AI is a tool for efficiency but a disaster when it starts making

AI is a tool for efficiency but a disaster when it starts making decisions, according to a new essay that warns against treating AI as a manager rather than an assistant. The author advocates a 'human-in-the-loop' workflow where AI handles grunt work but humans own the logic, using a three-phase approach: draft, friction, and refinement. The essay argues that the real danger is losing the 'human' part of work, resulting in polished but intellectually empty output, and that organizational knowledge is the only real moat left in the AI era.

AI is a tool for efficiency but a disaster when it starts making The real danger isn't that AI will take over the world, but that we've started treating it as the manager rather than the assistant. When you let the AI dictate the direction of a project or the tone of a strategic document without heavy intervention, you lose the "human" part of the work. You end up with a polished, corporate-sounding void—text that is grammatically perfect but intellectually empty. To actually make this work, you need a rigorous AI workflow that treats the model as a junior intern who is prone to confident lying. I've found that the most effective approach is a "human-in-the-loop" system where the AI handles the grunt work, but the human owns the logic. How to actually implement a servant-style AI workflow If you want to avoid the "AI master" trap, you have to change how you prompt and review. Instead of asking the AI to "write a strategy," ask it to "critique my strategy from the perspective of a skeptical CFO." 1. The Draft Phase: Use the AI for the "ugly first draft." This is where the speed is actually useful. Get the structure down, dump the raw data, and let the LLM organize it. 2. The Friction Phase: This is where most people fail. Instead of accepting the output, intentionally introduce friction. Challenge the AI's assumptions. If it suggests a solution, tell it why that solution won't work in your specific real-world context. 3. The Refinement Phase: Use specific prompt engineering to strip away the "AI-isms." Tell it to remove adjectives like "comprehensive," "tapestry," or "game-changing." For those trying to build this into a technical pipeline, keep your prompts modular. Don't ask one prompt to do five things. Break it down: { "step 1": "extract key facts", "step 2": "identify contradictions", "step 3": "draft summary based on verified facts" } By decoupling the extraction of facts from the synthesis of the narrative, you keep control over the truth. The goal isn't to see how much of the work we can offload, but how much of our own thinking we can amplify. When we stop chasing "first-pass" perfection and start using AI to stress-test our own ideas, that's when it actually becomes a superpower. If you're just using it to clear your inbox faster, you're missing the point. Organizational knowledge is the only real moat left in the AI era 2h ago /en/news/5666/ Typing is actually a thinking process that voice input can't 10h ago /en/news/5622/ Coding is no longer about syntax when LLMs can generate a 11h ago /en/news/5611/ Stop trusting AI to write your final commit because LLMs are 14h ago /en/news/5596/ Analyzing PSX charts and market news in 2026 requires a 15h ago /en/news/5589/ Why do AI models keep pushing the Japanese Communist Party? 19h ago /en/news/5568/ Next F1 standings and race calendars finally live on my desktop → /en/news/5661/