OpenAI researcher Sebastien Bubeck says GPT-5's math skills saved him a month of work. In a post on X, Bubeck reports that GPT-5 tackled a highly complex mathematical task for him. The model designed the solution path, ran a simulation to check a formula, and then wrote a complete proof, effectively a seamless calculation. While this process would have previously taken him around a month, GPT-5 finished it in just an afternoon. Bubeck calls it the "most impressive LLM output" he has seen to date.
Generative AI is becoming increasingly apparent in high-level mathematics—and not just because of gold medals in Math Olympiads. Mathematician Terence Tao recently noted that AI saved him several hours of work, though he used the tool to verify his theoretical assumptions rather than relying on it autonomously. An OpenAI report backs this up, showing how GPT-5 can save significant research time across various scientific fields.
Programmers who rely on AI assistants tend to ask fewer questions and learn more superficially, according to new research from Saarland University. A team led by Sven Apel found that students were less critical of the code suggestions they received when working with tools like GitHub Copilot. In contrast, pairs of human programmers asked more questions, explored alternatives, and learned more from one another.
In the experiment, 19 students worked in pairs: six in human-only teams and seven in human-AI teams. According to Apel, many of the AI-assisted participants simply accepted code suggestions because they assumed the AI's output was already correct. He noted that this habit can introduce mistakes that later require significant effort to fix. Apel said AI tools can be helpful for straightforward tasks, but complex problems still benefit from real collaboration between humans.