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.
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.
Have you ever noticed nobody wants to just say yes? Or no? Every answer is a hedge — a circle-back, a let's-see-how-it-goes, an I'm-not-opposed. Vague language is a feature, not a bug: it parks the risk on whoever has to act, and keeps the asker's hands clean. Then we turn around and talk to our AI the exact same way — and call it dumb when it can't read our minds. The model isn't hallucinating. You're mumbling.