How an astrophysicist leverages Codex to simulate black holes
Astrophysicist Chi-kwan Chan uses OpenAI's Codex to accelerate black hole simulations, leveraging AI to write code, debug, and handle routine engineering while focusing on the physics. His work tests Einstein's general relativity by comparing simulated black hole images with telescope observations, showcasing how AI tools are reshaping scientific research workflows.
If you've ever wondered how scientists study objects that swallow light itself, this story is for you. Astrophysicist Chi-kwan Chan has found an unexpected research partner in Codex, the AI coding tool from OpenAI, using it to help build simulations of black holes. In this article, you'll learn what his work involves, why an AI assistant makes sense for this kind of science, and what it tells us about the future of computational research.
The Challenge of Simulating a Black Hole
Black holes are among the strangest objects in the universe. Around them, gravity is so intense that space and time behave in ways that defy everyday intuition. To understand what happens near a black hole, scientists can't just visit one — they have to recreate the physics on a computer.
That means writing software that solves Einstein's equations of general relativity, models hot swirling matter, and tracks how light bends on its way to a virtual observer. These simulations are computationally enormous and mathematically demanding. A single mistake in the code can produce results that look plausible but are physically wrong, which is why researchers like Chan spend enormous care getting every detail right.
Chan, who works at the University of Arizona and has been involved in the team behind the famous first images of black holes, knows this territory well. His work connects directly to efforts to test whether observations of black holes match what Einstein's theory predicts.
Where Codex Fits In
Codex is designed to help developers write and refine code. Instead of manually typing out every routine, a researcher can describe what they need and let the tool draft implementations, suggest fixes, or explain existing code.
For someone like Chan, this changes the workflow in practical ways. Scientific software often involves long simulations with many moving parts: setting up initial conditions, handling numerical methods, and processing the output. An AI assistant can speed up the routine parts — writing utility functions, debugging errors, or translating ideas into working code — freeing the scientist to focus on the physics.
The value isn't that Codex understands black holes. It's that it accelerates the engineering around the science. When a researcher can iterate faster, they can test more ideas and catch problems sooner.
Testing Einstein's Theory One Simulation at a Time
Why go to all this trouble? Because black holes offer one of the most demanding tests of general relativity ever conceived. Einstein published his theory in 1915, long before anyone had observed a black hole directly. If his description of gravity is correct, black holes should cast a very specific "shadow" and bend light in predictable ways.
By building accurate simulations, researchers can compare what theory predicts with what telescopes actually record. If the simulated image of a black hole matches the observed one, that's evidence for Einstein. If it doesn't, something interesting is going on — possibly new physics.
Simulations also let scientists explore scenarios no telescope can capture, such as what happens when matter spirals inward at nearly the speed of light. Each improvement in simulation software sharpens the picture.
A Sign of How Science Is Changing
Chan's use of Codex reflects a broader shift. Research has always depended on tools — telescopes, particle accelerators, supercomputers. AI coding assistants are simply the newest addition to that toolbox, and they're arriving in fields far beyond software companies.
For students and early-career scientists, this suggests an important skill: knowing how to work alongside AI tools productively, while still verifying that the science is sound. The physicist remains responsible for the physics; the AI helps with the plumbing.
It's worth noting that AI-generated code should always be checked carefully in scientific contexts, where subtle numerical errors can have real consequences. Chan's expertise is what turns an AI assistant into a genuinely useful research instrument.
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Tags: Codex, black holes, astrophysics, AI in science, general relativity
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