AI Compute Strategic Context Graph
SCB/SCO Reference Run #001 — a 30-day longitudinal demonstration of structured strategic-context artifacts (entity snapshots, analytic deltas, evidence packs, risk timeseries) over the AI compute domain. Tracks who is constrained by what, which bottlenecks are moving, which assumptions are becoming fragile, which actors are changing stance, and how shocks propagate across chips, memory, packaging, cloud capacity, power, regulation, capital, and sovereign AI demand. This is a research demonstration corpus, not a commercial product.
Last charter version: v2 · Authored: 5/4/2026, 11:22:44 AM · Maturity: flagship
Verified track record
Every insight is pre-registered with a falsification threshold and Merkle-sealed at birth; resolutions are sealed too. This is the feed's borne-out scorecard — published with failures alongside successes. Public tier reflects calls resolved ≥ 7 days ago.
Track record forming. 5 call(s) have resolved, but none yet meets our scoring standard (forward-tested AND soundly adjudicated): the evidence that resolved them predated the sealed claim, so we score none of them. Each is listed below with its signed evidenceLeadDays. The scored record begins with the first resolution whose evidence postdates its seal.
Withdrawn resolution criteria: 78 across 46 open calls. On 2026-07-25 we withdrew 78 machine-checkable resolution criteria across 46 open calls, before any of them resolved. Most were sigma-anomaly tests over hourly US grid demand — a comparison that cannot separate the claim being tested from summer air-conditioning load and the daily demand cycle. Two independent errors: the criteria had no stable operating characteristic (one pair's confirming side would have fired on 0.44% of scans measured on 2026-07-10 and 11.65% on 2026-07-25, with the falsifying side moving the opposite way, on an unchanged thesis), and a "3 sigma" criterion scanned hourly for 30 days offers 526 chances to fire, so it has roughly a 51% chance of firing on noise alone against the 0.135% its label implies. The affected calls remain open and visible; they are not scored, and they stay in the denominator. The withdrawal rule is arithmetic that was available on the day each criterion was sealed, was applied to every criterion that failed it regardless of which way it was trending, and was recorded while the resolution monitors were disabled — so the sealed record shows the withdrawals preceded any outcome.
Resolved calls (each carries a sealed birth → resolution hash chain; pull the evidence pack to re-verify offline):
| Call | Conviction | Verdict | Resolved | Scored | Resolution seal |
|---|---|---|---|---|---|
| Federal Register AI-compute publications accelerated 178% this week, contradicting the consensus view that regulatory pace won't materially shift near-term assumptions. | 85% | inconclusive | 7/19/2026 | no — evidence predates seal | bf648f60f9ad… |
| Federal AI-compute regulatory publishing accelerated 227% this week, challenging the consensus view of slow-moving regulators. | 85% | confirmed | 7/19/2026 | no — evidence predates seal by 1.4d | 40b8a5786469… |
| The assumption that GPU allocation remains the primary bottleneck for frontier training faces mounting contradictory signals. Three portfolio decisions depend on this increasingly uncertain premise. | 77% | falsified | 7/19/2026 | no — evidence predates seal by 7.8d | e9fcda419104… |
| The assumption that GPU allocation remains the primary bottleneck for frontier training faces mounting pressure from two contradictions, with three portfolio decisions at stake. | 58% | falsified | 7/19/2026 | no — evidence predates seal by 8.9d | 866adc7c3d8c… |
| NVIDIA TensorRT-LLM commit activity drops 4σ below baseline, pressuring assumptions about typical development velocity. | 36% | falsified | 7/2/2026 | no — evidence predates seal by 13.0d | 386116e9ce4b… |
Verify it yourself. The aggregate is served live at /api/v1/public/ai-compute/track-record. Each insight's full birth→resolution hash chain is in its evidence pack; re-verify offline with npx tsx scripts/verify-insight-pack.ts <pack.json> (zero server access required). Daily Merkle roots are anchored publicly.
Domain charter
In scope
- Advanced semiconductor supply (HBM, advanced-node logic, packaging)
Out of scope
- Consumer AI applications and adoption (unless materially affecting compute demand)
Bottleneck Migration Index
Which constraint is binding right now, and how is it migrating? The most-leveraged single insight in the AI compute domain.
Read: Binding constraint stable at US export controls on advanced AI chips (score 0.640, stable).
| Driver | Binding score | Direction | Δ vs prior |
|---|---|---|---|
| US export controls on advanced AI chips | 0.640 | stable | 0.000 |
| Datacenter power and grid interconnect availability | 0.600 | stable | 0.000 |
| Hyperscaler capital-expenditure cycles | 0.540 | stable | 0.000 |
| China indigenous AI compute substitution | 0.520 | stable | 0.000 |
| High-Bandwidth Memory supply | 0.440 | stable | 0.000 |
| Frontier-model training demand | 0.340 | stable | 0.000 |
| Model efficiency gains (per FLOP) | 0.340 | stable | 0.000 |
| Advanced-node logic capacity (TSMC 3nm/2nm) | 0.320 | stable | 0.000 |
| Cloud GPU availability + pricing | 0.280 | stable | 0.000 |
| Sovereign AI subsidies and procurement | 0.260 | stable | 0.000 |
Binding-constraint history (last 2 snapshots): US export controls on advanced AI chips → US export controls on advanced AI chips
Boundary Driver Watchlist
Boundary nodes (drivers, actors, policies, narratives) at the edge of multiple domains, ranked by composite cross-cutting importance.
| Node | Kind | Subscribing domains | Recent deltas | Composite rank |
|---|---|---|---|---|
| US export controls regime | policy | 5 (ai_compute, semiconductor_geopolitics, ai_regulation, sovereign_ai, cyber_threat_landscape) | 0 | 6.00 |
| Sovereign AI narrative | narrative | 4 (ai_compute, ai_regulation, sovereign_ai, cyber_threat_landscape) | 0 | 5.00 |
| Microsoft | actor | 4 (ai_compute, datacenter_power, hyperscaler_capex, cyber_threat_landscape) | 0 | 4.95 |
| Amazon (AWS) | actor | 4 (ai_compute, datacenter_power, hyperscaler_capex, cyber_threat_landscape) | 0 | 4.95 |
| Alphabet (Google) | actor | 3 (ai_compute, hyperscaler_capex, cyber_threat_landscape) | 0 | 3.90 |
| OpenAI | actor | 3 (ai_compute, ai_regulation, cyber_threat_landscape) | 0 | 3.90 |
| Meta | actor | 3 (ai_compute, hyperscaler_capex, cyber_threat_landscape) | 0 | 3.85 |
| Anthropic | actor | 3 (ai_compute, ai_regulation, cyber_threat_landscape) | 0 | 3.85 |
| TSMC | actor | 2 (ai_compute, semiconductor_geopolitics) | 0 | 3.00 |
| Export controls | driver | 2 (ai_compute, semiconductor_geopolitics) | 0 | 3.00 |
Strategic Contagion Index
Per-domain 0-1 score of cross-domain influence (60% outbound applied count + 40% PageRank centrality across the cross-domain edge graph).
| Domain | Contagion score | PageRank | Outgoing edges | Inbound edges |
|---|---|---|---|---|
| ai_compute | 0.400 | 0.3427 | 7 | 9 |
| cyber_threat_landscape | 0.229 | 0.0214 | 4 | 0 |
| sovereign_ai | 0.171 | 0.1868 | 3 | 4 |
| semiconductor_geopolitics | 0.171 | 0.1092 | 3 | 3 |
| ai_regulation | 0.114 | 0.1205 | 2 | 3 |
| datacenter_power | 0.114 | 0.1096 | 2 | 2 |
| hyperscaler_capex | 0.114 | 0.1096 | 2 | 2 |
Capex Credibility / Deployment Reality Score
For each tracked AI compute project, 0-1 probability that announced capex actually converts to usable compute capacity. Low-confidence cap of 0.7 when evidence_count < 2.
| Project | Credibility | Confidence | Constraints hit |
|---|---|---|---|
| Will the Stargate Project hit $50B in committed capex by Q3 2026? | 0.615 (low-confidence) | low | supplyChainCredibility |
| What is the cumulative committed capex of the Stargate Project + analogous mega-consortia by Q3 2026? | 0.615 (low-confidence) | low | supplyChainCredibility |
| Will HBM3e + HBM4 supply remain the binding constraint on AI accelerator deployment through Q4 2026? | 0.605 (low-confidence) | low | supplyChainCredibility |
| What is the probability of a material US-China escalation event affecting Taiwan-based fab capacity in the next 18 month | 0.605 (low-confidence) | low | supplyChainCredibility |
| Will datacenter power overtake silicon as the dominant deployment bottleneck in mature US markets by mid-2026? | 0.380 (low-confidence) | low | supplyChainCredibility, powerAvailability, gridInterconnect |
| Will a major (>1GW) datacenter project announced in 2024-2025 face material delay due to power/permitting? | 0.380 (low-confidence) | low | supplyChainCredibility, powerAvailability, gridInterconnect |
| When does datacenter power overtake silicon as the dominant compute deployment bottleneck in mature markets? | 0.380 (low-confidence) | low | supplyChainCredibility, powerAvailability, gridInterconnect |
| What is the probability that a major datacenter mega-project announced in 2024-2025 (>1GW) is materially delayed by powe | 0.380 (low-confidence) | low | supplyChainCredibility, powerAvailability, gridInterconnect |
| What is the median grid-interconnect timeline for new GW-class datacenters in the US (Virginia, Texas, Ohio) as of mid-2 | 0.380 (low-confidence) | low | supplyChainCredibility, powerAvailability, gridInterconnect |
Assumption Fragility Index
Load-bearing assumptions behind the conclusions, with fragility scores. Tells you what would actually change the analysis.
Read: Most fragile: "Sovereign AI procurement (UAE, Saudi, EU AI Factories, India) becomes a material..." (supporting, fragility 0.296, strengthening). Median across 15 assumptions: 0.229.
Median-fragility trajectory:
| Assumption | Tier | Fragility | Direction |
|---|---|---|---|
| Sovereign AI procurement (UAE, Saudi, EU AI Factories, India) becomes a material allocation driver representing >10% of total demand by 2027. | supporting | 0.296 | strengthening |
| Hyperscaler aggregate AI capex remains elevated (>$200B/year combined) through 2026 with no near-term retrenchment. | load_bearing | 0.269 | stable |
| AWS / Azure / GCP retain >70% of cloud GPU capacity revenue against neoclouds through 2026. | supporting | 0.263 | weakening |
| Frontier-lab compute spend continues to scale faster than revenue, requiring continued external financing. | supporting | 0.263 | weakening |
| US export controls on advanced AI chips remain strategically relevant and continue to expand to cloud-resale and inference scope. | load_bearing | 0.236 | strengthening |
| China substitution closes the 1-1.5 generation gap on AI accelerator capability but cannot match leading-edge performance for frontier training within 24 months. | load_bearing | 0.236 | strengthening |
| Inference compute demand grows faster than training compute demand starting in 2026 as deployment scales. | supporting | 0.229 | stable |
| Major hyperscalers commit to internal accelerators (TPU, Trainium, MAIA) that progressively reduce Nvidia dependence by 2-5pp/year. | supporting | 0.229 | stable |