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Free concept playground · no account

The 5-Year Foresight Builder

Build your own AI future. Compare it to the forecast.

All 90 playgrounds Join the free cohort

The 5-Year Foresight Builder

AvailableFree

Set where you think each AI capability lands in five years. Only then does the builder reveal the maintained baseline forecast and measure the gap — the axis where you diverge most is the teaching moment, not the forecast.

% of code written by AIforecast 55%
+20 · bolder
% of support tickets auto-resolvedforecast 45%
+15 · bolder
Agent task autonomyforecast 40%
+15 · bolder
% of meetings AI-summarizedforecast 70%
+10 · bolder
AI regulation strengthforecast 50%
-5 · cautious
11/ 100 boldnessbalanced
avg gap 13 · 0/5 aligned
◆Prediction first, forecast secondthe gap is the lesson

You committed to your numbers before seeing the baseline. Your forecast reads as balanced forecast — the most surprising divergence is % of code written by AI (+20 vs a 55% baseline).

  • % of code written by AIyou 75% vs forecast 55% — +20 bolder
  • % of support tickets auto-resolvedyou 60% vs forecast 45% — +15 bolder
  • Agent task autonomyyou 55% vs forecast 40% — +15 bolder
  • % of meetings AI-summarizedyou 80% vs forecast 70% — +10 bolder
  • AI regulation strengthyou 45% vs forecast 50% — -5 cautious

As Engineering: build the provider-agnostic scaffolding and evals that hold up whether or not this axis moves as fast as you bet.

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The 5-Year Foresight Builder

Set where you think AI lands in five years — the share of code written by AI, tickets auto-resolved, agent autonomy, meetings summarized, regulation strength — and only then does the builder reveal the maintained baseline forecast and measure your gap on every axis. It scores how bold your bet is (0–100), calls your forecast consensus, balanced, or contrarian, and spotlights the single axis where you diverge most from the baseline. The moat is the order of operations: your prediction is elicited first and compared second, so the gap is the lesson — not somebody else's forecast handed to you as truth. Runs entirely in your browser (0 uploads, works offline).

How to use it

  1. Drag each of the five capability sliders to your five-year prediction, or load a preset — Cautious future, Match the forecast, or Bold future.
  2. Watch each axis reveal its baseline tick and a live gap chip: bolder (above the forecast), cautious (below), or aligned (dead on).
  3. Read the gauge: your boldness score, the verdict, the average gap, and how many axes you aligned.
  4. Pick a role lens to reframe the "what to build toward" takeaway, then open the X-ray to see your biggest divergence and copy or share the result.

What this clears up (the fundamentals)

  • Elicit before you reveal — a forecast you're shown feels authoritative; a forecast you're compared against exposes your own priors. The gap between your number and the baseline is where the learning is, not the baseline itself.
  • Boldness is directional — predicting more AI than the baseline is bolder, predicting less is cautious. Averaged and clamped to 0–100, it turns a hunch into a number you can defend or revise.
  • Contrarian isn't wrong — a high average gap makes you contrarian, a near-zero gap makes you consensus. Neither is a grade; it's a map of where you'd need to be right for your plan to pay off.
  • One axis carries the bet — the most-divergent axis is where your forecast is riskiest. Naming it turns "AI will change everything" into a specific, testable, buildable call.

Where it's used

A FREE, top-of-funnel foresight check — the fastest way to feel why a forecast you built beats a forecast you were told. It's a miatz build-lab concept playable: play it here, then learn to build the elicit-first comparison, the gap scoring, and the signature reveal yourself.

FAQ

Why does the tool hide the baseline until after I set my sliders?

That's the whole point. If you see the consensus first, you anchor to it. By committing to your own numbers first, the reveal measures your divergence honestly — the gap becomes a teaching moment instead of a number you nodded along to.

What makes a forecast "contrarian" vs "consensus"?

It's driven by your average absolute gap from the baseline across all five axes. A small average gap reads as consensus, a large one as contrarian, and the middle band as balanced. It's a deterministic rubric — no model call, no randomness.

How is the boldness score calculated?

Each axis contributes your prediction − baseline. The average of those signed gaps, clamped to 0–100, is your boldness score: predicting more AI than the baseline pushes it up, predicting less pulls it toward zero. Rounded to a whole number so it's stable and shareable.

Is the baseline forecast a real prediction I should trust?

No — it's a fixed teaching baseline, not investment or planning advice. It exists to give your prediction something concrete to diverge from. The skill being taught (build your view, then measure the gap) is exactly the real practice.

Is anything uploaded?

No. Your predictions and the whole comparison run entirely in your browser — nothing is transmitted, stored, or logged. Turn off your Wi-Fi and it still works.

Limits

A teaching rubric with five fixed axes and a hand-set baseline — it's a fast, opinionated way to feel the elicit-first move, not a validated market forecast. Real foresight depends on live signals, your domain, and how the axes interact; the baseline here is a stable target, not a source of truth. What it teaches — form your own view, then measure the gap — is the durable part.

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