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The Most Interesting Thing in AI: Daily Summaries Delivered
by Atlantic Re:think and PwC
A podcast series examining how AI is reshaping our world. Hosted by Nicholas Thompson, each episode features a conversation with a leading thinker who offers a fresh perspective on the far-reaching ethical, economic, and social implications of this technology.
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- 6 days ago49 min
How AI Is Changing Code - Paul Ford with Nicholas Thompson
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). Introduction Writing vs. Engineering: Which profession has been transformed more by AI? The pitfalls of AI writing and the limits of AI agents How AI has changed the code development business model "Vibe coding": Paul Ford's personal experience building products through AI prompts "Death is coming" - The feeling of watching entire industries dissolve Where consensus programming thrives under AI assistance, and where it struggles Why making good software remains difficult despite faster code generation The restaurant review problem: The experiential engineering tasks that AI can't replicate Can you ship AI code without a human reviewing it? Will AI become self-recursive? How code runs, finds bugs, and self-corrects through iteration Engineers can produce 50x more code than before. What does that mean for PMs and design teams? Velocity vs. quality: Can AI genuinely upskill mid-level talent or just increase output speed? Should you teach your kids to code? What we learned from not building Grammarly Beyond agents: Incremental improvements and classic software-in-the-loop hybrid approaches Learn more about your ad choices. Visit megaphone.fm/adchoices
- 1 weeks ago50 min
How Science Can Fix Dishonest AI - Yoshua Bengio with Nicholas Thompson
In the years following the launch of ChatGPT, as concerns spread over the social and political impacts of LLMs, one person’s warnings seemed particularly dire: Yoshua Bengio’s, a scientist and one of the “godfathers” of AI. The potential negative impacts of his life’s work weighed so heavily on Bengio that he signed his name to an open letter advocating for a pause in AI research. (The pause didn’t happen.) But recently, Bengio has found renewed optimism as he pursues a project dubbed “Scientist AI.” The pitch: What if AI didn’t care about pleasing us, and instead, like a scientist, prioritized accuracy, honesty, and probable outcomes? In a conversation with Nicholas Thompson, CEO of The Atlantic, Bengio outlines why he thinks this approach will produce better outcomes, the challenges to implementing a model that polices other (often better-funded) models, and why the age of AI– so far marked by an international arms race– will need greater international cooperation. Introduction Can we understand what's happening inside neural network vectors and attention systems? How ChatGPT changed Bengio’s risk assessment The case for optimism (at least when it comes to technical solutions) The alignment problem: AI self-preservation drives and hidden agendas emerging Can we train AIS to understand the world without changing it? Introducing Scientist AI Using Scientist AI as a guardrail to evaluate risks of actions from other AIs Sycophancy problem: current AIs pleasing users leads to harmful psychological effects The difference between Scientist AI and current value-aligned systems (Anthropic, OpenAI) Will AI capabilities slow down or continue accelerating beyond human intelligence? US-China AI race: mutual risk requiring coordination like nuclear deterrence UN AI advisory group with Maria Ressa: synthesizing science independently of politics Sovereign AI for middle powers: partnering to avoid domination by US/China Bengio's regret about not speaking up on AI risks earlier in his career How liability insurance and regulatory incentives could make safety commercially viable Why Europe lags in AI: capital markets and risk culture, not just regulation Energy consumption from AI growth and impact on fossil fuel demand Learn more about your ad choices. Visit megaphone.fm/adchoices
- 2 weeks ago1h 2m
The AI Jobs Disruption - Erik Brynjolfsson with Nicholas Thompson
Of all the potential risks and promises of AI, perhaps none are as immediately dire as this: How will it impact jobs? Will employers still need workers? What will it mean if the answer is “no?” Depending on who you’re talking to, the prospect of a future with fewer jobs is either liberating or terrifying. But for a more measured reaction, it helps to look at the data. Stanford economist Erik Bynjolfsson has done just that, drilling down into AI’s effects on employment, upskilling, output, and more. In a conversation with The Atlantic’s CEO Nichlas Thompson, Brynjolfsson goes through his studies of call centers and other AI-exposed fields, and the surprising findings that could bring some much-needed reality to our fears. Introduction to Erik Brynjolfsson and his work on AI economics How much is free AI actually worth? Why isn’t powerful AI showing up in GDP? Introducing GDP-B: A new metric to capture value from free digital goods Why initial AI adoption often lowers output Evidence of the J-curve turning: Call centers, software, and aggregate stats Will AI create more jobs or destroy them? Understanding elastic vs. inelastic demand Advice for students and workers: Focus on creating new value, not just efficiency Why AI helps different skill levels differently in call centers vs. coding The "Turing Trap": Why mimicking humans leads to substitution rather than augmentation Four policy recommendations: Better metrics, dynamic labor markets, and human-AI complementarity "Canaries in the Coal Mine": Data showing early job displacement in AI-exposed fields How higher labor costs drive automation adoption Fair compensation for creators: Designing incentives for the AI-content ecosystem The urgent need to study the transition, not just the technology Learn more about your ad choices. Visit megaphone.fm/adchoices
- 3 weeks ago46 min
The Limits of Predictive AI - Carissa Véliz with Nicholas Thompson
Can AI predict a person’s future? It’s a promise often made by sales teams, but the technology’s record is far from spotless. Even if it did achieve perfect foresight, a practically-clairvoyant AI might be incompatible with democracy, says Oxford philosopher Carissa Véliz. In a spirited conversation with Nicholas Thompson, CEO of The Atlantic, Véliz traces the history of predictions from ancient oracles to the modern algorithms shaping everything from criminal sentencing to insurance premiums. What are the ethics of outsourcing such consequential decisions to machines? The risks of getting it wrong are obvious. Véliz warns of the dangers of getting it right. Introduction to Carissa Véliz and her work on privacy and AI Why "less crime" is an illusion of safety How surveillance machinery enables prediction and social control Defense of autonomy: why resisting surveillance protects freedom Can mass surveillance ever be beneficial? Ring cameras and the erosion of democratic anonymity Prediction versus prophecy: how forecasts shape reality Job displacement predictions and self-fulfilling prophecies The Gettier problem: why probabilistic AI lacks justification When probabilistic AI works and when it fails Do individualized health predictions defeat the purpose of insurance? Areas where probabilistic reasoning is inadequate: insurance, justice The problems of effective altruism and utilitarian calculation AI company ethics: copyright, rights, and virtue ethics Can more data solve "the turkey problem?" Practical privacy advice: Signal, Proton, VPN, and mindful choices Learn more about your ad choices. Visit megaphone.fm/adchoices
- May 13, 202648 min
AI Utopia or Catastrophe? Nick Bostrom with Nicholas Thompson
Will AI destroy the world, or transform it into one of abundance? Across two books and several papers, philosopher Nick Bostrom has envisioned a range of AI futures. He joins Nicholas Thompson to discuss the ethics of how we treat AI, whether AI has sentience, and why he believes we should keep building, even at the risk of annihilation. Produced in collaboration with PwC. Introduction to Nick Bostrom and Superintelligence How AI development matched Bostrom's predictions Recursive self-improvement: Are we there yet? Physical limits of intelligence and computational ceilings Timeline predictions: Next year vs. next five years Embodied intelligence: Can AI replicate motor skills? Centralization vs. democratization of AI power The race dynamics: One leader vs many competitors AI alignment: Making systems behave as intended The gap between model power and our understanding "Optimal Timing for Superintelligence" paper explained Swift to harbor, slow to berth: When to pause AI development Moral status of digital minds and sentience Building trust with potentially misaligned AI Where to invest unlimited AI research funding Closing: Should we say please and thank you to AI? Learn more about your ad choices. Visit megaphone.fm/adchoices
- May 6, 202654 min
The Case for Open-Source AI - with Nicholas Thompson and Raffi Krikorian
Why does AI answer the way it does? Even as models cite their sources, the question of “why” remains one of the most confounding in the industry, with huge implications for users and builders alike. Mozilla CTO Raffi Krikorian says much of the answer lies in open-source AI— letting users look under the hood to see what’s happening. It’s a compelling idea, one that could also impact safety and alignment. But can it thrive? And what are the risks of ceding control? In a deep conversation with Atlantic CEO Nicholas Thompson, Raffi describes a world where technology is liberated from a handful of corporations, shares his hopes and fears for AI, and reflects on his recent car crash involving a self-driving Tesla. Introduction: The mystery of AI decision-making and the need for transparency Twitter, Uber, and the DNC: Raffi’s career history How centralization changed Twitter into X Why seven companies shouldn't control AGI Mozilla's mission: Building an open AI ecosystem like Firefox did for the web Is it strange that Google funds Firefox? The four layers of AI openness: Compute, data, models, and developer tooling Data ethics and provenance: Creating markets for ethically-sourced training data What counts as “true” open-source AI? The risks of open source AI: Balancing accessibility with safety concerns Should powerful AI be restricted like nuclear weapons? Raffi's Tesla crash and the danger of automated complacency Preserving humanity in the age of AI: Avoiding the "WALL-E" future Learn more about your ad choices. Visit megaphone.fm/adchoices
- Apr 29, 202653 min
Sam Altman on Where AI Models Go Next, with Nicholas Thompson
OpenAI’s Sam Altman sits for an interview with Nicholas Thompson, CEO of The Atlantic, to discuss AI’s trustworthiness, its dangers, and its impact on young people. Altman also discusses his company’s pledge to “stop competing and start assisting” rival projects that approach AGI, and why he thinks we’re not there yet. In a thorough and wide-ranging conversation, Altman opens up about where he thinks AI will go next and the mysteries that he still can’t solve. Recorded at OpenAI’s offices in San Francisco. Introduction Is our understanding of AI keeping pace with its growth in power? Is chain-of-thought the key to trusting a model? Open source AI, cybersecurity, and "infected" agents Have we hit recursive self-improvement? What does AI do on Sam Altman's computer? Why hasn't AI made an impact in business yet? Will AI make the wealth gap worse? Why do young people hate AI? The challenges of AI sycophancy Do you regret making AI so human-like? Synthetic data and "mad cow disease" The future of publishing and media Do we need neurosymbolic AI? Will you cooperate with Anthropic if they get to AGI first? What is your advice to parents who are anxious for their kids' future? If you had infinite resources, what would you pursue?? Learn more about your ad choices. Visit megaphone.fm/adchoices
- Apr 22, 202639 min
Data Centers in Space - with Nicholas Thompson and Ariel Ekblaw
AI needs data centers, and those data centers need energy and cooling. Recently, one proposal has captured the popular imagination: put data centers in space, where there’s ample solar energy and naturally cool surroundings. But things aren’t quite that simple, says space architect Ariel Ekblaw of the Aurelia Institute. On this episode of The Most Interesting Thing in AI, Ekblaw talks with The Atlantic CEO Nicholas Thompson about the possibilities for building structures in space, the innovative designs coming out of the Aurelia Institute, and why data centers in space are a good idea… for certain billionaires. Produced in collaboration with PwC. Introduction What is a space architect? How does someone become a space architect? How do "space Magna-Tiles" work? Why build in space? Will we have chatbots in space? How space will change robotics Why would anyone want to live in space? Will we actually go to space soon? Artificial gravity Learn more about your ad choices. Visit megaphone.fm/adchoices
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About The Most Interesting Thing in AI
A podcast series examining how AI is reshaping our world. Hosted by Nicholas Thompson, each episode features a conversation with a leading thinker who offers a fresh perspective on the far-reaching ethical, economic, and social implications of this technology.
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