The Most Interesting Thing in AI

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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