A self-taught scientist reflects on how unconventional homeschooling and avoidance of formal mathematics education shaped their career. Despite lacking traditional academic foundations, they developed problem-solving skills through self-directed learning and eventually pursued science through community college, though they struggled with pre-calculus.
Nearly five thousand mathematicians, including twenty-five Fields medalists, have signed a declaration opposing AI's approach to mathematics, arguing that while AI excels at solving problems, this overlooks mathematics' primary goal of conceptual understanding. The concern is that AI's rapid production of true/false statements may undermine the creative work of generating new mathematical ideas and concepts, which has historically been validated through prestigious problem-solving achievements.
The author critiques the tendency to focus solely on flaws and complaints, arguing that while identifying problems has value, there's equal importance in recognizing what works and building solutions. Drawing on references from Ratatouille and JFK, they encourage moving beyond cynicism to do the harder work of finding value and creating positive change.
Mathematicians, including Fields medalists, are concerned that AI's success in solving prestigious mathematical problems threatens conceptual understanding and idea-generation, the true intellectual work of mathematics. They worry that rapid production of problem solutions could overshadow the more difficult and valuable work of developing new mathematical concepts and frameworks.
Nearly 5,000 mathematicians, including 25 Fields medalists, have signed a declaration opposing AI's role in mathematics, arguing that while AI excels at solving problems (puzzle-solving), this obscures mathematics' true purpose: generating novel conceptual ideas and insights. The concern is that AI's rapid production of solutions may devalue idea-generation work and misalign our metrics for mathematical progress.
On September 8th, 2026, OpenAI announced an AI-generated solution to the Navier–Stokes problem, sparking debate about human-machine credit and mathematics' future. De Toffoli and Duede argue that AI providing answers does not constitute genuine mathematical solutions, which require intelligible proofs humans can understand and use, and that mathematics encompasses far more than problem-solving.
A tech worker reflects on why people pursue their careers, using personal experience to argue that most work motivations are mundane—paycheck, hours, personal neurosis—rather than noble ideals. The author describes falling in love with tech work not for transformative potential but for the dopamine hit of solving puzzles, and traces how that drive pushed them deeper into the stack, seeking ever-harder problems and the ultimate satisfaction of building solutions from scratch.