AGI Timeline
Page Status
Quality:59 (Adequate)⚠️
Importance:74.5 (High)
Last edited:2026-01-29 (3 days ago)
Words:2.0k
Structure:
📊 15📈 1🔗 34📚 21•15%Score: 14/15
LLM Summary:Comprehensive synthesis of AGI timeline forecasts showing dramatic acceleration: expert median dropped from 2061 (2018) to 2047 (2023), Metaculus from 50 years to 5 years since 2020, with current predictions clustering around 2027-2045 median (50% probability). Aggregates 9,300+ predictions across expert surveys, prediction markets, and lab leader statements, documenting key uncertainties around scaling limits, definitions, and technical bottlenecks.
Critical Insights (4):
- Quant.Expert AGI timeline predictions have accelerated dramatically, shortening by 16 years from 2061 (2018) to 2045 (2023), representing a consistent trend of timeline compression as capabilities advance.S:4.0I:4.5A:4.0
- DebateThere is a striking 20+ year disagreement between industry lab leaders claiming AGI by 2026-2031 and broader expert consensus of 2045, suggesting either significant overconfidence among those closest to development or insider information not reflected in academic surveys.S:4.5I:4.5A:3.5
- GapAGI definition choice creates systematic 10-15 year timeline variations, with economic substitution definitions yielding 2040-2060 ranges while human-level performance benchmarks suggest 2030-2040, indicating definitional work is critical for meaningful forecasting.S:3.5I:4.0A:4.5
Issues (2):
- QualityRated 59 but structure suggests 93 (underrated by 34 points)
- Links12 links could use <R> components
Quick Assessment
Section titled “Quick Assessment”| Dimension | Assessment | Evidence |
|---|---|---|
| Median Expert Forecast (2026) | 2040-2047 (50% HLMI) | AI Impacts 2023 Survey found 50% probability of HLMI by 2047, down 13 years from 2022 |
| Prediction Markets | 2027-2031 median | Metaculus forecasters predict median of November 2027 (1,700+ forecasters) |
| Lab Leader Estimates | 2026-2029 | Sam Altman, Dario Amodei, and Demis Hassabis converge on late 2020s |
| Timeline Trend | Rapidly shortening | Expert median dropped from 2061 (2018) → 2059 (2022) → 2047 (2023); Metaculus dropped from 50 years to 5 years since 2020 |
| Uncertainty Range | Very high (±15-20 years) | 80% confidence intervals span 2026-2045+ across forecasts |
| Definition Sensitivity | High | Different AGI definitions shift predictions by 10-20 years |
| Confidence Level | Low-Medium | Expert surveys show framing effects of 15+ years; historical predictions consistently too pessimistic |
Overview
Section titled “Overview”AGI timeline predictions represent attempts to forecast when artificial intelligence will match or exceed human cognitive abilities across all domains. Current expert consensus suggests a 50% probability of AGI development between 2040-2050, though estimates vary widely based on AGI definitions and measurement criteria.
Recent surveys show accelerating timelines compared to historical predictions. The 2023 AI Impacts survey↗🔗 web★★★☆☆AI Impacts2023 AI Impacts surveySource ↗Notes found median expert predictions of 2045 for “High-Level Machine Intelligence,” while Metaculus prediction markets↗🔗 web★★★☆☆MetaculusMetaculus prediction marketsSource ↗Notes aggregate to approximately 2040-2045. However, significant uncertainty remains around capability thresholds, measurement methodologies, and potential discontinuous progress.
AGI Timeline Factors
Section titled “AGI Timeline Factors”Loading diagram...
AGI Timeline Risk Assessment
Section titled “AGI Timeline Risk Assessment”| Factor | Assessment | Timeline Impact | Source |
|---|---|---|---|
| Expert Survey Median | 2040-2050 | Baseline estimate | AI Impacts 2023↗🔗 web★★★☆☆AI ImpactsAI Impacts 2023Source ↗Notes |
| Prediction Market Aggregate | 2040-2045 | Market consensus | Metaculus↗🔗 web★★★☆☆MetaculusMetaculusMetaculus is an online forecasting platform that allows users to predict future events and trends across areas like AI, biosecurity, and climate change. It provides probabilisti...Source ↗Notes |
| Lab Leader Statements | 2025-2035 | Optimistic bound | OpenAI↗🔗 web★★★★☆OpenAIOpenAISource ↗Notes, DeepMind↗🔗 web★★★★☆Google DeepMindGoogle DeepMindSource ↗Notes |
| Scaling Limitations | 2050+ | Conservative bound | Epoch AI↗🔗 web★★★★☆Epoch AIEpoch AIEpoch AI provides comprehensive data and insights on AI model scaling, tracking computational performance, training compute, and model developments across various domains.Source ↗Notes |
Expert Survey Results
Section titled “Expert Survey Results”Recent Survey Data (2023-2026)
Section titled “Recent Survey Data (2023-2026)”| Survey | Year | Sample Size | Median AGI Timeline | Key Finding | Source |
|---|---|---|---|---|---|
| AI Impacts ESPAI | 2023 | 2,778 experts | 2047 (HLMI) | 13-year drop from 2060 in 2022 | AI Impacts |
| Digital Minds Survey | 2025 | 67 experts | 2050 (50% probability) | 20% by 2030, 40% by 2040 | Digital Minds Report |
| AI Multiple Meta-Analysis | 2026 | 9,300 predictions | 2040 (aggregated) | Synthesized all public forecasts | AI Multiple |
| Metaculus Community | 2026 | 1,700+ forecasters | Nov 2027 median | 80% CI: July 2026 - Feb 2031 | Metaculus |
| Samotsvety Superforecasters | 2023 | 15 forecasters | 28% by 2030 | Professional forecasters more conservative | 80,000 Hours |
Timeline Acceleration Trends
Section titled “Timeline Acceleration Trends”Expert timelines have consistently shortened over the past decade, with dramatic acceleration since 2022:
| Year | Expert Median (HLMI) | Metaculus Median | Change from Previous |
|---|---|---|---|
| 2018 | 2061 | 2070+ | Baseline |
| 2022 | 2059-2060 | 2055 | -2 years |
| 2023 | 2045-2047 | 2040 | -13 to -15 years |
| 2024 | ≈2040 | 2035 | -5 years |
| 2025 | ≈2035 | 2030 | -5 years |
| 2026 | Varied | Nov 2027 | -3 years |
The 80,000 Hours analysis notes that “in four years, the mean estimate on Metaculus for when AGI will be developed has plummeted from 50 years to five years.” Historical expert predictions have consistently been too pessimistic—in 2022, researchers thought AI wouldn’t write simple Python code until ~2027, but AI met that threshold by 2023-2024.
Leading AI researchers↗🔗 web★★★★☆AnthropicLeading AI researchersSource ↗Notes increasingly cite rapid scaling of language modelsCapabilityLarge Language ModelsComprehensive analysis of LLM capabilities showing rapid progress from GPT-2 (1.5B parameters, 2019) to o3 (87.5% on ARC-AGI vs ~85% human baseline, 2024), with training costs growing 2.4x annually...Quality: 60/100 and emergent capabilities as evidence for shorter timelines.
Prediction Market Analysis
Section titled “Prediction Market Analysis”Metaculus Aggregates (January 2026)
Section titled “Metaculus Aggregates (January 2026)”| Question | Current Prediction | Confidence Interval | Forecasters | Source |
|---|---|---|---|---|
| First General AI Announced | Nov 30, 2027 median | July 2026 - Feb 2031 (80%) | 1,700+ | Metaculus |
| Weakly General AI | Nov 2033 | Dec 2028 - Sep 2045 | 1,800+ | Metaculus |
| Transformative AI | 2031 median | 2027-2045 (80%) | 1,000+ | AGI Dashboard |
| AGI by 2030 | ≈40% probability | 25-55% range | Aggregated | Market consensus |
| AGI by 2040 | ≈75% probability | 60-85% range | Aggregated | Market consensus |
Platform Comparison
Section titled “Platform Comparison”| Platform | AGI Median | 50% Probability Year | Key Difference |
|---|---|---|---|
| Metaculus | Mid-2030 | 2030-2031 | Stricter definition requiring robotics |
| Manifold | 2028 | ≈50% before 2028 | More aggressive, market-based |
| Polymarket | 2029-2030 | ≈45% by 2029 | Real-money incentives |
| Expert Surveys | 2040-2047 | 2040-2045 | Academic conservatism |
Market Dynamics
Section titled “Market Dynamics”Prediction markets show several notable patterns:
- Dramatic shortening: Metaculus dropped from 50 years to 5 years median since 2020
- Volatility spikes following major capability announcements (GPT-4, Claude 3, o1, o3)
- Shorter timelines in technical communities vs. academic surveys (10-15 year gap)
- Definition sensitivity with different AGI operationalizations varying by 10-20 years
Lab Leader Statements
Section titled “Lab Leader Statements”Industry Timeline Claims (Updated January 2026)
Section titled “Industry Timeline Claims (Updated January 2026)”| Organization | Leader | Claimed Timeline | Key Statement | Source |
|---|---|---|---|---|
| OpenAILabOpenAIComprehensive organizational profile of OpenAI documenting evolution from 2015 non-profit to commercial AGI developer, with detailed analysis of governance crisis, safety researcher exodus (75% of ...Quality: 46/100 | Sam Altman | 2025-2028 | ”We are now confident we know how to build AGI”; 2026 models will “amaze us” | Sam Altman Blog |
| AnthropicLabAnthropicComprehensive profile of Anthropic tracking its rapid commercial growth (from $1B to $7B annualized revenue in 2025, 42% enterprise coding market share) alongside safety research (Constitutional AI...Quality: 51/100 | Dario Amodei | 2026-2027 | ”AI may surpass humans in most tasks by 2027”; “rapidly running out of convincing blockers” | Lex Fridman Interview |
| DeepMindLabGoogle DeepMindComprehensive overview of DeepMind's history, achievements (AlphaGo, AlphaFold with 200M+ protein structures), and 2023 merger with Google Brain. Documents racing dynamics with OpenAI and new Front...Quality: 37/100 | Demis Hassabis | ”Within this decade” (by 2030) | Nature interview 2024↗📄 paper★★★★★Nature (peer-reviewed)Nature interview 2024Source ↗Notes | Internal planning |
| DeepMindLabGoogle DeepMindComprehensive overview of DeepMind's history, achievements (AlphaGo, AlphaFold with 200M+ protein structures), and 2023 merger with Google Brain. Documents racing dynamics with OpenAI and new Front...Quality: 37/100 | Shane Legg | 50% by 2028 | ”Minimal AGI” prediction (January 2026) | DeepMind cofounder |
| Meta | Yann LeCun | ”Many decades away” | Skeptical of current paradigm reaching AGI | Public statements 2024↗🔗 web★★★★☆Meta AIPublic statements 2024Source ↗Notes |
| xAI | Elon Musk | 2026 | AI “smarter than any single human” | Public statements |
Implied Timelines from Investment Plans
Section titled “Implied Timelines from Investment Plans”Several labs’ public roadmaps suggest aggressive acceleration:
| Metric | 2024 | 2025 | 2026 | 2027 | Source |
|---|---|---|---|---|---|
| Training Run Cost | ≈$100M | ≈$1B | $10B+ | $100B clusters | Dario Amodei |
| Compute per Training | Baseline | 3-10x | 30-100x | 300-1000x | Scaling projections |
| Data Center Power | 100-500 MW | 500 MW-1 GW | 1-5 GW | 5-10 GW | Industry reports |
| Researcher FTEs | 5,000+ | 10,000+ | 20,000+ | 50,000+ | Lab hiring plans |
Key Uncertainty Factors
Section titled “Key Uncertainty Factors”Definition Problems
Section titled “Definition Problems”| AGI Definition | Timeline Range | Key Challenge |
|---|---|---|
| Human-level performance | 2030-2040 | Benchmark gaming |
| Economic substitution | 2040-2060 | Deployment lags |
| Scientific breakthrough | 2035-2050 | Discovery vs. automation |
| Consciousness/sentience | 2050+ | Hard problem of consciousness |
Technical Bottlenecks
Section titled “Technical Bottlenecks”Current limitations that may extend timelines:
- Reasoning capabilitiesCapabilityReasoning and PlanningComprehensive survey tracking reasoning model progress from 2022 CoT to late 2025, documenting dramatic capability gains (GPT-5.2: 100% AIME, 52.9% ARC-AGI-2, 40.3% FrontierMath) alongside critical...Quality: 65/100: Current models struggle with complex multi-step reasoning
- Long-horizon planningCapabilityLong-Horizon Autonomous TasksMETR research shows AI task completion horizons doubling every 7 months (accelerated to 4 months in 2024-2025), with current frontier models achieving ~1 hour autonomous operation at 50% success; C...Quality: 65/100: Limited ability for extended autonomous operation
- Robustness: Brittleness to distribution shifts and adversarial examples
- Sample efficiency: Still require massive training data compared to humans
Scaling Constraints
Section titled “Scaling Constraints”| Constraint Type | Impact on Timeline | Mitigation Strategies |
|---|---|---|
| Compute hardwareAi Transition Model MetricCompute & HardwareComprehensive metrics tracking finds training compute grows 4-5x annually (30+ models at 10²⁵ FLOP by mid-2025), algorithmic efficiency doubles every 8 months (95% CI: 5-14), and NVIDIA holds 80-90...Quality: 67/100 | +5-10 years if hits limits | Advanced chip architectures |
| Data availability | +3-5 years | Synthetic data generation |
| Energy requirements | +2-5 years | Efficiency improvements |
| Regulatory barriers | +5-15 years | International coordination |
Current Capability Trajectory
Section titled “Current Capability Trajectory”2024 State Assessment
Section titled “2024 State Assessment”Recent capabilities suggest accelerating progress toward AGI:
- Multi-modal integration: Vision, text, and code in single models
- Tool useCapabilityTool Use and Computer UseTool use capabilities achieved superhuman computer control in late 2025 (OSAgent: 76.26% vs 72% human baseline) and near-human coding (Claude Opus 4.5: 80.9% SWE-bench Verified), but prompt injecti...Quality: 67/100: Effective API calls and workflow automation
- Emergent reasoning: Chain-of-thought and constitutional approaches
- Scientific researchCapabilityScientific Research CapabilitiesAI scientific research capabilities have achieved superhuman performance in narrow domains (AlphaFold's 214M protein structures, GNoME's 2.2M materials in 17 days vs. 800 years traditionally), with...Quality: 66/100: Automated hypothesis generation and testing
Projection Methods
Section titled “Projection Methods”| Approach | 2030 Prediction | Methodology | Limitations |
|---|---|---|---|
| Scaling laws | 85% human performance | Extrapolate compute trends | May hit diminishing returns |
| Expert elicitation | 60% probability | Survey aggregation | Bias and overconfidence |
| Benchmark tracking | 90% on specific tasks | Performance trajectory | Narrow evaluation |
| Economic modeling | 40% job automation | Labor substitution | Deployment friction |
Disagreement and Cruxes
Section titled “Disagreement and Cruxes”Major Points of Contention
Section titled “Major Points of Contention”Timeline Pessimists (2050+) argue:
- Current paradigms (transformers, scaling) will hit fundamental limits
- Alignment difficulty will require extensive safety research before deployment
- Economic and regulatory barriers will slow deployment
- Key cognitive capabilities (long-horizon planning, true reasoning) may require architectural breakthroughs
Timeline Optimists (2025-2035) contend:
- Scaling laws will continue with current paradigms through 2030+
- Emergent capabilities from larger models will bridge remaining capability gaps
- Competitive pressure and $100B+ investments will accelerate development
- Recent progress (o1, o3 reasoning, agents) shows faster-than-expected capability gains
Key Cruxes
Section titled “Key Cruxes”| Question | Impact on Timeline | Current Evidence | Optimist View | Pessimist View |
|---|---|---|---|---|
| Will scaling laws continue? | ±10 years | Mixed signals since GPT-4 | Compute scaling to $100B clusters will unlock new capabilities | Diminishing returns visible; new paradigms needed |
| Can transformers achieve AGI? | ±15-20 years | Chain-of-thought, o1/o3 reasoning | Architecture is sufficient with scale | Fundamental limits on reasoning and planning |
| How hard is alignment? | ±10-15 years | Constitutional AI, RLHF improvements | Tractable with current approaches | Requires deep unsolved problems |
| Will regulation slow progress? | ±5-15 years | EU AI Act, compute governance | Light touch will prevail | Precautionary regulation inevitable |
| Is AGI a single threshold? | ±10 years | Definitional debates | Continuous capability improvement | Discrete capability jumps required |
Timeline Implications
Section titled “Timeline Implications”Strategic Considerations
Section titled “Strategic Considerations”Different timelines imply varying urgency for:
- Safety research: Shorter timelines require immediate focus on alignment solutionsSolutionsComprehensive analysis of key uncertainties determining optimal AI safety resource allocation across technical verification (25-40% believe AI detection can match generation), coordination mechanis...Quality: 71/100
- Governance frameworks: International coordination becomes critical
- Economic preparation: Labor market disruption planning
- Coordination mechanismsAi Transition Model ParameterInternational CoordinationThis page contains only a React component placeholder with no actual content rendered. Cannot assess importance or quality without substantive text.: Preventing dangerous racing dynamics
Policy Relevance
Section titled “Policy Relevance”Timeline uncertainty affects regulation approachesCruxGovernment Regulation vs Industry Self-GovernanceComprehensive comparison of government regulation versus industry self-governance for AI, documenting that US federal AI regulations doubled to 59 in 2024 while industry lobbying surged 141% to 648...Quality: 54/100:
- Precautionary principle: Plan for shortest reasonable timelines
- Adaptive governance: Build flexible frameworks for multiple scenarios
- Research prioritization: Balance capability and safety advancement
Sources & Resources
Section titled “Sources & Resources”Primary Research
Section titled “Primary Research”| Category | Source | Key Contribution |
|---|---|---|
| Expert Surveys | AI Impacts 2023 Survey↗🔗 web★★★☆☆AI ImpactsAI Impacts 2023Source ↗Notes | Largest expert survey (2,778 respondents) |
| Prediction Markets | Metaculus AGI Questions↗🔗 web★★★☆☆MetaculusMetaculusMetaculus is an online forecasting platform that allows users to predict future events and trends across areas like AI, biosecurity, and climate change. It provides probabilisti...Source ↗Notes | Continuous probability tracking (1,700+ forecasters) |
| Technical Analysis | Epoch AI Scaling Reports↗🔗 web★★★★☆Epoch AIEpoch AIEpoch AI provides comprehensive data and insights on AI model scaling, tracking computational performance, training compute, and model developments across various domains.Source ↗Notes | Compute and training cost projections |
| Industry Perspectives | OpenAI Planning Documents↗🔗 web★★★★☆OpenAIOpenAISource ↗Notes | Lab development roadmaps |
| Meta-Analysis | 80,000 Hours Timeline Review | Synthesis of forecaster disagreements |
2025-2026 Key Sources
Section titled “2025-2026 Key Sources”| Source | Date | Key Finding | URL |
|---|---|---|---|
| Sam Altman “Gentle Singularity” | Jan 2025 | ”We know how to build AGI”; 2026 will see “systems that figure out novel insights” | Blog |
| Dario Amodei Lex Fridman Interview | Nov 2024 | ”Rapidly running out of convincing blockers”; 2026-2027 possible | Transcript |
| AI Multiple Meta-Analysis | Jan 2026 | 9,300 predictions analyzed; aggregated median ≈2040 | Analysis |
| Digital Minds Forecasting | 2025 | 67 experts: 20% by 2030, 50% by 2050 | Report |
| AGI Timelines Dashboard | Jan 2026 | Combined forecasts: 2031 median (80% CI: 2027-2045) | Dashboard |
Forecasting Organizations
Section titled “Forecasting Organizations”| Organization | Focus Area | Key Resources |
|---|---|---|
| AI Impacts↗🔗 web★★★☆☆AI ImpactsAI Impacts 2023Source ↗Notes | Expert surveys and trend analysis | Annual ESPAI survey reports |
| Metaculus↗🔗 web★★★☆☆MetaculusMetaculusMetaculus is an online forecasting platform that allows users to predict future events and trends across areas like AI, biosecurity, and climate change. It provides probabilisti...Source ↗Notes | Prediction markets | AGI timeline questions, AGI Horizons tournament |
| Epoch AIOrganizationEpoch AIEpoch AI provides empirical AI progress tracking showing training compute growing 4.4x annually (2010-2024), 300 trillion tokens of high-quality training data with exhaustion projected 2026-2032, a...Quality: 91/100 | Compute trends and scaling laws | Technical reports, training cost projections |
| Future of Humanity Institute↗🔗 web★★★★☆Future of Humanity Institute**Future of Humanity Institute**Source ↗Notes | Long-term forecasting | Academic papers (now closed) |
| Samotsvety Forecasting | Superforecaster aggregation | AGI probability estimates |
Related Analysis
Section titled “Related Analysis”- Scaling debates: See scaling law discussionCruxIs Scaling All You Need?Comprehensive survey of the 2024-2025 scaling debate, documenting the shift from pure pretraining to 'scaling-plus' approaches after o3 achieved 87.5% on ARC-AGI-1 but GPT-5 faced 2-year delays. Ex...Quality: 42/100
- Capability analysis: Review core capabilities development
- Timeline uncertainty: Explore forecasting methodologyAgi TimelineComprehensive synthesis of AGI timeline forecasts showing dramatic acceleration: expert median dropped from 2061 (2018) to 2047 (2023), Metaculus from 50 years to 5 years since 2020, with current p...Quality: 59/100
- Risk implications: Consider takeoff dynamics scenarios