From Physicist to AI Founder
What searching for dark matter taught me about building reliable AI systems for the enterprise.
People often ask me how I transitioned from theoretical particle physics to founding an enterprise AI company. On the surface, the two fields seem entirely disconnected. One is concerned with the fundamental nature of the universe, the other with automating regulatory reporting for insurance companies.
But the truth is, the intellectual rigorousness required to search for dark matter is exactly what is missing from most enterprise AI deployments today.
When I was completing my PhD at Sorbonne Université, my research focused on developing active learning algorithms to explore the parameter spaces of Beyond Standard Model (BSM) physics. We were looking for signals of dark matter—signals that are incredibly faint, buried in massive amounts of noise, and highly susceptible to systemic errors.
In that environment, you cannot rely on “vibes.” You cannot deploy a model because it looks accurate on a test set. You have to understand the underlying causal mechanisms. You have to quantify your uncertainty rigorously. You have to build systems that are robust against unexpected perturbations.
When I left academia and entered the world of enterprise AI, I was struck by how often these principles were ignored.
The Physics of Enterprise AI
In the enterprise, I saw teams deploying large language models with the same casual optimism one might use to launch a new website feature. They were treating probabilistic, non-deterministic systems as if they were standard software applications.
They were building demos that worked flawlessly in controlled environments, and then acting surprised when those same systems hallucinated or failed catastrophically in production.
They were missing the physics of the problem.
Building a reliable AI system is not just a software engineering challenge; it is a complex systems engineering challenge. It requires understanding the interactions between the data layer, the model layer, the orchestration layer, and the human operators. It requires anticipating failure modes and designing graceful degradation paths.
Most importantly, it requires a fundamental shift in how we evaluate success.
The Rigor of Evaluation
In particle physics, a discovery is not claimed until the signal reaches a statistical significance of 5 sigma—meaning there is less than a 1 in 3.5 million chance that the result is a statistical fluke.
While enterprise AI does not require 5-sigma certainty, it does require a level of rigor that goes far beyond the standard “accuracy” metrics used today.
When we build systems at Wangari, we apply the same rigorous evaluation frameworks I learned in physics. We do not just ask if the model is accurate; we ask if it is reliable. We measure its variance. We stress-test it against edge cases. We build automated test suites that continuously monitor for silent degradation.
We treat the AI system not as a black box, but as a complex instrument that must be calibrated, monitored, and governed.
The Causal Foundation
Perhaps the most profound lesson I brought from physics to AI is the importance of causality.
In physics, correlation is interesting, but causality is everything. You cannot understand the universe by simply observing patterns; you have to understand the underlying forces that drive those patterns.
The same is true in the enterprise. A correlative model might predict that a customer is likely to churn, but it cannot tell you why, or what intervention would prevent it. A causal model, on the other hand, provides a transparent chain of reasoning. It allows you to simulate interventions and understand the true drivers of behavior.
This is why Wangari is focused on building agentic and causal AI infrastructure. We believe that for AI to be truly transformative in the enterprise, it must move beyond pattern matching and embrace causal reasoning.
The Transition to AI-Native
The transition from academia to the startup world also mirrors the broader transition happening across the economy. As Jakob Nielsen points out, the only way to have 5 years of experience with AI by 2030 is to have started in 2025. The transition period we are in right now is the critical window for individuals and organizations to adapt.
This is particularly true for founders. The playbook for building a startup has fundamentally changed. As Henry’s Best Hits outlines, building a lean, AI-native startup in 2025 requires a completely different approach to team structure, product development, and go-to-market strategy. It requires leveraging AI not just as a feature, but as the core engine of the business.
Venture capitalists are also adapting to this new reality. They are no longer funding thin wrappers around foundation models. As the AI-Native Founder newsletter notes, [investors are increasingly looking for deep technical differentiation](https://ainativefounder.substack.com/p/ai-didnt-just-change-what-we-build) and teams with the domain expertise required to solve hard, unsexy problems [3].
The Importance of First Principles
In physics, when you encounter a problem you don’t understand, you return to first principles. You strip away the complexity and focus on the fundamental laws governing the system.
The enterprise AI industry needs a return to first principles. We need to stop chasing the latest benchmark scores and start focusing on the fundamental requirements of production systems: reliability, auditability, and causal understanding.
This is the philosophy that drives our work at Wangari. We are not interested in building the flashiest demo. We are interested in building the infrastructure that allows organizations to deploy AI safely and effectively in the real world.
The Journey Continues
The journey from Sorbonne to Wangari has been unconventional, but it has given me a unique perspective on the challenges and opportunities of enterprise AI.
The era of the impressive AI demo is over. The era of the reliable, auditable, causal AI system has begun. And the principles required to build those systems are the same principles that guide our understanding of the universe.
Meanwhile, at Wangari
My book, Soccer Analytics with Machine Learning, is now officially available from also in print, O’Reilly Media. It has been a joy to see readers engaging with machine learning concepts through the lens of the beautiful game. Thank you for all the readers who have already started reading it, exploring the GitHub repo, and engaging with the content.
One of my co-authors, Guanyu Hu, and myself gave an O’Reilly live session yesterday to explain some of the core concepts of the book in a fun and interactive way. It was a fantastic experience — we were quite moved by the level of enthusiasm in the audience.
Reads of the Week
Use the AI Transition Period to Transition Your Career by Jakob Nielsen: On the flip side of doom and gloom, compelling argument for why the current AI transition period is the perfect time to pivot your career and embrace new technologies. Nielsen emphasizes that early adoption is the only way to build the experiential knowledge required to lead in the AI era.
How to start a Lean, AI-Native Startup in 2025 by Henry Shi and Deedy: Still fresh enough a read, this is a practical playbook for founders looking to build AI-native companies from the ground up. The author details how small, highly technical teams can leverage AI to achieve the output of much larger organizations, fundamentally changing the economics of early-stage startups.
AI Didn’t Just Change What We Build by Mohamed F. Ahmed: An analysis of what venture capitalists are looking for in AI startups today, emphasizing team expertise and technical differentiation. The piece highlights the shift away from “wrapper” applications toward deep tech solutions that solve complex, domain-specific problems.