← Cockpit
SEM_049predictionAI/Softwarejobs

AI will soon fully automate software engineering, achieving massive cost reductions via iterative self-improvement.

Predictor: Alex Wissner-Gross

Prior probability
60.0%
Current probability
55.7%
evolves via intake + LBP
Conviction
4/5
Signal quality
A
Resolution
partial
Window
2026-01-01 – 2028-12-31
Edges in / out
8 / 1
Tickers exposed
32

Prediction text

AI will soon fully automate software engineering, achieving massive cost reductions via iterative self-improvement. | Long-horizon SWE benchmark results

Key catalyst: Long-horizon SWE benchmark results

Watch events: SWE-Bench + METR long-horizon eval results; enterprise code-gen market share

Resolution evidence

Status: partial

Claude Opus 4.7 + Claude Code 42-54% share of code-gen market. Cursor $1B ARR. Anthropic 'Programmer Equivalent' benchmarks showing continued capability growth.

Predictor: Alex Wissner-Gross

κ + Brier as of 2026-05-22
κ (discount)
0.844
Brier
0.0341
excellent
Hits / Misses
6 / 1
of 11 resolved
Hit rate
54.5%
Calibration plot (stated vs observed)

Evidence about this node from Alex Wissner-Gross 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: ai_capability_milestone_2y

Linked

AI reaches specific named capability (intern-level / world-class programmer / etc) within 2y of stated target

Base rate
5/15 historical
Inside weight
Outside weight
no pull
inside 55.7% → blend 55.7% 0.0pp)

Tetlock-style outside view: at TRF=1 (just predicted), outside view dominates (w_in=0.3). At TRF=0 (deadline), inside view dominates (w_in=1.0). The blend regularizes overconfident inside views toward the historical base rate.

Probability over time

7 prob_history rows
0%25%50%75%100%prior 60%2026-04-302026-05-032026-05-24
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 55.7%

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: 7 fired ✓
  1. 2026-04-16hitClaude Opus 4.7 hits 87.6% on SWE-Bench Verified
    How: Anthropic Claude Opus 4.7 releases with >=85% SWE-Bench Verified score
    Source: https://tokenmix.ai/blog/swe-bench-2026-claude-opus-4-7-winsconf 95%
    Notes: HIT — Claude Opus 4.7 leads SWE-Bench Verified at 87.6% with 1M context.
  2. 2026-04-30hitOpenAI deprecates SWE-Bench Verified as too contaminated
    How: OpenAI publishes statement that SWE-Bench Verified is no longer the frontier coding benchmark of record
    Source: https://openai.com/index/why-we-no-longer-evaluate-swe-bench-verified/conf 95%
    Notes: HIT — OpenAI explicitly retired SWE-Bench Verified, recommending SWE-Bench Pro.
  3. 2026-05-01hitClaude Mythos Preview crosses 90% on SWE-Bench Verified
    How: Any frontier model crosses 90% accuracy on SWE-Bench Verified leaderboard
    Source: https://www.marc0.dev/en/leaderboardconf 90%
    Notes: HIT — Claude Mythos Preview at 93.9% as of May 2026.
  4. 2026-09-01 → 2027-12-31pendingLong-horizon multi-day software-engineering benchmark crossed by Claude/GPT
    How: A frontier model completes a multi-day (>=8 hour) end-to-end real-world dev task on a public benchmark with >=70% pass rate
    Source: METR / Anthropic / OpenAI benchmark releasesconf 55%
    Notes: Cascade — currently METR documents <8-hour task horizon; needs 5-10x extension.
  5. 2026-09-01 → 2028-12-31pendingIterative self-improvement loop demonstrated on coding agent
    How: Published paper or product launch showing AI coding agent improves its own benchmark score across iterations without human gradient updates
    Source: arXiv, Anthropic/OpenAI/DeepMind research blogsconf 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: 56%)

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
LBP2026-05-24T02:00:02Z55.7%-7.5pp
Network propagation: 63.2% → 55.7%
4-iter LBP, residual 0.01000 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 806b02f8
intake_event_update2026-05-21T23:15:16Z63.2%+14.3pp
intake:7afeeb9a-f217-4dd2-b910-24ff14bdfc39 bayesian_v2 inside=0.632 blend=0.632 LLR=0.585 κ=0.84 no_blend
Raw metadata
{
  "trf": 0.8712612462476746,
  "kappa": 0.8438,
  "base_rate": null,
  "predictor": "Alex Wissner-Gross",
  "total_llr": 0.6931471805599453,
  "bayesian_v2": true,
  "prior_logit": -0.04500009648673667,
  "bayes_factor": "1.8:1 favoring",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.4887518739436685,
  "kappa_source": "predictor_table",
  "blend_applied": false,
  "contributions": [
    {
      "llr": 0.6931471805599453,
      "kappa": 0.8438,
      "label": "Multi-week autonomous task completion approaches 'soon fully automate' threshold.",
      "adjusted_llr": 0.5848775909564818
    }
  ],
  "evidence_kind": "intake_event_update",
  "inside_source": "history_v2",
  "inside_weight": 1,
  "outside_weight": 0,
  "posterior_prob": 0.6317839193673745,
  "evidence_origin": "daily_intake",
  "llm_suggestions": [
    {
      "polarity": "corroborates",
      "status_change": "unchanged",
      "evidence_strength": "moderate",
      "delta_prob_suggestion": 0.04
    }
  ],
  "posterior_logit": 0.5398774944697452,
  "predictor_brier": 0.03413,
  "evidence_doc_ids": [],
  "inside_posterior": 0.6317839193673745,
  "blended_posterior": 0.6317839193673745,
  "reference_class_id": null,
  "total_adjusted_llr": 0.5848775909564818,
  "predictor_n_resolved": 11
}
LBP2026-05-10T02:00:02Z48.9%-1.5pp
Network propagation: 50.3% → 48.9%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z50.3%-2.3pp
Network propagation: 52.6% → 50.4%
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%-2.6pp
resolution_terminal partial outcome=0.5 pre_resolution=0.526
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": -0.026029999999999998,
  "inside_posterior": 0.52603,
  "validation_notes": "Claude Opus 4.7 + Claude Code 42-54% share of code-gen market. Cursor $1B ARR. Anthropic 'Programmer Equivalent' benchmarks showing continued capability growth.",
  "validation_status": "hit",
  "pre_resolution_prob": 0.52603,
  "resolution_evidence": "Claude Opus 4.7 + Claude Code 42-54% share of code-gen market. Cursor $1B ARR. Anthropic 'Programmer Equivalent' benchmarks showing continued capability growth.",
  "does_not_update_current_prob": true
}
LBP2026-04-30T16:39:51Z52.6%-3.2pp
Network propagation: 55.8% → 52.6%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z55.8%-4.2pp
Network propagation: 60.0% → 55.8%
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
prereq247_058
Jury selection begins April 27, 2026 for Musk v OpenAI trialPeter Diamandis
71.4%0.6000.050-0.119
prereqSEM_042
2025 will be the definitive year that agentic systems finallKevin Weil
73.8%0.6000.050-0.106
prereqSEM_012
Nvidia quadrupled chip production output while only doublingJensen Huang
75.0%0.6000.050-0.098
killerTK04
Macro Recession 2026-27 (Structural Deleveraging)
25.0%0.0500.600-0.094
prereqSEM_008
Training runs costing $10 billion for a single model will coDario Amodei
76.9%0.6000.050-0.089

Top outgoing (children)

Predictions THIS node influences

KindNodeTheir probP(c|s=T)P(c|s=F)Δ implied
prereq234_036
Job displacement will be issue 6-10 not top 5 in 10 years; AAlex Wissner-Gross
28.8%0.4500.050-0.033

Ticker exposure

32 ticker(s) linked

Beneficiaries (23)

ADUSCOURDOCNFROGGTLBINODPLROLSPIRSRFMUDMYTEAMNFLXPLTRRDDTUBERAMZNBABASPOTGDDYGOOGLMETAMSFT

Adverse (6)

RHIBXPSLGMANKFYTNET

Prerequisites (8)

Predictions that must hit first
TypePredTitleDomainLag
prereq238_009Recursive self-improvement is already happening now (no longer three years out)AI
prereqSEM_008Training runs costing $10 billion for a single model will commence sometime in 2025.AI
prereq247_058Jury selection begins April 27, 2026 for Musk v OpenAI trialAI
prereqSEM_012Nvidia quadrupled chip production output while only doubling human headcount — achieved by deploying AI coding tools (Cursor, Claude Code) across engineering.AI/Manufacturing
prereqSEM_0422025 will be the definitive year that agentic systems finally hit the mainstream.AI/Agents
correlateS_NO_AI_PAUSE_5YNo major AI pause through 2031ai_regulatory_pause
killerTK04Macro Recession 2026-27 (Structural Deleveraging)
killerTK07Labor Political Backlash (UBI Mandate / AI Tax)

Dependents (1)

Predictions enabled by this
TypePredTitleDomainLag
prereq234_036Job displacement will be issue 6-10 not top 5 in 10 years; AI discoveries will dominateLabor/Jobs

Expected milestones (1)

From Sheet 17 Monitoring Triggers
Expected byDescriptionStatus
2027-09-30[Capability 2027-09] de / Cursor enterprise adoption metrics [SEM_049] SWE-Bench + METR long-horizon eval results; enterprise code-gen market sharepending

Validations (1)

Resolution events
Observed atStatusByNotes
2026-04-29hitthesis_timeline_v1.0_importClaude Opus 4.7 + Claude Code 42-54% share of code-gen market. Cursor $1B ARR. Anthropic 'Programmer Equivalent' benchmarks showing continued capability growth.

Linked documents (10)

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

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "mode": "FORECAST",
  "role": "Guest-VC/Physicist",
  "context": "Wissner-Gross extrapolates hardware-software recursion (Nvidia 4x/2x) into full software-engineering automation within near-term horizon.",
  "to_year": 2028,
  "conv_cues": "will soon; iterative self-improvement",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "near-term",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "Claude Opus 4.7 hits 87.6% on SWE-Bench Verified",
      "notes": "HIT — Claude Opus 4.7 leads SWE-Bench Verified at 87.6% with 1M context.",
      "source": "https://tokenmix.ai/blog/swe-bench-2026-claude-opus-4-7-wins",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -7,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://tokenmix.ai/blog/swe-bench-2026-claude-opus-4-7-wins",
      "expected_date": "2026-04-16",
      "observed_date": "2026-04-16",
      "research_origin": "deep_research",
      "measurement_criterion": "Anthropic Claude Opus 4.7 releases with >=85% SWE-Bench Verified score"
    },
    {
      "kind": "llm_pre_event",
      "label": "OpenAI deprecates SWE-Bench Verified as too contaminated",
      "notes": "HIT — OpenAI explicitly retired SWE-Bench Verified, recommending SWE-Bench Pro.",
      "source": "https://openai.com/index/why-we-no-longer-evaluate-swe-bench-verified/",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -6,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://openai.com/index/why-we-no-longer-evaluate-swe-bench-verified/",
      "expected_date": "2026-04-16",
      "observed_date": "2026-04-30",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2026-06-30",
        "from": "2026-02-01"
      },
      "measurement_criterion": "OpenAI publishes statement that SWE-Bench Verified is no longer the frontier coding benchmark of record"
    },
    {
      "kind": "prereq",
      "label": "Nvidia quadrupled chip production output while only doubling human headcount — achieved by deploying AI coding tools (Cursor, Claude Code) a",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -5,
      "source_id": "SEM_012",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Training runs costing $10 billion for a single model will commence sometime in 2025.",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -4,
      "source_id": "SEM_008",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "2025 will be the definitive year that agentic systems finally hit the mainstream.",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -3,
      "source_id": "SEM_042",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Jury selection begins April 27, 2026 for Musk v OpenAI trial",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -2,
      "source_id": "247_058",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Recursive self-improvement is already happening now (no longer three years out)",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -1,
      "source_id": "238_009",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "event",
      "label": "AI will soon fully automate software engineering, achieving massive cost reductions via iterative self-improvement.",
      "status": "partial",
      "weight": 1,
      "ordinal": 0,
      "source_id": "SEM_049",
      "expected_date": "2026-05-01",
      "observed_date": "2026-05-01"
    },
    {
      "kind": "llm_pre_event",
      "label": "Claude Mythos Preview crosses 90% on SWE-Bench Verified",

... (truncated)