Compound Fork — Compute scale × Humanoid deployment
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
| Compute scale ↓ × Humanoid deployment → | HUMANOID_FACTORY_2026 prior 40% | HUMANOID_ENTERPRISE_2028 prior 50% | HUMANOID_CONSUMER_2030 prior 20% | HUMANOID_MASS_2033 prior 10% |
|---|---|---|---|---|
COMPUTE_1GW_2027 prior 60% | 127 claims · Σ|Δ| 15.63 | 128 claims · Σ|Δ| 15.60 | 126 claims · Σ|Δ| 15.68 | 127 claims · Σ|Δ| 15.58 |
COMPUTE_10GW_2028 prior 40% | 129 claims · Σ|Δ| 16.18 | 127 claims · Σ|Δ| 15.97 | 127 claims · Σ|Δ| 16.18 | 128 claims · Σ|Δ| 16.10 |
COMPUTE_100GW_2030 prior 20% | 128 claims · Σ|Δ| 16.80 | 126 claims · Σ|Δ| 16.60 | 126 claims · Σ|Δ| 16.81 | 127 claims · Σ|Δ| 16.72 |
COMPUTE_STARGATE_FAILURE prior 15% | 125 claims · Σ|Δ| 15.48 | 126 claims · Σ|Δ| 15.44 | 124 claims · Σ|Δ| 15.53 | 125 claims · Σ|Δ| 15.43 |
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.