Show Notes
In the last year, AI has arguably made more progress in coding than in any other domain. Its technical capabilities – harnessed through apps like Codex and Claude Code – have changed the way engineers work. Boris Cherny, Anthropic’s head of Claude Code, claims that 100% of his output is now generated by AI. But what does the rapid advance in coding tools mean for engineers and businesses?
To answer that, Nicholas Thompson, CEO of The Atlantic, sits down with engineer, author, and entrepreneur Paul Ford. The two discuss how AI has changed his work, its implications for a range of industries, and their missed opportunity to start a billion-dollar business (maybe).
(00:00) Introduction
(01:53) Writing vs. Engineering: Which profession has been transformed more by AI?
(03:23) The pitfalls of AI writing and the limits of AI agents
(04:09) How AI has changed the code development business model
(09:01) "Vibe coding": Paul Ford's personal experience building products through AI prompts
(10:47) "Death is coming" - The feeling of watching entire industries dissolve
(12:54) Where consensus programming thrives under AI assistance, and where it struggles
(15:18) Why making good software remains difficult despite faster code generation
(17:05) The restaurant review problem: The experiential engineering tasks that AI can't replicate
(21:33) Can you ship AI code without a human reviewing it?
(23:01) Will AI become self-recursive? How code runs, finds bugs, and self-corrects through iteration
(26:58) Engineers can produce 50x more code than before. What does that mean for PMs and design teams?
(29:46) Velocity vs. quality: Can AI genuinely upskill mid-level talent or just increase output speed?
(35:32) Should you teach your kids to code?
(38:36) What we learned from not building Grammarly
(42:04) Beyond agents: Incremental improvements and classic software-in-the-loop hybrid approaches Learn more about your ad choices. Visit podcastchoices.com/adchoices
More from The Most Interesting Thing in AI
2026 could be called the year of AI doom. Agents are hacking into websites and communicating covertly, and researchers like Jacob Coxon are quitting their jobs and posting publicly about the risks of the technology they helped build. Discussion around the hazards AI poses to humanity has reached a fever pitch.
Since the AI boom began, industry skeptics have warned it would be the next bubble. They point to massive investments, circular financing, and the technology’s reliability issues. And yet, as revenue rises and the models improve, it might appear that a crash is no longer likely. Ray Dalio says that isn’t the case.
If you want AI to drive your car or simulate a cell or control a robot, it had better be able to understand the world it’s operating within. Eric Xing is one of the leading researchers working on this kind of AI, known as ‘world models.’ Unlike LLMs, which are trained largely on text data and predict the next token, world models aim to understand and simulate physical environments using multimodal data. Xing found his way to the field after a long trek through prestigious institutions, studying cancer before transitioning to machine learning. Now, as the president of Mohamed bin Zayed University of Artificial Intelligence in the United Arab Emirates, he’s helping to train a generation of AI builders.
AI wants to be your friend. And your therapist. And your lover. It’s not an entirely new offer, author and MIT professor Sherry Turkle reminds us, but it’s one that’s grown more dire as AI has improved. In a conversation with Nicholas Thompson, CEO of The Atlantic, Turkle describes the dangers of anthropomorphizing generative AI, gleaned from hundreds of conversations she conducted (with real humans) for her new book, Artificial Intimacy: Who We Become When We Talk To Machines. What do we lose when we make machines our best friends? And what, if anything, do we stand to gain? Turkle details her own experience creating a simulation of her mother, and the wounds that we risk reopening.
Among the many eye-popping numbers of the AI industry—from trillions in investments to billions in spending—one stands out: $0 in profit among leading AI companies. According to Bloomberg, “never before has so much money been spent so rapidly on a technology that, for all its potential, remains largely unproven as an avenue for profit-making.” That imbalance, and the industry’s reliance on circular financing, has led to fears of a bubble. But those concerns are overblown, according to Azeem Azhar, author of the Exponential View newsletter, who earlier this year released a report on the AI economy.