A 5-OOM (100,000x) effective-compute leap will occur between 2024-2027 — bridging GPT-4 high-schooler to fully automated AI researcher.
Predictor: Leopold Aschenbrenner
Prediction text
A 5-OOM (100,000x) effective-compute leap will occur between 2024-2027 — bridging GPT-4 high-schooler to fully automated AI researcher. | Epoch AI FLOP tracking; frontier model releases
Key catalyst: Epoch AI FLOP tracking; frontier model releases
Watch events: Frontier training FLOPs; published algorithmic-efficiency papers; emergent capability benchmarks.
Resolution evidence
Epoch AI tracking shows frontier training compute doubled ~every 6 months 2022-2025; algorithmic efficiency gains (MoE, distillation) material.
Predictor: Leopold Aschenbrenner
Calibration plot (stated vs observed)
Evidence about this node from Leopold Aschenbrenner is multiplied by κ in /api/intake. Lower κ = less weight; floors at 0.10 (effectively silenced) and caps at 1.00 (full weight).
Reference class: regulatory_freeze_window
Major-country regulatory pause/moratorium on AI capability research lasting >6 months
Tetlock-style outside view: at TRF=1 (just predicted), outside view dominates (w_in=0.3). At TRF=0 (deadline), inside view dominates (w_in=1.0). The blend regularizes overconfident inside views toward the historical base rate.
Probability over time
Milestone chain
- 2024-09-11overdueQ1 window check-in (25%)
- 2025-05-24overdueQ2 window check-in (50%)
- 2026-01-31hitTest-time compute / reasoning OOM unlocked via o1, o3, R1How: Three+ frontier reasoning models shipped (o1, o3, DeepSeek R1) demonstrating Aschenbrenner's predicted unhobbling OOMSource: Stockalarm Pro / Dwarkesh Patel — Aschenbrenner test-time compute call validatedconf 95%Notes: HIT — o1 (Sep 2024), o3 (late 2024/early 2025), R1 (Jan 2026) shipped. Unhobbling axis validated.
- 2026-02-02overdueQ3 window check-in (75%)
- 2026-04-30hitAschenbrenner 1GW per cluster prediction validated by 2026How: Public confirmation of 1GW-class AI training cluster operational, validating Aschenbrenner's 'compute scaling 0.5 OOM/year' axisSource: Stockalarm Pro 'Situational Awareness Two Years Later' — '1 GW per cluster by 2026: hit'conf 95%Notes: HIT — 1GW cluster milestone confirmed; 10GW under construction. Compute axis on track for 5-OOM stack.
- 2026-06-01 → 2026-12-31pendingFrontier model demonstrates 1 full OOM effective compute over GPT-4How: Public release of model with ≥10x effective compute vs GPT-4 (per Epoch AI FLOP estimation) — would mark cumulative ~3 OOM gain since 2024Source: Epoch AI tracking / OpenAI, Anthropic, Google releasesconf 75%
- 2026-09-01 → 2027-03-31pendingAlgorithmic efficiency gains tracked at 0.5+ OOM/year through 2026How: Epoch AI or peer publication confirms algorithmic efficiency improved by ≥0.5 OOM YoY over 2025-2026 windowSource: Epoch AI compute efficiency researchconf 65%
- 2027-01-01 → 2027-12-31pendingFull automated AI researcher milestone — autonomous research agentHow: Frontier lab publicly demonstrates AI system performing end-to-end ML research (hypothesis, experiment, paper) at level matching mid-tier human researcherSource: Anthropic, OpenAI, DeepMind research demonstrationsconf 40%Notes: Cascade endpoint of Aschenbrenner thesis — high uncertainty but on his original 2027 timeline.
What if this resolves?
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
Raw metadata
{
"trf": 0.3903346852891947,
"kappa": 0.6875,
"base_rate": 0.05,
"predictor": "Leopold Aschenbrenner",
"total_llr": -1.2163953243244932,
"grace_days": 7,
"bayesian_v2": true,
"prior_logit": -0.3896748373344811,
"bayes_factor": "2.3:1 against",
"blend_reason": "blend 72% inside / 27% outside (TRF=0.390, base_rate=0.050 from regulatory_freeze_window)",
"inside_prior": 0.4037955794452396,
"kappa_source": "predictor_table",
"n_milestones": 3,
"blend_applied": true,
"contributions": [
{
"llr": -0.4054651081081644,
"kind": "quartile_checkpoint",
"kappa": 0.6875,
"label": "Q1 window check-in (25%)",
"weight": 0.05,
"strength": "weak",
"confidence": null,
"source_url": null,
"adjusted_llr": -0.278757261824363,
"expected_date": "2024-09-11",
"measurement_criterion": null
},
{
"llr": -0.4054651081081644,
"kind": "quartile_checkpoint",
"kappa": 0.6875,
"label": "Q2 window check-in (50%)",
"weight": 0.05,
"strength": "weak",
"confidence": null,
"source_url": null,
"adjusted_llr": -0.278757261824363,
"expected_date": "2025-05-24",
"measurement_criterion": null
},
{
"llr": -0.4054651081081644,
"kind": "quartile_checkpoint",
"kappa": 0.6875,
"label": "Q3 window check-in (75%)",
"weight": 0.05,
"strength": "weak",
"confidence": null,
"source_url": null,
"adjusted_llr": -0.278757261824363,
"expected_date": "2026-02-02",
"measurement_criterion": null
}
],
"evidence_kind": "metadata_milestone_miss_sweep",
"inside_source": "history_v2",
"inside_weight": 0.7267657202975637,
"outside_weight": 0.2732342797024363,
"posterior_prob": 0.15505421390054963,
"posterior_logit": -1.2259466228075702,
"predictor_brier": 0.04167,
"inside_posterior": 0.2268916488382616,
"blended_posterior": 0.15505421390054963,
"reference_class_id": "regulatory_freeze_window",
"total_adjusted_llr": -0.836271785473089,
"predictor_n_resolved": 3
}Network propagation neighbors
Top incoming (parents)
Edges that influence THIS node's belief
| Kind | Node | Their prob | P(c|s=T) | P(c|s=F) | Δ implied |
|---|---|---|---|---|---|
| killer | TK02 AI Compute Supply Shock (TSMC/Taiwan Disruption) | 12.0% | 0.050 | 0.550 | +0.088 |
Top outgoing (children)
Predictions THIS node influences
No outgoing edges.
Ticker exposure
Beneficiaries (11)
Prerequisites (5)
| Type | Pred | Title | Domain | Lag |
|---|---|---|---|---|
| correlate | S_AGI_MID_2029 | AGI mid: Kurzweil 2029 path | agi_general_capability | — |
| correlate | S_AGI_FAST_2027 | AGI fast: drop-in remote worker by 2027-09 | agi_general_capability | — |
| correlate | S_AGI_WINTER_2036PLUS | AGI delayed: capability plateau or AI winter | agi_general_capability | — |
| correlate | S_AI_PAUSE_2026 | Major-country AI pause beginning 2026 | ai_regulatory_pause | — |
| killer | TK02 | AI Compute Supply Shock (TSMC/Taiwan Disruption) | — | — |
Dependents (0)
| Type | Pred | Title | Domain | Lag |
|---|---|---|---|---|
| No dependents | ||||
Linked documents (10)
Raw metadata
{
"nia": false,
"qty": "5 OOMs (100,000x)",
"mode": "FORECAST",
"role": "Cited-Researcher",
"caveats": "Unhobbling gains hardest to quantify; algorithmic efficiency may saturate.",
"context": "Same size jump as GPT-2 → GPT-4 (5 OOM). Derived from three vectors: physical compute (~0.5 OOM/yr), algorithmic efficiency (~0.5 OOM/yr), and unhobbling (RLHF, CoT, tools, memory).",
"to_year": 2027,
"conv_cues": "specific quantitative target; derivation provided",
"direction": "NUMERIC_TARGET",
"from_year": 2024,
"timeframe": "2024-2027",
"conv_level": "HIGH",
"milestones": [
{
"kind": "quartile_checkpoint",
"label": "Q1 window check-in (25%)",
"status": "overdue",
"weight": 0.05,
"ordinal": -6,
"source_id": null,
"expected_date": "2024-09-11",
"observed_date": null,
"miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
"miss_emitted_by": "metadata_milestone_sweep"
},
{
"kind": "quartile_checkpoint",
"label": "Q2 window check-in (50%)",
"status": "overdue",
"weight": 0.05,
"ordinal": -5,
"source_id": null,
"expected_date": "2025-05-24",
"observed_date": null,
"miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
"miss_emitted_by": "metadata_milestone_sweep"
},
{
"kind": "llm_pre_event",
"label": "Test-time compute / reasoning OOM unlocked via o1, o3, R1",
"notes": "HIT — o1 (Sep 2024), o3 (late 2024/early 2025), R1 (Jan 2026) shipped. Unhobbling axis validated.",
"source": "Stockalarm Pro / Dwarkesh Patel — Aschenbrenner test-time compute call validated",
"status": "hit",
"weight": 0.4,
"ordinal": -4,
"source_id": null,
"confidence": 0.95,
"source_url": "https://www.dwarkesh.com/p/leopold-aschenbrenner",
"expected_date": "2026-01-31",
"observed_date": "2026-01-31",
"research_origin": "deep_research",
"measurement_criterion": "Three+ frontier reasoning models shipped (o1, o3, DeepSeek R1) demonstrating Aschenbrenner's predicted unhobbling OOM"
},
{
"kind": "quartile_checkpoint",
"label": "Q3 window check-in (75%)",
"status": "overdue",
"weight": 0.05,
"ordinal": -3,
"source_id": null,
"expected_date": "2026-02-02",
"observed_date": null,
"miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
"miss_emitted_by": "metadata_milestone_sweep"
},
{
"kind": "llm_pre_event",
"label": "Aschenbrenner 1GW per cluster prediction validated by 2026",
"notes": "HIT — 1GW cluster milestone confirmed; 10GW under construction. Compute axis on track for 5-OOM stack.",
"source": "Stockalarm Pro 'Situational Awareness Two Years Later' — '1 GW per cluster by 2026: hit'",
"status": "hit",
"weight": 0.4,
"ordinal": -2,
"source_id": null,
"confidence": 0.95,
"source_url": "https://pro.stockalarm.io/blog/situational-awareness-two-years-later",
"expected_date": "2026-04-30",
"observed_date": "2026-04-30",
"research_origin": "deep_research",
"measurement_criterion": "Public confirmation of 1GW-class AI training cluster operational, validating Aschenbrenner's 'compute scaling 0.5 OOM/year' axis"
},
{
"kind": "llm_pre_event",
"label": "Frontier model demonstrates 1 full OOM effective compute over GPT-4",
"source": "Epoch AI tracking / OpenAI, Anthropic, Google releases",
"status": "pending",
"weight": 0.4,
"ordinal": -1,
"source_id": null,
"confidence": 0.75,
"source_url": "https://epochai.org/data/notable-ai-models",
"expected_date": "2026-09-15",
"research_origin": "training",
"expected_date_range": {
"to": "2026-12-31",
"from": "2026-06-01"
},
"measurement_criterion": "Public release of model with ≥10x effective compute vs GPT-4 (per Epoch AI FLOP estima
... (truncated)