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The four stages

The four stages of AI: a sequence that compounds.

Foundation, then Generative, then Agentic, then Physical. Each stage produces the fuel for the next.

Updated

Know · Make · Do · Move

Each stage feeds the next.

The race is won in the handoffs.

  1. 01 · The floor

    Foundation

    Data, classical ML, analytics and the model layer. The floor decides the ceiling.

    Feeds the next: Clean, connected data and a model census the next stage can trust.

    Life cycle
    MDLC · 12 phases
    Source
    The AI Flywheel, Ch 4
    Model Engineer →

  2. 02 · Table stakes

    Generative

    AI that produces content directly. Fast, useful and no longer a lasting edge on its own.

    Feeds the next: Early returns, governed pipelines and the trust to let AI act.

    Life cycle
    GDLC · 12 phases
    Source
    The AI Flywheel, Ch 5
    Generative AI Practitioner →

  3. 03 · The value frontier

    Agentic

    Agents that plan and act through tools. Harder, gated by governance, and so defensible.

    Feeds the next: Telemetry and decision traces that seed the next stage.

    Life cycle
    ADLC · 10 phases · G1–G6
    Source
    The AI Flywheel, Ch 6
    Agentic AI Engineer →

  4. 04 · The industrial frontier

    Physical

    Robotics, vehicles and embodied systems. The deepest moat: you cannot rent a body.

    Feeds the next: Real-world data that flows back to the floor.

    Life cycle
    RDLC · 12 phases
    Source
    The AI Flywheel, Ch 7
    Physical AI and Robotics Engineer →

Quick answers

The stages, answered.

What are the four stages of AI?

Foundation AI (data, classical ML, analytics and models), Generative AI (AI that produces content), Agentic AI (agents that plan and carry out work) and Physical AI (robotics and embodied systems). They are a sequence that compounds, not a menu: each stage produces the fuel for the next.

Which stage should a company start with?

Foundation, because the floor sets the ceiling. Unready data is the number-one cause of AI failure, so run the Data-Readiness Checklist on the slice your first use case needs. Most teams then prove one generative or agentic turn before they scale.

Next

The DRIVE method: five phases from idea to value.

Diagnose, Resource, Implement, Validate, Expand. A cycle, not a line. Every life cycle runs inside it.

DRIVE →Take the Flywheel Check