The Greatest Mathematician of Our Time

You don't usually see crowds gathering for a math professor, but Terence Tao is anything but ordinary. By the age of two, he taught himself to read and count. By nine, he was studying university-level math. And by 20, he graduated with a Ph.D. from Princeton. Most math geniuses are considered a little strange. Isaac Newton was intense and obsessive. John Nash was paranoid with delusions. Grigori Perelman withdrew almost completely from public life. On the other hand, Tao is super normal. That's a credit to his parents. His mom, a high school math teacher from Hong Kong, and his father, a pediatrician, raised him and his two younger brothers in Adelaide, Australia. They didn't push him relentlessly, ensuring he would develop emotionally and socially even though they knew he was special. When he was two, he taught children twice his age how to count using wooden blocks. As a child, he was assessed to have an IQ over 160, and he scored 760 on the math portion of the SAT when he was just 8 years old. He was hooked on math when he realized it could help explain the world, like how satellites can guide millions of drivers to within a few meters using precise geometry and orbits. What sets Tao apart is how he thinks. When he struggled with a problem involving rotating waves, this is how he came up with the solution. I tried to visualize being the wave that the equation was modeling, and I remember having my eyes closed on the floor. I was rolling around trying to imagine how different parts of the wave evolved. In fact, I was staying at my aunt's place at the time, and she saw me rolling on the floor, and I couldn't explain to her what I was doing. But I did actually understand. I found a way to transform the solution to make it easier to solve, and it actually helped quite a lot to physically do that. For the longest time, he didn't think being a mathematician was a real career, and figured he'd end up as a shopkeeper balancing the books. But when his undergraduate advisor stepped in, everything changed. His advisor encouraged him to focus on mathematical research and to study abroad. When Tao applied to Princeton, his application included a letter of recommendation from the great Hungarian mathematician Paul Erdős. I am sure he will develop into a first-rate mathematician, and perhaps into a really great one. I recommend him in the highest possible terms. Tao would become famous for tackling problems involving prime numbers, one of the reasons he won the Fields Medal, the Nobel Prize of Math, in 2006. Prime numbers are divisible only by one and themselves, and as numbers grow larger, primes become rarer, with ever-increasing gaps between them. For centuries, mathematicians wondered, do primes behave randomly, or is there hidden order beneath the surface? In 2004, Tao and British mathematician Ben Green proved that no matter how sparse prime numbers become, you can always find long sequences of evenly spaced primes, called arithmetic progressions, if you look far enough. 3, 5, and 7 are prime numbers spaced evenly by 2. That's an arithmetic progression of length 3. There are longer ones, 7, 37, 67, and 97 are all prime, each separated by 30, with an arithmetic progression of length 4. However far out you go, sequences like this exist for whatever length you desire. They didn't prove it by hunting for patterns one by one, they used powerful tools from different branches of math to show that the structure of the primes forces these progressions to exist. Tao compared the idea to counting grains of sand in a box. You don't count each grain one by one. You weigh the box, subtract the container, and divide by the average weight of a single grain to get an accurate count of how many are likely to be inside. Unlike many mathematicians who spend a lifetime specializing in one area, Tao moves freely across math fields, often working collaboratively. One day in 2004, Stanford mathematician Emmanuel Condès brought Tao a problem he'd run into while working with radiologists. They were trying to make MRI scans faster, but the math just wasn't working. That conversation led them to launch an entirely new field, compressed sensing. To understand compressed sensing, think about an image. If you delete pixels, the information is gone and the image is ruined. But compressed sensing doesn't see pixels, it sees patterns. If an image has sufficient underlying structure, you don't need to record every pixel to recover it. Even if some of the information is discarded, you can still reconstruct the original image. Here's a simple analogy. You can probably read this without much trouble. Even if lots of letters are missing, you can still read it because English has structure. Their work led to faster MRI scans, and also attracted the military, where the goal is often to see an area using many simple sensors, rather than relying on extremely expensive ones. Work like this made him quickly stand out. He became a tenured professor at UCLA when he was 24, teaching students who were almost the same age. He later married Laura, a former student of his, who went on to become an engineer at NASA's Jet Propulsion Laboratory. Together they have two children, and he's passed his spark of curiosity onto them. When his son was four, Tao would ask questions like, if there are ten cookies and five people in the living room, how many does each person get? His son also showed a strong inclination toward languages, unlike Tao, who struggled with English and Latin in school. He preferred mathematics, where answers were concrete, true or false, right or wrong. At one point, a hedge fund approached him, eager to put his extraordinary mathematical mind to work in finance, a job that would have been extremely lucrative, but he turned it down for the pure joy of staying in academia. And while he's certainly not hurting for money, his UCLA professorship pays handsomely, with public records showing he earned $784,000 in 2024. He's been more interested in the questions that fascinate him than in the dollars they might generate. One of those fascinating questions is whether AI can solve long-standing open problems in pure mathematics. Tao has been tracking how AI tools like advanced versions of ChatGPT tackle unsolved puzzles by Paul Erdős, even maintaining a public database of these attempts. Recently, ChatGPT solved Erdős's problem number 281, generating a proof that has held up under expert review. Tao called it perhaps the most unambiguous instance of AI independently solving an open problem. Today, Terence Tao is one of the most recognizable mathematicians in the world. He is so sought after that his website includes a simple notice. I reached out as well, knowing full well the answer might be no. If I do hear back, I'll leave an update in a pinned comment. Despite the attention, the Mozart of math has remained remarkably humble. That attitude reflects the Chinese philosophy he grew up with, which emphasizes humility. As his father put it, Even if you are very successful, you tend to say, no, I'm just pretty average. In Terence Tao's case, that couldn't be further from the truth. Tao wants to teach more non-mathematicians how to think mathematically, saying, I believe you can teach this to almost anybody. And he's right, if you're taught the right way. That's one of the reasons I love using Brilliant. It's a completely different way of learning. Instead of passively sitting through lectures, you're actively solving problems step-by-step learning intuitively. I've been playing around with Brilliant's exponential functions. What's great is how interactive it is. It just clicks way more naturally. Brilliant has a range of courses in algebra and calculus that meet you at your skill level, while letting you challenge yourself and grow. They also have a whole host of courses in coding, science, and AI, all built around learning by doing. You can start learning for free at brilliant.org slash newsthink, or scan the QR code on your screen. Brilliant's also giving you 20% off your annual premium subscription if you use my custom link in the description, giving you unlimited access to their thousands of lessons. Thanks for watching. For Newsthink, I'm Cindy Pom.