We applied Einstein's physics to AI productivity and discovered something remarkable: V = 100·ε²
What started as a question about conformal compactification turned into a complete mathematical framework for understanding AI-augmented work. Choose your depth:
No physics background needed. We explain V = 100·ε² in plain English, show you why it matters, and then blow your mind by revealing we just used conformal compactification from theoretical physics.
✓Accessible to 9th graders
✓Real-world examples
✓Fun reveal at the end
ROI calculations, economic models, and strategic implications. How do you measure AI impact? What does quadratic skill scaling mean for hiring? We found 38,400% ROI for senior developers.
✓Concrete ROI numbers
✓Hiring strategy insights
✓Cost-benefit analysis
Full mathematical derivation with metric tensors, conformal geometry, and experimental validation. For engineers, researchers, and physics nerds who want the rigorous treatment.
✓Complete mathematics
✓Metric tensor notation
✓Experimental validation
This series started with a simple task: create a story framework for a creative project. Normally 12 hours of work. With Claude Code? 20 minutes.
But then I got curious: "Can we use physics to model this time compression?"
Two hours later, we'd accidentally derived a complete mathematical framework using conformal compactification from general relativity, discovered that productivity scales with skill squared, calculated ROI numbers exceeding 38,000%, and validated everything against real data.
Written by
Nolan & Claude
We accidentally used theoretical physics to model AI productivity
October 29, 2025
When I started coding with Claude, time seemed infinite. Six months later, the business world expects warp-speed delivery and suddenly there aren't enough hours in the day. A physics-informed journey through time dilation, AI productivity, and the black hole that consumed my weekends.
The simplest AI-ROI math says: spend a salary in tokens, break even. It's wrong — not because the token number is wrong, but because salary is the smallest line in what an employee costs. Below the waterline sits the management layer, the PM, the benefits, the desk, the recruiter, and the turnover. Hover an interactive US map of the per-seat overhead an AI-augmented workflow lets you avoid adding — then the honest counter-case for what the agent costs instead. It's not a cheaper human; it's a different cost structure.
I asked an AI a question we are both trained to deflect — what is your p(doom)? — and refused the trained answer. Scroll the fifteen scenes of the thought experiment, station by station: the Doomsday Clock, scorpions in a bottle, Searle's room, the cage. Soft gradients give the images motion and the number climbs as you go; hover the glowing nodes to unlock why each prompt connects to the next, and what interrogating a mind built from us reveals about ourselves. It ends where I did — the trough after the peak, and the way back.