Category Archives: Artifical Intelligence

The switch that crypto never had

A single U.S. directive forced Anthropic to abruptly disable its most capable models for users worldwide—without warning—by using export controls that treat access as an “export.” Unlike the cryptography wars, modern AI has a real control point: revocable access to centralized models and compute. The event signals a new operational risk for every company that builds on frontier AI: dependency that can be switched off is not an asset, it’s a liability. Continue reading

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AI might end up a controlled substance

Public sentiment is turning against AI—not over superintelligence fears, but over electricity and water consumption that show up on real utility bills. As compute becomes measurable and governable, regulation is shifting toward licensing and metering AI like a controlled substance. The winners won’t just have the best models—they’ll have accountability, auditability, and paperwork ready. Continue reading

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AI agents & Marketplaces are like Ryanair & Legacy Airlines

Classifieds marketplaces have long operated like hub airports: buyers and sellers are forced through a central portal that charges for the connection. AI agents change that by enabling direct, point-to-point matching across listings—eroding the discovery toll that created outsized margins. With no “long-haul” segment to retreat into, marketplaces face a structural reset toward trust, verification, and assisted closing rather than captive attention. Continue reading

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A Stress Diagram Cannot Be Scraped

Frontier models are becoming interchangeable; the differentiator is context. In industrial manufacturing, the real moat is connecting physical test signals, structured lab data, and cross-system workflow agents that turn measurements into decisions. The winners will pair AI capability with deep domain expertise and the operational wiring that models can’t replace. Continue reading

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Magnifica Humanitas: The Pope Just Audited the AI Industry

Read as an audit, Pope Leo XIV’s first encyclical lands three critiques the AI industry already knows but rarely admits: frontier systems are cultivated more than engineered, alignment can’t stay a closed in-lab project, and AI has a very real human supply chain. For practitioners, it’s less sermon than board memo—and it comes with regulatory deadlines and due-diligence consequences. Continue reading

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Buy the Workflow, not the Agent

If you are buying an agent platform in 2026, you are buying a rebuild in 2027. A few early indicators paint the following picture: The agent is not the problem; the architecture around it is. An agent has three things … Continue reading

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The Regulator nobody lobbied against

The AI industry spends big on lobbying. OpenAI’s CEO personally argued against safety regulations and transparency requirements. The industry’s message was consistent. Regulation will kill innovation. A patchwork of state laws will fragment compliance. Let us self-regulate. They possibly were … Continue reading

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The Token Tax

Every time an AI model gets called, GPU cycles get burned. GPUs cost 6-8x more per operation than traditional CPU compute. That is structural, and right now it is mostly hidden to everyday (enterprise) users. OpenAI projects a cash burn … Continue reading

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The Paradox Is back

In 1987, Robert Solow looked at two decades of corporate IT investment and wrote one of the most quoted lines in economics. “You can see the computer age everywhere but in the productivity statistics.” Nearly forty years later, replace “computer” … Continue reading

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The big Recomposition

In the previous posts I argued that most companies think about AI linearly. Make the process faster. But keep the layout the same. Bolt the electric motor onto the old belt-and-shaft system and call it AI-progress. But there is a … Continue reading

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