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What AI can and can't do

Prediction is not destiny

Predictions estimate patterns under assumptions. They do not reveal destiny, eliminate uncertainty, or justify every decision made from a correlation.

~14 min this pagePrediction limits

1Try it yourself

Playground

Can or can’t?

Tap Can or Can’t for each claim. Don’t fall for the hype.

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Summarize a long email in a few bullets

2Learn the idea

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The core idea

Predictions estimate patterns under assumptions. They do not reveal destiny, eliminate uncertainty, or justify every decision made from a correlation.

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A practical lens

Use this three-part method:

  1. Ask what outcome and time period were measured. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.
  2. Inspect who was represented in the data. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.
  3. Keep consequential judgment and appeals with accountable people. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.

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Worked example

Walk through A hiring score predicts which applicants resemble past hires, then gets presented as a statement about each person’s future potential.. Label three moments where “Prediction is not destiny” changes what you trust: (1) the first fluent answer, (2) the first missing source or permission, and (3) the decision a human must own. Write the before/after task so the model only does the slice that evidence supports. Keep one sentence that states how this page’s idea differs from a generic “AI is smart/dumb” score.

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Common traps and better moves

  • Turning group patterns into certainty about an individual. This shortcut removes useful friction, but it also hides an assumption that should be tested. Replace it with an observable check.
  • Ignoring changed conditions. This shortcut removes useful friction, but it also hides an assumption that should be tested. Replace it with an observable check.
  • Optimizing a measurable proxy that is not the real goal. This shortcut removes useful friction, but it also hides an assumption that should be tested. Replace it with an observable check.

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Build the habit

Before you close the tab, capture a reusable habit for Prediction is not destiny inside What AI can and can't do: name the observable check, the evidence you would open, and the stop condition. Rehearse it once on a low-stakes example, then once on a higher-stakes variant. The habit succeeds when you can explain the check without reopening this lesson. Target outcome: Explain AI capabilities as task-specific and conditional rather than magical.

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Before you start

Why this matters

A hiring score predicts which applicants resemble past hires, then gets presented as a statement about each person’s future potential.

Check your understanding

Page assessment

Answer from memory. Completion is saved from this evidence, not from opening the next page.

Local focus for Prediction is not destiny (What AI can and can't do): write the smallest test that would falsify a confident claim on this page, name the evidence you would open first, and note who must approve if the cost of being wrong is more than a redo. Keep the note under ten lines so you will actually reuse it.

1. Explain the page’s core distinction without using the word “smart.”
2. Which fact, source, permission, or test would most change your judgment in the opening scenario?
3. Name one low-consequence use where a light check is enough and one high-consequence use where independent review is required.
4. What should a responsible user do when the available evidence cannot support the requested conclusion?

All responses are required.