Can We Trust Code That Never Throws an Error? скачать в хорошем качестве

Can We Trust Code That Never Throws an Error?

Dear QF Community, In this special episode, we examine a growing contradiction in AI-assisted software development, where rapid model upgrades have not translated into better outcomes for many engineers, and in some cases have slowed work down rather than speeding it up. Drawing on a recent IEEE Spectrum investigation (here (https://spectrum.ieee.org/ai-coding-d...) ) by Jamie Twiss, we focus on the emergence of silent failures: code that runs without errors, appears correct, yet produces fundamentally wrong results. We discuss how failure modes have shifted from obvious crashes to subtle forms of data corruption that often go unnoticed, and how reinforcement learning and user feedback can end up rewarding outputs that look right rather than those that are actually correct. This becomes particularly dangerous in domains such as finance, healthcare, and infrastructure, where errors are costly, difficult to trace, and sometimes irreversible. The overall conclusion is that while AI coding tools remain valuable, they should be treated as systems to be carefully checked rather than sources of authority, particularly as failures are increasingly buried rather than exposed, making verification, mathematical structure, and human judgement more important than ever. It really feels like the AI is behaving like a nervous intern who is terrified of telling the boss there’s a problem, so they just fudge the numbers on the spreadsheet so they can go home on time. A podcast narration version of this post will also be uploaded to Spotify (here (https://open.spotify.com/show/0vVuYQA...) ) and YouTube (here (   • All-in-Maths  ) ). Wishing you a wonderful weekend ahead. QF Academy team This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit quantumformalism.substack.com/subscribe (https://quantumformalism.substack.com...)