Product recruiter Chris Lee explains why job seekers should tailor their LinkedIn narratives to a specific target, treating the search as a test of candidate-market fit. He also walks through how recruiters scan profiles for stage, domain, seniority and hands-on AI experience before deciding whom to contact.

OpenAI’s Ian Silber argues that designers rattled by AI’s rapid advance are confronting an uncertain role, not an obsolete one. He describes a field where curiosity, systems thinking and a willingness to ship imperfect ideas matter as much as pixel-level polish.

Town’s mayor argues that AI assistants should build themselves around users’ inboxes, calendars and work habits, turning private data into an evolving personal wiki. The conversation weighs the appeal of proactive software against the privacy, trust and job-displacement questions that follow.

Benchmark partner Sarah Tavel traces AI’s consumer future from Google’s technical beginnings to the product-led rise of social platforms. She argues that the next major AI company may turn prompt-sharing, personal agents and network effects into a genuinely social experience.

At the Toronto Product Conference, product leaders argue that side projects are becoming essential training for an AI-shaped workplace. Building real products, navigating technical tradeoffs and finding distribution can sharpen the judgment that PRDs alone cannot teach.

Tom Verrilli, Whatnot’s chief product officer, makes the case for fewer, sharper product managers and more engineers and designers with the context to make decisions themselves. The conversation doubles as a critique of tech’s bureaucratic middle layers and a defense of hands-on leadership in an AI-accelerated era.

Casey Newton talks with Granola co-founder Chris Pedregal about why AI meeting note takers have become indispensable office software, and what it means to turn workplace conversations into a permanent layer of machine-readable context. Their conversation circles the promise of better tools, the risks of surveillance, and the uneasy possibility that AI may make meetings easier without helping anyone work less.

A Checkr executive explains why some big-company operators thrive at startups while others wash out, tracing the answer to humility, hands-on experience, and a precise fit between what a company needs and what a leader actually knows how to build. The conversation widens into a playbook for moving upmarket into enterprise sales, designing incentives, using AI in production, and making decisions that scale without smothering a company in process.

Formal methods consultant Hillel Wayne argues that mathematical verification remains too costly for most day-to-day software, even as AI makes specification work easier at the margins. The conversation ranges from why software engineers resemble other engineers more than they admit to how tools like TLA+ and Alloy expose rare concurrency and distributed-systems bugs that ordinary testing misses.

Pavel Samsonov argues that many companies mistake speed and feature output for product thinking, shipping polished solutions before they’ve defined the problem or understood the service around it. The conversation ranges from UX and service design to AI hype, making the case that good products come from clear problem framing, human research and systems-level thinking across organizational silos.

Gamma co-founder John Nerona traces the company’s pivot from a middling remote-work startup to an AI presentation juggernaut by attacking the blank page problem. He reflects on how early constraints, viral prosumer adoption, and an unplanned rush into enterprise forced the team to rethink product design, pricing, and what craftsmanship means in the age of fast-improving models.

Every’s team breaks down how a new premium tier built around discounted AI tools produced the biggest subscription-revenue jump in the company’s history. The conversation doubles as a snapshot of how builders are using agents to automate the dull parts of work, orchestrate software stacks, and turn expensive experimentation into something more accessible.

As unions confront the prospect of humanoid robots on factory floors, Raycast co-founder Thomas Paul Mann argues that AI is also making software creation radically more personal. The conversation traces a shift from bloated, one-size-fits-all tools toward custom Mac apps, disposable software, and workplace systems assembled for the way people actually work.

After a layoff, product leader Eric Goehring rebuilt his search around referrals, careful storytelling and pointed reverse interviews, landing a new role in six weeks. Along the way, he argues that while AI can sharpen the work behind a search, hiring still lags badly in figuring out how to evaluate it.

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.

Plaid COO Eric Sager describes how companies stay steady through shocks by treating acquisitions, pandemics and AI booms as milestones rather than identities, and by building a culture that prizes customers over short-term optics. He argues that organizational resilience comes from clear decision-making, spare capacity, frontline intimacy and leaders who reward behavior that strengthens the mission, not just the metrics.

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.

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.