S_COMPUTE_10GW_2028scenariocompute_scale
Compute: 10GW total by Dec 2028
Prior probability
40.0%
Current probability
40.0%
evolves via intake + LBP
Conviction
—
Signal quality
—
Resolution
pending
Window
2026-04-30 – 2028-12-31
Edges in / out
0 / 28
Tickers exposed
0
Prediction text
Multiple gigawatt clusters online totaling 10GW. Stargate's stated 10GW target. Likely slips by quarters.
Predictor calibration
Not assigned
This node has no predictor assigned. Default κ=0.5 applies to any intake evidence about it.
Reference class
Not linked
This node isn't linked to a reference class. The Bayesian update applies without outside-view blending.
Probability over time
0 prob_history rows
No probability history yet.
Milestone chain
Pre-event signals (upstream prereqs + window checkpoints) → resolution event → downstream cascades. Status/dates update from linked nodes; re-derive nightly via scripts/ops/derive_milestones.py.
No milestone chain derived yet. Run scripts/ops/derive_milestones.py to populate from prereq edges + window checkpoints.
What if this resolves?
Clamp this prediction TRUE or FALSE and run a counterfactual Gibbs sample. Surfaces the predictions whose marginals shift most under that assumption.
(live posterior: 40%)
Click a button to clamp this prediction and run a Gibbs sample. Returns the predictions whose marginals shift most. ~30s per run; ideal for stress-testing "if X resolves, what else moves?"
Evidence chain
Every probability update with full Bayesian provenance — chronological, latest first
No probability history yet. The first evidence will arrive via /api/intake or the daily milestone sweep / weekly LBP run.
Network propagation neighbors
Top edges sorted by latest LBP cross-impact
Top incoming (parents)
Edges that influence THIS node's belief
No incoming edges.
Top outgoing (children)
Predictions THIS node influences
| Kind | Node | Their prob | P(c|s=T) | P(c|s=F) | Δ implied |
|---|---|---|---|---|---|
| prereq | 246_036 Terafab will deliver 1 terawatt/year AI compute, 50x current — Peter Diamandis | 46.4% | 0.650 | 0.050 | -0.174 |
Prerequisites (0)
Predictions that must hit first
| Type | Pred | Title | Domain | Lag |
|---|---|---|---|---|
| No prerequisites | ||||
Dependents (28)
Predictions enabled by this
| Type | Pred | Title | Domain | Lag |
|---|---|---|---|---|
| prereq | 246_036 | Terafab will deliver 1 terawatt/year AI compute, 50x current global output of 20 gigawatt. | AI | — |
| correlate | 241_052 | Standard AI data centers being built at 400 MW scale, half-mile long | Energy | — |
| correlate | CMQ_040 | AI-driven demand has triggered an 'unprecedented supercycle' for dominant global memory suppliers (Samsung, SK Hynix, Micron). | Semis/Memory | — |
| correlate | CMQ_038 | Micron has initiated HVM production of HBM4 36GB module delivering 2.8 TB/s bandwidth — optimized to reduce 'time to first token' for long-context LLM inference. | Semis/Memory | — |
| correlate | INF_017 | xAI Colossus Phase 2 will consume approximately 1.5 gigawatts of power — city-scale demand for a single facility, making it among the first AI DCs to exceed 1 GW peak draw. | Energy | — |
| correlate | CMQ_024 | 'Tokens per Watt' is the defining Key Performance Indicator (KPI) for the modern digital economy. | AI/Compute | — |
| correlate | CMQ_017 | US electricity production must grow by 'tens of percent' by end of decade to support hundreds of millions of frontier-AI GPUs. | Energy | — |
| correlate | INF_010 | US data-center demand will reach 74 GW by 2028 against a projected ~49 GW power-access shortfall — structural gap driven by retiring coal/gas, aging grid, and ~7-year interconnection queues. | Energy | — |
| correlate | INF_048 | Global data-center energy demand will reach approximately 945 Terawatt-hours by 2030 — a roughly 175% increase from 2023 levels — requiring complete grid overhauls in major markets. | Energy | — |
| correlate | SEM_001 | Compute clusters will scale from $10B → $100B → $1T by end of decade; 'another zero every six months' on boardroom projections. | AI/Compute | — |
| correlate | 235_022 | US will add a record 86 gigawatts of utility-scale capacity this coming year. | Energy | — |
| correlate | 231_030 | US AI industry needs 80 gigawatts of power in the next 3-5 years (Eric Schmidt cited). | Energy | — |
| correlate | 241_016 | 92 GW power shortage in America between now and 2030 | Energy | — |
| correlate | 241_017 | Equivalent of ~60 nuclear plants needed by 2030 but essentially zero being built | Energy | — |
| correlate | 241_020 | 1 GW of power = ~$50 billion in hardware/software/data centers | Energy | — |
| correlate | 241_023 | 10% of US electricity will be used by data centers | Energy | — |
| correlate | INF_012 | AI data-center power consumption will grow by approximately 126 GW annually through 2028 — comparable to Canada's total annual power demand, and representing nearly 20% of projected global power growth through 2030. | Energy | — |
| correlate | 240_034 | Morgan Stanley forecasts 13-40GW data center power shortfall through 2028 | Energy | — |
| correlate | 240_035 | Meta secured 6.6 GW of clean nuclear power for 2035 | Energy | — |
| correlate | 238_046 | xAI will build every new data center at 1.2 gigawatts scale | Energy | — |
| correlate | COD_AI_002 | At least one 1 GW Vera Rubin frontier-training cluster reaches deployment by Q1 2027 | AI/Compute | — |
| correlate | 238_047 | US will add 86 GW of new grid capacity in 2026, 51% from solar | Energy | — |
| correlate | 232_045 | Tesla and SpaceX each aim to generate 100 gigawatts of solar per year. | Energy | — |
| correlate | INF_006 | Over time, very large fractions of the earth's land surface will be given over to data centers as AI compute demand scales — hyperscaler DC footprint transitions from discrete campuses to regional-scale compute territories. | AI | — |
| correlate | INF_014 | Data-center industry could emit up to 2.5 billion tons of CO2 cumulatively through 2030 if powered primarily by fossil fuels — a scale that materially delays global climate-pledge compliance. | Energy | — |
| correlate | 238_066 | Pete Donovan aspires to build 20,000 distributed 10MW data centers across US high-school-football towns | Energy | — |
| correlate | INF_056 | Helion will supply OpenAI with 5 gigawatts of fusion electricity by 2030 (roughly 100 Helion 50-MW reactors, equivalent to ~73% of Grand Coulee Dam) — a 100x step-up from Helion's existing Microsoft commitment. | Energy | — |
| correlate | INF_057 | Helion will scale to 50 gigawatts of fusion-generated power to OpenAI by 2035 — equivalent to more than 7x Grand Coulee Dam output and roughly 1,000 Helion 50-MW reactors. | Energy | — |
Linked documents (10)
Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
| Sim | Source | Title | Market prob | Polarity | Reviewed | Published |
|---|---|---|---|---|---|---|
| 0.588 | arxiv | A new completely parameter-free clustering algorithm for unsupervised classification of BATSE gamma-ray bursts | — | mentions | pending | 2026-05-28 |
| 0.585 | arxiv | Squeezed-state radiation in shockwave scattering: QCD-Gravity double copy | — | mentions | pending | 2026-05-04 |
| 0.580 | arxiv | Constraints on the inflationary vacuum and reheating era from NANOGrav | — | mentions | pending | 2026-05-06 |
| 0.575 | edgar_8k | IPG PHOTONICS CORP (IPGP) (CIK 0001111928) | — | mentions | pending | 2026-05-15 |
| 0.573 | edgar_8k | Globalstar, Inc. (GSAT) (CIK 0001366868) | — | mentions | pending | 2026-05-15 |
| 0.562 | edgar_8k | Constellation Energy Corp (CEG) (CIK 0001868275) | — | mentions | pending | 2026-06-02 |
| 0.562 | edgar_8k | Constellation Energy Corp (CEG) (CIK 0001868275) | — | mentions | pending | 2026-05-11 |
| 0.562 | edgar_8k | Constellation Energy Corp (CEG) (CIK 0001868275) | — | mentions | pending | 2026-05-01 |
| 0.551 | manifold | Free Lottery (GRB 170817A) | 17% | mentions | pending | 2026-05-07 |
| 0.539 | edgar_8k | WEC ENERGY GROUP, INC. (WEC) (CIK 0000783325) | — | mentions | pending | 2026-06-01 |
Raw metadata
From Thesis_Timeline_v1.0_FINAL workbook
{
"phase": "compute_10gw",
"fork_key": "compute",
"dimension": "compute_scale",
"family_type": "cumulative",
"family_label": "Compute scale",
"family_order": 2
}