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Long Bet Project

ASecurity

Make accountable long-term predictions with skin in the game, forcing rigorous thinking about future outcomes through public commitments and charitable stakes

22 stars
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Added 9/20/2026
blockchaingotestingperformance

Security Analysis

A100/100

Scanned 9/20/2026

Install to Claude Code

$npx -y skills add lev-os/agents --skill long-bet-project --agent claude-code

Installs into .claude/skills of the current project.

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SKILL.md
---
name: long-bet-project
description: Make accountable long-term predictions with skin in the game, forcing rigorous thinking about future outcomes through public commitments and charitable stakes
---

# Long Bet Project

## Overview

The Long Bet Project, created by the Long Now Foundation in 2002, is a public arena for accountable predictions about the future. It addresses a critical problem: pundits, CEOs, and thought leaders routinely make confident predictions with zero accountability. Media's short attention span lets them escape consequences when wrong.

Long Bets demands predictors put their name, a solid argument, and money down in support of their statement. Winnings go to charity. This mechanism transforms cheap talk into costly signals - if you're wrong, a cause you oppose might benefit. The minimum term is 2 years with no maximum, encouraging thinking across decades or centuries.

The framework operationalizes prediction accountability: your forecast becomes a matter of public record, your reasoning is documented, and resolution is guaranteed by institutional continuity. Warren Buffett's famous 10-year bet against hedge funds (won in 2017) demonstrated the model's power to settle debates definitively.

## When to Use

- Making long-term predictions you want held accountable
- Settling debates that won't resolve for years or decades
- Forcing rigorous thinking about future outcomes
- Creating skin in the game for forecasts
- Building institutional memory around predictions
- Testing expertise claims publicly
- Funding charities through competitive prediction
- Challenging overconfident forecasters to back their words

## The Process

### Step 1: Formulate a Clear, Falsifiable Prediction

State exactly what will happen by when. Binary outcomes only - no hedging.

**Poor prediction:** "AI will change everything by 2030"
**Strong prediction:** "By December 31, 2030, a commercially available AI system will have won a Grammy for Best New Artist"

### Step 2: Build Your Argument

Document WHY you believe this outcome. Include:
- Causal mechanism (what forces make this outcome likely)
- Evidence supporting your position
- Acknowledgment of key uncertainties
- Why the timeline is appropriate

**Example argument:** "Foundation models demonstrate emergent musical composition. Grammy's 'Best New Artist' eligibility requires human performance, but AI-generated personas with human performers already chart. Commercial incentives push labels toward AI collaboration. Timeline based on 10-year cycles in music industry disruption."

### Step 3: Set Meaningful Stakes

Minimum $200 per side, no maximum. Consider:
- Amount large enough to create genuine accountability
- Amount you can afford to lose entirely
- Charity you'd want to fund if you win
- Charity you'd prefer NOT fund (opponent's choice)

**Stakes design:** Higher stakes = stronger signal of conviction. Buffett bet $1M. Match stakes to confidence level.

### Step 4: Publish and Invite Challenge

Make your prediction public. Either:
- Post on longbets.org ($50 publication fee)
- Announce publicly with clear terms and escrow mechanism

**Critical:** True names required. Anonymous predictions don't create accountability.

### Step 5: Wait and Let the Future Adjudicate

Resist the urge to hedge or revise. The prediction stands as written.

**Resolution:** Mutual agreement between parties if both alive, otherwise Long Now Foundation adjudicates. Public announcement and charitable disbursement.

### Step 6: Analyze the Outcome (Win or Lose)

Win: Document what you got right and why your reasoning held.
Lose: Conduct honest post-mortem - where did your model break?

**Key insight:** The learning value exists regardless of outcome. The process of rigorous prediction improves forecasting ability over time.

## Example Application

**Situation:** Tech industry debate about cryptocurrency future (2018).

**Application:**
- **Prediction:** "By December 31, 2028, at least one sovereign nation will use Bitcoin as its primary legal tender for all domestic transactions"
- **Argument:** El Salvador adoption trend, hyperinflation in emerging markets, Lightning Network scalability, generational shift in money perception
- **Stakes:** $5,000 per side
- **Charities:** Winner's choice - Electronic Frontier Foundation; Opponent's choice - Institute for Humane Studies
- **Publication:** Posted on longbets.org with full reasoning

**Outcome tracking:** El Salvador adopted BTC as legal tender in 2021, but "primary" for "all domestic transactions" remains unmet. Prediction continues.

## Scoring Rubric

- **Practitioner Weight:** 8/10 - Real-money stakes from notable figures (Buffett, Bezos donors)
- **Clarity/Executability:** 9/10 - Extremely clear process with institutional support
- **ROI:** 7/10 - Indirect benefits through improved forecasting discipline
- **Novelty:** 8/10 - Unique mechanism combining prediction markets with charity
- **Cross-domain:** 8/10 - Applicable to any falsifiable future claim

**Total: 40/50**

## Anti-Patterns

- Making vague predictions that can't be definitively resolved
- Setting stakes too low to create real accountability
- Using pseudonyms or anonymous accounts (defeats accountability)
- Refusing to publish reasoning (hides from scrutiny)
- Revising predictions after initial publication
- Betting on unfalsifiable outcomes ("AI will be important")
- Ignoring the post-mortem learning opportunity
- Only making predictions you're highly confident about (no learning)

## Related

- superforecasting (systematic prediction accuracy improvement)
- tetlocks-10-commandments (forecasting best practices)
- skin-in-the-game (accountability through personal risk)
- cathedral-thinking (multi-generational timeframes)
- thinking-in-bets (probabilistic decision-making)

Attribution

lev-oslev-os
View sourceMore from lev-os →
SSkills DirectorySkills Directory

Your tool, in front of Claude Code builders.

3 founder slots · $299/mo · GSC-verified traffic · sponsors can never buy grades.

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SSkills DirectorySkills Directory

Your tool, in front of Claude Code builders.

3 founder slots · $299/mo · GSC-verified traffic · sponsors can never buy grades.

See placements

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