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INF_029predictionMacro/Economyoutcome-economics

Enterprise AI will shift the software economy from licensing to 'outcome-based economics' — corporations will pay data-center operators directly for finalized outcomes (optimized supply chains, completed legal briefs, delivered marketing campaigns) rat...

Predictor: Peter Diamandis / Salim Ismail / Andrew Yang

Prior probability
55.0%
Current probability
45.9%
evolves via intake + LBP
Conviction
4/5
Signal quality
B
Resolution
partial
Window
2026-01-01 – 2028-12-31
Edges in / out
5 / 0
Tickers exposed
27

Prediction text

Enterprise AI will shift the software economy from licensing to 'outcome-based economics' — corporations will pay data-center operators directly for finalized outcomes (optimized supply chains, completed legal briefs, delivered marketing campaigns) rather than subscribing to tools. | Big-4 consulting outcome-pricing pilots

Key catalyst: Big-4 consulting outcome-pricing pilots

Watch events: Enterprise procurement shifts; agent-API pricing disclosures; Gartner enterprise-SaaS forecasts

Resolution evidence

Status: partial

Anthropic 'pay per agent-task' pricing experiments; OpenAI ChatGPT agents billed on task completion; law-firm LLM deployments moving toward per-matter pricing.

Predictor: Peter Diamandis / Salim Ismail / Andrew Yang

κ + Brier as of 2026-05-22
κ (discount)
0.583
Brier
0.0025
excellent
Hits / Misses
0 / 0
of 1 resolved
Hit rate
0.0%
Calibration plot (stated vs observed)

Evidence about this node from Peter Diamandis / Salim Ismail / Andrew Yang 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

4 prob_history rows
0%25%50%75%100%prior 55%2026-04-302026-05-012026-05-03
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 45.9%

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: 3 overdue ⏱
  1. 2026-01-31overdueQ1 window check-in (25%)
  2. 2026-03-02overdueQ2 window check-in (50%)
  3. 2026-04-01overdueQ3 window check-in (75%)
  4. 2026-04-01 → 2026-12-31pendingBig-4 consultancy launches outcome-based AI pricing pilot
    How: McKinsey, BCG, Bain, or Deloitte publicly announces outcome-based pricing model for AI engagements (paid for delivered outcomes, not consultant-hours)
    Source: https://www.mckinsey.com/about-us/new-at-mckinsey-blog/mckinsey-and-google-cloud-launch-the-mckinsey-google-transformation-group-to-scale-enterprise-impact-for-the-ai-era — McKinsey-Google partnership signals shiftconf 55%
  5. 2026-04-01 → 2027-06-30pendingMajor SaaS company announces shift to usage/outcome pricing
    How: Top-50 SaaS company (Salesforce, Adobe, ServiceNow, peer) announces material shift from per-seat to usage- or outcome-based pricing for AI-augmented products
    Source: Earnings calls, company announcements, Stratechery/Ben Thompson coverageconf 60%
    Notes: Salesforce already announced Agentforce consumption-pricing model in late 2024; broader shift inevitable.
  6. 2026-06-01 → 2027-12-31pendingHyperscaler (AWS, Azure, GCP) launches outcome-based AI service tier
    How: AWS, Azure, GCP launches new service tier where customer pays for completed business outcomes (e.g., 'managed marketing campaigns') rather than compute
    Source: AWS re:Invent, Azure Build, Google Cloud Next announcementsconf 45%
  7. 2026-09-01 → 2028-06-30pendingIndustry analyst declares 'outcome economy' as dominant pricing trend
    How: Gartner, Forrester, or peer publishes report declaring outcome-based pricing as dominant trend in enterprise AI software
    Source: Gartner research, Forrester reportsconf 40%

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: 46%)

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
LBP2026-05-03T02:00:01Z45.9%-1.4pp
Network propagation: 47.3% → 45.9%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
resolution_terminal2026-05-01T00:00:00Z50.0%+2.7pp
resolution_terminal partial outcome=0.5 pre_resolution=0.473
Raw metadata
{
  "source": "backfill_resolution_history.py",
  "status": "partial",
  "bayesian_v2": false,
  "outcome_prob": 0.5,
  "evidence_kind": "resolution_terminal",
  "posterior_prob": 0.5,
  "delta_to_outcome": 0.027339999999999975,
  "inside_posterior": 0.47266,
  "validation_notes": "Anthropic 'pay per agent-task' pricing experiments; OpenAI ChatGPT agents billed on task completion; law-firm LLM deployments moving toward per-matter pricing.",
  "validation_status": "hit",
  "pre_resolution_prob": 0.47266,
  "resolution_evidence": "Anthropic 'pay per agent-task' pricing experiments; OpenAI ChatGPT agents billed on task completion; law-firm LLM deployments moving toward per-matter pricing.",
  "does_not_update_current_prob": true
}
LBP2026-04-30T16:39:51Z47.3%-2.6pp
Network propagation: 49.9% → 47.3%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z49.9%-5.1pp
Network propagation: 55.0% → 49.9%
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

KindNodeTheir probP(c|s=T)P(c|s=F)Δ implied
killerTK05
Rate Regime Persistence (10y > 5% through 2028)
30.0%0.0500.550-0.059
killerTK04
Macro Recession 2026-27 (Structural Deleveraging)
25.0%0.0500.550-0.034
killerTK10
$100T Sovereign Debt Crisis
12.0%0.0500.550+0.031
killerTK14
Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates)
20.0%0.0500.550-0.009
killerTK07
Labor Political Backlash (UBI Mandate / AI Tax)
18.0%0.0500.550+0.001

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

27 ticker(s) linked

Beneficiaries (21)

FSLRARGANWULFAPLDIRENEQIXCRWVFLNCNBISIRMMETAMSFTETNORCLSFTBYSTXAAPLAMZNAMTGOOGLHUBB

Prerequisites (5)

Predictions that must hit first
TypePredTitleDomainLag
killerTK05Rate Regime Persistence (10y > 5% through 2028)
killerTK04Macro Recession 2026-27 (Structural Deleveraging)
killerTK14Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates)
killerTK07Labor Political Backlash (UBI Mandate / AI Tax)
killerTK10$100T Sovereign Debt Crisis

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Validations (1)

Resolution events
Observed atStatusByNotes
2026-04-29hitthesis_timeline_v1.0_importAnthropic 'pay per agent-task' pricing experiments; OpenAI ChatGPT agents billed on task completion; law-firm LLM deployments moving toward per-matter pricing.

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "mode": "FORECAST",
  "role": "Host/Guest-Panel",
  "context": "Shift reallocates enterprise spend from SaaS to compute-metered outcomes. Mentioned on Moonshots Podcast; aligned with Dario Amodei prediction that by 2026 generative AI will grant SMBs analytical tools rivaling Fortune 500.",
  "to_year": 2028,
  "conv_cues": "paradigm-shift framing; multi-expert agreement",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "2026-2028",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -3,
      "source_id": null,
      "expected_date": "2026-01-31",
      "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": -2,
      "source_id": null,
      "expected_date": "2026-03-02",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -1,
      "source_id": null,
      "expected_date": "2026-04-01",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "event",
      "label": "Enterprise AI will shift the software economy from licensing to 'outcome-based economics' — corporations will pay data-center operators dire",
      "status": "partial",
      "weight": 1,
      "ordinal": 0,
      "source_id": "INF_029",
      "expected_date": "2026-05-01",
      "observed_date": "2026-05-01"
    },
    {
      "kind": "llm_pre_event",
      "label": "Big-4 consultancy launches outcome-based AI pricing pilot",
      "source": "https://www.mckinsey.com/about-us/new-at-mckinsey-blog/mckinsey-and-google-cloud-launch-the-mckinsey-google-transformation-group-to-scale-enterprise-impact-for-the-ai-era — McKinsey-Google partnership signals shift",
      "status": "pending",
      "weight": 0.4,
      "ordinal": 1,
      "source_id": null,
      "confidence": 0.55,
      "source_url": "https://www.mckinsey.com/about-us/new-at-mckinsey-blog/mckinsey-and-google-cloud-launch-the-mckinsey-google-transformation-group-to-scale-enterprise-impact-for-the-ai-era",
      "expected_date": "2026-08-16",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2026-12-31",
        "from": "2026-04-01"
      },
      "measurement_criterion": "McKinsey, BCG, Bain, or Deloitte publicly announces outcome-based pricing model for AI engagements (paid for delivered outcomes, not consultant-hours)"
    },
    {
      "kind": "llm_pre_event",
      "label": "Major SaaS company announces shift to usage/outcome pricing",
      "notes": "Salesforce already announced Agentforce consumption-pricing model in late 2024; broader shift inevitable.",
      "source": "Earnings calls, company announcements, Stratechery/Ben Thompson coverage",
      "status": "pending",
      "weight": 0.4,
      "ordinal": 2,
      "source_id": null,
      "confidence": 0.6,
      "expected_date": "2026-11-14",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2027-06-30",
        "from": "2026-04-01"
      },
      "measurement_criterion": "Top-50 SaaS company (Salesforce, Adobe, ServiceNow, peer) announces material shift from per-seat to usage- or outcome-based pricing for AI-augmented products"
    },
    {
      "kind": "llm_pre_event",
      "label": "Hyperscaler (AWS, Azure, GCP) launches outcome-based AI service tier",
      "source": "AWS re:Invent, Azure Build, Google Clo
... (truncated)