# Audit: Current AI systems have already achieved Artificial General Intelligence (AGI).

- Generated: 2026-04-06
- Reader summary: [proof_agi.md](proof_agi.md)
- Proof script: [proof_agi.py](proof_agi.py)

## Claim Specification

Source: proof_agi.py JSON summary `claim_formal`.

| Field | Value |
|-------|-------|
| Subject | Current AI systems (as of March 2026) |
| Property | Achievement of Artificial General Intelligence (AGI) |
| Operator | >= |
| Threshold | 3 |
| Proof direction | disprove |
| Operator note | This is a disproof. We search for authoritative sources that reject the claim that current AI systems have achieved AGI. AGI is interpreted using the most widely-cited frameworks: (1) Google DeepMind's "Levels of AGI" paper (Morris et al., 2023), which classifies current frontier models as Level 1 ("Emerging") AGI — not yet "Competent" (Level 2) on most cognitive tasks; (2) OpenAI's internal 5-level framework, which places current systems at Level 2 ("Reasoners") out of 5 levels needed for full AGI; (3) Expert survey consensus that AGI has not been achieved. The threshold of 3 independent authoritative sources rejecting the claim is conservative. If >= 3 verified sources explicitly state AGI has NOT been achieved, the claim is DISPROVED. |

## Fact Registry

Source: proof_agi.py JSON summary `fact_registry`.

| ID | Key | Label |
|----|-----|-------|
| B1 | deepmind_levels | Google DeepMind "Levels of AGI" framework — classifies current AI as Level 1 (Emerging) |
| B2 | gary_marcus | Gary Marcus (NYU) — current AI is not AGI, conflates statistical approximation with intelligence |
| B3 | cogni_analysis | Expert analysis — AGI not achieved, current systems lack autonomous goals and transfer learning |
| B4 | tim_dettmers | Tim Dettmers (UW) — AGI will not happen due to physical computation limits |
| A1 | — | Verified source count meeting disproof threshold |

## Full Evidence Table

### Type A (Computed) Facts

Source: proof_agi.py JSON summary `fact_registry`.

| ID | Fact | Method | Result |
|----|------|--------|--------|
| A1 | Verified source count meeting disproof threshold | count(verified citations) = 4 | 4 |

### Type B (Empirical) Facts

Source: proof_agi.py JSON summary `citations`.

| ID | Fact | Source | URL | Quote (truncated) | Status | Method | Credibility |
|----|------|--------|-----|---------------------|--------|--------|-------------|
| B1 | DeepMind Levels of AGI framework | Google DeepMind (Morris et al., 2023) | https://arxiv.org/abs/2311.02462 | "We propose a framework for classifying the capabilities and behavior of AGI models..." | verified | fragment (81.4%) | Tier 4 (academic) |
| B2 | Gary Marcus on AGI | Gary Marcus — Substack | https://garymarcus.substack.com/p/rumors-of-agis-arrival-have-been | "Current AI systems are powerful and increasingly useful tools, but they do not exhibit..." | verified | full_quote | Tier 2 (unknown) |
| B3 | Cogni analysis on AGI | Cogni Down Under — Medium | https://medium.com/@cognidownunder/... | "Current models lack autonomous goal formation..." | verified | full_quote | Tier 2 (unknown) |
| B4 | Tim Dettmers on AGI | Tim Dettmers (UW) | https://timdettmers.com/2025/12/10/why-agi-will-not-happen/ | "For linear improvements, we previously had exponential growth as GPUs..." | verified | full_quote | Tier 2 (unknown) |

## Citation Verification Details

Source: proof_agi.py JSON summary `citations`.

**B1 (deepmind_levels):**
- Status: verified
- Method: fragment match, coverage_pct = 81.4%
- Fetch mode: live

**B2 (gary_marcus):**
- Status: verified
- Method: full_quote
- Fetch mode: live

**B3 (cogni_analysis):**
- Status: verified
- Method: full_quote
- Fetch mode: live

**B4 (tim_dettmers):**
- Status: verified
- Method: full_quote
- Fetch mode: live

## Computation Traces

Source: proof_agi.py inline output (execution trace).

```
  Confirmed sources: 4 / 4
  verified source count vs disproof threshold: 4 >= 3 = True
```

## Independent Source Agreement (Rule 6)

Source: proof_agi.py JSON summary `cross_checks`.

Four independent sources were consulted from different institutions and individuals:

| Source | Institution | Reasoning approach | Verification status |
|--------|------------|-------------------|-------------------|
| B1 | Google DeepMind | Formal AGI taxonomy | verified |
| B2 | Gary Marcus (NYU/independent) | Philosophy of mind / cognitive science | verified |
| B3 | Cogni Down Under (independent) | Capability gap analysis | verified |
| B4 | Tim Dettmers (University of Washington) | Physical computation limits | verified |

All four sources reach the same conclusion — AGI has not been achieved — via fundamentally different reasoning approaches, providing strong independent corroboration.

## Adversarial Checks (Rule 5)

Source: proof_agi.py JSON summary `adversarial_checks`.

**Check 1: Has any credible AI researcher or organization officially declared AGI achieved?**
- Searched for: "AGI achieved 2026 claims"
- Finding: Jensen Huang (Nvidia CEO) is the only major industry figure to declare AGI achieved (March 2026). His claim was immediately challenged by researchers who note it conflates benchmark performance with general intelligence. No major AI research lab has endorsed the claim. 76% of 475 AI researchers surveyed by AAAI said scaling current AI is unlikely to result in AGI.
- Breaks proof: No

**Check 2: Do current AI systems pass any widely-accepted AGI benchmark or test?**
- Searched for: "AGI benchmark test passed 2026"
- Finding: No widely-accepted AGI benchmark has been passed. Current systems show "jagged intelligence" — winning math olympiad gold medals but failing elementary problems. DeepMind's 2026 cognitive framework shows large gaps in 5 of 10 cognitive abilities needed for AGI.
- Breaks proof: No

**Check 3: Is there expert consensus that AGI timelines are imminent?**
- Searched for: "AGI timeline expert survey 2025 2026"
- Finding: Expert consensus places AGI arrival well into the future. Even optimistic forecasters give only 25% probability by 2029. No mainstream expert survey claims AGI is already here.
- Breaks proof: No

## Source Credibility Assessment

Source: proof_agi.py JSON summary `citations[].credibility`.

| Fact ID | Domain | Type | Tier | Note |
|---------|--------|------|------|------|
| B1 | arxiv.org | academic | 4 | Known academic/scholarly publisher |
| B2 | substack.com | unknown | 2 | Unclassified domain — author is Gary Marcus, established NYU professor and prominent AI critic |
| B3 | medium.com | unknown | 2 | Unclassified domain — independent AI analysis blog |
| B4 | timdettmers.com | unknown | 2 | Unclassified domain — author is Tim Dettmers, University of Washington AI researcher |

Note: 3 sources have tier 2 (unclassified) credibility. However, the authors behind B2 and B4 are established, well-known AI researchers whose expertise is independently verifiable. Gary Marcus is a former NYU professor, bestselling author on AI limitations, and frequent Congressional witness on AI policy. Tim Dettmers is a University of Washington researcher known for foundational work on quantization and efficient deep learning. The tier 2 rating reflects the publishing platform (personal blog/Substack), not the authors' credentials.

## Extraction Records

Source: proof_agi.py JSON summary `extractions`.

For this qualitative/consensus proof, extractions record citation verification status per source rather than numeric values:

| Fact ID | Value (status) | Countable | Quote snippet |
|---------|---------------|-----------|---------------|
| B1 | verified | Yes | "We propose a framework for classifying the capabilities and behavior of Artifici" |
| B2 | verified | Yes | "Current AI systems are powerful and increasingly useful tools, but they do not e" |
| B3 | verified | Yes | "Current models lack autonomous goal formation. They respond brilliantly to promp" |
| B4 | verified | Yes | "For linear improvements, we previously had exponential growth as GPUs which canc" |

## Hardening Checklist

- Rule 1: N/A — qualitative consensus proof, no numeric value extraction
- Rule 2: Every citation URL fetched and quote checked via `verify_all_citations()`
- Rule 3: N/A — no time-dependent computation (auto-pass)
- Rule 4: Claim interpretation explicit with operator rationale in `CLAIM_FORMAL["operator_note"]`
- Rule 5: Three adversarial checks searched for independent counter-evidence supporting AGI achievement
- Rule 6: 4 distinct source references from independent institutions/authors
- Rule 7: N/A — no constants or formulas (auto-pass)
- validate_proof.py result: **PASS with warnings** (14/15 checks passed, 0 issues, 1 warning about missing else branch in verdict assignment)

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Generated by [proof-engine](https://github.com/yaniv-golan/proof-engine) v1.7.0 on 2026-04-06.