Analogy check

Analogy Check

Analogies are how people reason about the genuinely new — and how they most often fool themselves. "AI is like the internet" can illuminate or mislead depending entirely on which parts you claim transfer. Used well, a precedent gives you a base rate and a mechanism. Used lazily, it smuggles a conclusion by feel: the two things rhyme, so the outcome must too. This skill forces every analogy to declare its terms — what maps, what doesn't, and what the precedent actually predicts — before it's allowed to support anything.

The three questions every analogy must answer

1. What transfers? (the mapping)

Name the specific mechanism claimed to carry across, not a vibe of similarity. "Like electrification" could mean many things — say which:

• General-purpose technology whose payoff lags because organizations must redesign around it? (Electricity took ~40 years to reach 50% adoption because factories kept steam-era layouts before rebuilding around distributed motors.)
• Diffusion that outran the institutions meant to govern it? (The printing press spread to hundreds of towns in decades, and information outpaced ecclesiastical and state control — producing decades of upheaval before new norms formed.)
• Capital capturing productivity gains long before labor did? (UK output per worker rose far faster than real wages for ~50 years — "Engels' Pause" — until bargaining power shifted.) State the one mechanism you're actually borrowing. An analogy that "just feels apt" is borrowing none.

2. What breaks? (the disanalogy — mandatory)

Every analogy has points where it fails, and naming them is not optional — it's what separates analysis from rhetoric. For each precedent, state where the target differs:

• Different diffusion speed? (AI's marginal-infrastructure cost is far lower than a national electrical grid, so adoption may compress the timeline — or compute concentration could reproduce the same access gaps. Both are live; say which your argument needs.)
• Different agency/control assumptions? (Every "prior GPT" analogy assumes humans stay in operational control throughout. If the target scenario doesn't, the precedent falls out of scope entirely.)
• Different mechanism, same surface? (Two eras can produce the same statistic for opposite reasons — and which reason holds changes what intervention works.) If you can't name a disanalogy, you haven't examined the analogy — you've adopted it.

3. What base rate does it imply? (and does it survive it?)

A precedent isn't just a story, it's a data point with an outcome. State the outcome and check whether the argument survives it:

• How long did the transition actually take, and does your forecast honor that lag or quietly assume it's instant?
• Did the precedent's access gaps close on their own, or persist for generations? (Electrification is still incomplete a century-plus on, concentrated in the same regions — precedent against "it'll equalize naturally.")
• Did confident forecasts based on that precedent pan out? Beware the analogies that feel strongest.

The exposure-is-not-outcome trap

A recurring failure worth its own flag: technical-susceptibility forecasts dressed as outcome forecasts. A famous 2013 study rated 47% of US jobs at high automation risk within a decade; the occupations rated highest-risk sometimes grew, and unemployment fell to multi-decade lows. Task-level exposure measures what's technically automatable, not what happens — outcomes depend on bargaining power, retraining, demand, and institutions the exposure metric ignores. When an analogy runs "X% exposed, therefore X% displaced," stop it there. (See also hype-check and protect-the-skill.)

The forecaster's-bias flag

Watch for predictions that always sit "15–25 years out" regardless of when they're made — far enough to feel unfalsifiable, near enough to feel urgent. That clustering is a fact about the psychology of forecasting, not evidence the timeline is right. Discount single-point precedent-based timelines accordingly; prefer conditional ranges (see scenario-branch).

Procedure

1. Extract the analogy and the conclusion it's being used to support.
2. Mapping: name the specific mechanism claimed to transfer (not a general resemblance).
3. Disanalogy: name at least one concrete way the target differs — speed, agency, mechanism, scale.
4. Base rate: state the precedent's actual outcome and timeline; check whether the argument honors it.
5. Trap check: is this exposure-dressed-as-outcome? Is it a forever-15-years timeline?
6. Verdict: does the analogy still support the conclusion, support a weaker one, or collapse? Rewrite the claim to what survives.

Output template

ANALOGY: <"X is like Y"> | USED TO ARGUE: <the conclusion it's carrying>

TRANSFERS: <the specific mechanism claimed to map across>
BREAKS: <at least one concrete disanalogy — speed / agency / mechanism / scale>
BASE RATE IT IMPLIES: <precedent's real outcome + timeline; does the argument honor it?>
TRAP CHECK: <exposure≠outcome? forever-15-years timeline? other>

SURVIVES AS: <the claim the analogy can actually support — often weaker>

Example (compressed)

Analogy: "AI won't cost jobs — the ATM analogy proves it. ATMs increased bank teller employment."

TRANSFERS: the real mechanism is "automation of a task cut the cost per branch,
  so banks opened more branches, so total tellers rose even as tellers-per-branch
  fell." That's a genuine, specific mechanism — credit it.
BREAKS: it requires that cheaper unit-cost expands the market and that the
  remaining human tasks stay valuable. AI targets the cognitive tasks that were
  the teller's *remaining* value, and doesn't obviously trigger branch expansion.
  The demand-expansion step is exactly what may not transfer.
BASE RATE: the ATM transition played out over ~30 years with the human kept as
  relationship/again-value worker. A forecast citing it inherits that lag and
  that "humans keep the valuable residual" condition — both of which the argument
  hides.
TRAP CHECK: this is the inverse of exposure-panic — a single reassuring precedent
  generalized to a different task structure.

SURVIVES AS: "automation *can* raise employment when it expands the market and
  humans keep a valuable residual task" — a conditional, not a proof. Whether
  those conditions hold for AI is the actual question, which the analogy skipped.

Gotchas

• Analogies illuminate; they don't prove. The best outcome is usually a conditional — "this holds if X" — not a settled conclusion.
• The disanalogy is mandatory. An analogy presented without its breaking points is rhetoric. Always name at least one.
• Multiple precedents beat one. If three different transitions all imply the same thing, that's stronger than any single rhyme. If they diverge, that divergence is the finding.
• Pairs with scenario-branch (precedents seed the branches), hype-check (exposure≠outcome), and grade-the-evidence (analogical reasoning is tier 5 — flag it).