OpenAI's GPT 6 Astra Is Near "AGI" Level & Solving The Impossible
GPT 6 Astra claims it solved 10 open math problems with Lean proofs, inside OpenAI's biggest AGI claim yet OpenAI's next model family, Astra, is at the center of the boldest AI news of the year: an internal checkpoint reportedly solved ten decades-old problems in group theory, geometry, Ramsey theory, circuit complexity, quantum information and lattice cryptography, then backed the claims with machine-checkable Lean 4 proofs. This breaks down what OpenAI actually published on August 1, 2026, and why it matters for anyone tracking gpt 6, machine learning progress, and the real distance between today's models and artificial intelligence that can do genuine research. The video walks through the receipts: a 249-page manuscript, a public GitHub repo of Lean certificates with a zero "sorry" count, Sebastien Bubeck's reaction, and the roughly $2,000 compute bill OpenAI says it took to generate the proofs. It also covers the May 2026 result disproving the 1946 Erdős unit distance conjecture, reviewed by Fields Medalist Tim Gowers, and an Anthropic researcher's same-day reproduction of five of the ten proofs using a public model. Then it pressure-tests the hype against Terence Tao's own encrypted-proof experiment, the Riemann-Bench research-math benchmark where frontier models scored under 10%, and Mistral's Leanstral 1.5 hitting 100% on miniF2F, a competition benchmark, not open research. Along the way it touches the June 2026 Leiden Declaration on AI credit in mathematics and asks what any of this means for ai agents, world of ai speculation, and the AGI timeline Sam Altman and others keep pushing. For builders and the AI-curious trying to separate real progress from press releases, this is the grounded version of the Astra story. Chapters: 0:00 Three Escalating AI Jumps 0:24 Ten Impossible Problems, Solved 1:35 OpenAI Shows Its Working 2:42 The Machine That Can't Be Fooled 3:52 The Proof From Three Months Back 5:19 The $2,000 Math Breakthrough 6:24 A Rival Lab Checks The Math 7:21 Did They Ask The Wrong Question 8:22 When Nobody Picks The Problems 9:25 Full Marks, Wrong Exam 10:16 Tao Puts It To The Test 11:19 Who Actually Gets The Credit 12:24 So How Close To AGI Is This Tools & resources mentioned: - OpenAI Ten Advances in Mathematics: https://openai.com/index/ten-advances-in-mathematics/ - ten-proofs GitHub repo: https://github.com/openai/ten-proofs - Lean 4: https://lean-lang.org/ - Leanstral 1.5 (Mistral) - Riemann-Bench paper - miniF2F benchmark - PutnamBench About The Stack The Stack helps you build with AI. Each video takes one tool, model, or workflow and shows how it works in a few focused minutes, with the real benchmarks and real costs. We go deep on Claude Code and Cursor for AI coding, AI agents and MCP servers, the open-source AI tools and GitHub repos most people miss, RAG and vector search, fine-tuning, and running local LLMs on your own machine with Ollama and LM Studio. We compare models like ChatGPT and Claude, test AI automation with Zapier, Make, and n8n, and flag the tools that actually ship. Subscribe for new breakdowns: https://www.youtube.com/@the-stack-ai?sub_confirmation=1 #gpt6 #openai #astra #aiagi #artificialintelligence
