Original research · claim-level source ledger
AI, AGI, ASI, and Abundance Evidence Ledger
A reviewed claim-level ledger separating observed findings, institutional definitions, projections, inferences, scenarios, and unresolved questions in the path from AI capability to universal access.
Version 1.0.0 · Published · Reviewed · 30-day review cadence
10
claim records
9
source statements verified
10
analytical dimensions
Reviewed claims
“Verified” means the source states the attributed fact or method. It does not convert a laboratory goal, scenario, or definition into an independently observed future outcome.
| Claim | Class / status | Source | Metric | Uncertainty | Counterevidence | Verified |
|---|---|---|---|---|---|---|
| AGI definitionOpenAI defines AGI as highly autonomous systems that outperform humans at most economically valuable work.openai-charter-agi-definition | Attributed definitionverified | OpenAI Charter (opens in a new tab)OpenAI · Tier 1 | No numeric metric | The definition is authoritative for OpenAI but is not a universal standard or proof that a system meets it. | Google DeepMind proposes a multidimensional levels framework rather than one binary threshold. | Global institutional definition |
| AGI measurementGoogle DeepMind’s Levels of AGI framework separates depth of performance from breadth of generality and discusses autonomy as a deployment consideration.deepmind-levels-framework | Measured findingverified | Levels of AGI for Operationalizing Progress on the Path to AGI (opens in a new tab)Google DeepMind · Tier 2 | No numeric metric | The ontology is proposed by its authors and is not a binding certification system. | Other organizations use different threshold definitions focused on economic work or autonomy. | Global research framework |
| AGI-to-ASI pathwaysGoogle DeepMind analyzes four possible AGI-to-ASI pathways: scaling AGI, AI paradigm shifts, recursive improvement, and large-scale multi-agent collectives.deepmind-four-asi-paths | Scenarioverified | From AGI to ASI (opens in a new tab)Google DeepMind · Tier 2 | Proposed pathway count4 pathwaysDenominator: Pathways analyzed in the report | The report presents pathways and open research questions, not probabilities or dates. | Unknown future paradigms may not fit the four categories, and material frictions may limit every path. | Global theoretical analysis |
| ASI definitionGoogle DeepMind describes artificial general superintelligence intuitively as more intelligent and cognitively capable than large organizations of humans.deepmind-asi-definition | Attributed definitionverified | From AGI to ASI (opens in a new tab)Google DeepMind · Tier 2 | No numeric metric | No accepted operational test or verified current instance follows from this intuitive definition. | Domain-specific superhuman performance does not establish general superiority over human organizations. | Global theoretical analysis |
| Institutional objectiveOpenAI’s 2026 plan states a goal of giving everyone on Earth a personal AGI.openai-personal-agi-goal | Verified product factverified | Built to benefit everyone: our plan (opens in a new tab)OpenAI · Tier 1 | No numeric metric | This records a goal, not delivery, coverage, affordability, or an independently verified AGI capability. | The same source says transformative technologies can concentrate power and that broad benefit will not happen automatically. | Global stated objective |
| Intelligence accessOpenAI’s 2026 plan identifies making advanced AI abundant, affordable, safe, useful, and easy to use as a central task.openai-abundant-affordable-intelligence | Attributed definitionverified | Built to benefit everyone: our plan (opens in a new tab)OpenAI · Tier 1 | No numeric metric | No universal affordability threshold, coverage result, or completion date is supplied. | Access to intelligence alone does not establish access to housing, energy, healthcare, food, or productive capital. | Global stated objective |
| Labor and distributionAnthropic’s Economic Policy Framework considers a scenario in which AI generates unprecedented abundance while acting as a general substitute for labor.anthropic-abundance-labor-scenario | Scenarioverified | Policy on the AI Exponential: Economic Policy Framework (opens in a new tab)Anthropic · Tier 1 | No numeric metric | Anthropic explicitly says the pace of capability development and diffusion is uncertain. | The scenario is not a measured forecast of unemployment, productivity, or abundance. | United States policy proposal |
| Agent capability measurementMETR defines a 50% task-completion time horizon as the human-expert task duration at which an agent is predicted to succeed half the time.metr-time-horizon-definition | Measured findingverified | Task-Completion Time Horizons of Frontier AI Models (opens in a new tab)METR · Tier 3 | Illustrative reliability threshold50 percent predicted successDenominator: Tasks at the reported human-expert duration in the evaluated suite | METR says measurements above 16 hours are unreliable with its current suite, and performance does not generalize to all jobs. | Real jobs include context, people, ambiguous goals, and outcomes that are difficult to score automatically. | Public frontier models; task suite is primarily software engineering, machine learning, and cybersecurity |
| Generalization benchmarkARC-AGI-3 uses Relative Human Action Efficiency to score level completion and action efficiency against controlled human baselines.arc-rhae-method | Measured findingverified | ARC-AGI-3 Scoring Methodology (opens in a new tab)ARC Prize · Tier 3 | Total score range0–100 percentDenominator: Average of all game scores | The benchmark is one task environment and does not operationalize every dimension of AGI. | A model can perform strongly in a benchmark environment while remaining unreliable or narrow elsewhere. | Global evaluation environment |
| AI-to-abundance transmissionHigher productive capacity does not by itself establish delivered affordability or universal access.capacity-access-distinction | Inferencesupported | Built to benefit everyone: our plan (opens in a new tab)OpenAI · Tier 1 | No numeric metric | The size and order of the bottlenecks vary by sector, country, technology, and policy. | In competitive, elastic markets, higher capacity can lower prices without an explicit redistribution program; that still does not prove universal coverage. | General analytical distinction |
Cite this dataset
Akoum, Muhamad J. “AI, AGI, ASI, and Abundance Evidence Ledger.” Version 1.0.0. Akoum.me. Reviewed 2026-07-26. https://akoum.me/research/ai-abundance-evidence-ledger