AI has crossed a historic tipping point — traditional Moore's Law is obsolete; AI now follows a highly accelerated, domain-specific experience curve.

AI has crossed a historic tipping point — traditional Moore's Law is obsolete; AI now follows a highly accelerated, domain-specific experience curve. | Algorithmic-efficiency trendlines vs Moore extrapolations

Key catalyst: Algorithmic-efficiency trendlines vs Moore extrapolations

Watch events: Algorithmic efficiency research; FP4 / ternary adoption; experience-curve slopes across AI sub-domains.

FP4 training, MoE architectures, distillation, and algorithmic efficiency gains all compound beyond Moore — Chinese quant breakthroughs reinforce.

No propagation data yet. Run inference/.venv/bin/python scripts/ops/run_loopy_belief_propagation.py on the droplet, or wait for the Sunday 02:00 UTC weekly cron.

{
  "nia": false,
  "mode": "THESIS",
  "role": "Host",
  "context": "Wissner-Gross's scaling-law thesis; reframes computational progress as experience-curve not transistor-density driven.",
  "to_year": 2030,
  "conv_cues": "crossed tipping point; framework claim",
  "direction": "HAPPEN",
  "from_year": 2025,
  "timeframe": "2025+",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "DeepSeek-class efficiency events demonstrate domain-specific experience curve",
      "notes": "HIT — DeepSeek's $6M frontier-class model directly evidences the domain-specific accelerated experience curve.",
      "source": "https://markets.financialcontent.com/wral/article/tokenring-2026-1-9-the-deepseek-revolution-how-a-6-million-model-shattered-the-ai-compute-moat",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -8,
      "source_id": null,
      "confidence": 0.99,
      "source_url": "https://markets.financialcontent.com/wral/article/tokenring-2026-1-9-the-deepseek-revolution-how-a-6-million-model-shattered-the-ai-compute-moat",
      "expected_date": "2025-01-27",
      "observed_date": "2025-01-27",
      "research_origin": "deep_research",
      "measurement_criterion": "Single algorithmic-efficiency event produces 10-20x cost reduction for frontier model capability, demonstrating the experience-curve / Wright's-law effect operating much faster than hardware Moore's Law"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -7,
      "source_id": null,
      "expected_date": "2025-05-14",
      "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": -6,
      "source_id": null,
      "expected_date": "2025-09-24",
      "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": "OpenAI documents 44x algorithmic efficiency gain since 2012 (vs Moore's 11x)",
      "notes": "HIT — direct quantitative evidence for accelerated experience curve outpacing Moore's Law.",
      "source": "https://openai.com/index/ai-and-efficiency/",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -5,
      "source_id": null,
      "confidence": 0.97,
      "source_url": "https://openai.com/index/ai-and-efficiency/",
      "expected_date": "2025-12-31",
      "observed_date": "2025-12-31",
      "research_origin": "deep_research",
      "measurement_criterion": "OpenAI / Epoch AI / Anthropic publishes data showing algorithmic efficiency for AlexNet-level capability has improved 44x since 2012, vs Moore's Law extrapolation of 11x"
    },
    {
      "kind": "llm_pre_event",
      "label": "AI compute efficiency continues to double every ~6 months",
      "notes": "HIT — IEEE Spectrum reports AI training outpacing Moore's Law dramatically; doubling every ~6 months observed.",
      "source": "MLPerf benchmarks, Epoch AI tracking",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -4,
      "source_id": null,
      "confidence": 0.85,
      "source_url": "https://spectrum.ieee.org/ai-training-mlperf",
      "expected_date": "2025-12-31",
      "observed_date": "2025-12-31",
      "research_origin": "deep_research",
      "measurement_criterion": "MLPerf / Epoch AI / industry tracker reports AI compute efficiency doubles every ~6 months in 2025, materially faster than Moore's Law's ~2-3 year doubling"
    },
    {
      "kind": "llm_pre_event",
      "label": "Pat Gelsinger / industry leader publicly acknowledges Moore's Law slowing to ~3-year doubling",
      "notes": "HIT — Gelsinger publicly stated doubling caden
... (truncated)