The Future of Coding Agents with Sasha Rush (Cursor/Cornell)

We talked with Sasha Rush, researcher at Cursor and professor at Cornell, about what it actually feels like to we in the heart of the AI revolution and build coding agents right now. Sasha shared how these systems are changing day-to-day work and how it feels to develop these systems. A big part of the conversation was about why coding has become such a powerful setting for these tools. We discussed what makes code different from other domains, why agents seem to work especially well there, and how much of today’s progress comes not just from better models, but from better ways of using them. Sasha also gave an inside look at how Cursor thinks about training coding models, long-running agents, context limits, bug finding, and the balance between autonomy and human oversight. We also talked about the broader shift happening in software engineering. Are developers moving to a higher level of abstraction? Is this just a phase where we “babysit” models, or the beginning of a deeper change in how software gets built? Sasha had a very thoughtful perspective here, including what he’s seeing from students, researchers, and engineers who are growing up native to these tools. More broadly, this episode is about what it means to do serious technical work in a moment when the tools are changing incredibly fast. Sasha brought both optimism and skepticism to the discussion, and that made this a really grounded conversation about where coding agents are today, what they are already surprisingly good at, and where all of this might be going next. Timeline 00:00 Intro and Sasha joins us 01:11 What “coding agents” actually mean 02:34 Why coding became the breakout use case 08:56 Long-running agents and autonomous workflows 15:08 How these tools are changing the work of engineers 17:15 Are people just babysitting models right now? 22:11 How Cursor builds its coding models 26:29 Rewards, training, and what makes agents work 34:53 Memory, continual learning, and agent communication 38:00 How context compaction works in practice 41:29 Why coding agents recently got much better 50:31 Refactoring, maintenance, and self-improving codebases 52:16 Bug finding, oversight, and verification 54:43 Will this pace of progress continue? 56:42 Can this spread beyond coding? 58:27 The future of Cursor and coding agents 1:03:08 Model architectures beyond standard transformers 1:05:37 World models, diffusion, and what may come next Music: "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0. "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0. Changes: trimmed About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.