CMQ_059predictionAIlocal-compute-drivers

Shift to local compute is driven by: (1) absolute data privacy, (2) cost control for high-volume inference, (3) running uncensored/customized LLMs unavailable on major clouds.

Predictor: Alex Finn

Prior probability

82.0%

Current probability

62.4%

evolves via intake + LBP

Conviction

4/5

Signal quality

Resolution

in_progress

Window

2026-01-01 – 2026-09-30

Edges in / out

1 / 0

Tickers exposed

Prediction text

Shift to local compute is driven by: (1) absolute data privacy, (2) cost control for high-volume inference, (3) running uncensored/customized LLMs unavailable on major clouds. | Enterprise on-prem AI deployment rates

Key catalyst: Enterprise on-prem AI deployment rates

Watch events: Enterprise on-prem AI deployment; open-source model fine-tune volume; privacy-first AI startup funding.

Resolution evidence

Status: in_progress

Enterprise data-sovereignty requirements accelerating; local-model inference cost for specific workloads approaching break-even vs cloud APIs.

Predictor: Alex Finn

κ + Brier as of 2026-05-22

Full calibration →

κ (discount)

0.643

Brier

0.0122

excellent

Hits / Misses

1 / 0

of 2 resolved

Hit rate

50.0%

Calibration plot (stated vs observed)

Evidence about this node from Alex Finn 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 = 62.4%

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: 4 overdue ⏱ · 2 pending

2026-02-25overdueQ1 window check-in (25%)
2026-04-21overdueQ2 window check-in (50%)
2026-01-01 → 2026-09-30overdueEnterprise on-prem AI inference reaches 55% of total AI workloads
How: IDC, Gartner, or NVIDIA enterprise survey confirms ≥55% of enterprise AI inference performed on-premises/edge by 2026
Source: https://renewator.com/the-rise-of-local-llms-privacy-and-sovereignty-in-2026/conf 75%
2026-01-01 → 2026-09-30overdueOpen-weight local model inference cost gap widens to 18x cheaper
How: Total cost per million tokens comparison (open-weight on-prem vs major cloud API) shows ≥18x cost advantage at scale
Source: https://www.aiintime.com/post/on-premise-llm-deploymentconf 70%
2026-06-15pendingQ3 window check-in (75%)
2026-01-01 → 2026-12-31pendingData privacy ranked top barrier by majority of enterprise IT leaders
How: Enterprise IT survey (e.g., Gartner CIO survey) shows >50% of CIOs/IT leaders rank data privacy as top barrier to cloud AI adoption
Source: https://www.accrets.com/general/on-premise-llm-deployment/conf 80%
2026-08-09pendingShift to local compute is driven by: (1) absolute data privacy, (2) cost control for high-volume inference, (3) running uncensored/customize
2026-04-01 → 2026-12-31pendingUncensored/customized open-source LLMs dominate Hugging Face downloads
How: Hugging Face Hub monthly download data shows uncensored/dolphin/abliterated models in top 10 model downloads
Source: https://www.bentoml.com/blog/navigating-the-world-of-open-source-large-language-modelsconf 65%
2026-06-01 → 2027-06-30pendingAverage enterprise on-prem AI ROI realized within 4 months
How: Public case studies (≥5 enterprise deployments) document ROI on on-prem LLM infrastructure within 4 months of go-live
Source: https://www.accrets.com/general/on-premise-llm-deployment/conf 55%

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: 62%)

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_sweep2026-05-30T22:15:00Z62.4%-8.4pp

metadata_milestone_miss_sweep bayesian_v2 n=2 inside=0.624 blend=0.624 LLR=-0.378 κ=0.64 no_blend

Raw metadata

{
  "trf": 0.4487974555581937,
  "kappa": 0.6429,
  "base_rate": null,
  "predictor": "Alex Finn",
  "total_llr": -0.8109302162163288,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": 0.8846384436672471,
  "bayes_factor": "1.5:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.7077824976358063,
  "kappa_source": "predictor_table",
  "n_milestones": 2,
  "blend_applied": false,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "llm_pre_event",
      "kappa": 0.482175,
      "label": "Enterprise on-prem AI inference reaches 55% of total AI workloads",
      "weight": 0.4,
      "strength": "weak",
      "confidence": 0.75,
      "source_url": "https://renewator.com/the-rise-of-local-llms-privacy-and-sovereignty-in-2026/",
      "adjusted_llr": -0.19550513850205417,
      "expected_date": "2026-05-17",
      "measurement_criterion": "IDC, Gartner, or NVIDIA enterprise survey confirms ≥55% of enterprise AI inference performed on-premises/edge by 2026"
    },
    {
      "llr": -0.4054651081081644,
      "kind": "llm_pre_event",
      "kappa": 0.45003,
      "label": "Open-weight local model inference cost gap widens to 18x cheaper",
      "weight": 0.4,
      "strength": "weak",
      "confidence": 0.7,
      "source_url": "https://www.aiintime.com/post/on-premise-llm-deployment",
      "adjusted_llr": -0.1824714626019172,
      "expected_date": "2026-05-17",
      "measurement_criterion": "Total cost per million tokens comparison (open-weight on-prem vs major cloud API) shows ≥18x cost advantage at scale"
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.6858417811092643,
  "outside_weight": 0.3141582188907357,
  "posterior_prob": 0.6240236070054134,
  "posterior_logit": 0.5066618425632757,
  "predictor_brier": 0.0122,
  "inside_posterior": 0.6240236070054134,
  "blended_posterior": 0.6240236070054134,
  "reference_class_id": null,
  "total_adjusted_llr": -0.37797660110397135,
  "predictor_n_resolved": 2
}

LBP2026-05-10T02:00:02Z70.8%+2.0pp

Network propagation: 68.8% → 70.8%

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

LBP2026-05-03T02:00:01Z68.8%+4.1pp

Network propagation: 64.6% → 68.8%

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

metadata_milestone_miss_sweep2026-05-02T22:07:21Z64.6%-10.8pp

metadata_milestone_miss_sweep bayesian_v2 n=2 inside=0.646 blend=0.646 LLR=-0.521 κ=0.64 no_blend

Raw metadata

{
  "trf": 0.5517581791161151,
  "kappa": 0.6429,
  "base_rate": null,
  "predictor": "Alex Finn",
  "total_llr": -0.8109302162163288,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": 1.1244204006333227,
  "bayes_factor": "1.7:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.7548077345060716,
  "kappa_source": "predictor_table",
  "n_milestones": 2,
  "blend_applied": false,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.6429,
      "label": "Q1 window check-in (25%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.2606735180027389,
      "expected_date": "2026-02-25",
      "measurement_criterion": null
    },
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.6429,
      "label": "Q2 window check-in (50%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.2606735180027389,
      "expected_date": "2026-04-21",
      "measurement_criterion": null
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.6137692746187193,
  "outside_weight": 0.38623072538128067,
  "posterior_prob": 0.6463591284427734,
  "posterior_logit": 0.6030733646278449,
  "predictor_brier": 0.0122,
  "inside_posterior": 0.6463591284427734,
  "blended_posterior": 0.6463591284427734,
  "reference_class_id": null,
  "total_adjusted_llr": -0.5213470360054778,
  "predictor_n_resolved": 2
}

LBP2026-04-30T16:39:51Z75.5%-2.4pp

Network propagation: 77.9% → 75.5%

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

LBP2026-04-30T02:18:57Z77.9%-4.1pp

Network propagation: 82.0% → 77.9%

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	TK02 AI Compute Supply Shock (TSMC/Taiwan Disruption)	12.0%	0.050	0.820	+0.104

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Prerequisites (1)

Predictions that must hit first

Type	Pred	Title	Domain	Lag
killer	TK02	AI Compute Supply Shock (TSMC/Taiwan Disruption)	—	—

Dependents (0)

Predictions enabled by this

Type	Pred	Title	Domain	Lag
No dependents

Validations (1)

Resolution events

Observed at	Status	By	Notes
2026-04-29	partial	thesis_timeline_v1.0_import	Enterprise data-sovereignty requirements accelerating; local-model inference cost for specific workloads approaching break-even vs cloud APIs.

Linked documents (10)

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

Sim	Source	Title	Market prob	Polarity	Reviewed	Published
0.672	arxiv	Benchmarking Local LLMs for Natural-Language-to-SQL Querying in Biopharmaceutical Manufacturing: An Empirical Benchmark on Consumer-Grade Hardware	—	mentions	pending	2026-05-31
0.658	arxiv	Zero-Shot Confidence Estimation for Small LLMs: When Supervised Baselines Aren't Worth Training	—	mentions	pending	2026-05-04
0.656	arxiv	Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference	—	mentions	pending	2026-06-01
0.648	arxiv	Data Flow Control: Data Safety Policies for AI Agents	—	mentions	pending	2026-06-04
0.641	arxiv	Differentially Private Datastore Generation for Retrieval-Augmented Inference	—	mentions	pending	2026-05-31
0.634	arxiv	Attention Once Is All You Need: Efficient Streaming Inference with Stateful Transformers	—	mentions	pending	2026-05-13
0.629	arxiv	Stochastic Matching via Local Sparsification	—	mentions	pending	2026-05-13
0.628	arxiv	Fair Finetuning Mitigates Distribution Inference Attacks	—	mentions	pending	2026-06-01
0.626	arxiv	Heterogeneous Tasks Offloading in Vehicular Edge Computing: A Federated Meta Deep Reinforcement Learning Approach	—	mentions	pending	2026-05-18
0.625	arxiv	Understanding Generalization through Decision Pattern Shift	—	mentions	pending	2026-05-13

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook

{
  "nia": false,
  "mode": "THESIS",
  "role": "Cited-Analyst",
  "context": "Three-pillar thesis for sustained local-compute adoption; none of which are going away.",
  "to_year": 2030,
  "conv_cues": "structural drivers; framework",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "2026+",
  "conv_level": "MEDIUM",
  "milestones": [
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -6,
      "source_id": null,
      "expected_date": "2026-02-25",
      "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": "2026-04-21",
      "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": "Enterprise on-prem AI inference reaches 55% of total AI workloads",
      "source": "https://renewator.com/the-rise-of-local-llms-privacy-and-sovereignty-in-2026/",
      "status": "overdue",
      "weight": 0.4,
      "ordinal": -4,
      "source_id": null,
      "confidence": 0.75,
      "source_url": "https://renewator.com/the-rise-of-local-llms-privacy-and-sovereignty-in-2026/",
      "expected_date": "2026-05-17",
      "miss_emitted_at": "2026-05-30T22:15:00.756418+00:00",
      "miss_emitted_by": "metadata_milestone_sweep",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2026-09-30",
        "from": "2026-01-01"
      },
      "measurement_criterion": "IDC, Gartner, or NVIDIA enterprise survey confirms ≥55% of enterprise AI inference performed on-premises/edge by 2026"
    },
    {
      "kind": "llm_pre_event",
      "label": "Open-weight local model inference cost gap widens to 18x cheaper",
      "source": "https://www.aiintime.com/post/on-premise-llm-deployment",
      "status": "overdue",
      "weight": 0.4,
      "ordinal": -3,
      "source_id": null,
      "confidence": 0.7,
      "source_url": "https://www.aiintime.com/post/on-premise-llm-deployment",
      "expected_date": "2026-05-17",
      "miss_emitted_at": "2026-05-30T22:15:00.756418+00:00",
      "miss_emitted_by": "metadata_milestone_sweep",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2026-09-30",
        "from": "2026-01-01"
      },
      "measurement_criterion": "Total cost per million tokens comparison (open-weight on-prem vs major cloud API) shows ≥18x cost advantage at scale"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -2,
      "source_id": null,
      "expected_date": "2026-06-15",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "Data privacy ranked top barrier by majority of enterprise IT leaders",
      "source": "https://www.accrets.com/general/on-premise-llm-deployment/",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -1,
      "source_id": null,
      "confidence": 0.8,
      "source_url": "https://www.accrets.com/general/on-premise-llm-deployment/",
      "expected_date": "2026-07-02",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2026-12-31",
        "from": "2026-01-01"
      },
      "measurement_criterion": "Enterprise IT survey (e.g., Gartner CIO survey) shows >50% of CIOs/IT leaders rank data privacy as top barrier to cloud AI adoption"
    },
    {
      "kind": "event",
      "label": "Shift to local compute is driven by: (1) absolute data privacy, (2) cost control for high-volume inference, (3) running uncensored/customize
... (truncated)