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.
You asked a simple question. What came back looks like a compliance filing, full of codes you can't tell apart — and you can't tell whether AI-03 is something you should already know, something the model looked up, or something it invented thirty seconds ago to keep a list tidy. So you skim. Flip the toggle to see the same meeting summary written for a machine and written for you, watch which codes survive, and find out why the document doesn't become wrong so much as poisoned. Then the honest mechanism behind the habit, two prompts you can copy the next time a response is unreadable, and the 2017 AI panic that was false in every particular and accidentally right about the direction.
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