{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Fundamentals of Software Engineering","title":"Open Source, AI Tooling, and the Coming Token Crisis with Dan Vega and Nate Schutta","html":"","width":"100%","height":180,"duration":3684,"description":"In this episode of Fundamentals of Software Engineering, Nate and I dig into the increasingly tense overlap between open source maintainers and the new wave of AI-generated contributions. We start with the policies maintainers are putting in place to refuse AI-driven pull requests, and why a flood of low-signal 50,000 line diffs is genuinely threatening the open-source contribution model. From there we get into the patterns we're seeing in the wild: code that compiles but solves the wrong problem, the trouble AI has actually deleting code, and the way junior engineers can lean on AI in a way that hollows out the deeper learning that builds real engineering judgment.From there, Nate and I get into the commercial pressure building underneath all of this. We talk about the Silicon Valley loss-leader playbook, why current AI tooling pricing is almost certainly going to climb sharply once teams are dependent on it, and what that means for engineering organizations who have built their workflows around a tool that may not stay this cheap. We also talk about what good practice looks like when you're using AI day to day, where you should still be writing the code yourself, and how to keep your sharpness as an engineer while these tools keep evolving.__________________________________________________Key Highlights🚫 Maintainers Saying No to AI PRs: Some open source maintainers have explicitly banned AI-generated pull requests, and we look at why this matters for the future of community contributions.📈 The 50,000 Line Diff Problem: Students are now submitting massive AI-generated diffs that overwhelm reviewers, and we discuss why volume without judgment hurts everyone in the loop.🗑️ AI Is Bad at Deleting Code: One of the consistent weaknesses in current AI tooling is removing unused or stale code. We talk about why you have to push it in that direction explicitly.💸 The Darth Vader Moment in AI Pricing: Current AI tooling follows the Silicon Valley playbook: cheap to get you...","thumbnail_url":"https://img.transistorcdn.com/1BiOcr3jOEw_uiwQk5MInsKiSAl8JXHgE7p7L1stz0g/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS82NmM2/MmE3OWEzYWVkMWFl/MWUxNzhkOWY1YzY1/Njg2Ny5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}