The Most Interesting Thing in AI

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.

(00:00) Introduction to Erik Brynjolfsson and his work on AI economics 

(02:51) How much is free AI actually worth? 

(05:25) Why isn’t powerful AI showing up in GDP?

 (06:48) Introducing GDP-B: A new metric to capture value from free digital goods 

(07:23) Why initial AI adoption often lowers output 

(09:05) Evidence of the J-curve turning: Call centers, software, and aggregate stats 

(14:48) Will AI create more jobs or destroy them? Understanding elastic vs. inelastic demand

(19:36) Advice for students and workers: Focus on creating new value, not just efficiency 

(21:34) Why AI helps different skill levels differently in call centers vs. coding 

(25:47) The "Turing Trap": Why mimicking humans leads to substitution rather than augmentation

(30:50) Four policy recommendations: Better metrics, dynamic labor markets, and human-AI complementarity 

(37:52) "Canaries in the Coal Mine": Data showing early job displacement in AI-exposed fields 

(47:08) How higher labor costs drive automation adoption

(52:53) Fair compensation for creators: Designing incentives for the AI-content ecosystem 

(59:03) The urgent need to study the transition, not just the technology

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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.

(00:00) Introduction to Carissa Véliz and her work on privacy and AI 

(01:29) Why "less crime" is an illusion of safety 

(02:30) How surveillance machinery enables prediction and social control 

(03:06) Defense of autonomy: why resisting surveillance protects freedom 

(04:02) Can mass surveillance ever be beneficial?

(05:36) Ring cameras and the erosion of democratic anonymity 

(07:01) Prediction versus prophecy: how forecasts shape reality 

(08:52) Job displacement predictions and self-fulfilling prophecies 

(13:15) The Gettier problem: why probabilistic AI lacks justification 

(16:06) When probabilistic AI works and when it fails 

(18:52) Do individualized health predictions defeat the purpose of insurance?

(25:24) Areas where probabilistic reasoning is inadequate: insurance, justice 

(27:48) The problems of effective altruism and utilitarian calculation 

(32:37) AI company ethics: copyright, rights, and virtue ethics 

(37:47) Can more data solve "the turkey problem?" 

(39:15) Practical privacy advice: Signal, Proton, VPN, and mindful choices

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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.

(00:00) Introduction to Nick Bostrom and Superintelligence 

(01:56) How AI development matched Bostrom's predictions 

(04:48) Recursive self-improvement: Are we there yet? 

(07:40) Physical limits of intelligence and computational ceilings 

(09:40) Timeline predictions: Next year vs. next five years 

(11:46) Embodied intelligence: Can AI replicate motor skills? 

(14:32) Centralization vs. democratization of AI power 

(16:52) The race dynamics: One leader vs  many competitors

(19:57) AI alignment: Making systems behave as intended 

(21:37) The gap between model power and our understanding 

(23:25) "Optimal Timing for Superintelligence" paper explained 

(28:14) Swift to harbor, slow to berth: When to pause AI development 

(35:20) Moral status of digital minds and sentience 

(41:23) Building trust with potentially misaligned AI 

(44:11) Where to invest unlimited AI research funding 

(47:07) Closing: Should we say please and thank you to AI?

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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.

(00:00) Introduction: The mystery of AI decision-making and the need for transparency 

(02:25) Twitter, Uber, and the DNC: Raffi’s career history

(05:01) How centralization changed Twitter into X

(08:30) Why seven companies shouldn't control AGI 

(11:38) Mozilla's mission: Building an open AI ecosystem like Firefox did for the web 

(14:21) Is it strange that Google funds Firefox?

(16:40) The four layers of AI openness: Compute, data, models, and developer tooling

(22:20) Data ethics and provenance: Creating markets for ethically-sourced training data 

(26:44) What counts as “true” open-source AI?

(32:04) The risks of open source AI: Balancing accessibility with safety concerns 

(35:56) Should powerful AI be restricted like nuclear weapons? 

(39:17) Raffi's Tesla crash and the danger of automated complacency 

(46:56) Preserving humanity in the age of AI: Avoiding the "WALL-E" future

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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.

(00:00) Introduction

(02:33) Is our understanding of AI keeping pace with its growth in power?

(05:10) Is chain-of-thought the key to trusting a model?

(11:07) Open source AI, cybersecurity, and "infected" agents

(16:26) Have we hit recursive self-improvement?

(19:18) What does AI do on Sam Altman's computer?

(21:15) Why hasn't AI made an impact in business yet?

(24:04) Will AI make the wealth gap worse?

(27:01) Why do young people hate AI? 

(30:23) The challenges of AI sycophancy 

(33:36) Do you regret making AI so human-like? 

(36:53) Synthetic data and "mad cow disease" 

(39:22) The future of publishing and media 

(41:54) Do we need neurosymbolic AI? 

(43:40) Will you cooperate with Anthropic if they get to AGI first? 

(47:42) What is your advice to parents who are anxious for their kids' future? 

(49:46) If you had infinite resources, what would you pursue??

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