Robotics & Autonomous Systems

Intelligence
in the physical world

Nine years building AI software products has given me a systems perspective I believe is directly applicable to physical AI. This page is my public thinking about where I'm going next — and why.

Why Robotics

I'm transitioning intentionally into robotics and autonomous systems — not as a pivot away from AI product, but as its natural extension.

The product problems in physical AI are harder, the stakes are higher, and the impact is more tangible. Every principle I've developed in search, ranking, and LLM products — evaluation rigor, feedback loop design, uncertainty handling, human-in-the-loop systems — applies directly to autonomous systems product management.

I'm studying, building perspective, and actively seeking roles and collaborations at the frontier of physical AI product development.

Areas of Focus

Four domains I'm studying

Autonomous Driving

AD systems are the most complex product problem of our generation: real-time decision-making under uncertainty, multi-modal sensor fusion, and a safety envelope that tolerates zero ambiguity. The product discipline required to ship safe AV systems will define the next era of product management.

  • Sensor fusion architecture and perception stack design
  • Behavioral planning under uncertainty and edge cases
  • Safety-critical evaluation frameworks
  • Operational domain expansion strategy

Robotics Product Management

Physical AI product management combines the rigor of embedded systems with the user-centricity of consumer software. The unique challenge: your product breaks in the real world, in real time, with real consequences — and you can't roll back a hardware deployment.

  • Hardware-software co-design for productization
  • Teleoperation to autonomy transition strategy
  • Robot learning from human demonstration
  • Fleet management and operational scaling

AI Agents in Physical Space

The same agent design principles from software — goal decomposition, tool use, state tracking, recovery from failure — apply in robotics, but with latency constraints and physical consequences. This is where my LLM product background connects directly to physical AI.

  • Task planning and execution in unstructured environments
  • Failure recovery and graceful degradation design
  • Human-robot interaction and trust calibration
  • Sim-to-real transfer and evaluation methodology

Embodied Intelligence

Embodied AI represents a fundamental shift: intelligence that exists in and acts on the physical world. Understanding how context, affordances, and physical constraints shape intelligent behavior is the defining product challenge of physical AI development.

  • Foundation models for robotic manipulation
  • Affordance understanding and scene comprehension
  • Dexterous manipulation and fine motor control
  • Long-horizon task planning with world models

Transferable Expertise

From software AI to physical AI

Evaluation frameworks for LLMs

transfers to

Evaluation frameworks for robotic behaviors

Human-in-the-loop LLM systems

transfers to

Human-in-the-loop robot teleoperation

A/B experimentation at scale

transfers to

Sim-to-real transfer validation

Feedback loop design for ranking

transfers to

Reward signal design for RL

Cold start problems in search

transfers to

Limited data problems in robot learning

Agent orchestration patterns

transfers to

Task planning for autonomous systems

Active Reading

Foundation

Scaling Laws for Neural Language Models

Kaplan et al.

Robotics

RT-2: Vision-Language-Action Models

Google DeepMind

Agents

Do As I Can, Not As I Say

Google Research

Data

Open X-Embodiment

Google DeepMind

Product

Physical Intelligence — Pi0

Physical Intelligence

Research

Foundation Models for Decision Making

Yang et al.

Building something at the frontier of physical AI?

Let's talk