Figma’s CFO traces a rise from early-career finance hire to public-company executive, crediting luck, first-principles thinking and a willingness to grab the work no one else owned. The conversation widens into how finance leaders build trust, make bets in the AI era and prepare companies for the scrutiny of public markets long before an IPO.

Riyaz Factory’s founders describe a biologically driven alternative to plastic recycling, using enzymes and AI to break polymers back into their original monomers instead of degrading them with heat. Their conversation traces the science, the limits of traditional recycling, and a startup effort to build a low-energy circular process that could plug into existing industrial supply chains.

Neuroscientist and physician Mithu Storoni argues that in an AI-saturated workplace, human efficiency should be measured by the quality of ideas rather than the quantity of output. She explains how attention, creativity, learning, and even boredom follow distinct brain states that managers can better support through flexible schedules, protected focus time, and work designed around natural cognitive rhythms.

Anders Hejlsberg traces the accidents, lawsuits, and internal battles that shaped Turbo Pascal, C#, and TypeScript, from Java’s legal fallout to Microsoft’s uneasy embrace of open source. The conversation doubles as a philosophy of language design: tooling matters as much as syntax, AI still needs deterministic systems beneath it, and great developer platforms are built on decade-long bets.

Joanna Stern joins Big Technology to talk through her year of using AI for nearly everything, from parenting and health questions to work, wearables, and even a flirtation with an AI boyfriend. The conversation treats AI less as a futuristic gimmick than as an intimate, sometimes unsettling layer in ordinary life, where convenience, companionship, and bad habits start to blur.

Noah Breyer describes an unusually practical AI stack: an Obsidian vault synced to a home server, cloud coding agents that search and summarize his notes, and a phone-based workflow that turns dead time into deep work. The conversation broadens into a larger argument about probabilistic software, bureaucratic organizations, and why AI is often more useful as a reader and thinking partner than as a writer.

Casey Newton and Ella Marciano open a new Platformer series with survey data showing AI adoption is highest among managers and top earners, while junior workers and administrative staff remain more skeptical of its benefits. A later conversation with Box CEO Aaron Levie argues that enterprise software will be remade by agents, but that most jobs will shift rather than disappear as human work moves to the harder, higher-value last mile.

A new report from CircleCI suggests AI coding tools are widening the gap between elite software teams and everyone else: the fastest shops are shipping dramatically more code, while the middle muddles through bugs, stalled projects, and cleanup. Paul Ford and Rich Ziatti argue that the payoff comes less from the tools themselves than from disciplined process, strong technical judgment, and organizations willing to confront AI’s confident mistakes.

Ryan Nystrom describes how AI has collapsed the distance between planning, meetings and shipping at Notion, from auto-generated standups to spec-driven coding that turns spoken ideas into working features. The conversation frames engineering less as hand-writing every line and more as designing systems, verification loops and plain-English specs that agents can execute.

Joanna Stern joins Nilay Patel to talk through a year of living with AI, the gap between flashy demos and useful products, and the privacy costs hidden inside convenience. The conversation also turns to her leap from The Wall Street Journal to her new venture, New Things, and what it takes to build an independent tech-media business around YouTube, newsletters and a mainstream audience.

Eric Ries argues that good companies rarely collapse because of competition; they are more often hollowed out by governance, incentives and financial pressure that turn success into a liability. Drawing on examples from Anthropic, Novo Nordisk, Cloudflare and Philip Morris’s disastrous purchase of Vectura, he makes the case that founders must build mission protection into both culture and corporate structure before it is too late.

A sprawling Friday news roundtable connects Anthropic’s massive compute deal with SpaceX, the OpenAI trial’s newly public text messages, and a wave of AI-era layoffs. Beneath the gossip and spectacle runs a sharper argument over whether the boom is driven by real product demand, IPO maneuvering, and a genuine rewiring of how companies operate.

Anthropic’s platform leads sketch how AI infrastructure is shifting from a bare completion endpoint to managed, long-running agents with memory, tools and cloud scaffolding. They argue that the real bottleneck is no longer prompt craft but the dreary work of production infrastructure, and imagine a near future where Claude chooses its own model, spins up sub-agents and compresses work down to an outcome and a budget.

Perplexity’s chief business officer argues that the industry’s rush toward agentic “super apps” is less a pivot than an attempt to monetize AI where it already matters most: work. The conversation weighs whether computer-controlling assistants are durable businesses or just the next novelty spike, and what it means to trust them with calendars, email, taxes, and the modern payroll budget.

A conversation about AI in schools moves past cheating panics to the deeper question of what education is for when machines can generate polished work on demand. Teachers and researchers describe a fractured landscape in which generative tools may save time at the margins, but also erode learning, judgment, and trust in the classroom.

Claire Vo races through Anthropic’s Code with Claude announcements, from scheduled routines in Claude Code to managed-agent features like outcome-based rubrics, multi-agent teams, and experimental memory tools. The conversation stays grounded in product work, sketching practical uses for newsletters, PRDs, and the growing appeal of agent systems that can grade, iterate, and remember.

A once-clunky coding tool is recast here as a desktop command center for modern office work, with hosts arguing that the real contest in AI is over agent interfaces that sit between workers and their apps. The conversation moves from theory to practice, showing how Codex and similar tools now draft emails, triage workflows, assemble strategy docs, and turn fragmented digital labor into something closer to managed delegation.

ZenBusiness CEO John Kim lays out an internal AI marketplace where employees post “quests,” share reusable skills, and compete on token-consumption leaderboards to turn curiosity into company-wide adoption. The conversation argues that AI works best not as a mandate to move faster, but as a way for marketers, operators, and leaders to build joyful, bespoke tools that would never survive a normal roadmap.

Simon Willison joins Joe Russo for a grounded assessment of AI coding tools as they move from autocomplete novelty to reliable engineering partners, reshaping review, mentorship, pricing and the pace of software work. Their conversation treats software development as a stubbornly human discipline even as models become powerful amplifiers of experience and increasingly capable collaborators.

MG Siegler and Alex Kantrowitz size up Apple’s unusually modest AI spending against the rest of Big Tech’s infrastructure binge, arguing that the company may be either prudently waiting out a commoditized market or dangerously ceding the future to rivals. They also trace the loosening Microsoft-OpenAI alliance and the murky reality of Stargate, where grand promises of owned infrastructure are giving way to outsourced capacity and financial improvisation.