AI Is Becoming the Next Cloud Computing Disaster

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.

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.

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.

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.

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.