Compound Fork — Compute scale × $1T+ IPO
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 ↓ × $1T+ IPO → | IPO_TRILLION_2026 prior 25% | IPO_TRILLION_2027 prior 40% | IPO_TRILLION_2028 prior 25% | IPO_TRILLION_NONE_5Y prior 10% |
|---|---|---|---|---|
COMPUTE_1GW_2027 prior 60% | 126 claims · Σ|Δ| 15.34 | 127 claims · Σ|Δ| 15.48 | 127 claims · Σ|Δ| 15.54 | 127 claims · Σ|Δ| 15.65 |
COMPUTE_10GW_2028 prior 40% | 131 claims · Σ|Δ| 16.30 | 132 claims · Σ|Δ| 16.43 | 132 claims · Σ|Δ| 16.49 | 132 claims · Σ|Δ| 16.59 |
COMPUTE_100GW_2030 prior 20% | 129 claims · Σ|Δ| 16.87 | 130 claims · Σ|Δ| 17.00 | 130 claims · Σ|Δ| 17.06 | 130 claims · Σ|Δ| 17.17 |
COMPUTE_STARGATE_FAILURE prior 15% | 125 claims · Σ|Δ| 15.23 | 126 claims · Σ|Δ| 15.37 | 126 claims · Σ|Δ| 15.43 | 126 claims · Σ|Δ| 15.53 |
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.