Advertise on podcast: The Most Interesting Thing in AI

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. Produced independently of The Atlantic’s editorial staff. Supporting sponsor: PwC. (00:00) Introduction (03:45) The surprising return of relational machines  (07:35) Why Sherry wrote her new book, Artificial Intimacy  (11:18) The problem with delegating parenting, education, and therapy to AI  (19:07) Why people seek emotional support from  chatbots  (24:40) Sherry’s concept of “dual consciousness” (29:43) Regulating generative AI use for kids (32:42) Can college students still read and write? (37:32) Distinguishing between useful AI tools and harmful delegations  (41:32) How chatbots mimicking deceased loved ones can cause new psychological harm  (50:56) What Sherry would do with unlimited AI funds Learn more about your ad choices. Visit podcastchoices.com/adchoices

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. In conversation with Nicholas Thompson, CEO of The Atlantic, Azhar expands on his findings. He says AI’s finances are sound: the demand is there, revenue is growing, and the technology will advance enough to deliver on the necessary ROI. Produced independently of The Atlantic’s editorial staff. Supporting sponsor: PwC. (00:00) Introduction (03:40) Mapping the AI economy through forensic accounting (07:50) How to think about infrastructure spend  (16:30) The "J-curve" of AI adoption (24:00) How cheaper tokens will change the market  (27:17) China's AI strategy (40:22) Is there something that could break Azeem’s model?  (48:34) What could happen when the AI firms IPO  (53:34) The effects of the culture in Silicon Valley  (56:34) Which bubble-breaker scenario worries Azeem the most (59:16) What Azeem would do if he had an unlimited budget to invest in AI Learn more about your ad choices. Visit podcastchoices.com/adchoices

Technological waves often arrive with a promise. The internet was going to democratize information. Blockchain was going to decentralize power. Now, AI's boosters say it will bring unprecedented prosperity. Few people are better positioned to speak to that possibility than Jeremy Allaire. As the CEO of the fintech company Circle—issuer of the USDC stablecoin—and a longtime internet entrepreneur, Allaire has been at the center of the first two technological waves. With AI, he says we’re going through a change that’s “bigger than the Industrial Revolution.” He sees a future of financially-empowered agents—and believes this technology could be the one that finally fulfills the promise of shared wealth.  In a conversation with Nicholas Thompson, CEO of The Atlantic, Allaire describes how the agentic economy might work, why regulation will be critical to its success, and what’s missing from the conversation around AI’s effect on society. Produced independently of The Atlantic’s editorial staff. Supporting sponsor: PwC. (00:00) Introduction (03:33) Jeremy’s early fascination with open networks (07:14) Creating the first online archive of Noam Chomsky's work  (10:54) The promise of the early web (17:00) How the internet centralized power through social media giants (22:53) Founding Circle with the vision of creating "HTTP for money" (31:18) Navigating crypto's reputation challenges (38:18) Why Jeremy wrote The Agentic Economy Treatise (43:30) How AI agents could change the fundamental structure of companies (53:34) The risks of extreme capital concentration with AI (59:40) The choice we face with AI regulation (1:05:03) What Jeremy would do if he had an unlimited budget to invest in AI Learn more about your ad choices. Visit podcastchoices.com/adchoices

As AI’s capabilities improve, reports of “rogue” agents and misaligned systems are becoming more frequent. In response, some activists, academics, and politicians have called for a pause in development, warning of the dangers that superintelligent AI could pose. But Richard Socher, the CEO of AI startup Recursive as well as the AI search engine You.com, sees another path. In his new book, The Eureka Machine, Socher lays out his vision for how self-improving AI could lead to breakthroughs in science, medicine, economics, and more. In conversation with Nicholas Thompson, CEO of The Atlantic, he details some of the ways we can mitigate its risks, explains why the limits of our own minds could hold AI back, and considers whether we’re projecting too many fears about human behavior onto an insensate technology. Produced independently of The Atlantic’s editorial staff. (00:00) Introduction (02:25) Richard's childhood in East Germany and Ethiopia (10:23) What Richard got wrong about AI hype and the DeepMind acquisition (12:14) The idea behind The Eureka Machine (17:18) How AIs can compete to make each other safer (20:30) Are fears about synthetic data overblown? (22:32) When hallucinations are actually helpful (28:16) How human intelligence could hold AI back (34:52) The metacognition tension (39:13) The OpenAI-Hugging Face hack (46:15) Would narrower AI goals have put us in a better spot? (50:30) Richard’s debate with Yoshua Bengio: are we building a dominant species? (54:29) What breakthrough will finally win over AI skeptics? (59:26) The case for widely available superintelligence (1:04:20) What Richard would do if he had an unlimited budget to invest in AI Learn more about your ad choices. Visit podcastchoices.com/adchoices

For years, tech CEOs have promised that AI will take on the menial, repetitive tasks that have long been a part of office life. Typically, those tasks are assigned to younger, less experienced workers—and poll after poll shows Gen Z is increasingly concerned about what AI will mean for their job prospects. Clara Shih says those fears are, at least partly, justified. As the former head of Meta's Business AI Group, and the CEO of Salesforce AI before that, Shih knows firsthand how companies are deploying AI and where it's affecting hiring. Now, as the founder of the New Work Foundation, Shih is building tools to help young workers navigate this complex hiring maze. In conversation with Nicholas Thompson, CEO of The Atlantic, Shih details the skills students should focus on to prepare for the AI era, how employees can leverage the technology, and why more people—of all ages—might soon find themselves working for an algorithm. Produced independently of The Atlantic’s editorial staff. (0:00) Introduction (02:35) What New Work Foundation is doing for Gen Z job seekers (04:00) Is your major cooked? Testing the "Field Report" tool (06:50) What AI exposure scores don’t capture (10:20) How colleges should prepare students for the AI era (17:30) Why beginners shouldn't use AI to learn something new (22:00) Is college still worth it? (23:42) Why applying for a job is harder than it used to be (29:52) AI will make big companies more efficient—and less fun  (33:12) How the bitter lesson could apply to entire organizations (36:12) The three buckets all white-collar work might soon fall into (42:51) Are companies actually laying people off because of AI? (47:54) What Clara would do if she had an unlimited budget to invest in AI Learn more about your ad choices. Visit podcastchoices.com/adchoices

Kara Swisher has done a bit of everything. She’s covered the rise of Silicon Valley’s tech giants, founded and sold her own startup publication, and dabbled in Hollywood, with appearances in films such as The Devil Wears Prada 2 and Tron: Ares.  She’s now the host of multiple podcasts about tech and politics, as well as Kara Swisher Wants To Live Forever, a CNN series about the longevity industry. After decades of chronicling the Valley’s foibles, Swisher has many thoughts on AI—where the technology currently is and where it’s going. In conversation with Nicholas Thompson, CEO of The Atlantic, Swisher goes deep on whether the Big Tech companies are capable of managing AI responsibly; if the technology will deliver on the hype they’ve generated; and how media companies and creators can best leverage it.  Recorded in Bar Harbor, Maine. Produced independently of The Atlantic’s editorial staff. Supporting sponsor: PwC. (00:00) Introduction (02:25) What Kara’s most excited about with AI (08:50) What the political backlash against data centers shows (11:15) Are we repeating the dot-com bubble with massive AI spending? (13:45) OpenAI's self-inflicted wounds (16:40) What the US should do about open-source AI (19:15) Will AI-driven social algorithms push politics toward the center? (21:25) Why young people are turning on Big Tech (25:10) How AI is reshaping the relationship between creators and media institutions (28:35) Will AI make trusted media brands more valuable? (37:30) How media business models should adapt in the age of AI (45:35) What Kara would do if she had an unlimited budget to invest in AI Learn more about your ad choices. Visit podcastchoices.com/adchoices

AI may have leveled the playing field in coding and app development, but it’s also put potentially dangerous tools in reach of nefarious actors. Now, empowered by AI, state-backed intelligence agencies and non-state actors alike can access tools that enable everything from mass surveillance to cyberattacks to bioweapon development. This dynamic could lead to a catastrophe, warns Eurasia Group’s Ian Bremmer. In conversation with Nicholas Thompson, CEO of The Atlantic, Bremmer describes the risks of open-source AI, the opportunities for international collaboration (including an “AI stability board”), and how to increase investment in safety and alignment. Will AI cause socially-destabilizing inequality? What are the chances it helps democracy rather than hurt it? And how concerned should we be about AI companions?  Produced independently of The Atlantic’s editorial staff. Supporting sponsor: PwC. (00:00) Introduction (02:04) Why cyberattacks aren’t the primary threat of AI (05:58) What we should (and shouldn't) take away from the Mythos incident (08:53) How AI could enable the creation of bioweapons within a year (13:32) The surveillance trilemma: balancing existential risk mitigation, privacy rights, and open-source freedom  (16:11) Could a FINRA-style global AI stability board enforce safety standards? (19:54) Why market dynamics alone cannot ensure AI safety (23:14) Could a US-China bilateral agreement be enough to address governance?  (29:51) Does AI solve social media’s polarization issue? (34:13) Will AI-driven inequality threaten democratic foundations? (40:31) The vertical vs. horizontal race: can civil society and nonprofits build ethical guardrails fast enough to catch up?  (45:21) China’s AI strategy: prioritizing industrial robotics and defense (49:19) What role will Europe play in the AI era? (52:10) The emerging anti-AI backlash: a new wave of economic populism driven by displaced white-collar professionals  (56:42) Career advice for the AI era (58:17) How AI impacts consultancies like the Eurasia Group (01:00:00) What Ian would do if he had an unlimited budget to invest in AI Learn more about your ad choices. Visit podcastchoices.com/adchoices

Steven Johnson spent decades figuring out the best ways to turn his research into words on the page. Now, he’s adapting those methods for Gemini Notebook, Google’s AI-powered interactive bibliography. (Until recently, it was called NotebookLM.) But in building a tool designed to synthesize ideas, is there a risk of taking humans too far out of the writing process? In conversation with The Atlantic’s Nicholas Thompson, Johnson discusses the best practices, ethics, and limitations of using AI as a collaborator. The Atlantic has a licensing agreement with Gemini Notebook, and Google also advertises with The Atlantic. This episode was not a part of those deals. Produced independently of The Atlantic’s editorial staff. Supporting sponsor: PwC. (00:00) Introduction (05:10) The evolution of Steven's writing process  (08:22) Managing the "iceberg" of research (11:45) The ethics of AI-assisted writing (19:02) What happens when AI becomes as good at writing as humans (24:17) Why some genres could change drastically with AI (29:10) How AI might undo polarization  (34:45) Designing educational guardrails for AI (44:26) What will AI do to writing styles? (49:10) Licensing author personas and protecting creators (56:52) A rule of thumb for how to use AI  (58:00) What Steven would do if he had an unlimited budget to invest in AI Learn more about your ad choices. Visit podcastchoices.com/adchoices

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 Learn more about your ad choices. Visit podcastchoices.com/adchoices

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? Learn more about your ad choices. Visit podcastchoices.com/adchoices

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 Learn more about your ad choices. Visit podcastchoices.com/adchoices

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 Learn more about your ad choices. Visit podcastchoices.com/adchoices

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

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 Learn more about your ad choices. Visit podcastchoices.com/adchoices

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 Learn more about your ad choices. Visit podcastchoices.com/adchoices