Using the label of Artificial Intelligence (AI) to describe a lot of different technology capabilities gives us a useful map. It helps us recognize a broader technological movement, shared foundations, and economy-wide implications.
But the label can also blur the landscape by obscuring important differences in:
- Capability: predicting, generating, recommending, perceiving, or acting.
- Mechanism: language model, vision system, recommendation engine, agent, or robot.
- Maturity: laboratory demonstration, released product, or reliable operational system.
- Risk: drafting an email is not equivalent to approving a loan or controlling machinery.
- Value: technical performance does not automatically produce productivity or better outcomes.
- Accountability: saying the AI decided can hide the people who designed, deployed, or relied upon it.
The single label also encourages anthropomorphism and AI-washing: ordinary automation can be presented as intelligence, while narrow competence can be mistaken for general understanding.
AI is useful for discussing the technological wave, but insufficient for evaluating a particular system.
Use the broad term when discussing historical, economic, or societal change. When making a real decision, specify:
Which system? Which task? Which data? How much autonomy? What evidence? What consequences?
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