Netflix product and technology officer Elizabeth Stone argues that generative AI is blurring job boundaries without erasing the value of craft, pushing companies to prize systems thinkers, stronger infrastructure and cultures built on talent density, autonomy and accountability. She sketches a future where agents accelerate product development, analysis and entertainment production, while human judgment and storytelling remain the core differentiators.

Proton CTO Bart Butler makes the case that the privacy company is really selling trust, then traces how encryption, Swiss jurisdiction, and a foundation-controlled ownership model are meant to keep that promise intact. The conversation turns from corporate ideals to harder tests: FBI requests routed through Switzerland, looming European surveillance laws, child-safety demands, age verification, and the problem of building AI tools without surrendering user control.

Granola CEO Chris Pedregal talks with Every’s Dan Shipper about the knife-fight reality of building through the AI boom, where even breakout products feel precarious. Their conversation widens from meeting notes to the bigger contest over AI-native work: the interfaces, context, and invisible systems that could remake how people think and collaborate on the job.

Casey Newton and David Pierce sort the genuinely useful AI aids from the new wave of productivity theater, arguing that simple systems and reliable capture matter more than ever. Along the way, they weigh where AI actually saves time, from transcription to summarizing source material, and where it mostly adds friction, false confidence, and more work.

NVIDIA automotive chief Zhinzhou Wu argues that the software-defined car is finally arriving, with central computers, shared data, and AI models reshaping how automakers build vehicles. The conversation traces the industry’s uneven path to autonomy, from China’s EV head start and Tesla’s camera-only bet to the safety case for LiDAR and the economics of putting ever more compute inside a car.

A frantic day of AI releases sharpened the industry’s new fault line: not just which model scores highest, but which gets close enough for far less money. Weighing OpenAI’s new Sol, Terra and Luna against Anthropic, xAI and Meta, the conversation argues that price-performance, not raw benchmark supremacy, may decide where people actually work AI into coding, finance and everyday software tasks.

Instagram chief Adam Mosseri sketches a workplace where AI shrinks product teams, blurs job boundaries and makes taste, judgment and strategy more valuable than sheer execution. He also argues that an internet flooded with synthetic media may ultimately reward authenticity, even as Instagram struggles to label, rank and moderate what is real.

A wide-ranging AMA traces Gergely Orosz’s shift from Uber manager to independent publisher, then circles through AI hiring, code quality, startup culture and the engineers still finding leverage in a choppy market. Along the way, he argues that AI is less a doctrine than a tool, and that careers are future-proofed less by credentials than by proximity to relevant work.

A writer and technologist describes using AI as both intoxicant and tool, building bespoke software while guarding his mornings and attention so the work that matters does not get flattened into productivity theater. The conversation moves from vibe coding and the collapse of old SaaS moats to the stubborn value of weird books, deep focus and human particularity in an epochal technological shift.

Casey Newton tests Raycast’s Glaze by building a Nightwing-themed to-do list, a custom Platformer archive search tool, and a half-finished source tracker, using the experience to argue that AI-made software is getting more visual, more personal, and more immediately useful. The appeal is less technical novelty than the thrill of instantly reshaping the tools that used to make users live with their compromises.

A Gusto product leader traces how one company’s AI adoption moved from informal demos to quarterly hackathons, shared tooling and an expectation that everyone, not just engineers, learns to build. The conversation argues that sustained time, paired practice and connected data matter more than slogans if companies want AI fluency to change how work gets done.

At Cannes, Digitas North America CEO Amy Lanzi argues that advertising’s AI boom resembles the overhyped promises of programmatic, with platforms selling automation while agencies and brands still need human judgment, strategy, and data fluency. The conversation also traces the rise of creators as full-fledged marketing businesses and the growing fight over who controls the relationship between brands, audiences, and the platforms in between.

Natalia, Every’s head of consulting, describes how AI agents are moving from novelty to everyday infrastructure: managing sales ops, triaging email, building family care systems, and turning research into personalized learning tools. The conversation also argues for a clearer division of labor, with human judgment and off-the-shelf software still essential even as custom agents absorb administrative work.

Edwin Chen, the founder of data-labeling and evaluation firm Surge, describes training frontier models as a kind of schooling for AGI, where benchmarks now stretch from middle-school math to research-level discovery. The conversation widens into a debate over whether AI should optimize for human flourishing or the same engagement traps that warped social media, and what deeply personal data may be worth in teaching models taste, judgment, and voice.

AWS chief Matt Garman argues that AI will reorder white-collar work rather than erase it, even as Amazon automates more tasks and trims parts of its own workforce. The conversation ranges from college AI degrees and shaky labor data to enterprise adoption, data center backlash, and the question of whether efficiency gains create new jobs fast enough.

At Cloaked, head of product Kyler describes how an internal AI “harness” evolved from a personal fix for prompt-copying drudgery into companywide infrastructure installed on every managed computer. The conversation traces a distinctly AI-native operating model, with Slack agents for nontechnical staff, coding agents for power users, and layered guardrails designed to make automation useful without making it reckless.

Patreon CEO Jack Conte says the company has outgrown its old role as a payments layer and is rebuilding itself as a discovery, hosting and community platform for creators squeezed by algorithmic feeds, Apple’s fees and AI-fueled slop. The conversation ranges from social media’s failures to the payment and moderation pressures of running a creator business that now competes more directly with Instagram, TikTok and Substack.

Anthropic engineering leader Fiona Fung describes a software world where code is abundant, initiative matters more than syntax, and managers rely on AI to track quality, feedback, and the work itself. The conversation traces how engineering, product management, and team culture are being remade by agents, even as loneliness, fear, and accountability become harder problems to solve.

A weekend scramble over Anthropic’s newly released Claude Fable 5 became a test case for how the Trump administration may regulate AI: through formal safety policy or through panicked, personalized power. Nilay Patel and Verge reporter Hayden Field trace the model’s abrupt shutdown, the export controls that followed, and the industry’s growing fear that political risk now sits alongside technical risk.

GitHub’s Kyle Daigle describes a software world remade by coding agents, where pull requests and commits are surging, nondevelopers are building apps, and maintainers need new controls to keep pace. The conversation follows the business and product consequences of that shift, from model routing and token costs to the long game of training tools on a developer’s habits and context.