OpenAI's Mark Chen & Jakub Pachocki on GPT-5, Automated Research, and AI's Future

GPT-5 and Advanced Reasoning

• 💡 GPT-5 was launched to bring reasoning into the mainstream, enabling more agentic behavior by default.
• 🧠 The model combines instant responses with the ability to think for a long time to provide the best answer, simplifying user experience.

Measuring AI Progress

• 📊 Traditional evaluations (evals) are becoming saturated, with models reaching near-perfect scores.
• 🎯 OpenAI is now focused on models that can discover new things and demonstrate economically relevant movement, particularly in math and programming competitions.

The Automated Researcher Vision

• 🚀 A primary research target is to create an automated researcher capable of discovering new ideas, including in ML and other sciences.
• 📈 This involves extending the model's reasoning horizon and ability to plan over very long periods, from hours to months or years.

Reinforcement Learning's Impact

• ✅ Reinforcement Learning (RL) has proven to be a highly versatile and effective method, especially when combined with natural language understanding.
• 🛠️ While reward modeling is currently complex, it is expected to evolve and simplify, moving towards more human-like learning paradigms.

Evolving Coding and Research

• 💻 GPT-5 Codecs focuses on making AI intelligence useful for real-world coding, handling messy environments and optimizing for problem difficulty.
• 💡 The ultimate goal is to transition from "vibe coding" to "vibe researching," where AI assists in the discovery of new ideas, requiring persistence and learning from failures.

OpenAI's Research Culture

• 🔑 OpenAI maintains a culture of fundamental research, attracting talent by focusing on ambitious, frontier problems rather than copying competitors.
• 🧠 The organization balances research and product by protecting dedicated research space, prioritizing algorithmic advances, and allocating compute resources strategically.