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7/19/2026
The original title is about evaluating people who are "good at AI" using the ABCD2 framework. Let me rewrite this for a mobile feed.

The original title is about evaluating people who are "good at AI" using the ABCD2 framework. Let me rewrite this for a mobile feed.

Original: How Do You Evaluate People Who Are "Good at AI"? The ABCD2 Framework

Short summary

The essay proposes the ABCD2 framework for evaluating who is truly 'good at AI,' arguing that the common framing of AI as a tool to operate is wrong. Instead, AI should be treated like a junior colleague — the skilled person is the one who helps AI do work well through knowledge building and context pipelining. Traditional evaluation methods like coding tests measure execution ability, which is exactly what AI now handles, making them poor predictors of AI competence.

  • •AI competence is not tool proficiency — it's the ability to help AI do work well via knowledge building and context pipelining
  • •Traditional yardsticks (coding tests, certifications) measure execution, which has moved to the AI side
  • •Ontology — structuring organizational terms and judgment criteria — becomes critical as AI becomes the execution engine

Generated with AI, which can make mistakes.

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