231_012predictionAIAI-timing

Within 6 months to a year physics problems will be massively addressed by AI.

Predictor: Peter Diamandis · ep#231 "Top AI News: Sonnet 4.6, Grok 4.2, Gemini 3 Deep Think, and OpenClaw | EP #231" · source

Prior probability

65.0%

Current probability

51.1%

evolves via intake + LBP

Conviction

4/5

Signal quality

Resolution

pending

Window

2026-01-01 – 2027-08-31

Edges in / out

9 / 5

Tickers exposed

Prediction text

Within 6 months to a year physics problems will be massively addressed by AI. | today the first uh the first hints at physics and six months from now if not a year from now uh we'll be talking about how all these physics problems have been been addressed.

Verbatim quote

From episode "Top AI News: Sonnet 4.6, Grok 4.2, Gemini 3 Deep Think, and OpenClaw | EP #231"

today the first uh the first hints at physics and six months from now if not a year from now uh we'll be talking about how all these physics problems have been been addressed.

Predictor: Peter Diamandis

κ + Brier as of 2026-05-22

Full calibration →

κ (discount)

0.875

Brier

0.0367

excellent

Hits / Misses

10 / 0

of 15 resolved

Hit rate

66.7%

Calibration plot (stated vs observed)

Evidence about this node from Peter Diamandis 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

Not linked

This node isn't linked to a reference class. The Bayesian update applies without outside-view blending.

Probability over time

5 prob_history rows

intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 51.1%

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.

Leading chain: 6 fired ✓ · 1 overdue ⏱ · 2 pending

2026-02-01hitGoogle DeepMind AlphaEvolve cited for new mathematical structures
How: AlphaEvolve research/blog post documents discovery of new mathematical structures improving SOTA on long-standing open problems
Source: https://www.nextbigfuture.com/2026/04/2026-is-breakthrough-year-for-reliable-ai-world-models-and-continual-learning-prototypes.htmlconf 85%
2026-04-01hitOpenAI launches FrontierScience benchmark for AI scientific discovery
How: OpenAI publishes FrontierScience benchmark testing models against frontier scientific (incl. physics) problems
Source: https://time.com/7341081/openai-frontierscience-benchmark/conf 92%
2026-04-29hitNvidia quadrupled chip production output while only doubling human headcount — achieved by deploying AI coding tools (Cursor, Claude Code) a
2026-04-29hitTraining runs costing $10 billion for a single model will commence sometime in 2025.
2026-04-29hit2025 will be the definitive year that agentic systems finally hit the mainstream.
2026-04-29hitRecursive self-improvement is already happening now (no longer three years out)
2026-04-30overduePhysics-based AI simulations operational at military-grade fidelity (WarMatrix)
How: US Air Force WarMatrix or equivalent physics-AI simulation reaches operational use at 10,000x real-time speed
Source: https://www.crescendo.ai/news/latest-ai-news-and-updatesconf 75%
2026-06-01 → 2027-08-31pendingAI co-author on peer-reviewed physics paper resolving prior open problem
How: Peer-reviewed physics paper (Nature/PRL) lists AI system as primary contributor to resolving documented open problem
Source: https://www.technologyreview.com/2026/01/05/1130662/whats-next-for-ai-in-2026/conf 55%
Notes: Diamandis predicted 'physics problems massively addressed' within 6mo-1yr — needs concrete physics-paper outcomes, not just code/math.
2026-09-01 → 2027-08-31pendingWorld-model AI demonstrates novel physical-system prediction at SOTA
How: Genie-style world model demonstrates novel SOTA on real-world physics prediction task (fluid dynamics, materials)
Source: https://www.nextbigfuture.com/2026/04/2026-is-breakthrough-year-for-reliable-ai-world-models-and-continual-learning-prototypes.htmlconf 60%
2027-03-16pendingWithin 6 months to a year physics problems will be massively addressed by AI.
2028-06-25pendingWe're exiting the industrial age permanently as recursive self-improvement unfolds.
2028-09-07pendingBy 2028, AI systems will reach 'independent researcher' level — driving autonomous scientific discoveries without human intervention.
2033-07-30pendingRay Kurzweil predicts Longevity Escape Velocity (LEV) by 2033.
2033-08-10pendingASI will arrive within 2 years to 5 years to this next decade
2039-09-22pendingTrue artificial general intelligence will be achieved between 2032 and 2042 — 'first we solve AI, then use AI to solve everything else'.

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: 51%)

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

LBP2026-05-10T02:00:02Z51.1%+1.8pp

Network propagation: 49.3% → 51.1%

6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29

metadata_milestone_miss_sweep2026-05-08T22:15:34Z49.3%-6.6pp

metadata_milestone_miss_sweep bayesian_v2 n=1 inside=0.493 blend=0.493 LLR=-0.266 κ=0.88 no_blend

Raw metadata

{
  "trf": 0.7892463222938595,
  "kappa": 0.875,
  "base_rate": null,
  "predictor": "Peter Diamandis",
  "total_llr": -0.4054651081081644,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": 0.23703488041371287,
  "bayes_factor": "1.3:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.5589828132915797,
  "kappa_source": "predictor_table",
  "n_milestones": 1,
  "blend_applied": false,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "llm_pre_event",
      "kappa": 0.65625,
      "label": "Physics-based AI simulations operational at military-grade fidelity (WarMatrix)",
      "weight": 0.4,
      "strength": "weak",
      "confidence": 0.75,
      "source_url": "https://www.crescendo.ai/news/latest-ai-news-and-updates",
      "adjusted_llr": -0.2660864771959829,
      "expected_date": "2026-04-30",
      "measurement_criterion": "US Air Force WarMatrix or equivalent physics-AI simulation reaches operational use at 10,000x real-time speed"
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.44752757439429836,
  "outside_weight": 0.5524724256057016,
  "posterior_prob": 0.4927376115823738,
  "posterior_logit": -0.02905159678227001,
  "predictor_brier": 0.03667,
  "inside_posterior": 0.4927376115823738,
  "blended_posterior": 0.4927376115823738,
  "reference_class_id": null,
  "total_adjusted_llr": -0.2660864771959829,
  "predictor_n_resolved": 15
}

LBP2026-05-03T02:00:01Z55.9%-2.3pp

Network propagation: 58.2% → 55.9%

6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9

LBP2026-04-30T16:39:51Z58.2%-2.7pp

Network propagation: 60.9% → 58.2%

5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3

LBP2026-04-30T02:18:57Z60.9%-4.1pp

Network propagation: 65.0% → 60.9%

5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v1 · run 592311ef

Network propagation neighbors

Top edges sorted by latest LBP cross-impact

All propagation →

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	TK03 AI Regulatory Moratorium (EU/US Capability Freeze)	10.0%	0.050	0.650	+0.079
killer	TK01 AGI Capability Plateau (2026-27 Training Stall)	15.0%	0.050	0.650	+0.049
prereq	SEM_042 2025 will be the definitive year that agentic systems finall — Kevin Weil	73.8%	0.650	0.050	-0.024
killer	TK14 Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates)	20.0%	0.050	0.650	+0.019
prereq	SEM_012 Nvidia quadrupled chip production output while only doubling — Jensen Huang	75.0%	0.650	0.050	-0.016

Top outgoing (children)

Predictions THIS node influences

Kind	Node	Their prob	P(c\|s=T)	P(c\|s=F)	Δ implied
prereq	232_055 We're exiting the industrial age permanently as recursive se — Peter Diamandis	35.5%	0.700	0.050	+0.033
prereq	SEM_034 True artificial general intelligence will be achieved betwee — Demis Hassabis	28.7%	0.550	0.050	+0.023
prereq	235_030 Ray Kurzweil predicts Longevity Escape Velocity (LEV) by 203 — Ray Kurzweil	39.2%	0.750	0.050	+0.021
prereq	CMQ_002 By 2028, AI systems will reach 'independent researcher' leve — Sam Altman	31.4%	0.550	0.050	-0.004
prereq	241_043 ASI will arrive within 2 years to 5 years to this next decad — Peter Diamandis	35.9%	0.650	0.050	+0.003

Ticker exposure

33 ticker(s) linked

Beneficiaries (23)

SOUN CRWV SITM NVDA ARM GTLB BBAI TSM APLD CEVA AI MSFT MRVL SFTBY ORCL QCOM AVGO BABA AMD GOOGL IBM AMZN META

Adverse (6)

WNS CHGG CTSH IBM INFY ACN

Prerequisites (9)

Predictions that must hit first

Type	Pred	Title	Domain	Lag
prereq	238_009	Recursive self-improvement is already happening now (no longer three years out)	AI	—
prereq	SEM_008	Training runs costing $10 billion for a single model will commence sometime in 2025.	AI	—
prereq	SEM_012	Nvidia quadrupled chip production output while only doubling human headcount — achieved by deploying AI coding tools (Cursor, Claude Code) across engineering.	AI/Manufacturing	—
prereq	SEM_042	2025 will be the definitive year that agentic systems finally hit the mainstream.	AI/Agents	—
correlate	S_ASI_MID_2034	ASI mid: Schmidt 'ASI in 6 years'	asi_recursive_self_improvement	—
correlate	S_AGI_SLOW_2031	AGI slow: Schmidt/Hassabis 5-10 year path	agi_general_capability	—
killer	TK14	Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates)	—	—
killer	TK01	AGI Capability Plateau (2026-27 Training Stall)	—	—
killer	TK03	AI Regulatory Moratorium (EU/US Capability Freeze)	—	—

Dependents (5)

Predictions enabled by this

Type	Pred	Title	Domain	Lag
prereq	235_030	Ray Kurzweil predicts Longevity Escape Velocity (LEV) by 2033.	Biotech/Longevity	—
prereq	232_055	We're exiting the industrial age permanently as recursive self-improvement unfolds.	AI	—
prereq	241_043	ASI will arrive within 2 years to 5 years to this next decade	AI	—
prereq	CMQ_002	By 2028, AI systems will reach 'independent researcher' level — driving autonomous scientific discoveries without human intervention.	AI	—
prereq	SEM_034	True artificial general intelligence will be achieved between 2032 and 2042 — 'first we solve AI, then use AI to solve everything else'.	AI/AGI	—

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook

{
  "nia": false,
  "url": "https://www.youtube.com/watch?v=HklyjXKYFng",
  "mode": "PREDICTION",
  "role": "Host",
  "context": "And we just saw I mean today the first uh the first hints at physics and six months from now if not a year from now uh we'll be talking about how all these physics problems have been been addressed. Can't wait.",
  "to_year": 2027,
  "verbatim": "today the first uh the first hints at physics and six months from now if not a year from now uh we'll be talking about how all these physics problems have been been addressed.",
  "conv_cues": "we'll be talking",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "6 months to 1 year",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "Google DeepMind AlphaEvolve cited for new mathematical structures",
      "source": "https://www.nextbigfuture.com/2026/04/2026-is-breakthrough-year-for-reliable-ai-world-models-and-continual-learning-prototypes.html",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -9,
      "source_id": null,
      "confidence": 0.85,
      "source_url": "https://www.nextbigfuture.com/2026/04/2026-is-breakthrough-year-for-reliable-ai-world-models-and-continual-learning-prototypes.html",
      "expected_date": "2026-02-01",
      "observed_date": "2026-02-01",
      "research_origin": "deep_research",
      "measurement_criterion": "AlphaEvolve research/blog post documents discovery of new mathematical structures improving SOTA on long-standing open problems"
    },
    {
      "kind": "llm_pre_event",
      "label": "OpenAI launches FrontierScience benchmark for AI scientific discovery",
      "source": "https://time.com/7341081/openai-frontierscience-benchmark/",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -8,
      "source_id": null,
      "confidence": 0.92,
      "source_url": "https://time.com/7341081/openai-frontierscience-benchmark/",
      "expected_date": "2026-04-01",
      "observed_date": "2026-04-01",
      "research_origin": "deep_research",
      "measurement_criterion": "OpenAI publishes FrontierScience benchmark testing models against frontier scientific (incl. physics) problems"
    },
    {
      "kind": "prereq",
      "label": "Nvidia quadrupled chip production output while only doubling human headcount — achieved by deploying AI coding tools (Cursor, Claude Code) a",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -7,
      "source_id": "SEM_012",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Training runs costing $10 billion for a single model will commence sometime in 2025.",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -6,
      "source_id": "SEM_008",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "2025 will be the definitive year that agentic systems finally hit the mainstream.",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -5,
      "source_id": "SEM_042",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Recursive self-improvement is already happening now (no longer three years out)",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -4,
      "source_id": "238_009",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "llm_pre_event",
      "label": "Physics-based AI simulations operational at military-grade fidelity (WarMatrix)",
      "source": "https://www.crescendo.ai/news/latest-ai-news-and-updates",
      "status": "overdue",
      "weight": 0.4,
      "ordinal": -3,
      "source_id": null,
      "confidence": 0.75,
      "source_url": "https://www.crescendo.ai/news/latest-ai-news-and-updates",
      "expected_date": "2026-04-30",
      "miss_emitted_at": "2026-05-08T22:15:34.476563+00:00",
 
... (truncated)