Building One with Tomer Cohen

Payroll isn't exactly the kind of problem that gets Silicon Valley excited. But for Gusto co-founder and Head of Technology Eddie Kim, that was part of the opportunity. In this episode of Building One, LinkedIn Chief Product Officer Tomer Cohen sits down with Eddie to explore how Gusto turned one of the least glamorous parts of running a business into a platform serving small businesses across America. Eddie shares the thinking behind Gusto's early days, why solving unglamorous problems can create extraordinary companies, and how the company is reimagining its role in an AI-powered world. The conversation explores what happens when software evolves from a tool that helps business owners complete tasks into something closer to a highly capable, always-available COO. Eddie also makes the case for experimentation without an immediate objective—and why giving people room to explore can lead to unexpected breakthroughs. It's a conversation about spotting opportunity where others aren't looking, building for the long term, and how AI could fundamentally change what it means to run a small business.

What if you could build breakthrough companies systematically? In this episode of Building One, Tomer Cohen sits down with Noubar Afeyan, founder and CEO of Flagship Pioneering, the company-building platform behind more than 100 companies, including Moderna. Noubar has spent nearly four decades building at the edge of science. His approach starts with a deceptively simple question: “What if?” At Flagship, teams explore roughly 100 of these questions each year. The most promising become proto-companies, which are then actively stress-tested — and sometimes killed — before becoming full-fledged companies. It’s a radically different approach to innovation: don’t start with what already exists and incrementally improve it. Move beyond the boundaries of what we know and work backwards from what might be possible. Tomer and Noubar discuss: • How Flagship developed a repeatable system for creating companies from scratch• Why the most interesting opportunities can exist beyond the edge of expertise• Noubar’s philosophy of “paranoid optimism” and “urgency-oriented patience”• Why Flagship prototypes companies, not just products• How a “what if” question eventually led to Moderna• Why platforms can become product-generation engines• How little we still understand about the human body — and why drug development remains so difficult• Why Noubar believes AI could fundamentally transform the process of scientific discovery• His idea of “polyintelligence” — bringing together human, machine, and nature’s intelligence• Why breakthrough innovation is about “taking the fiction out of science fiction” It’s a conversation about science, company building, AI, and what it takes to pursue ideas that initially sound unreasonable. Listen to the full episode of Building One with Noubar Afeyan.

Netflix feels remarkably simple. Open the app. Find something to watch. Press play. But creating that experience is anything but simple. On this episode of Building One, Tomer Cohen sits down with Elizabeth Stone, Netflix's Chief Technology Officer and Chief Product Officer, to explore how one of the world's most iconic consumer products continues to evolve while staying remarkably intuitive. Netflix is no longer just movies and TV. It's live events, games, podcasts, mobile experiences, and AI-powered personalization. Elizabeth shares how Netflix is expanding into entirely new forms of entertainment—without making the product feel more complicated for its members. Before joining Netflix, Elizabeth built products across healthcare, transportation, and finance. That unique perspective shapes how she thinks about product strategy, organizational design, and solving complex problems at scale. In this episode, Tomer and Elizabeth discuss: How Netflix balances world-class content with world-class product and technology Why expanding from one product to many is one of the hardest challenges in product management How Netflix introduces new experiences without overwhelming its members Why content teams and product teams shouldn't operate the same way—and how Netflix bridges the gap How Netflix measures value across movies, games, live events, and podcasts Why AI raises the bar for product quality, taste, and judgment The future of entertainment: more personalized, immersive, and interactive experiences Whether you're building consumer products, leading cross-functional teams, or thinking about the future of AI and entertainment, this conversation offers a rare look inside the product philosophy behind one of the world's most influential technology companies.

Some builders don't just create breakthrough products. They create entirely new ways of thinking about how innovation happens. In this episode of Building One, Tomer Cohen sits down with Astro Teller, Co-founder and Captain of Moonshots at X, Google's legendary moonshot factory. Astro has helped build one of the world's most remarkable innovation engines—an organization responsible for projects like Waymo, Google Brain, Wing, Taara, Google Glass, Loon, and many more. But this conversation isn't just about ambitious technology. It's about building a system that makes ambitious technology more likely. At X, innovation isn't treated as inspiration or creative genius. It's treated as a discipline. Teams are rewarded for disproving their own ideas, attacking the hardest assumptions first, and learning faster than everyone else. Success isn't measured by how long a project survives—but by how quickly you discover whether it deserves to. In this episode, Tomer and Astro discuss: Why X calls itself a "moonshot factory"—and what it takes to build innovation as a repeatable system Why the goal isn't to prove your ideas right—but to discover when they're wrong Why 10x thinking can actually be easier than incremental improvement The famous "teach the monkey before you build the pedestal" framework for attacking risk in the right order Lessons from Waymo, Google Brain, Taara, Google Glass, and Loon—and why some of X's biggest "failures" produced its greatest insights How culture, incentives, and organizational design determine whether breakthrough ideas survive If you've ever wondered why some organizations consistently produce world-changing products while others struggle to innovate, this conversation offers one of the clearest frameworks you'll hear. It's a masterclass on building not just products—but the systems that build products.

What happens when AI enters a world where being wrong isn’t an inconvenience — it’s a liability? In this episode of Building One, Tomer Cohen sits down with Gabe Pereyra, co-founder of Harvey, to explore what it actually takes to build AI for one of the most complex and high-stakes industries in the world: legal. Harvey works with leading law firms and enterprises to draft, analyze, and reason through complex legal work — contracts, filings, cases, and internal workflows where precision, accountability, and trust are non-negotiable. On paper, legal is a perfect domain for AI.It’s language-heavy. Logic-heavy. High value. In reality, it’s one of the hardest. Every word matters.Every output has consequences.And “almost right” doesn’t count. In this conversation, Tomer and Gabe discuss: Why the hardest problem in AI today isn’t intelligence — it’s coordination What makes vertical AI companies like Harvey durable as foundation models improve Why legal systems must be auditable, permissioned, and accountable How conflicts and data isolation create unique infrastructure challenges in legal AI Why the future of work may look less like individuals using tools — and more like teams of humans and AI agents working together And why the next bottleneck in AI may not be generation — but human review and trust Gabe also shares his journey from aspiring professional soccer player to finance, AI research, and eventually co-founding Harvey — along with the contrarian thinking that led him to bet early on the future of AI. This episode is about what it takes to move AI from impressive demos into real-world systems — where the stakes are high, trust is fragile, and the tolerance for error is near zero.