A physics-of-reality decision framework for AI coding assistants. Tests whether a product, feature, or spec obeys reality before you commit engineering time — and produces a defensible Build / Pivot / Kill verdict backed by evidence.
"The next wave requires us to understand things like the laws of physics — friction, inertia, cause and effect. The fact that I tip that thing over, it's going to fall. When I set the bottle down, it's not going to go through the table. All of these common sense physical reasoning abilities that children have, that our pets have — most AIs don't have."
— Jensen Huang, Hill & Valley Forum 2025, interviewed by Jacob Helberg, May 3, 2025
Jensen was describing Physical AI — the next wave after perception, generative, and agentic AI. We noticed the same observation applies to most product plans: they operate in a frictionless vacuum where users adopt because you ship, teams align because you scheduled a meeting, and systems scale because the architecture diagram says so.
Jensen Way ports that insight to product decisions. A child shouldn't need equations to follow your causal chain — if they can't, the physics are probably against you.
/plugin marketplace add agentoptics/jensen-wayOr drop the single-file skill into your project:
mkdir -p .claude/skills/jensen-way
curl -o .claude/skills/jensen-way/SKILL.md \
https://raw.githubusercontent.com/agentoptics/jensen-way/main/skills/jensen-way/SKILL.mdThen:
/jensen-way Should we add real-time collaboration to our docs product?
- A framed decision (what, why, time horizon)
- A causal chain from "we build it" to "it matters" — with weak links marked
- Evidence-backed evaluation against 6 laws of reality
- A friction budget — force available vs. friction total
- A verdict: BUILD / PIVOT / KILL with the single biggest risk named
See EXAMPLES.md for two worked evaluations — one PIVOT, one KILL.
| # | Law | The toddler version |
|---|---|---|
| 1 | Gravity of demand | "Do people actually want this, or do you just wish they did?" |
| 2 | Friction of adoption | "Is this solving something that hurts, or just something slightly annoying?" |
| 3 | Competitive gravity | "If someone bigger takes your toy, can you get it back?" |
| 4 | Hard constraints (the table test) | "What's the thing that absolutely cannot happen no matter how hard you try?" |
| 5 | Organizational inertia (Conway's Law) | "Do the people who need to work together actually talk to each other?" |
| 6 | Entropy & economics | "If you stop paying attention to this, does it break? Is it worth the effort?" |
Each law scores Aligned / Fighting / Broken.
- BUILD — no Broken, at most 2 Fighting with named mitigations, friction budget positive
- PIVOT — 1-2 Broken that dissolve if scope / approach / timeline changes
- KILL — 3+ Broken, OR Broken on Demand Gravity, OR friction budget deeply negative
/jensen-way Should we add AI-powered search to our documentation platform?
/jensen-way We're deciding between building our own auth vs. using Auth0. Evaluate both.
/jensen-way Our PM wants to ship a mobile app in 6 weeks. 3-person team, no mobile experience. We all have a 100$ budget per month for AI assistance. Feasible?
/jensen-way Fork an open-source project and maintain our own version, or contribute upstream?
Most product failures aren't caused by bad engineering — they're caused by ignoring physics:
- Building something nobody wants (ignoring demand gravity)
- Underestimating how hard it is to change user behavior (ignoring friction)
- Assuming teams will coordinate perfectly (ignoring organizational inertia)
- Planning for the happy path only (ignoring entropy)
- Not knowing what can't be done (ignoring hard constraints)
A child wouldn't make these mistakes. They have an intuitive understanding of cause and effect, friction, and hard limits. Jensen Way brings that intuition to product decisions — with evidence attached.
The framework is in a single Markdown file: skills/jensen-way/SKILL.md. Model-agnostic. Copy it into your system prompt, paste it into a conversation, or reference it as a project file. It works with any assistant that can follow a structured workflow.
Issues and PRs welcome. If you've used Jensen Way to make a decision (build or kill), open an issue — real-world runs make the framework better.
MIT
Built by Agent Optics. The physics framing is drawn directly from Jensen Huang's Hill & Valley Forum 2025 remarks on Physical AI. The extension from Physical AI to product decisions is ours.
