Anyone who’s used AI to generate code has seen it make mistakes. But the real danger isn’t the occasional wrong answer; it’s in what happens when those errors pile up across a codebase. Issues that seem small at first can compound quickly, making code harder to understand, maintain, and evolve. To really see that danger, you have to look at how AI is used in practice—which for many developers starts with vibe coding. Vibe coding is an exploratory, prompt-first approach to software development where developers rapidly prompt, get code, and iterate. When the code seems close but not quite right, […]

We can’t not talk about power these days. We’ve been talking about it ever since the Stargate project, with half a trillion dollars in datacenter investment, was floated early in the year. We’ve been talking about it ever since the now-classic “Stochastic Parrots” paper. And, as time goes on, it only becomes more of an issue. Stochastic Parrots deals with two issues: AI’s power consumption and the fundamental nature of generative AI; selecting sequences of words according to statistical patterns. I always wished those were two papers, because it would be easier to disagree about power and agree about parrots. […]

Earlier this year, when my colleague Casey Crownhart and I spent six months researching the climate and energy burden of AI, we came to see one number in particular as our white whale: how much energy the leading AI models, like ChatGPT or Gemini, use up when generating a single response.  This fundamental number remained elusive even as the scramble to power AI escalated to the White House and the Pentagon, and as projections showed that in three years AI could use as much electricity as 22% of all US households.  The problem with finding that number, as we explain […]