Compound Fork — AGI × Compute scale
Each cell = a joint scenario "Both branches fire". Cell intensity = total |Δ| across the 200 highest-conviction tradeable predictions vs their unconditional posterior. Joint probabilities approximated via log-odds combination (assumes conditional independence given the prediction — coarse but bounded). Click a cell to drill into the dominant single-fork view.
Pick two fork families
| AGI ↓ × Compute scale → | COMPUTE_1GW_2027 prior 60% | COMPUTE_10GW_2028 prior 40% | COMPUTE_100GW_2030 prior 20% | COMPUTE_STARGATE_FAILURE prior 15% |
|---|---|---|---|---|
AGI_FAST_2027 prior 30% | 132 claims · Σ|Δ| 19.42 | 135 claims · Σ|Δ| 20.47 | 133 claims · Σ|Δ| 21.00 | 131 claims · Σ|Δ| 19.29 |
AGI_MID_2029 prior 35% | 128 claims · Σ|Δ| 19.02 | 133 claims · Σ|Δ| 20.10 | 131 claims · Σ|Δ| 20.63 | 127 claims · Σ|Δ| 18.89 |
AGI_SLOW_2031 prior 25% | 137 claims · Σ|Δ| 19.93 | 141 claims · Σ|Δ| 21.03 | 139 claims · Σ|Δ| 21.55 | 136 claims · Σ|Δ| 19.80 |
AGI_WINTER_2036PLUS prior 10% | 134 claims · Σ|Δ| 19.83 | 138 claims · Σ|Δ| 20.87 | 138 claims · Σ|Δ| 21.53 | 133 claims · Σ|Δ| 19.70 |
Method note
Joint conditional probability is approximated via log-odds combination: logit(P(pred|A,B)) ≈ logit(P(pred|A)) + logit(P(pred|B)) − logit(P(pred)). This is the closed-form Bayesian update assuming A and B are conditionally independent given the prediction. It's correct when the two scenarios act on the prediction through different causal paths; it's pessimistic when they overlap. The exact joint requires running the Gibbs sampler with both scenarios clamped, which would be N×M=16 sampling runs (~12 minutes per refresh) instead of N+M=8 — a 2× cost for higher fidelity.