CMQ_046predictionSemis/Memoryagentic-DRAM

Sustained memory contexts required for agent 'memory' will drive additional 15-45 exabytes of DRAM demand by 2027 — up to 77% of global supply.

Predictor: Morgan Stanley

Prior probability

68.0%

Current probability

62.7%

evolves via intake + LBP

Conviction

5/5

Signal quality

Resolution

pending

Window

2026-01-01 – 2026-12-31

Edges in / out

0 / 0

Tickers exposed

Prediction text

Sustained memory contexts required for agent 'memory' will drive additional 15-45 exabytes of DRAM demand by 2027 — up to 77% of global supply. | Global DRAM supply/demand; content-per-server trends

Key catalyst: Global DRAM supply/demand; content-per-server trends

Watch events: Global DRAM shipments; DRAM content-per-server trends; agentic workflow adoption rate.

Resolution evidence

Status: pending

Agentic workflows already driving DRAM content-per-server up 2-3x 2024-2026; extended-context LLMs (2M+ tokens) amplify further.

Predictor: Morgan Stanley

κ + Brier as of 2026-05-22

Full calibration →

κ (discount)

0.633

Brier

0.0442

excellent

Hits / Misses

1 / 0

of 2 resolved

Hit rate

50.0%

Calibration plot (stated vs observed)

Evidence about this node from Morgan Stanley 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

1 prob_history rows

intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 62.7%

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 fired ✓ · 1 overdue ⏱ · 4 pending

2026-03-21overdueQ1 window check-in (25%)
2026-04-22hitMorgan Stanley publishes 15-45 EB DRAM agentic forecast
How: Morgan Stanley research note publishes 15-45 exabyte additional DRAM demand from agentic AI by 2030
Source: Morgan Stanley research via ANI / Daily Hunt Apr 2026conf 99%
Notes: HIT — MS confirmed the 15-45EB figure equating to 26-77% of 2027 DRAM supply, matching CMQ_046 thesis exactly.
2026-04-23hitMS Memory Stocks bottleneck shift report published
How: Morgan Stanley publishes 'Memory stocks: how to play the new AI bottleneck' research validating memory-as-bottleneck thesis
Source: Investing.com / Yahoo Finance — MS memory stocks AI bottleneckconf 95%
2026-06-09pendingQ2 window check-in (50%)
2026-06-01 → 2026-09-30pendingMicron / SK Hynix earnings cite agentic AI as DRAM demand driver
How: ≥2 DRAM majors explicitly cite 'agentic AI' or 'agent memory' as 2027 demand driver in Q2/Q3 2026 earnings calls
Source: Micron, SK Hynix, Samsung earnings transcriptsconf 70%
2026-08-28pendingQ3 window check-in (75%)
2026-07-01 → 2026-12-31pendingDRAM industry 2026 capex announcements support multi-year supply ramp
How: Top-3 DRAM makers (SK Hynix, Samsung, Micron) collectively announce 2026 capex >$80B, with explicit citation of agentic AI demand
Source: SK Hynix, Samsung, Micron quarterly earningsconf 75%
2026-11-16pendingSustained memory contexts required for agent 'memory' will drive additional 15-45 exabytes of DRAM demand by 2027 — up to 77% of global supp
2027-04-01 → 2028-03-31pendingGlobal DRAM 2027 demand growth tracker confirms 25%+ YoY
How: TrendForce / IDC reports global DRAM bit demand grew ≥25% YoY in 2027 (vs 8-12% baseline), confirming agentic share emergence
Source: TrendForce / IDC DRAM bit demand trackerconf 50%

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: 63%)

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

metadata_milestone_miss_sweep2026-05-02T22:07:21Z62.7%-5.3pp

metadata_milestone_miss_sweep bayesian_v2 n=1 inside=0.627 blend=0.627 LLR=-0.237 κ=0.58 no_blend

Raw metadata

{
  "trf": 0.6650500679109432,
  "kappa": 0.5833,
  "base_rate": null,
  "predictor": "Morgan Stanley",
  "total_llr": -0.4054651081081644,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": 0.7537718023763803,
  "bayes_factor": "1.3:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.68,
  "kappa_source": "predictor_table",
  "n_milestones": 1,
  "blend_applied": false,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.5833,
      "label": "Q1 window check-in (25%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.2365077975594923,
      "expected_date": "2026-03-21",
      "measurement_criterion": null
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "prior_prob",
  "inside_weight": 0.5344649524623397,
  "outside_weight": 0.4655350475376603,
  "posterior_prob": 0.6265077768950755,
  "posterior_logit": 0.517264004816888,
  "predictor_brier": 0.01,
  "inside_posterior": 0.6265077768950755,
  "blended_posterior": 0.6265077768950755,
  "reference_class_id": null,
  "total_adjusted_llr": -0.2365077975594923,
  "predictor_n_resolved": 1
}

Network propagation neighbors

Top edges sorted by latest LBP cross-impact

All propagation →

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.

Ticker exposure

18 ticker(s) linked

Beneficiaries (17)

MU NVDA AI BBAI GTLB SOUN META MSFT ORCL PLTR SHOP STX AMD AMZN ATEYY GOOGL IBM

Prerequisites (0)

Predictions that must hit first

Type	Pred	Title	Domain	Lag
No prerequisites

Dependents (0)

Predictions enabled by this

Type	Pred	Title	Domain	Lag
No dependents

Linked documents (10)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT

Sim	Source	Title	Market prob	Polarity	Reviewed	Published
0.681	arxiv	Storage Is Not Memory: A Retrieval-Centered Architecture for Agent Recall	—	mentions	pending	2026-05-06
0.671	arxiv	Beyond Similarity: Trustworthy Memory Search for Personal AI Agents	—	mentions	pending	2026-06-04
0.667	arxiv	What Happens Inside Agent Memory? Circuit Analysis from Emergence to Diagnosis	—	mentions	pending	2026-05-05
0.667	arxiv	MEME: Multi-entity & Evolving Memory Evaluation	—	mentions	pending	2026-05-12
0.665	arxiv	Don't Ask the LLM to Track Freshness: A Deterministic Recipe for Memory Conflict Resolution	—	mentions	pending	2026-05-31
0.660	arxiv	RAMPART: Registry-based Agentic Memory with Priority-Aware Runtime Transformation	—	mentions	pending	2026-06-03
0.660	arxiv	From Volume to Value: Preference-Aligned Memory Construction for On-Device RAG	—	mentions	pending	2026-05-18
0.654	arxiv	Efficient Serving for Dynamic Agent Workflows with Prediction-based KV-Cache Management	—	mentions	pending	2026-05-07
0.644	arxiv	RedKnot: Efficient Long-Context LLM Serving with Head-Aware KV Reuse and SegPagedAttention	—	mentions	pending	2026-06-04
0.575	polymarket	Will Valve add Cache to the Map Pool by April 30, 2026?	1%	mentions	pending	2026-02-27

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook

{
  "nia": false,
  "qty": "+15-45 EB DRAM",
  "mode": "FORECAST",
  "role": "Cited-Firm",
  "context": "Agentic AI long-running-task memory requirements dramatically amplify total DRAM TAM; compounds HBM supercycle thesis.",
  "to_year": 2027,
  "cited_by": "Synthesis report",
  "conv_cues": "specific quantitative; MS research",
  "direction": "NUMERIC_TARGET",
  "from_year": 2026,
  "timeframe": "by 2027",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -7,
      "source_id": null,
      "expected_date": "2026-03-21",
      "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": "Morgan Stanley publishes 15-45 EB DRAM agentic forecast",
      "notes": "HIT — MS confirmed the 15-45EB figure equating to 26-77% of 2027 DRAM supply, matching CMQ_046 thesis exactly.",
      "source": "Morgan Stanley research via ANI / Daily Hunt Apr 2026",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -6,
      "source_id": null,
      "confidence": 0.99,
      "source_url": "https://www.newkerala.com/news/a/morgan-stanley-agentic-ai-shifts-value-from-gpus-161.htm",
      "expected_date": "2026-04-22",
      "observed_date": "2026-04-22",
      "research_origin": "deep_research",
      "measurement_criterion": "Morgan Stanley research note publishes 15-45 exabyte additional DRAM demand from agentic AI by 2030"
    },
    {
      "kind": "llm_pre_event",
      "label": "MS Memory Stocks bottleneck shift report published",
      "source": "Investing.com / Yahoo Finance — MS memory stocks AI bottleneck",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -5,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://www.investing.com/news/stock-market-news/memory-stocks-morgan-stanley-explains-how-to-play-the-new--ai-bottleneck-4451697",
      "expected_date": "2026-04-23",
      "observed_date": "2026-04-23",
      "research_origin": "deep_research",
      "measurement_criterion": "Morgan Stanley publishes 'Memory stocks: how to play the new AI bottleneck' research validating memory-as-bottleneck thesis"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q2 window check-in (50%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -4,
      "source_id": null,
      "expected_date": "2026-06-09",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "Micron / SK Hynix earnings cite agentic AI as DRAM demand driver",
      "source": "Micron, SK Hynix, Samsung earnings transcripts",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -3,
      "source_id": null,
      "confidence": 0.7,
      "expected_date": "2026-07-31",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2026-09-30",
        "from": "2026-06-01"
      },
      "measurement_criterion": "≥2 DRAM majors explicitly cite 'agentic AI' or 'agent memory' as 2027 demand driver in Q2/Q3 2026 earnings calls"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -2,
      "source_id": null,
      "expected_date": "2026-08-28",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "DRAM industry 2026 capex announcements support multi-year supply ramp",
      "source": "SK Hynix, Samsung, Micron quarterly earnings",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -1,
      "source_id": null,
      "confidence": 0.75,
      "source_url": "https://news.skhynix.com/2026-market-outlook-focus-on-the-hbm-led-memory-supercycle/",
      "expected_date": "2026-09-30",
      "research_origin": "deep_research",
      "expect
... (truncated)