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From Vibe Coding to Vibe Researching: OpenAI’s Mark Chen and Jakub Pachocki

What comes after vibe coding? Maybe vibe researching. OpenAI’s Chief Scientist, Jakub Pachocki, and Chief Research Officer, Mark Chen, join a16z general partners Anjney Midha and Sarah Wang to go deep on GPT-5—how they fused fast replies with long-horizon reasoning, how they measure progress once benchmarks saturate, and why reinforcement learning keeps surprising skeptics. They explore agentic systems (and their stability tradeoffs), coding models that change how software gets made, and the bigger bet: an automated researcher that can generate new ideas with real economic impact. Plus: how they prioritize compute, hire “cave-dweller” talent, protect fundamental research inside a product company, and keep pace without chasing every shiny demo. Timecodes: 0:00 Introduction 0:25 The Launch of GPT-5 2:28 Evaluating Progress: Evals & Milestones 5:07 Surprising Capabilities of GPT-5 7:10 The Future of Automated Research 8:59 Agency, Reasoning, and Model Planning 10:18 Extending Progress Beyond Verifiable Domains 12:11 The Role and Success of Reinforcement Learning 14:44 Reward Modeling and Best Practices 15:54 The Evolution of Coding with AI 21:39 What Makes a Great Researcher? 27:20 Building and Sustaining a Winning Research Culture 31:40 Balancing Product and Fundamental Research 38:36 Prioritization, Compute, and Resource Allocation 41:19 The Intersection of Academia and Frontier AI 46:56 Maintaining Speed and Learning at Scale 48:52 Trust and Collaboration at OpenAI Resources: Find Jakub on X: https://x.com/merettm Find Mark on X: https://x.com/markchen90 Find Sarah on X: https://x.com/sarahdingwang Find Anjney on X: https://x.com/AnjneyMidha Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://x.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

Jakub PachockiguestMark ChenguestAnjney MidhahostSarah Wanghost
Sep 25, 202553mWatch on YouTube ↗

Episode Details

EPISODE INFO

Released
September 25, 2025
Duration
53m
Channel
a16z
Watch on YouTube
▶ Open ↗

EPISODE DESCRIPTION

What comes after vibe coding? Maybe vibe researching. OpenAI’s Chief Scientist, Jakub Pachocki, and Chief Research Officer, Mark Chen, join a16z general partners Anjney Midha and Sarah Wang to go deep on GPT-5—how they fused fast replies with long-horizon reasoning, how they measure progress once benchmarks saturate, and why reinforcement learning keeps surprising skeptics. They explore agentic systems (and their stability tradeoffs), coding models that change how software gets made, and the bigger bet: an automated researcher that can generate new ideas with real economic impact. Plus: how they prioritize compute, hire “cave-dweller” talent, protect fundamental research inside a product company, and keep pace without chasing every shiny demo. Timecodes: 0:00 Introduction 0:25 The Launch of GPT-5 2:28 Evaluating Progress: Evals & Milestones 5:07 Surprising Capabilities of GPT-5 7:10 The Future of Automated Research 8:59 Agency, Reasoning, and Model Planning 10:18 Extending Progress Beyond Verifiable Domains 12:11 The Role and Success of Reinforcement Learning 14:44 Reward Modeling and Best Practices 15:54 The Evolution of Coding with AI 21:39 What Makes a Great Researcher? 27:20 Building and Sustaining a Winning Research Culture 31:40 Balancing Product and Fundamental Research 38:36 Prioritization, Compute, and Resource Allocation 41:19 The Intersection of Academia and Frontier AI 46:56 Maintaining Speed and Learning at Scale 48:52 Trust and Collaboration at OpenAI Resources: Find Jakub on X: https://x.com/merettm Find Mark on X: https://x.com/markchen90 Find Sarah on X: https://x.com/sarahdingwang Find Anjney on X: https://x.com/AnjneyMidha Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://x.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

SPEAKERS

  • Jakub Pachocki

    guest

    Chief Scientist at OpenAI, leading frontier model research and long-horizon reasoning efforts.

  • Mark Chen

    guest

    Chief Research Officer at OpenAI, overseeing research portfolio, compute allocation, and product-research integration (e.g., Codex).

  • Anjney Midha

    host

    a16z investor and former founder who interviews leaders in AI and applied research.

  • Sarah Wang

    host

    a16z investor who co-hosts interviews with AI research and product leaders.

EPISODE SUMMARY

In this episode of a16z, featuring Jakub Pachocki and Mark Chen, From Vibe Coding to Vibe Researching: OpenAI’s Mark Chen and Jakub Pachocki explores openAI leaders on GPT-5, evals, RL, and automated research vision GPT-5 is positioned as a unification move that brings “reasoning by default” to mainstream users, reducing confusion between instant-response GPT models and longer-thinking O-series models.

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