Dave Linthicum Is Not AI

David Linthicum
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Welcome to "Dave is not AI." I'm David Linthicum, and I take a skeptical look at the exploding AI marketplace. Forget the hype. We explore the true reality behind AI technology, its capabilities, and its limitations. Discover why enterprises and humans are struggling with AI today, and gain expert insights on how to best navigate a future where AI is everywhere. Join me for grounded, unbiased analysis to master the AI landscape. Because while AI might be the buzzword, clear understanding is your best strategy. Subscribe now for the real AI story.

  1. 20h ago

    Bill Gates Is Wrong About AI Taking Developer Jobs…Here's Why

    Every few years, the technology industry falls in love with a new prediction: this time, the tools are so powerful that programmers are finished. We heard it with expert systems. We heard it with code generators. We heard it with fourth-generation languages, CASE tools, object-oriented programming, component-based development, offshore development, cloud platforms, and low-code/no-code tools. Each one was promoted as the end of software development as we knew it. And each time, the opposite happened. Developers did not disappear. They moved up a layer of abstraction, learned the new tools, and became more productive.Now we are hearing the same story about AI. Bill Gates and others are warning that AI could eliminate huge numbers of jobs, including knowledge work. I respect the concern, but I think the conclusion is wrong. AI coding tools are powerful, but they are not replacing architecture, engineering judgment, accountability, integration, or business understanding. They are removing routine work and changing the developer's role. That matters because fear is starting to dominate the AI conversation. Instead of panic, we need logic. History tells us that better tools usually create more software, more demand, and more need for skilled people.

  2. 3d ago

    Microsoft Is Betting AI Can Replace Its Own Brain

    Microsoft is offering voluntary exits to a large group of employees while pouring enormous resources into artificial intelligence, and the strategic question is not whether AI matters — it clearly does — but whether the company is underestimating the human expertise that keeps complex enterprise platforms running. In this episode, David Linthicum takes a skeptical look at Microsoft's AI-era workforce strategy and the risk of trading durable operational knowledge for short-term balance-sheet relief. The danger is not simply losing headcount. The danger is losing the people who know why systems behave the way they do, how enterprise customers actually use the products, where the fragile dependencies live, and which problems cannot be automated away with a Copilot prompt. If Microsoft's most experienced and marketable employees take the exit package, rivals could inherit talent, customers could feel the support impact, remaining teams could burn out, and the company could discover that AI is powerful but not mature enough to absorb the hidden work performed by seasoned engineers, product leaders, and customer-facing experts.

  3. Oct 23

    Enterprise AI Is About to Change Forever

    This video explores why enterprise AI is about to move beyond the current obsession with large foundation models and frontier LLMs. While these systems are powerful and useful for productivity, communication, customer service, scheduling, coding, analysis, and other general business tasks, they are unlikely to remain the center of enterprise AI architecture. The next phase will be driven by small and medium language models that are narrowly focused, business specific, and built around enterprise data. These models can operate closer to the systems, workflows, and data that matter most, giving organizations better control, lower costs, improved privacy, stronger governance, and more practical business value.  Rather than relying on one massive model to do everything, enterprises will increasingly build tiered AI architectures where frontier models support broad language and reasoning tasks, while smaller models handle the core operational work. This shift will change how CIOs, CTOs, architects, and business leaders think about AI investment, risk, data sovereignty, and long term strategy. If your organization is putting all its AI bets on the biggest models, this video explains why the smarter move may be building focused AI systems that solve specific business problems.

  4. Oct 17

    AI Companies Are Selling the Future Like It Already Works

    Right now, the AI industry is operating like a gold rush where everyone insists they have already found the mother lode. Startup founders are stretching timelines, enterprise vendors are relabeling old automation as intelligence, executives are pretending pilots are strategies, and customers are quietly exaggerating how much AI has improved their work. The technology is real and powerful, which is exactly what makes the exaggeration so easy to sell. A good demo can look like magic, even if the production version still hallucinates, leaks context, fails audits, or needs a team of consultants to babysit it. This is happening because the incentives are badly aligned. Investors reward massive visions. Buyers reward confident vendors. Boards reward leaders who appear ahead of the curve. Employees reward tools that make them look more productive. Nobody wants to be the person saying, "This is promising, but not ready," while competitors are shouting about transformation, disruption, and human-level intelligence. The result is a market full of half-truths, inflated claims, and strategic optimism. AI may change everything eventually, but for now many people are selling tomorrow's breakthrough as today's finished product. That is not innovation; it is theater with a subscription contract and a confident slide deck.

  5. Oct 16

    How AI Can Learn from Your Data Without Seeing It

    What if organizations could build powerful AI together without ever sharing their private data? This video explores federated learning marketplaces, an emerging cloud computing idea that could reshape how hospitals, banks, governments, and companies collaborate on artificial intelligence. Instead of moving sensitive records into one central database, each organization trains an AI model locally on its own data. Only the model updates are shared, combined, tested, and then made available through a marketplace where buyers can license trusted AI models. The key innovation is provenance: a clear record showing who contributed, what type of data was used, how the model was trained, and how well it performed. This creates a new way to build AI that is private, auditable, and commercially valuable. We will break down how the process works, why it matters for regulated industries, what risks still exist, and why model provenance could become as important as the model itself. If cloud computing is moving from simple storage and servers to trusted AI collaboration, federated learning marketplaces may be one of the most important ideas hiding in plain sight. This is not just about better models; it is about making AI collaboration possible where data sharing was once impossible.

  6. Oct 13

    Why AI Clones Could Destroy Adobe, Microsoft, and the Software Rental Model

    AI is changing the software business in a way few companies expected: it is becoming far easier to recreate familiar applications without rebuilding every capability the old-fashioned way. This video examines the growing backlash against perpetual software subscriptions, price increases, and cancellation policies that leave customers feeling trapped in products they no longer own. Adobe is the obvious flash point, but the implications reach Microsoft, Autodesk, Salesforce, Intuit, and every vendor whose business depends on recurring fees for mature applications.   We look at how AI-assisted development and clean-room reverse engineering can reproduce software behaviors with newly written code, why "good enough" alternatives can change consumer expectations, and why copying a user experience is not the same as copying proprietary code. We also explore the legal, security, compatibility, governance, and support questions that determine whether a free clone becomes a real replacement or merely a demonstration. The central issue is simple: when customers believe subscription pricing is unfair, AI gives developers a dramatically cheaper way to build alternatives. Over the next five years, software giants will need to justify their prices through trusted ecosystems, integrated data, enterprise controls, reliability, and continuous innovation — not just features that competitors can increasingly imitate.

  7. Oct 9

    Consulting Firms Are Wrong About Agentic AI

    Agentic AI is being sold as the next inevitable enterprise architecture — autonomous agents, frontier models, orchestration layers, and massive transformation programs. But before companies rush into another expensive consulting-led technology cycle, they need to ask a much harder question: do they actually need agentic AI, or AI at all? In this video, David Linthicum challenges the assumptions behind the major consulting reports and argues that many of them are pushing a vendor-friendly vision rather than an engineering-first reality.   The real future of agentic AI will not be giant, remotely hosted, multi-agent meshes tied to expensive frontier LLMs. It will be smaller, simpler, distributed agents designed for narrow business functions, often running close to the data they need. These agents will be cheaper, faster, easier to test, easier to govern, and better aligned with how enterprises actually operate. Some use cases will still justify frontier models, but most will not. This is not an anti-AI argument. It is an anti-waste argument. If companies skip the need test, cost model, architecture discipline, and operational reality, they will not get transformation — they will get another overfunded science project dressed up as strategy.

  8. Oct 2

    NeoClouds Explained in 8 Minutes

    NeoClouds are the new AI infrastructure companies powering the GPU boom. While most people focus on AI models, the real fight is over compute: GPUs, data centers, power, cooling, networking, training, and inference.   This video explains the NeoCloud market through four simple groups: full-stack AI infrastructure clouds, developer GPU clouds, inference-first AI clouds, and data-center capacity players.   We'll cover names like CoreWeave, Nebius, Lambda, Crusoe, Nscale, Fluidstack, Vultr, Runpod, Civo, Together AI, Groq, Voltage Park, Applied Digital, Core Scientific, and GMI Cloud.   The key idea: NeoClouds are not just GPU rental companies. Some want to become the AWS of AI. Others focus on fast inference, easy GPU access, or building the physical data centers behind AI.   As AI demand grows, the winners may be the companies that control the compute layer underneath every major model.

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Ratings & Reviews

5
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About

Welcome to "Dave is not AI." I'm David Linthicum, and I take a skeptical look at the exploding AI marketplace. Forget the hype. We explore the true reality behind AI technology, its capabilities, and its limitations. Discover why enterprises and humans are struggling with AI today, and gain expert insights on how to best navigate a future where AI is everywhere. Join me for grounded, unbiased analysis to master the AI landscape. Because while AI might be the buzzword, clear understanding is your best strategy. Subscribe now for the real AI story.

Information

  • Creator
    David Linthicum
  • Years Active
    2025 - 2026
  • Episodes
    95
  • Rating
    Clean
  • Copyright
    © 2025
  • Show Website
    Dave Linthicum Is Not AI

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