
The Vibe Coding Stall: Why Your AI-Assisted Code Never Ships
AI-assisted code works in demos. It stalls in production. Here's why the gap exists and how to close it.
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This is where long-form thinking lives: practical essays on strategy, prioritization, and how strong PM leaders improve over time.
Tagged “ai product strategies”

AI-assisted code works in demos. It stalls in production. Here's why the gap exists and how to close it.
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MCP isn't a technical detail—it's a strategic decision about whether your AI product can evolve independently of your model vendor. Here's how to think about it.
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Anthropic's internal analytics went from 21% to 95% accuracy with zero model improvement. What that means for where AI moats actually live—and why you're fighting for the wrong layer.
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Speed and feature parity are the trap. The moat moves toward embedded domain knowledge and workflow depth before foundation models commodify the gap.
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When AI accelerates your team, bottlenecks don't disappear—they migrate. Where they surface reveals exactly what your organization doesn't yet know how to do. Here's how to read the signal and fix the gap.
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If an essay named a stall you're in, tell me where you're stuck. I'll tell you if I can help close the gap from demo to done.