Building With AI
How to actually use AI: skills, harnesses, and turning capability into results.
Where the Harness Pays Off, and Where It Does Not
Most AI budgets fund the wrong remedy because the workflow type was never diagnosed. Capability-bound vs friction-bound is the missing one-page policy.
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AI Is a Mirror: Stanford's Lesson From 51 Deployments
Stanford studied 51 successful enterprise AI deployments. The difference was never the AI model. It was the organization. Here is what that means.
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Skills Beat Agents: The AI Layer CEOs Keep Missing
Agent count is the visible metric. Skill depth is the layer that actually produces returns, survives vendor changes, and becomes IP the company owns.
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What Zuckerberg's AI Deputy Reveals About Your Information Gap
Zuckerberg is building an AI agent to bypass the layers between him and his data. The real story is the information gap every CEO shares.
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You're Not Using AI Wrong. You're Building Wrong.
McKinsey tested 25 factors. The single biggest predictor of AI profitability was workflow redesign. Not better models, not bigger budgets.
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The Case for Using AI Less
More AI is not always better AI. When cognitive dependency replaces cognitive effort, the advantage reverses.
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