The Most Interesting Thing in AI

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
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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

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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

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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
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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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