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We're measuring AI productivity the wrong way
When we talk about AI and productivity, we ask how much time it saved us. That's the wrong question. The one that matters is how much time it saved everyone. AI didn't remove the cost of communication. It shifted it from the author to the audience, and we're celebrating the wrong side of the ledger.
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Every configuration option is a question we couldn't answer
Open your favorite AI coding agent and you're greeted with a surprising number of decisions. Which model? How much reasoning? Which MCP servers? Most of us don't know the right answers. We treat that as the cost of cutting-edge AI. It's a sign the technology hasn't matured yet.
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The next literacy is externalizing judgment
When ChatGPT took off, everyone wanted to learn prompt engineering. We've been optimizing the wrong thing. The biggest productivity gains from LLMs come from learning to externalize judgment.
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You can't judge an AI agent until you know what the task is worth
We keep asking whether AI coding agents are good or bad, fast or slow, cheap or expensive. But good compared to what? Until you know what a task is actually worth to you, you have no way to tell whether the agent's output was a bargain or a rip-off.
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ProxyStat is now available for Windows
Use ProxyStat to easily see if you have system proxy configured on Windows
