The hidden strategy map inside every robot
All about robotics stack + BOM - probably one of the most important metrics to track in robotics
Humanoid-robot economics look complex. Until you zoom into the Bill of Materials (BOM).
BOM isn’t just a cost sheet. It’s a "strategy map".
It reveals where cost hides leverage, where complexity hides moat, and where hardware and software meet or clash.
When you know how to read it, the BOM tells you:
Which subsystems drive cost vs. differentiation
Where integration complexity creates or erodes margin
What IP is worth owning (mechanical, model, or data)
How geography impacts iteration speed
Why some companies scale like SaaS and others get stuck in hardware
Let’s walk through the robot stack with the BOM lens on👇🏻
Actuation drives cost and moat
At the base of every robot is actuation. That’s where nearly half the BOM cost sits and where most of the defensibility lives today.
Motors, gearboxes, torque systems:
have complex supply chains
require high levels of integration
are extremely competitive
But they’re also strategic strongholds.
Vendors like LeaderDrive, Harmonic Drive, and Beite used to dominate here with hard-to-copy IP built on:
Tight tolerances
Decades of iteration
End-to-end vertical integration
If you control actuator design, you control a robot’s core physics.
These systems though are now getting replaced AI-centric ones, which favors on dynamic behavior and cost, being less IP-centric and hedge more on strong manufacturing and good overall product design.
🧠 Takeaway: high cost + high IP = hardware moat
Software eats margin
But moving up the stack, the leverage flips.
Compute, sensing, and model layers might each take up ~10% of the BOM, but they disproportionately define:
Adaptability
Autonomy
Cost compression over time
Chips are cheap. Torque isn’t. But paired with the right stack, compute compounds fast.
That’s why companies like NVIDIA don’t stop at silicon. They own:
Isaac → simulation tooling
GR00T → foundational control models
Fleet learning loops → systems that get smarter with usage
This is the GR00T playbook: Own the model, the data, and the learning loop and suddenly, your 10% of the BOM controls 80% of the product’s differentiation.
This is where margin scales like code:
Fleet-level learning loops
API-style unit economics
Lower per-unit cost over time
Usage → data → model → autonomy → usage → margin
🧠 Takeaway: low cost + model/data IP = software moat
Integration creates margin or kills it
Hardware margins are brutal - unless you own precision complexity.
The question is: are you assembling parts? Or engineering behavior?
A company might carry high COGS if it:
Makes its own actuators
Tunes torque curves for specific behaviors
Designs electromechanical systems end-to-end
But that’s not just about defensibility, it’s a risk-for-margin tradeoff.
Owning this layer means taking on supply chain complexity, firmware tuning, and precision control. But if done right, it can knock 25–50% off costs which is a serious edge for hardware-heavy players.
🔍 Case study: Unitree’s $16K humanoid
Unitree didn’t win only on AI breakthroughs. They won by engineering the BOM:
Sourcing actuators locally (cheaper motors) + in house manufactured (no vendor margins)
Skipping multi-plane LiDAR (cutting sensor cost)
Avoiding radical architecture; just tight control over the stack
Co-locating supply chain; 90% of vendors within hours of Hangzhou
Here's a high level breakdown of the BOM for Unitree's Quadruped:
Western companies often can’t match this - not because of tech gaps, but because they lack supply chain density.
That slows iteration and bloats the BOM.
🧠 Takeaway: low BOM + tight integration = speed + margin
Cost ≠ Commodity
A low BOM doesn’t guarantee margin.
A high BOM doesn’t mean weak strategy.
It depends where exactly the cost sits and whether it’s backed by:
Model/data flywheels
Mechanical IP
Tight subsystem integration
In commoditized layers, cost savings often just pass through to the buyer.
But in leveraged layers, they unlock compounding advantage.
🧠 Takeaway: Cost only matters if it carries leverage
The invisible BOM matters more
What’s not in the BOM is often what defines long-term edge.
You won’t find fleet logs, ROS tuning configs, or simulation policies listed, but these layers determine:
Autonomy behavior
Adaptability in edge cases
Learning across robots
Margin expansion over time
This is the invisible layer:
data → model → behavior → more data
Whoever owns this loop doesn’t just reduce cost, they also bend the performance curve.
🧠 Takeaway: What’s not in the BOM might define long-term defensibility more than what is.
How to read the BOM like a strategy map
BOM tells you exactly where leverage lives:
Actuation → high cost, high moat, low margin
Compute → low cost, high leverage, high margin
Comms/frame → low cost, low moat, low margin
Sensors → medium cost, increasingly commoditized
So the BOM becomes a proxy for margin profile:
High BOM + high IP → hardware moat (gears, drives)
Low BOM + model/data ownership → software moat (GR00T stack)
High BOM + low IP → worst of both worlds (commodity assemblers)
As robotics systems will evolve, I feel BOM will offer a uniquely grounded lens to assess technical constraints, economic structure, and strategic leverage of the robotics stack.
