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.
In 1933 Peter Wessel Zapffe compared human consciousness to the Irish elk's antlers: a trait grown too big for the animal carrying it. For eighteen months, tech professionals have lived a version of that idea. AI showed us something about our work we can't stop seeing. Developers on TikTok miss the three-hour flow state that kept the antlers trimmed; mathematicians watched AI settle Erdős problems, and Steven Strogatz told the Times about a student who asked what the point was. But there is a path through: Anthropic went from 2,500 merged changes a day to 30,000, and it took people to decide how. Choose your profession — developer, mathematician, accountant, lawyer, physician, IT and security, project manager or manufacturing engineer — for your antlers and three paths through.
When the British Army swapped cloth caps for steel helmets, the story goes, the hospitals saw more head wounds — because men who would have died were now wounded. Your AI rollout will do the same thing to your dashboard. Four ways a count rises when things get better, then choose your floor: two software examples (GitLab issues climbing because more people use what you built; incidents climbing because you ship thirty small changes instead of one big one) and five from the plant floor, three of them from central Wisconsin — a paper mill, a cheese plant, and near-miss reporting in Spanish and Hmong. Then the cases where the alarm is real, and the one-page memo to write before go-live.
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