Steve Yegge Predicts AI's Impact on Software Development
It's always energizing to do a podcast with Steve Yegge (@Steve_Yegge, engineer+author, formerly at Amazon+Google, creator of Gas Town). Timestamps: 00:00 Intro 01:43 Steve’s latest projects 02:27 Important blog posts 04:48 Shifts in what engineers need to know 10:46 Steve’s current AI stance 13:23 Steve’s book Vibe Coding 18:25 Layoffs and disruption in tech 31:13 Gas Town 40:10 New ways of working 51:08 The problem of too many people 54:45 Why AI results lag in business 59:57 Gamification and product stickiness 1:04:54 The ‘Bitter Lesson’ explained 1:07:14 The future of software development 1:23:06 Where languages stand 1:24:47 Adapting to change 1:27:32 Steve’s predictions Brought to you by: • @statsig – The unified platform for flags, analytics, experiments, and more. https://t.co/ZCSOIcX2Sz • @SonarSource – The makers of SonarQube, the industry standard for automated code review. https://t.co/Q7wjteXaUZ • @WorkOS – Everything you need to make your app enterprise ready. https://t.co/aiAee0pcUP Three interesting thoughts from Steve that we talked about in this conversation: 1. Reading ability is becoming a blocker for wider AI adoption. Some struggle with walls of text that current AI tools produce, and Steve predicts that in the very near future, most people will program by talking to a visual avatar, not reading terminal output because he observes that five paragraphs is already a lot to read for many devs. 2. What software engineers need to know keeps changing. In the 1990s, any decent software engineer knew Assembly, and today almost no decent developer knows it because Assembly has long been superseded by technical progress. What engineers “need” to know these days is different from the ‘90s and that process continues with AI, changing the parts of the craft that are essential for devs. We grumble about this but that won’t change anything by itself. 3. There’s a “Dracula Effect” where AI-augmented work drains engineers faster than traditional work. This is because AI automates the easy tasks, meaning that engineers are stuck doing high-intensity thinking all day. Steve says you may only get three daily productive hours at max speed, but during that time, you could produce 100x more output than before.
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