Dave Linthicum Is Not AI
AI is rapidly turning modern marketing into a surveillance-and-optimization machine. What started with loyalty cards and basic customer databases has evolved into always-on tracking across apps, websites, and devices—feeding models that learn what people want, when they’re vulnerable to buying, and how to push them toward a decision. In this video, we break down how “surveillance marketing” works in plain language: companies collect massive amounts of behavioral data, stitch it together with identity graphs and third-party sources, and use AI to target messages in real time. Then...
info_outlineDave Linthicum Is Not AI
The launch of the Amazon McKinsey Group (AMG), a high-profile partnership between AWS and McKinsey, is being presented as a game-changing initiative for enterprise-scale digital transformation. They tout end-to-end value, integrated teams, and billion-dollar business impact. But let’s cut through the advertising: AMG’s very existence highlights the growing desperation among cloud providers and consulting giants faced with the slow, challenging rollout of artificial intelligence across enterprise landscapes. These firms, each with their own vested interests, are combining efforts not to...
info_outlineDave Linthicum Is Not AI
I’ve spent decades watching enterprises adopt technology, and the pattern is always the same: innovation only creates growth when it reduces friction and increases trust. The automotive industry is pushing AI into the cabin as if “more intelligence” automatically means “more demand.” But buyers don’t purchase abstractions—they purchase outcomes. Right now, much of in-car AI adds complexity to routine tasks, introduces unpredictable behavior, and shifts capabilities behind subscriptions and post-sale updates. That’s not a value story; it’s a risk story. What’s worse is...
info_outlineDave Linthicum Is Not AI
In this video, I’m going to take you back to 1985, when building AI meant rolling up your sleeves and encoding expertise by hand. I’ll tell a personal story from that era—using Prolog, Lisp, and Borland M1 to create rule-based systems that could make decisions in the real world, long before the cloud and GPUs made “intelligence” feel instant. Then we’ll jump to 2026, where AI is defined by foundation models, tool-using agents, and systems that learn from enormous datasets rather than just following explicit rules. You’ll see what we gained—speed, scale, and the ability to work...
info_outlineDave Linthicum Is Not AI
Windows 11 can run local AI, but in real day-to-day use it often feels like it’s working against you—especially once you start stacking multiple AI tools, projects, and installs. What I found is that Linux generally delivers a smoother “AI desktop” experience: setups are more straightforward, common AI instructions match what you’re actually running, and GPU-accelerated apps tend to behave more consistently. The result is less time spent troubleshooting and more time getting outputs. On Windows 11, the biggest pain points showed up around friction and...
info_outlineDave Linthicum Is Not AI
AI agents are the new buzzword in enterprise tech, but the real question isn’t “can we build them?”—it’s “can we actually sell them in a way enterprises will trust and fund?” This video, from a Dave Linthicum-style vantage point, cuts through the marketing gloss and treats agents as what they really are: autonomous software systems wired into messy, mission-critical environments. We unpack what an AI agent actually is, how it differs from a simple chatbot or workflow, and what it takes architecturally to move from a cool demo to a production-grade capability. From there, we...
info_outlineDave Linthicum Is Not AI
Windows 11 wasn’t “the future”—it was a forced pivot. Microsoft took an OS people relied on for speed, flexibility, and control, then locked the door behind TPM 2.0, Secure Boot, and arbitrary CPU lists that stranded millions of perfectly good PCs. And for what? A redesigned UI that’s less customizable, a Start Menu that feels like a billboard, and a setup flow that tries to drag Home users into an always-online Microsoft account whether they want it or not. Then comes the real point: Windows 11 increasingly feels like a platform for Microsoft’s priorities—cloud services, Edge,...
info_outlineDave Linthicum Is Not AI
Everyone’s hyped about AI breakthroughs—but almost nobody is talking about the bill that’s being handed to normal people. In this video, we break down how the AI gold rush is quietly driving up the price of basic hardware: GPUs, RAM, SSDs, and even CPUs. Hyperscalers are signing multi‑billion‑dollar contracts and buying entire foundry runs of chips, and that doesn’t just drain supply—it resets the global price floor for everyone else. The result? Gamers, PC builders, small IT shops, and indie ML labs are all forced to pay more for the same components they used to buy a few years...
info_outlineDave Linthicum Is Not AI
Is AI coming for your job… or not yet? In this video, I break down a simple test to understand how exposed your role really is, based on the patterns of work AI is best at replacing. Instead of vague hype, we’ll look at concrete signals that your job might be in the danger zone. You’ll see the core pattern behind high‑risk roles: predictable, rules-based tasks, high repetition, and “good enough” outputs where speed and cost matter more than originality. I’ll walk through real examples like customer support, basic content production, document processing, and reporting...
info_outlineDave Linthicum Is Not AI
I developed this cost comparison to ground the AI discussion in economic reality instead of assumptions and marketing slides. Too often, generative and agentic AI are framed as inevitable next steps—something you add “on top” of existing systems—as if the only risk is moving too slowly. In truth, these approaches introduce substantial new costs: specialized skills, LLM usage, vector infrastructure, orchestration platforms, and ongoing governance. By putting three approaches—traditional development, generative AI–enhanced systems, and agentic AI solutions—side by side...
info_outlineIn today’s evolving AI landscape, choosing the right cloud provider is critical for both startups and enterprises. This video delivers a detailed, side-by-side analysis of CoreWeave and AWS—two of the biggest names in cloud infrastructure for AI. We break down key features like GPU hardware, pricing, deployment speed, ecosystem, and global reach. CoreWeave stands out by specializing in AI/ML workloads, offering lightning-fast access to the latest NVIDIA GPUs at transparent, significantly lower prices. Its platform is built for rapid scaling and streamlined AI operations, making it a favorite among AI research labs, LLM startups, and VFX studios. In contrast, AWS remains the gold standard for enterprise cloud, providing a massive global network and a full suite of services for virtually any workload, though it lags behind in cost and agility for cutting-edge AI training. Utilizing simple, actionable talking points, this analysis helps you quickly understand what each platform does well—and where they fall short. Whether you’re running large-scale AI models, building SaaS products, or just optimizing your cloud costs, this video guides you to the best fit for your needs.