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Amateur armed with ChatGPT solves an Erdős problem

Even experts are surprised by AI’s latest ‘vibe-mathing’ advance

scientificamerican.com

April 25, 2026

4 min read

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63/100

Summary

Liam Price, a 23-year-old without advanced mathematics training, solved a 60-year-old problem previously unsolved by mathematicians using ChatGPT Pro. Recent advancements in artificial intelligence have led to solutions for several conjectures known as Erdős problems.

Key Takeaways

  • Liam Price, a 23-year-old without advanced mathematics training, solved a 60-year-old mathematical problem using ChatGPT Pro.
  • The solution involves special sets of whole numbers called "primitive sets," where no number can be evenly divided by another.
  • Price's method for solving the problem appears to be novel and may have broader applications in mathematics.
  • Experts caution that many AI-generated solutions to Erdős problems have been less original than they seem, highlighting the complexity of evaluating AI's mathematical capabilities.
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Community Sentiment

Positive

Positives

  • The ability of LLMs to make novel connections across different fields is a significant advancement, showcasing their potential to contribute to scientific breakthroughs.
  • The successful proof of an Erdős problem by an amateur using ChatGPT indicates that AI models are becoming increasingly capable and sophisticated in tackling complex mathematical challenges.
  • This milestone demonstrates that LLMs are not only improving in performance but also revealing new insights and connections that were previously overlooked by human mathematicians.
  • The collaborative idea of creating a GitHub repository for unsolved problems highlights the potential for community-driven AI applications to accelerate mathematical discoveries.

Concerns

  • Some users experienced limitations with the free version of ChatGPT, indicating that access to advanced features may hinder broader participation in solving complex problems.
  • Concerns were raised about the depth of understanding required to evaluate the proofs generated by LLMs, suggesting that reliance on AI may not fully substitute for human expertise in mathematics.

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