The Physics of Getting Sh*t Done
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:
The Simple Version
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
The Business Case
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
The Complete Framework
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
How This Happened
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
January 26, 2025