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IND_026predictionLabor/Jobsscaffolding-eaten-orchestrators

Non-coders and engineers alike must build for 'where the models are going, not where they are today' — 'this is the worst the models will ever be'. Next-generation models will 'eat your scaffolding for breakfast'; manual software configuration, standar...

Predictor: Kevin Weil

Prior probability
68.0%
Current probability
60.8%
evolves via intake + LBP
Conviction
5/5
Signal quality
B
Resolution
in_progress
Window
2025-01-01 – 2027-12-31
Edges in / out
2 / 0
Tickers exposed
0

Prediction text

Non-coders and engineers alike must build for 'where the models are going, not where they are today' — 'this is the worst the models will ever be'. Next-generation models will 'eat your scaffolding for breakfast'; manual software configuration, standard API integrations, static codebase management entirely replaced by living system orchestrators that generate and deploy applications autonomously. Labor market for mid-level developers collapses rapidly. | Next major coding-assistant capability leap

Key catalyst: Next major coding-assistant capability leap

Watch events: Mid-level developer hiring statistics; agentic-dev-platform revenue

Resolution evidence

Status: in_progress

Claude Code, Cursor Agent, Lovable, Bolt demonstrate scaffolding-replacement; mid-level developer hiring slowing 2024-2026.

Predictor: Kevin Weil

κ + Brier as of 2026-05-22
κ (discount)
0.688
Brier
0.0200
excellent
Hits / Misses
2 / 0
of 3 resolved
Hit rate
66.7%
Calibration plot (stated vs observed)

Evidence about this node from Kevin Weil 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
0%25%50%75%100%prior 68%2026-04-302026-05-032026-05-17
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 60.8%

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 ✓ · 2 overdue ⏱ · 4 pending
  1. 2025-08-02overdueQ1 window check-in (25%)
  2. 2026-03-04overdueQ2 window check-in (50%)
  3. 2026-04-23hitGPT-5.5 ships with frontier agentic-coding benchmarks
    How: OpenAI ships GPT-5.5 scoring 82.7% on Terminal-Bench 2.0, 73.1% Expert-SWE, 84.9% GDPval — validating 'this is the worst the models will ever be'
    Source: Artificial Analysis — 'OpenAI's GPT-5.5 is the new leading AI model'conf 99%
    Notes: HIT — GPT-5.5 shipped with massive jump on long-horizon agentic coding metrics.
  4. 2026-04-15hitGemini 3.1 Pro doubles ARC-AGI-2 over predecessor
    How: Gemini 3.1 Pro scores 77.1% on ARC-AGI-2 (double predecessor), 78.80% on SWE-bench Verified, validating non-linear improvement curve
    Source: LM Council Benchmarks April 2026conf 95%
  5. 2026-10-04pendingQ3 window check-in (75%)
  6. 2026-06-01 → 2027-06-30pendingMid-level developer headcount declines at major tech firms
    How: Public reporting confirms net mid-level (L4/L5 equivalent) software engineer headcount decline at >=2 of (Google, Meta, Microsoft, Amazon) attributed to AI agent productivity
    Source: AI Forces Over 50,000 Layoffs 2025 — National CIO Reviewconf 70%
  7. 2026-06-01 → 2027-12-31pendingClaude Code or equivalent crosses 50% Fortune 100 deployment
    How: GitHub Copilot or Claude Code reports >=50% Fortune 100 enterprise deployment with autonomous multi-file refactor as primary use case
    Source: Microsoft / Anthropic enterprise disclosuresconf 65%
  8. 2026-09-01 → 2027-12-31pendingHand-coded scaffolding pattern becomes obsolete in industry parlance
    How: Stack Overflow Developer Survey or equivalent shows >=60% of devs report agent-driven workflows as primary mode, with hand-built scaffolding/boilerplate cited as legacy practice
    Source: Trend extrapolation from vibe coding adoption + GPT-5.5 capability jumpconf 55%

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: 61%)

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
LBP2026-05-17T02:00:01Z60.8%+1.5pp
Network propagation: 59.3% → 60.8%
5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96
LBP2026-05-10T02:00:02Z59.3%+3.0pp
Network propagation: 56.3% → 59.3%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z56.3%+6.0pp
Network propagation: 50.3% → 56.3%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
metadata_milestone_miss_sweep2026-05-02T22:07:21Z50.3%-13.6pp
metadata_milestone_miss_sweep bayesian_v2 n=2 inside=0.503 blend=0.503 LLR=-0.558 κ=0.69 no_blend
Raw metadata
{
  "trf": 0.5549161103469683,
  "kappa": 0.6875,
  "base_rate": null,
  "predictor": "Kevin Weil",
  "total_llr": -0.8109302162163288,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": 0.5701739326829037,
  "bayes_factor": "1.7:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.6388033082389746,
  "kappa_source": "predictor_table",
  "n_milestones": 2,
  "blend_applied": false,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.6875,
      "label": "Q1 window check-in (25%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.278757261824363,
      "expected_date": "2025-08-02",
      "measurement_criterion": null
    },
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.6875,
      "label": "Q2 window check-in (50%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.278757261824363,
      "expected_date": "2026-03-04",
      "measurement_criterion": null
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.6115587227571222,
  "outside_weight": 0.3884412772428778,
  "posterior_prob": 0.5031648099924518,
  "posterior_logit": 0.012659409034177727,
  "predictor_brier": 0.02,
  "inside_posterior": 0.5031648099924518,
  "blended_posterior": 0.5031648099924518,
  "reference_class_id": null,
  "total_adjusted_llr": -0.557514523648726,
  "predictor_n_resolved": 3
}
LBP2026-04-30T16:39:51Z63.9%-1.4pp
Network propagation: 65.3% → 63.9%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z65.3%-2.7pp
Network propagation: 68.0% → 65.3%
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
killerTK06
China-Taiwan Military Conflict
8.0%0.0500.680+0.022
killerTK11
Autonomous Regulatory Block (Level 4 Halt)
10.0%0.0500.680+0.009

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Prerequisites (2)

Predictions that must hit first
TypePredTitleDomainLag
killerTK11Autonomous Regulatory Block (Level 4 Halt)
killerTK06China-Taiwan Military Conflict

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Validations (1)

Resolution events
Observed atStatusByNotes
2026-04-29partialthesis_timeline_v1.0_importClaude Code, Cursor Agent, Lovable, Bolt demonstrate scaffolding-replacement; mid-level developer hiring slowing 2024-2026.

Linked documents (5)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.679arxivProgramBench: Can Language Models Rebuild Programs From Scratch?mentionspending2026-05-05
0.643arxivScaffold, Not Vocabulary? A Controlled, Two-Tier, Pre-Registered Study of a Popperian Code-Generation Skillmentionspending2026-06-04
0.637arxivCode2LoRA: Hypernetwork-Generated Adapters for Code Language Models under Software Evolutionmentionspending2026-06-04
0.599github_releasefacebookresearch/ProgramBench v1.0.0mentionspending2026-05-05
0.580github_releasefacebookresearch/spdl v0.1.4mentionspending2025-09-10

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "mode": "FORECAST",
  "role": "Cited-Other",
  "context": "Fourth Weil entry (SPC_022 AI-science 2026, ROB_002 99% code, ROB_003 25yr science in 5, AUT_019 GPU spike). Specific scaffolding-eaten coinage + mid-level developer collapse framing.",
  "to_year": 2027,
  "conv_cues": "coined phrasing; specific future-infrastructure framing",
  "direction": "HAPPEN",
  "from_year": 2025,
  "timeframe": "late-2025 to 2027",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -8,
      "source_id": null,
      "expected_date": "2025-08-02",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q2 window check-in (50%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -7,
      "source_id": null,
      "expected_date": "2026-03-04",
      "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": "GPT-5.5 ships with frontier agentic-coding benchmarks",
      "notes": "HIT — GPT-5.5 shipped with massive jump on long-horizon agentic coding metrics.",
      "source": "Artificial Analysis — 'OpenAI's GPT-5.5 is the new leading AI model'",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -6,
      "source_id": null,
      "confidence": 0.99,
      "source_url": "https://artificialanalysis.ai/articles/openai-gpt5-5-is-the-new-leading-AI-model",
      "expected_date": "2026-04-23",
      "observed_date": "2026-04-23",
      "research_origin": "deep_research",
      "measurement_criterion": "OpenAI ships GPT-5.5 scoring 82.7% on Terminal-Bench 2.0, 73.1% Expert-SWE, 84.9% GDPval — validating 'this is the worst the models will ever be'"
    },
    {
      "kind": "llm_pre_event",
      "label": "Gemini 3.1 Pro doubles ARC-AGI-2 over predecessor",
      "source": "LM Council Benchmarks April 2026",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -5,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://lmcouncil.ai/benchmarks",
      "expected_date": "2026-04-30",
      "observed_date": "2026-04-15",
      "research_origin": "deep_research",
      "measurement_criterion": "Gemini 3.1 Pro scores 77.1% on ARC-AGI-2 (double predecessor), 78.80% on SWE-bench Verified, validating non-linear improvement curve"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -4,
      "source_id": null,
      "expected_date": "2026-10-04",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "Mid-level developer headcount declines at major tech firms",
      "source": "AI Forces Over 50,000 Layoffs 2025 — National CIO Review",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -3,
      "source_id": null,
      "confidence": 0.7,
      "source_url": "https://nationalcioreview.com/articles-insights/extra-bytes/ai-forces-over-50000-layoffs-in-2025-at-leading-technology-firms/",
      "expected_date": "2026-12-15",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2027-06-30",
        "from": "2026-06-01"
      },
      "measurement_criterion": "Public reporting confirms net mid-level (L4/L5 equivalent) software engineer headcount decline at >=2 of (Google, Meta, Microsoft, Amazon) attributed to AI agent productivity"
    },
    {
      "kind": "llm_pre_event",
      "label": "Claude Code or equivalent crosses 50% Fortune 100 deployment",
      "source": "Microsoft / Anthropic enterprise disclosures",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -2,
... (truncated)