However you feel about Meta, Andrew Bosworth has likely played a part in it. The long-serving right hand to Mark Zuckerberg and current Chief Technology Officer, “Boz” has helped shape almost every stage of the company’s growth. He’s credited with introducing the Facebook News Feed in 2006, and, more recently, with overseeing the company’s efforts in the metaverse.

Now, Boz is a key figure behind Meta's artificial intelligence strategy. In late June, he joined Nicholas Thompson, CEO of The Atlantic, for a conversation covering the state of Meta AI: from the privacy concerns around its glasses to its controversial (and now halted) program training models on its employees’ keystrokes and mouse movements to its pivot from open-source AI to a proprietary model.

(00:00) Introduction

(02:34) Can AI glasses get past the developer ecosystem Catch-22?

(05:45) What’s true and false about NameTag, facial recognition, and Meta’s glasses 

(09:22) Why Meta tracked employee keystrokes to teach AI how to use computers—and why it stopped

(12:20) The legal complications of tracking employee data 

(15:58) Why unique professional workflows are more valuable than generic internet text 

(18:15) Boz’s vision for AI: a tool to maximize individual human potential, not replace workers 

(21:15) How AI produces faster coding but slower thinking, leading to "well-executed bad products" 

(24:10) If AI doubles a worker's productivity, do companies hire fewer people or double the team size? 

(28:38) Why Boz is optimistic about life "under the algorithm"

(32:08) Meta’s pivot from open source (Llama) to closed models (Muse Spark)

(37:25) How long will scaling laws hold?

(40:26) Where Meta already excels in AI

(46:27) Why energy production is the upstream constraint for the future of AI

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AI agents can scour the internet for us, reply to our messages, and add events to our calendars. But in order to do so, they need sweeping access to our data. Handing that over presents an unprecedented threat to privacy, says Signal Foundation president Meredith Whittaker, who directs the Signal messaging app. In conversation with The Atlantic CEO Nicholas Thompson, Whittaker discusses the risks of agentic AI, Signal’s approach to AI coding, and why it’s uncompromising in its commitment to protecting user data. 

This episode was recorded prior to Keir Starmer’s resignation announcement.

(00:00) Introduction

(02:52) AI’s threat to privacy

(03:28) The end of the operating system as a safe boundary

(04:15) The dangers of off-device compute and the limits of Apple’s privacy promises

(05:30) Prompt injection vulnerabilities, and the risks of giving agents access to calendars, contacts, messages

(06:50) How Signal mitigates the risk of AI software like Microsoft Recall

(09:15) The accessibility trade-off: Why protecting privacy (at this moment) comes at the cost of screen readers for blind/low-vision users

(12:20) Are AI agents the new root users?

(15:28) The problem of proprietary OS providers

(17:40) Why don’t consumers care about privacy?

(19:35) Can a focus on enterprise customers fix AI’s privacy problems?

(23:30) How government surveillance can undermine private industry privacy efforts

(28:13) How Signal worked with Apple to fix the iOS notification exploit

(30:50) Meredith Whittaker’s critiques of the UK government’s content-scanning proposal

(36:20) The risks of compromising on privacy

(42:30) Would Signal take a different approach to privacy if it were the world's largest messenger app?

(45:36) What would you do with unlimited funding?

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Take a scroll through Facebook or TikTok today, and you’ll likely find some AI-generated content: an influencer with impossibly-radiant skin, a stunt that bends the laws of physics, Shrimp Jesus. And while there might not be much social benefit to AI slop, that doesn’t mean that we should shun AI from our feeds entirely, says Eli Pariser. As the author of “The Filter Bubble,” Pariser has been a longtime critic of social media. But he’s also the cofounder of Upworthy, the bubbly bright news site that perfected the clickbait headline. Today, Pariser is building Roundabout, a kind of socially-conscious answer to Nextdoor, and he’s hopeful that AI agents can mend the thumb-shaped hole in our social fabric. But is more tech really the answer to our tech woes? Nicholas Thompson, CEO of The Atlantic, sits down with Eli to find out.

(00:00) Introduction

(02:00) How we can avoid social media's worst mistakes

(03:36) Would subscription models have fixed social media?

(05:03) How the agentic interface will change news consumption

(07:19) If AI replaces social media, where is the new digital town square?

(09:29) Nextdoor, engagement, and online utility

(11:42) Agents replace utilitarian uses; humans stay in trusted spaces

(13:37) The future of group chats: custom micro-platforms with add-on features

(21:30) Can a social network built around community stewards and offline events thrive? The case for Roundabout

(27:26) Upworthy grew fast but VC pressure drove it to clickbait

(33:45) AI agents = filter bubbles on steroids with deep personalization

(39:45) Fund external AI governance structures rather than internal alignment

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You've probably heard it already: AI is going to radically change the way we work. But the details change depending on who's making the prediction: AI will wipe out the C-suite, or entry-level jobs, or make us all into prompt engineers. Those scenarios are far-fetched, says Dan Priest. In his role as the Chief AI Officer at PwC, Priest sees firsthand how companies across the industrial landscape are utilizing AI– often in ways that clash with those  fatalistic prognostications. In a vital conversation with Nicholas Thompson, CEO of The Atlantic, Priest unpacks what he's learned about AI implementation, agents, and how businesses can adapt in the age of AI.

(00:00) Introduction

(02:30) The two extremes of AI adoption

(04:46) Will we see a billion-dollar one-person company?

(06:24) Why technology advances faster than implementation 

(07:32) Where is AI actually being deployed? From IT/code generation to CFO/CHRO functions

(10:17) Specialist vs. generalist roles in an AI world 

(12:17) Should every employee have their own agent? 

(14:39) Agent performance limits: Task length, concentration windows, and why multi-model checks and balances matter

(18:11) Who gains most from AI: top performers or early career workers?

(21:37) Agentic applications 

(26:12) The hourglass organization: Replacing pyramid and diamond models with expanded entry points plus leadership, compressing middle management

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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

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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.

(00:00) Introduction

(05:00) Can we understand what's happening inside neural network vectors and attention systems?

(07:00) How ChatGPT changed Bengio’s risk assessment 

(09:37) The case for optimism (at least when it comes to technical solutions) 

(12:30) The alignment problem: AI self-preservation drives and hidden agendas emerging

(14:26) Can we train AIS to understand the world without changing it? Introducing Scientist AI

(15:52) Using Scientist AI as a guardrail to evaluate risks of actions from other AIs

(19:37) Sycophancy problem: current AIs pleasing users leads to harmful psychological effects

(22:20) The difference between Scientist AI and current value-aligned systems (Anthropic, OpenAI)

(24:06) Will AI capabilities slow down or continue accelerating beyond human intelligence?

(29:58) US-China AI race: mutual risk requiring coordination like nuclear deterrence

(31:57) UN AI advisory group with Maria Ressa: synthesizing science independently of politics

(33:18) Sovereign AI for middle powers: partnering to avoid domination by US/China

(37:54) Bengio's regret about not speaking up on AI risks earlier in his career

(40:12) How liability insurance and regulatory incentives could make safety commercially viable

(42:42) Why Europe lags in AI: capital markets and risk culture, not just regulation

(46:43) Energy consumption from AI growth and impact on fossil fuel demand

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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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