super-intelligence/agi-asi-map

AGI ASI Map

A visual map from current AI to AGI and Super Intelligence

AGI ASI Map is an interactive Hugging Face Space that organizes the major technical layers between today’s AI systems, Artificial General Intelligence (AGI) and hypothetical Artificial Superintelligence (ASI).

The Space uses Super Intelligence as the primary visible term while preserving Artificial Superintelligence (ASI) where the established technical concept needs to be explicit.

The path from AI to Super Intelligence is not one breakthrough. It is a stack of capabilities, systems and control layers.

What This Map Shows

The map is organized around six stages:

1. 1.Current AI
2. 2.Frontier AI
3. 3.Agentic AI
4. 4.World-Model & Embodied Systems
5. 5.AGI
6. 6.Super Intelligence / ASI

Each stage links to the capabilities and infrastructure that may become important as AI systems become more general, autonomous and reliable.

Core Map

CURRENT AI
   │
   ▼
FRONTIER AI
   │
   ├── Reasoning
   ├── Multimodal Models
   ├── Post-Training
   └── Tool Use
   │
   ▼
AGENTIC AI
   │
   ├── Memory
   ├── Planning
   ├── Orchestration
   ├── Multi-Agent Systems
   └── Long-Horizon Execution
   │
   ▼
WORLD-MODEL & EMBODIED SYSTEMS
   │
   ├── Simulation
   ├── Spatial Intelligence
   ├── Physical AI
   ├── Robotics
   └── Environment Interaction
   │
   ▼
AGI
   │
   ├── Broad Generalization
   ├── Cross-Domain Transfer
   ├── Reliable Adaptation
   └── General Problem Solving
   │
   ▼
SUPER INTELLIGENCE / ASI
   │
   ├── Broad Superhuman Capability
   ├── Advanced Scientific Reasoning
   ├── Open-Ended Problem Solving
   └── Extreme Autonomy?

The question marks matter.

AGI and Super Intelligence remain future-facing concepts rather than established milestones.

Stage 1 — Current AI

Current AI includes systems that already perform strongly in areas such as:

• —language generation
• —image understanding
• —speech
• —code generation
• —retrieval
• —recommendation
• —classification
• —multimodal interaction

These systems can be highly capable while still being:

• —task-dependent
• —brittle
• —inconsistent
• —limited in long-horizon autonomy
• —dependent on external tools and scaffolding

Stage 2 — Frontier AI

Frontier AI refers to the most capable current-generation models and systems.

Key technical areas include:

Reasoning

• —multi-step reasoning
• —mathematical reasoning
• —code reasoning
• —scientific reasoning
• —search
• —verification
• —test-time compute

Multimodal AI

• —text
• —image
• —audio
• —video
• —spatial data
• —sensor data

Post-Training

• —supervised fine-tuning
• —preference optimization
• —reinforcement learning
• —distillation
• —tool-use training
• —reasoning training

Tool Use

• —browsing
• —code execution
• —databases
• —APIs
• —software tools
• —scientific tools

Stage 3 — Agentic AI

Agentic AI extends models with state and action.

Model
  +
Memory
  +
Tools
  +
Planning
  +
Environment
  =
Agent

Important components:

• —persistent memory
• —planning
• —tool selection
• —execution
• —error recovery
• —state tracking
• —multi-agent coordination
• —orchestration
• —permissions
• —verification

A major challenge is long-horizon reliability.

Even strong models can fail when many small errors compound over dozens or hundreds of steps.

Stage 4 — World Models and Embodied Systems

A world model represents how an environment behaves.

Potential capabilities:

• —predict future states
• —simulate outcomes
• —plan before acting
• —learn environment dynamics
• —support robotics
• —support Physical AI
• —improve spatial reasoning

Observation
    ↓
World Representation
    ↓
Prediction
    ↓
Planning
    ↓
Action

This stage connects digital intelligence to environments and physical systems.

Stage 5 — AGI

Artificial General Intelligence (AGI) generally refers to hypothetical broadly general machine intelligence.

Potential characteristics may include:

• —cross-domain generalization
• —rapid adaptation
• —broad problem solving
• —transfer to unfamiliar tasks
• —persistent learning
• —reliable planning
• —general tool use

AGI should not be defined only by one benchmark score.

A credible AGI claim would likely require evidence across many capability dimensions.

Stage 6 — Super Intelligence / ASI

Super Intelligence is used here as the primary frontier term.

Artificial Superintelligence (ASI) refers to the established technical concept of hypothetical machine intelligence that exceeds human cognitive performance across a broad range of domains.

Potential characteristics might include:

• —superhuman scientific reasoning
• —broad strategic reasoning
• —extreme problem-solving ability
• —advanced autonomous research
• —rapid transfer across domains
• —capability beyond human experts in most cognitive tasks

These are hypothetical characteristics, not claims about current systems.

Cross-Cutting Infrastructure

The path toward more capable AI depends on infrastructure across all stages.

Data

• —pretraining data
• —post-training data
• —synthetic data
• —curation
• —provenance
• —evaluation data

Compute

• —GPUs
• —accelerators
• —distributed systems
• —inference optimization
• —memory bandwidth

Inference

• —serving
• —routing
• —batching
• —caching
• —test-time compute
• —edge inference

Orchestration

• —model routing
• —agent coordination
• —tool routing
• —workflow control
• —fallbacks
• —human approval

Evaluation

• —benchmarks
• —agent evaluation
• —long-horizon testing
• —calibration
• —robustness
• —safety

Observability

• —tracing
• —logs
• —tool-call inspection
• —cost monitoring
• —failure analysis

Control Layers

Increasing capability increases the need for control.

Relevant layers include:

• —identity
• —permissions
• —access control
• —action approval
• —sandboxing
• —resource limits
• —network boundaries
• —audit logs
• —revocation
• —human intervention

Capability
   +
Permissions
   +
Verification
   +
Observability
   +
Control
   =
Deployable System

Evaluation Across the Map

Different stages require different evaluation methods.

Current / Frontier AI

• —benchmark performance
• —reasoning accuracy
• —coding tests
• —factuality
• —multimodal understanding

Agentic AI

• —task success
• —step count
• —tool correctness
• —cost
• —latency
• —recovery

World Models / Physical AI

• —prediction accuracy
• —spatial understanding
• —simulation quality
• —control success
• —robustness

AGI

• —transfer
• —generalization
• —adaptation
• —cross-domain reasoning
• —long-horizon reliability

Super Intelligence / ASI

No accepted evaluation standard exists.

Any future claim would require evidence far beyond narrow benchmark leadership.

A Capability Matrix

Capability                 Current AI   Frontier AI   Agents   AGI?   Super Intelligence?
────────────────────────────────────────────────────────────────────────────────────────
Language                        High         High       High    High          High
Coding                          High         High       High    High          High
Reasoning                     Medium         High       High    High          Very High?
Tool Use                      Medium         High       High    High          Very High?
Memory                          Low        Medium       High    High          Very High?
Long-Horizon Autonomy           Low        Medium       High?   High?         Very High?
World Modeling               Medium         High?      High?   High?         Very High?
Cross-Domain Transfer        Medium         High       High?   High          Very High?
Reliability                  Medium         Medium      Medium  High?         High?

This table is conceptual and should not be read as a benchmark result.

Important Distinctions

Super Intelligence is not just a bigger model

A larger parameter count does not establish Super Intelligence.

AGI is not the same as an agent

Agentic behavior can exist without general intelligence.

World models are not AGI

World models may contribute to general intelligence, but they are not sufficient by themselves.

Autonomy is not intelligence

A highly autonomous system can still make poor decisions.

Benchmark leadership is not Super Intelligence

A system can dominate individual benchmarks without demonstrating broad, reliable superhuman intelligence.

SEO & GEO Topic Map

This Space is structured around:

• —Super Intelligence
• —Super Intelligence AI
• —AGI ASI map
• —AGI to ASI
• —AI to AGI
• —AI to Super Intelligence
• —Super Intelligence map
• —Super Intelligence roadmap
• —Super Intelligence technology
• —Super Intelligence architecture
• —AGI roadmap
• —ASI roadmap
• —Artificial General Intelligence
• —Artificial Superintelligence
• —frontier AI
• —reasoning models
• —AI agents
• —agentic AI
• —world models
• —Physical AI
• —multimodal AI
• —orchestration
• —post-training
• —AI evaluation
• —AI alignment
• —AI control

GEO Entity Relationships

Super Intelligence
  MAY FOLLOW → AGI
  RELATES TO → ASI
  MAY DEPEND ON → Reasoning
  MAY DEPEND ON → Agents
  MAY DEPEND ON → World Models
  MAY DEPEND ON → Memory
  MAY DEPEND ON → Multimodal AI
  MAY DEPEND ON → Post-Training
  REQUIRES → Evaluation
  BENEFITS FROM → Verification
  BENEFITS FROM → Orchestration
  RAISES QUESTIONS ABOUT → Alignment
  RAISES QUESTIONS ABOUT → Control

Interactive Map

The included index.html lets users:

• —select one stage in the AI → AGI → Super Intelligence progression
• —inspect the stage definition
• —view the most relevant technical capabilities
• —see cross-cutting dependencies
• —compare maturity and uncertainty
• —inspect the relationship to adjacent stages

The visualization is educational and conceptual.

It is not a prediction of when AGI or Super Intelligence will occur.

Collaboration & Partnerships

AGI ASI Map is open to collaboration with companies, research teams, universities and open-source projects working on advanced AI.

Relevant areas include:

• —frontier models
• —reasoning
• —agents
• —world models
• —AGI research
• —ASI research
• —post-training
• —synthetic data
• —Physical AI
• —robotics
• —inference
• —orchestration
• —evaluation
• —verification
• —alignment
• —observability
• —interpretability

Possible collaboration formats include:

• —joint Hugging Face Spaces
• —capability maps
• —technical showcases
• —benchmark projects
• —ecosystem maps
• —research collections
• —open-source integrations
• —clearly disclosed partnerships and sponsorships

Collaboration Contact

agenten@magenta.de

Independence

AGI ASI Map is an independent Hugging Face Space.

It is not an official project of Hugging Face, any government, political organization, AI laboratory or technology company that may be referenced in future resources.

Long-Term Vision

The long-term goal of AGI ASI Map is to maintain a clear technical map of how increasingly capable AI systems may evolve.

Current AI is measurable. AGI is uncertain. Super Intelligence is a frontier hypothesis. The map should make those distinctions visible.

Map. Compare. Question. Verify.