ChatGPT solves the Erdesh problem in 80 minutes: Amateur math enthusiasts use AI to overcome number theory conjectures

Core conclusion

On April 26, 2026, an amateur mathematics enthusiast used ChatGPT to successfully solve the Erdős number theory conjecture problem 1176 in 80 minutes - an unsolved problem that has plagued the mathematics community for decades. This incident triggered a hot discussion on Hacker News (747 points), marking a fundamental shift in the paradigm of AI-assisted mathematics research.

Key Points

• Time of incident: 2026-04-26 -Affected objects: mathematical research, AI-assisted scientific research, large language model reasoning ability
• Core changes: Non-professional mathematicians used AI tools to solve professional-level open problems, proving that LLM's mathematical reasoning capabilities have evolved from "auxiliary calculations" to "collaborative proofs"

Background and trigger events

Erdős problem 1176 comes from the set of number theory conjectures of Paul Erdős, one of the greatest mathematicians of the 20th century, and involves the summation convergence problem of the "primitive set". This problem has made some progress in the mathematical community - the known upper bound is about 1.399, but whether it can be proved that its sum is always less than 1+o(1) has always been an open problem.

Last week, an anonymous user shared his test on Hacker News: without using Internet search, relying only on ChatGPT and a carefully designed mathematical problem framework, the AI ​​​​gave a complete, rigorous, and creative unconditional proof in 80 minutes. ChatGPT not only gives a "yes" answer, but also provides a stronger conclusion than the question asks for (i.e., a tighter upper bound can be proved for any constant).

Source: Scientific American report + HN homepage ranked 2nd (747 votes) + ChatGPT public conversation record

Key Impact (by Dimension)

Adaptation suggestions

• Introduce "AI reasoning loop" into daily workflow: like EvanFlow's TDD model, let AI first generate hypotheses, then verify, and then iterate
• Treat LLM as a "reasoning collaborator" rather than a "content generator": in scenarios that require innovative solutions, set constraints + sufficient thinking time for AI
• Add the "AI quality self-inspection" step to the publishing automation process: use LLM to pre-review the text and pre-check logical breakpoints

Task List (Example)

• Add the "AI inference quality inspection" link to the content production pipeline
• Use tools such as Claude Code to build a positive feedback loop of "requirements → solutions → verification"
• Pay attention to the evolution trend of benchmark tests such as SWE-bench

Example: Proof idea in ChatGPT conversation (simplified)

x，A⊂[x,∞)（）， ∑_{a∈A} 1/(a log a) < 1+o(1) ？ ChatGPT
1. von MangoldtΛ(n)
2. ""（first-entry）
3. ，
4. Mertens
5. ：δ>0，1+O(δ)
``` ![](https://plus.unsplash.com/premium_photo-1661698447811-2f0b68cf2522?q=80&w=1200&auto=format&fit=crop)

## Related extended information
- [YouTube: Erdős 1176ChatGPT](https://www.youtube.com/results?search_query=erdos+problem+1176)
- [Bilibili: AI](https://www.bilibili.com/video/BV1qN4y1D7xR)

## Tool entry (trigger tool floating card)

Among the AI-assisted mathematics research routes, **ChatGPT**, **Claude**, and **DeepSeek** each have their own strengths in reasoning tasks. **OpenAI**'s ChatGPT performed particularly well in this demonstration. At the same time, AI Agent tools such as **Claude Code** and **Hermes Agent** can also be used to build "reasoning → verification → iteration" content and research automated workflows.

## Internal link guidance
- Want to learn how? See: [Claude Code automated writing practice: build an AI content production pipeline in 30 minutes](https://waytoclawearn.com/tutorials/claude-code-content-automation-tutorial)
- Real case: [Indie Developer: n8n + OpenClaw Automation Workflow Earning $5,000/mo](https://waytoclawearn.com/cases/indie-developer-n8n-automation-5000-month-demo)