Most cosmologists agree that our universe had a beginning. But the finer details about the Big Bang remain a mystery. A history of everything would explain all, or so theoretical physicists hoped. In his final years, Stephen Hawking working with Thomas Hertog proposed a striking idea: The laws of physics were not precisely determined before the Big Bang; they evolved as the universe evolved.

In this episode of The Joy of Why, Hertog speaks with co-host Janna Levin about his work and partnership with Hawking. Hertog, now at KU Leuven in Belgium, explains why they rejected the popular multiverse theory and instead explored the idea that the universe’s properties are a result of cosmological natural selection. According to Hertog and Hawking, these properties must be viewed through the lens of human observers, who are also the consequence of natural selection.

So, how could the universe have created the conditions needed for life to emerge? Listen to the episode below to find out.

Listen on Apple Podcasts, Spotify, TuneIn or your favorite podcasting app, or you can stream it from Quanta.

Climate models have changed the way we view the world. While effective, these models are imperfect, and scientists are constantly looking at ways to improve their accuracy and predictability.

MIT professor Elfatih Eltahir has spent decades developing complex models to understand how climate change affects vulnerable regions like the Nile Basin and Singapore. In this episode of The Joy of Why, Eltahir tells co-host Steven Strogatz how growing up near the Nile in Sudan helped him realize that climate change doesn’t occur in isolation. To better understand climate-related impacts and to create more effective adaptation strategies, Eltahir says we need regional models that incorporate contextual data like disease spread and population growth. Eltahir also discusses his “Equation of the Future of Africa,” and he introduces the concept of “outdoor days,” which he hopes can improve public perception about climate change.

Listen on Apple Podcasts, Spotify, TuneIn or your favorite podcasting app, or you can stream it from Quanta.

Born in the 18th century when Leonhard Euler solved the puzzle of the seven bridges of Königsberg, graph theory has become a foundational tool in mathematics. It studies relationships through nodes (vertices) and the links (edges) that connect them, transforming the complexity of systems — from friendship networks to airline routes — into elegant abstractions that reveal underlying structure and interaction.

Maria Chudnovsky from Princeton University is a leading mathematician in the field. In this episode of The Joy of Why, Chudnovsky talks with co-host Janna Levin about how she got into graph theory, solved the decades-old perfect graph problem, and used it to plan her wedding seating chart. Chudnovsky also reflects on her appearance in commercials as a “superstar mathematician,” and how her background primed her for a discipline that transcends language, culture and time.

What links a Möbius strip, brain folds and termite mounds? The answer is Harvard University’s L. Mahadevan, whose career has been devoted to using mathematics and physics to explore the form and function of common phenomena.

Mahadevan, or Maha to his friends and colleagues, has long been fascinated by questions one wouldn’t normally ask — from the equilibrium shape of inert objects like a Möbius strip, to the complex factors that drive biological systems like morphogenesis or social insect colonies.

In this episode of The Joy of Why, Mahadevan tells co-host Steven Strogatz what inspires him to tackle these questions, and how gels, gypsum and LED lights can help uncover form and function in biological systems. He also offers some provocative thoughts about how noisy random processes might underlie our intuitions about geometry.

For decades, string theory has been hailed as the leading candidate for the theory of everything in our universe. Yet despite its mathematical elegance, the theory still lacks empirical evidence.

One of its most intriguing, yet vexing, implications is that if all matter and forces are composed of vibrations of tiny strands of energy, then this allows for a vast landscape of possible universes with different physical properties, varieties of particles and complex spacetimes. How, then, can we possibly pinpoint our own universe within a field of almost infinite possibilities?

Since 2005, Cumrun Vafa(opens a new tab) at MIT has been working to weed out this crowded landscape by identifying which hypothetical universes lie in a ‘swampland’ with properties inconsistent with the world we observe. In this episode of The Joy of Why, Vafa talks to co-host Janna Levin about the current state of string theory, why there are no more than 11 dimensions, how his swampland concept got an unexpected lift from the BICEP array, and how close we may be to testable predictions.

Geometry is one of the oldest disciplines in human history, yet the worlds it can describe extend far beyond its original use. What began thousands of years ago as a way to measure land and build pyramids was given rigor by Euclid in ancient Greece, became applied to curves and surfaces in the 19th century, and eventually helped Einstein understand the universe.

Yang-Hui He sees geometry as a unifying language for modern physics, a mutual exchange in which each discipline can influence and shape the other. In the latest episode of The Joy of Why, He tells co-host Steven Strogatz how geometry evolved from its practical roots in ancient civilizations to its influence in the theory of general relativity and string theory — and speculates how AI could further revolutionize the field. They also discuss the tension between formal, rigorous mathematics and intuition-driven insight, and why there are two types of mathematicians — “birds” who have a broad overview of ideas from above, and “hedgehogs” who dig deep on one particular idea.

Large language models (LLMs) are becoming increasingly more impressive at creating human-like text and answering questions, but whether they can understand the meaning of the words they generate is a hotly debated issue. A big challenge is that LLMs are black boxes; they can make predictions and decisions on the order of words, but they cannot communicate the reasons for doing so.

Ellie Pavlick at Brown University is building models that could help understand how LLMs process language compared with humans. In this episode of The Joy of Why, Pavlick discusses what we know and don’t know about LLM language processing, how their processes differ from humans, and how understanding LLMs better could also help us better appreciate our own capacity for knowledge and creativity.

Quantum gravity is one of the biggest unresolved and challenging problems in physics, as it seeks to reconcile quantum mechanics, which governs the microscopic world, and general relativity, which describes the macroscopic world of gravity and space-time.

Efforts to understand quantum gravity have been focused almost entirely at the theoretical level, but Monika Schleier-Smith at Stanford University has been exploring a novel experimental approach — trying to create quantum gravity from scratch. Using laser-cooled clouds of atoms, she is testing the idea that gravity might be an emergent phenomenon arising from quantum entanglement.

In this episode of the Joy of Why podcast, Schleier-Smith discusses the thinking behind what she admits is a high-risk, high-reward approach, and how her experiments could provide important insights about entanglement and quantum mechanical systems even if the end goal of simulating quantum gravity is never achieved.