From a Million Orbiting Stars to the Human Sitting Next to You

Look at a globular cluster through a backyard telescope and it appears frozen—a quiet snow globe of a million stars packed into a sphere a few parsecs wide. Speed up the clock to an 800-million-year relaxation time (τ_rh), however, and that snow globe turns into a restless gravitational engine. Heavy black holes lose kinetic energy to lighter stars, sink to the core, and lock into tight binary dances.

In my doctoral work at UT Arlington, I ran and synthesized 3,240 MOCCA cluster simulations across 8,946 galaxies within 30 Mpc. That cosmic census revealed 17.88 million inspiralling binary black holes, a local merger rate of η ≃ 587 Gpc⁻³ yr⁻¹, and 17 LISA “standard sirens” pinpointed tightly enough (ΔV ≤ 1 Mpc³) to measure the expansion of the universe independently of starlight.

Sitting at a red light at an empty intersection one morning, I found myself doing the math: why do cities still run intersections on blind, fixed timers when approaching cars already carry GPS sensors? Even if only a fraction of vehicles share telemetry, pairing that sparse data with physical conservation laws (like Stephen Wolfram’s symbolic-neural hybrids) lets a system infer queue lengths and clear traffic dynamically.

The same structural puzzle came back in advertising. A serving path has to answer inside the latency budget the platform allows, and the trade-off is stated bluntly in Rethinking Digital Advertising: real-time ad platforms cannot tolerate the latency that AppLovin’s models can, and that headroom is exactly what buys larger models and better personalisation. So I wrote the system down instead of the slogan — a real-time ingestion pipeline and feature store feeding a labelling and profile service, which feeds a dynamic ad-serving path with in-session creative switching, backed by distilled user profiles so candidate generation stays cheap.

The rest of the argument matters more than the plumbing. If you optimise only for immediate clicks, you exhaust the audience: that is the Growth Trap, where consumer fatigue, regulatory risk and competitive erosion meet. A system that serves a decision in milliseconds still has to answer the slower question underneath it — whether the decision is worth making at all.

In late 2024 I stepped outside pure software loops to start Kin Domi in Palo Alto—spending afternoons in living rooms, community centers, and passenger seats with older adults. Watching seniors use Tesla’s Full Self-Driving (FSD) showed me something no benchmark captures: older drivers have the most to gain from autonomous mobility, yet cockpit alerts are built for thirty-year-old engineers. Sudden takeover chimes and tiny icons create panic at the exact moment calm reassurance is needed.

When I wrote Reclaiming Play as a Life Skill, several older readers gently told me they disliked the word “play” because it sounded childish. That conversation reshaped how I build: what people want at every age is dignified self-reliance, low-stakes curiosity, and a partner that respects their judgment—whether in Open Learning Lab, baby-AI, or iMessage AI Router.

Why do some teams compound curiosity into generational breakthroughs while others with ten times the capital stall? Whether comparing Boeing and SpaceX, studying eVTOL airworthiness across the US and China, or reflecting on our own responsibilities as builders, the bottleneck is rarely raw IQ. It is whether incentives reward first-principles honesty over short-term optics.

In my Substack essays and the civic notebook LibertyEarth, I use a simple mathematical picture for healthy collaboration: treat shared ethical values as the Greatest Common Divisor (GCD) that keeps baseline trust high, and treat complementary human + AI skills as the Least Common Multiple (LCM) that expands what we can build together—anchored day to day by the eighth revision of the work model I keep in my founder's log.

Shared values and complementary skills are the mechanism; what they are for is collective competence. The institutions I have worked in still organize people the way an organized farm organizes capital: interchangeable units, measured by output, replaceable when the season turns. That shape does not survive the move to organizing intelligence with empowered individuals. So I am trying to measure a resilient, capable team instead: collective competence — what the group can do that none of its members could do alone, held together by one mission, and sustained by leaving every person's difference intact. All three have to hold at once. Take the mission away and the team is a queue; take the individuality away and it is a crowd. The Avengers are the reference case — nobody interchangeable, everybody committed to the same thing.