What Machines Want to Become

Note: The insights from the experts in this chapter have been synthesized and presented in my direct voice, reflecting their core contributions to the principles of three-body coordination.

Three-Body Engineering Patterns

In 2019, Autodesk’s generative AI designed a partition for an Airbus A320 that was 45% lighter than the human-designed version while being stronger and using less material.

The design looked alien—organic curves and lattice structures that no human engineer would imagine. It violated every aesthetic principle of traditional aerospace design.

But it worked perfectly because it was optimized for something humans can’t do: coordinating structural requirements, manufacturing constraints, and material properties to create impossible solutions.

The Generative Design Three-Body System:

Engineer Goals ←→ AI Generation ←→ Manufacturing Reality

• Human: “Make it light, strong, and manufacturable”

• AI: Explores millions of design variations

• Manufacturing: Physical constraints and production methods

• Result: Solutions that coordinate all three bodies to create designs humans couldn’t imagine

This isn’t AI replacing engineers. It’s three-body coordination creating engineering capabilities that neither humans nor AI possess alone.

Why Traditional Engineering Hits Limits:

Human engineering (two-body):

• Engineer expertise ←→ Design requirements

• Optimize based on experience and intuition

• Limited to designs humans can imagine

• Constrained by cognitive capacity

AI engineering (also two-body):

• AI optimization ←→ Specified objectives

• Explore vast design spaces

• Limited to objectives that can be formalized

• Constrained by lack of contextual judgment

Ternary engineering (three-body):

• Human intent ←→ AI exploration ←→ Physical constraints

• Coordinate human judgment with AI capability with manufacturing reality

• Create solutions neither could generate alone

• Emergence from coordination

The Three-Body Engineering Principle:

Human Design Intent (What Layer)

• Goals, requirements, priorities

• Contextual judgment and trade-offs

• The problem definition

• What we’re trying to achieve

AI Exploration (How Layer)

• Generative algorithms exploring possibilities

• Optimization across design space

• The solution generation

• How we might achieve it

Physical Constraints (Reality Layer)

• Manufacturing methods, material properties, physics

• What’s actually possible to build

• The feasibility filter

• What actually works in reality

Traditional engineering optimizes what we can imagine within constraints. Ternary engineering coordinates human intent with AI exploration with physical reality to discover what we couldn’t imagine.

This is engineering reality itself—not just designing products, but coordinating possibility spaces to create new realities.

7.1 AI-Assisted Design (Autodesk Generative)

Engineer Goals ←→ AI Generation ←→ Manufacturing Reality

Case Study: General Motors Seat Bracket

GM needed a seat bracket for electric vehicles. Traditional approach: engineer designs based on experience, iterates through testing, arrives at solution.

Generative design approach: coordinate three bodies to explore possibilities.

The Coordination Process:

Step 1: Human Intent (Engineer Layer)

• Requirements: Support 300 pounds, withstand crash forces, minimize weight

• Constraints: Must fit in existing space, connect to specific mounting points

• Priorities: Weight reduction most important (affects EV range)

• Manufacturing: Must be producible with available methods

Step 2: AI Exploration (Generation Layer)

• AI generates thousands of design variations

• Each variation meets basic requirements

• Explores design space no human would consider

• Optimizes for multiple objectives simultaneously

Step 3: Reality Filter (Manufacturing Layer)

• Which designs can actually be manufactured?

• What materials work with each design?

• What production methods are required?

• What costs are associated with each option?

Step 4: Coordination Iteration

• Engineer reviews AI-generated options

• Selects promising candidates based on judgment

• AI refines selected directions

• Manufacturing evaluates feasibility

• Cycle repeats until convergence

The Result:

A bracket that is 40% lighter, 20% stronger, and reduces 8 parts to 1—impossible with traditional engineering because:

• No human would imagine that shape

• No optimization algorithm would know which shape to try

• No manufacturing analysis would suggest it without seeing it

The solution emerged from coordinating human judgment with AI exploration with manufacturing reality.

Three-Body Design Emergence

What Makes Ternary Engineering Revolutionary:

Traditional CAD: Human draws what they imagine

• Limited by human visualization

• Incremental improvements

• Constrained by convention

Topology Optimization: AI optimizes given structure

• Limited by initial structure

• Local optimization

• Constrained by starting point

Generative Design: Coordinate human intent, AI exploration, manufacturing reality

• Unconstrained by human imagination

• Global exploration

• Bounded only by physics and feasibility

The Coordination Architecture:

1. Define Coordination Space

• What are we trying to achieve? (human)

• What’s possible to explore? (AI)

• What’s feasible to build? (manufacturing)

2. Enable Bidirectional Learning