want to understand why the cloud keeps your data sticky, this is the breakdown you need before you sign anything — or before you hit "download" on a petabyte.
The Future of Cloud Computing Is in Space
AI data centers are getting so massive and power-hungry that some companies are looking beyond Earth for the next cloud computing frontier. In this video, we look at Starcloud, a startup trying to build data …
Show notes
AI data centers are getting so massive and power-hungry that some companies are looking beyond Earth for the next cloud computing frontier. In this video, we look at Starcloud, a startup trying to build data centers in space using satellites packed with AI hardware, solar panels, radiators, and high-speed communications. The idea sounds like science fiction, but Starcloud has already launched an Nvidia H100 GPU into orbit and raised major funding to scale its vision.
The pitch is simple: Earth-based data centers are running into serious limits, including power shortages, land constraints, cooling costs, grid delays, water usage, and local permitting battles. Space could offer constant solar energy, natural cooling through radiation, and room to build compute infrastructure at a scale that is difficult on the ground.
But the challenges are enormous. Launch costs, radiation, orbital debris, latency, regulation, hardware reliability, and connecting thousands of GPUs in orbit could make this one of the hardest infrastructure bets in tech. If it works, the future of cloud computing may not be in Virginia, Texas, or Silicon Valley — it may be in low Earth orbit.
AI Workloads Are Breaking Cloud Budgets. Does Managed Cloud Fix That?
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 …
Show notes
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 Data Center Lie Is Falling Apart
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 …
Show notes
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.
The Hidden Career Trap Holding Women in Tech Back
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 …
Show notes
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 the Cloud…Duh!
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 …
Show notes
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.
From Automation to Automated Automation: The Next AI Revolution
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 …
Show notes
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.
Why Enterprises Keep Getting Multicloud Wrong
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 …
Show notes
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.
We Were Lied to About Cloud Costs — And I Have the Videos to Prove It
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 …
Show notes
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.
Why Some Cloud Tech Workers Have To Pay To Quit
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 …
Show notes
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 Is Lying to You (We Have the Data)
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 …
Show notes
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.
Big Tech Analyst Firms Have a Credibility Problem
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 …
Show notes
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.
5 Cloud Training Hacks Nobody Talks About
Cloud training has never been more accessible, but that does not mean most people are learning it the smart way. In fact, a lot of aspiring cloud professionals waste time following the same crowded paths …
Show notes
Cloud training has never been more accessible, but that does not mean most people are learning it the smart way. In fact, a lot of aspiring cloud professionals waste time following the same crowded paths, buying the same generic courses, and missing the hidden opportunities that can accelerate their skills much faster. In this video, we are going beyond the mainstream advice and looking at the insider strategies that serious learners use to get ahead in cloud computing. These are the overlooked shortcuts, little-known training resources, and practical hacks that can help you build real-world cloud skills without spending a fortune or getting stuck in endless theory. We will cover how to find training that employers may actually pay for, where to uncover certification discounts, how to get hands-on lab access without overspending, why small study communities can outperform solo learning, and where mentorship programs quietly open doors for faster career growth. If you want to break into cloud computing or level up your expertise in a way that feels more strategic and less conventional, this video is for you. These are the cloud training secrets that can give you an edge in 2027 and beyond.
The AI Goat Rodeo: Rich CEOs, Reckless Claims, Real Consequences
Welcome to the AI goat rodeo, where the biggest names in tech are charging into the arena, waving billion-dollar checkbooks, shouting about the future, and trying to convince us they've got everything under …
Show notes
Welcome to the AI goat rodeo, where the biggest names in tech are charging into the arena, waving billion-dollar checkbooks, shouting about the future, and trying to convince us they've got everything under control. They don't. What we're watching in today's AI market is not a clean, disciplined race toward innovation. It's a loud, dusty, chaotic spectacle where every company claims it has the smartest model, the safest platform, the best agents, the biggest ecosystem, and the one true path to artificial general intelligence. Meanwhile, products are rushed out the door, pricing changes by the month, partnerships flip into rivalries overnight, and customers are left wondering who is actually solving real business problems and who is just hanging on for dear life. That's why the goat rodeo metaphor fits so perfectly. In a real goat rodeo, there's motion, noise, confusion, and a lot of people pretending they meant to do exactly what just happened. Sound familiar? In this video, we're going to break down why the current AI marketplace looks less like a master plan and more like a comic struggle to stay in the saddle. And listen closely: the buzzwords are often louder than the actual business value being delivered.
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 …
Show notes
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.
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 …
Show notes
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.
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 …
Show notes
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.
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 …
Show notes
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.
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 …
Show notes
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.
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 …
Show notes
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.
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 …
Show notes
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.
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 …
Show notes
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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