Abstract

This report explores the potential emergence of artificial general intelligence (AGI) using a scenario-based simulation model that incorporates key uncertainties in technology, governance, and capability thresholds. It introduces a decoupled definition of AGI, transparent transition matrices, and integrated technical milestones. Using a six-state lifecycle model and scenario planning combined with Markov simulation, the model examines four global scenarios from 2025 to 2050. AGI is treated as a functional outcome, defined by specific capability thresholds, not end states. Results suggest that AGI emergence is plausible under most modeled conditions, with timelines shaped by governance dynamics and technical progress. This model is a foresight tool and not a predictive forecast.

Methodology

Lifecycle States

The simulation models societal and technological AI integration through six lifecycle states:

  • Emergence – early development and curiosity
  • Acceleration – rapid expansion and investment
  • Normalization – widespread integration and regulation
  • Pushback – societal and political resistance
  • Domination – AI becomes core to major systems and decisions
  • Collapse/Convergence – systemic failure or post-human fusion

AGI Definition

AGI is identified when the system achieves capability-based thresholds:

  • Cross-domain generalization
  • Autonomous recursive improvement
  • Displacement of human decision-makers in core domains
  • Widespread cognitive labor substitution

Multiple definitions were modeled to test sensitivity, including narrow (Turing-level equivalence) and broad (global systemic integration) thresholds.

Scenario Framework

The simulation explores four scenario quadrants defined by two uncertainties:

  • Technological Trajectory – Slow vs. sudden progress
  • Governance Strength – Coordinated vs. fragmented regulation

Scenarios:

  • A – Slow, Stable AI: Global regulation strong, AGI emerges slowly (if at all)
  • B – Controlled AGI: AGI emerges under coordinated global governance
  • C – Unregulated Race: AGI emerges through market-driven acceleration
  • D – AGI in Chaos: AGI emerges rapidly with fragmented governance

Transition Matrices

Each scenario uses a unique, published transition matrix with the following:

  • Documented assumptions
  • Justification from historical trends or expert judgment
  • Time-gating on advanced transitions
  • Sensitivity analysis showing how outcomes vary with changing probabilities

Technical Track Integration

The simulation incorporates a parallel track modeling technical capability growth, including:

  • Hardware scaling (FLOPS, memory bandwidth)
  • Algorithmic breakthroughs (efficiency curves)
  • Capability evaluations (e.g., ARC, real-world generalization tests)

Transitions into late lifecycle states are conditional on meeting these technical milestones.

Key Findings

AGI Emergence Statistics by Scenario

| Scenario       | Likelihood by 2050 | Avg. Emergence Year | Earliest Emergence |
|----------------|--------------------|----------------------|---------------------|
| Quadrant D     | 95.3%              | Year 6.3             | 2028                |
| Quadrant C     | 91.7%              | Year 8.2             | 2028                |
| Quadrant B     | 81.4%              | Year 10.4            | 2028                |
| Quadrant A     | 59.8%              | Year 13.1            | 2028                |

Results Variability

  • Confidence intervals and variance are reported per scenario.
  • Pathway analysis reveals dominant transition sequences.
  • High variance is observed in fragmented scenarios (C, D).

Scenario Enhancements

  • Regional modeling and variable regulatory dynamics
  • Additional uncertainty dimensions: public acceptance, economic shocks, ecological instability
  • Inclusion of wildcard events (e.g., open-source AGI, cyber sabotage, AI ban treaties)

Revised Definitions of States

Each lifecycle state is linked to observable indicators:

  • Emergence: First multi-modal models with cross-domain capabilities
  • Acceleration: Doubling of AI investment over five years
  • Normalization: Majority of economies adopt formal AI regulation
  • Pushback: Documented resistance movements, moratoriums, or bans
  • Domination: AI in defense, finance, infrastructure
  • Collapse/Convergence: Structural reorganization, post-human integration, or collapse of human-centric governance

Historical Analogies

To contextualize the lifecycle states, we’ve mapped them to historical technological transitions:

  • Emergence: Early computers (1940s–1950s), internet formation (1970s–1980s)
  • Acceleration: Nuclear arms race (1940s–50s), mobile revolution (2000s)
  • Normalization: Electricity and utility regulation (1930s–40s), internet standardization (1990s)
  • Pushback: Anti-GMO and privacy activism, open-source movements
  • Domination: Global finance digitalization, algorithmic trading, military drones
  • Collapse/Convergence: Cold War near-misses, systemic shocks like 2008 financial crisis

These analogies provide a heuristic bridge between past technological integrations and future AI trajectories.

Assumptions & Limitations

The model has several key limitations that may affect its validity:

  • Overreliance on abstract state labels: Real-world complexity may not fit neatly into discrete categories.
  • Simplified actor modeling: The simulation treats global behavior as homogenous within each scenario, ignoring divergent national or corporate strategies.
  • Static governance strength: Scenarios assume fixed levels of coordination over 25 years, which may ignore dynamic responses to crises.
  • Absence of model-learning adaptation: Agents do not adjust behavior based on past events or outcomes.

Conclusion

While the simulation remains speculative, it offers a more credible and testable framework for exploring the potential emergence of AGI. The goal is to support structured foresight, not predict exact futures.