242_046predictionAIAI-scaling

Custom chip designs per use case will unlock 10x+ efficiency improvements

Predictor: Dave Blundin · ep#242 "Elon Enters the Chip Race, the S&P 500 Repricing, and Human Drivers Will Become Illegal | EP #242" · source

Prior probability

60.0%

Current probability

43.2%

evolves via intake + LBP

Conviction

4/5

Signal quality

Resolution

pending

Window

2026-04-30 – 2029-03-31

Edges in / out

6 / 0

Tickers exposed

Prediction text

Custom chip designs per use case will unlock 10x+ efficiency improvements | What this unlocks is chip designs that are specific to the use case that are probably about a factor of 10 more efficient and maybe more

Verbatim quote

From episode "Elon Enters the Chip Race, the S&P 500 Repricing, and Human Drivers Will Become Illegal | EP #242"

What this unlocks is chip designs that are specific to the use case that are probably about a factor of 10 more efficient and maybe more

Predictor: Dave Blundin

κ + Brier as of 2026-05-22

Full calibration →

κ (discount)

0.821

Brier

0.0491

excellent

Hits / Misses

3 / 2

of 9 resolved

Hit rate

33.3%

Calibration plot (stated vs observed)

Evidence about this node from Dave Blundin 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

6 prob_history rows

intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 43.2%

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: 2 overdue ⏱ · 6 pending

2024-08-01overdueGroq LPU delivers 10x throughput vs H100 on Llama 70B (already proven)
How: Groq LPU benchmarked at 300+ tokens/sec on Llama 2 70B vs H100 baseline ~30 tokens/sec — 10x throughput at lower power
Source: https://algeriatech.news/ai-inference-cloud-groq-cerebras-2026/conf 95%
Notes: Already validated — 10x is conservative for inference-specific ASIC vs general GPU.
2026-03-22overdueAWS Trainium2 delivers 40% energy savings (custom-chip thesis)
How: AWS Trainium2 production deployment delivers 10-15 TOPS/W with 40% energy savings vs comparable GPU workload — public Anthropic deployment
Source: https://techcrunch.com/2026/03/22/an-exclusive-tour-of-amazons-trainium-lab-the-chip-thats-won-over-anthropic-openai-even-apple/conf 95%
2026-01-01 → 2027-06-30pendingAnthropic / OpenAI announce internal custom-silicon strategy
How: At least one frontier-model lab (Anthropic, OpenAI, xAI) publicly commits to internal custom inference/training silicon — beyond cloud-rented Trainium
Source: https://www.tradingkey.com/analysis/stocks/us-stocks/261770188-anthropic-moving-toward-ai-chips-claude-nvidia-buy-in-2026-tradingkeyconf 80%
Notes: Anthropic moving toward custom chips per public reporting; OpenAI and xAI have rumored programs.
2026-10-24pendingQ1 window check-in (25%)
2026-12-31pendingAI ASIC market grows 44%+ in 2026 (Gartner/IDC validation)
How: AI ASIC global market revenue grows >=40% YoY in 2026 per Gartner / IDC tracking
Source: Gartner / IDC AI silicon market reportsconf 70%
2027-04-19pendingQ2 window check-in (50%)
2027-10-13pendingQ3 window check-in (75%)
2027-01-01 → 2029-03-31pendingPer-watt inference efficiency gap vs general GPU exceeds 10x
How: MLPerf or equivalent third-party benchmark shows top custom inference ASIC >=10x perf-per-watt advantage over best general GPU on production-relevant workload
Source: MLPerf Inference benchmarksconf 65%
2028-04-07pendingCustom chip designs per use case will unlock 10x+ efficiency improvements

No downstream cascades — this prediction is a leaf in the dependency graph.

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: 43%)

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

LBP2026-05-17T02:00:01Z43.2%+1.0pp

Network propagation: 42.2% → 43.2%

5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96

LBP2026-05-10T02:00:02Z42.2%+2.0pp

Network propagation: 40.2% → 42.2%

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

LBP2026-05-03T02:00:01Z40.2%+3.8pp

Network propagation: 36.3% → 40.2%

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

metadata_milestone_miss_sweep2026-05-02T22:07:21Z36.3%-15.5pp

metadata_milestone_miss_sweep bayesian_v2 n=2 inside=0.363 blend=0.363 LLR=-0.633 κ=0.82 no_blend

Raw metadata

{
  "trf": 0.9972591226262507,
  "kappa": 0.8214,
  "base_rate": null,
  "predictor": "Dave Blundin",
  "total_llr": -0.8109302162163288,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": 0.07122413071858065,
  "bayes_factor": "1.9:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.5177985091786997,
  "kappa_source": "predictor_table",
  "n_milestones": 2,
  "blend_applied": false,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "llm_pre_event",
      "kappa": 0.78033,
      "label": "Groq LPU delivers 10x throughput vs H100 on Llama 70B (already proven)",
      "weight": 0.4,
      "strength": "weak",
      "confidence": 0.95,
      "source_url": "https://algeriatech.news/ai-inference-cloud-groq-cerebras-2026/",
      "adjusted_llr": -0.3163965878100439,
      "expected_date": "2024-08-01",
      "measurement_criterion": "Groq LPU benchmarked at 300+ tokens/sec on Llama 2 70B vs H100 baseline ~30 tokens/sec — 10x throughput at lower power"
    },
    {
      "llr": -0.4054651081081644,
      "kind": "llm_pre_event",
      "kappa": 0.78033,
      "label": "AWS Trainium2 delivers 40% energy savings (custom-chip thesis)",
      "weight": 0.4,
      "strength": "weak",
      "confidence": 0.95,
      "source_url": "https://techcrunch.com/2026/03/22/an-exclusive-tour-of-amazons-trainium-lab-the-chip-thats-won-over-anthropic-openai-even-apple/",
      "adjusted_llr": -0.3163965878100439,
      "expected_date": "2026-03-22",
      "measurement_criterion": "AWS Trainium2 production deployment delivers 10-15 TOPS/W with 40% energy savings vs comparable GPU workload — public Anthropic deployment"
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.30191861416162447,
  "outside_weight": 0.6980813858383755,
  "posterior_prob": 0.3631844907902947,
  "posterior_logit": -0.5615690449015072,
  "predictor_brier": 0.0491,
  "inside_posterior": 0.3631844907902947,
  "blended_posterior": 0.3631844907902947,
  "reference_class_id": null,
  "total_adjusted_llr": -0.6327931756200879,
  "predictor_n_resolved": 9
}

LBP2026-04-30T16:39:51Z51.8%-2.8pp

Network propagation: 54.6% → 51.8%

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

LBP2026-04-30T02:18:57Z54.6%-5.4pp

Network propagation: 60.0% → 54.6%

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
prereq	S_AGI_MID_2029 AGI mid: Kurzweil 2029 path	35.0%	0.600	0.050	-0.189
killer	TK03 AI Regulatory Moratorium (EU/US Capability Freeze)	10.0%	0.050	0.600	+0.113
killer	TK02 AI Compute Supply Shock (TSMC/Taiwan Disruption)	12.0%	0.050	0.600	+0.102
killer	TK01 AGI Capability Plateau (2026-27 Training Stall)	15.0%	0.050	0.600	+0.086
killer	TK09 Energy Grid Cap (Data Center Power Wall)	35.0%	0.050	0.600	-0.024

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

37 ticker(s) linked

Beneficiaries (24)

MU WULF IREN EQIX ALAB APLD ASMIY ASML PLAB NVDA NBIS CRWV AAPL AMT AMZN DELL GOOGL IRM LNVGY META MSFT ORCL SFTBY STX

Adverse (6)

ACN GEN CHGG IBM WNS LRN

Prerequisites (6)

Predictions that must hit first

Type	Pred	Title	Domain	Lag
prereq	S_AGI_MID_2029	AGI mid: Kurzweil 2029 path	agi_general_capability	—
killer	TK09	Energy Grid Cap (Data Center Power Wall)	—	—
killer	TK05	Rate Regime Persistence (10y > 5% through 2028)	—	—
killer	TK01	AGI Capability Plateau (2026-27 Training Stall)	—	—
killer	TK02	AI Compute Supply Shock (TSMC/Taiwan Disruption)	—	—
killer	TK03	AI Regulatory Moratorium (EU/US Capability Freeze)	—	—

Dependents (0)

Predictions enabled by this

Type	Pred	Title	Domain	Lag
No dependents

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook

{
  "nia": false,
  "qty": "10x efficiency",
  "url": "https://www.youtube.com/watch?v=wMLcIWLlcWg",
  "mode": "PREDICTION",
  "role": "Host",
  "context": "chip designs that are specific to the use case that are probably about a factor of 10 more efficient and maybe more",
  "verbatim": "What this unlocks is chip designs that are specific to the use case that are probably about a factor of 10 more efficient and maybe more",
  "conv_cues": "probably",
  "direction": "UP",
  "timeframe": "unspecified future",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "Groq LPU delivers 10x throughput vs H100 on Llama 70B (already proven)",
      "notes": "Already validated — 10x is conservative for inference-specific ASIC vs general GPU.",
      "source": "https://algeriatech.news/ai-inference-cloud-groq-cerebras-2026/",
      "status": "overdue",
      "weight": 0.4,
      "ordinal": -8,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://algeriatech.news/ai-inference-cloud-groq-cerebras-2026/",
      "expected_date": "2024-08-01",
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep",
      "research_origin": "deep_research",
      "measurement_criterion": "Groq LPU benchmarked at 300+ tokens/sec on Llama 2 70B vs H100 baseline ~30 tokens/sec — 10x throughput at lower power"
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    {
      "kind": "llm_pre_event",
      "label": "AWS Trainium2 delivers 40% energy savings (custom-chip thesis)",
      "source": "https://techcrunch.com/2026/03/22/an-exclusive-tour-of-amazons-trainium-lab-the-chip-thats-won-over-anthropic-openai-even-apple/",
      "status": "overdue",
      "weight": 0.4,
      "ordinal": -7,
      "source_id": null,
      "confidence": 0.95,
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      "expected_date": "2026-03-22",
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    {
      "kind": "llm_pre_event",
      "label": "Anthropic / OpenAI announce internal custom-silicon strategy",
      "notes": "Anthropic moving toward custom chips per public reporting; OpenAI and xAI have rumored programs.",
      "source": "https://www.tradingkey.com/analysis/stocks/us-stocks/261770188-anthropic-moving-toward-ai-chips-claude-nvidia-buy-in-2026-tradingkey",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -6,
      "source_id": null,
      "confidence": 0.8,
      "source_url": "https://www.tradingkey.com/analysis/stocks/us-stocks/261770188-anthropic-moving-toward-ai-chips-claude-nvidia-buy-in-2026-tradingkey",
      "expected_date": "2026-09-30",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2027-06-30",
        "from": "2026-01-01"
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      "measurement_criterion": "At least one frontier-model lab (Anthropic, OpenAI, xAI) publicly commits to internal custom inference/training silicon — beyond cloud-rented Trainium"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
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    {
      "kind": "llm_pre_event",
      "label": "AI ASIC market grows 44%+ in 2026 (Gartner/IDC validation)",
      "source": "Gartner / IDC AI silicon market reports",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -4,
      "source_id": null,
      "confidence": 0.7,
      "expected_date": "2026-12-31",
      "research_origin": "training",
      "measurement_c
... (truncated)