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Hello,
We live in a fast-paced environment, where we are pressured to deliver on deadlines, and figure answers quickly. AI is raising the expected efficiency bar. On the other hand, generating quick answers may miss the educational process required to discover a solution.
People's adoption of AI seems to be on the wrong direction, where AI is used to skip a human's task. That is inconsistent with the fact that there plenty of LLM Wiki or Context Management tools which aim to instruct AI to follow humans' directions. We should see a shift towards more fundamental thinking where a human or a team design their own First Principles. In this way, AI generation will be easily verified, because it is based on principles a human understands very well.
Example in Finance. A human could state risk-adjusted return pattern to be (portfolio return) - (risk-free rate) / (portfolio volatility). Then ask AI to compute many risk-adjusted analysis following the same pattern. What I observe in practice is that AI generates over-complicated analysis and people do accept it in the name of productivity.
Discussion. Why? Why we don't see a shift towards more fundamental thinking? It feels weird that the majority prefer not to spend some time on foundational patterns, to benefit from AI alignment, and save time in reviewing AI's answer. Do we need to educate the public on thinking by first-principles in an AI-native world? Is there a communication problem in how AI is branded?
Hypothesis. I think there are intentional efforts by people in power to control the general public. Instead of empowering the community by educating them, many investments are poured into AI for replacing humans. Productivity is communicated in the name of lowering working hours by skipping tasks.
Discussion. How could we communicate that humans should spend more time working, and shift thinking to higher-cognitive tasks, because of AI?
Hello,
We live in a fast-paced environment, where we are pressured to deliver on deadlines, and figure answers quickly. AI is raising the expected efficiency bar. On the other hand, generating quick answers may miss the educational process required to discover a solution.
People's adoption of AI seems to be on the wrong direction, where AI is used to skip a human's task. That is inconsistent with the fact that there plenty of LLM Wiki or Context Management tools which aim to instruct AI to follow humans' directions. We should see a shift towards more fundamental thinking where a human or a team design their own First Principles. In this way, AI generation will be easily verified, because it is based on principles a human understands very well.
Example in Finance. A human could state risk-adjusted return pattern to be (portfolio return) - (risk-free rate) / (portfolio volatility). Then ask AI to compute many risk-adjusted analysis following the same pattern. What I observe in practice is that AI generates over-complicated analysis and people do accept it in the name of productivity.
Discussion. Why? Why we don't see a shift towards more fundamental thinking? It feels weird that the majority prefer not to spend some time on foundational patterns, to benefit from AI alignment, and save time in reviewing AI's answer. Do we need to educate the public on thinking by first-principles in an AI-native world? Is there a communication problem in how AI is branded?
Hypothesis. I think there are intentional efforts by people in power to control the general public. Instead of empowering the community by educating them, many investments are poured into AI for replacing humans. Productivity is communicated in the name of lowering working hours by skipping tasks.
Discussion. How could we communicate that humans should spend more time working, and shift thinking to higher-cognitive tasks, because of AI?