From Vibe Coding to Vibe Researching: OpenAI’s Mark Chen and Jakub Pachocki
🎯 Summary
[{“key_takeaways”=>[“The primary research target is building an ‘automated researcher’ capable of discovering new, economically relevant ideas autonomously.”, “GPT-5’s main focus was integrating reasoning and more agentic behavior by default, moving beyond simple instant responses.”, “Current evaluations (like math/programming competitions) are becoming saturated, necessitating a shift to benchmarks that measure genuine discovery and long-horizon operation.”, “Reinforcement Learning (RL) remains a highly effective and versatile method, successfully combined with large language models to execute complex objectives.”, “The new Codex for real-world coding is designed to handle messy environments and optimize latency based on problem difficulty, moving past the ‘vibe coding’ default.”, “Great research requires persistence, a high tolerance for failure, and the ability to be maximally truth-seeking even when deeply convicted in an idea.”, “OpenAI maintains its talent edge by focusing on frontier, fundamental research rather than copying competitors, and by valuing deep technical fundamentals over social media visibility.”], “overview”=>”OpenAI’s Mark Chen and Jakub Pachocki discuss the evolution of AI research, focusing on the shift from instant-response models to reasoning capabilities exemplified by GPT-5, and the ultimate goal of creating an ‘automated researcher.’ They emphasize the necessity of moving evaluation benchmarks beyond saturated metrics toward economically relevant discovery and long-horizon agency.”, “themes”=>[“The Future of AI Research (Automated Researcher)”, “GPT-5 and Reasoning Capabilities”, “Evolution and Deficiencies of AI Evaluations (Evals)”, “The Role and Success of Reinforcement Learning (RL)”, “AI in Coding and Developer Workflow”, “The Nature of Scientific Research and Researcher Culture”, “Talent Acquisition and Organizational Culture at OpenAI”]}]
🏢 Companies Mentioned
💬 Key Insights
"The biggest things that OpenAI has going for it in terms of keeping the best people motivated and excited, as I said, is that we are in the business of doing fundamental research. We aren't the type of company that looks around and says, oh, what model did company X build, what model did company Y build? We have a fairly clear and crisp definition of what it is we're out to build. We like innovating at the frontier. We really don't like copying."
"I've really kind of felt like, okay, this is no longer the way. Like, you can do a 35-year-old refactor pretty much perfectly in 15 minutes, you kind of have to use it."
"The next set of evals and milestones that we're looking at will involve actual discovery and actual movement on things that are economically relevant."
"The big thing that we are targeting is producing an automated researcher. So automating the discovery of new ideas."
"We don't purely look for who did the most visible work or is the most visible on social media. I think one thing that we look for is having solved hard problems in any field."
"I do feel like already it's kind of transformed the default for coding. This past weekend, I was talking to some high schoolers and they're saying, oh, actually the default way to code is vibe coding."