From Swipe to Scale: How Tinder Became #1

a16z General Partners Martin Casado, Sarah Wang, and Matt Bornstein unpack the story of Cursor: how a small, product-obsessed team entered one of the most competitive markets in technology, took on incumbents with seemingly unbeatable advantages, and repeatedly made decisions that ran against conventional startup wisdom. They revisit the early bet that the interface between humans and AI would matter more than building a coding-specific foundation model, why Cursor built its own product rather than a VS Code plugin, and how the founders' ability to say "no" became one of the company's defining strengths. They also discuss Cursor's rapid evolution from IDE to agent and model platform, and why the team was willing to cannibalize its own products as AI capabilities improved. The conversation gets into what founders can learn from Cursor's approach to competition, hiring, enterprise sales, M&A, and company culture, including why the team remained unfazed by competitors from Microsoft to Anthropic and how its obsessive focus on product ultimately extended into every part of building the company.

Anish Acharya joins Jen Kha to break down the next frontier of AI, from the evolving model landscape and open-source AI to why the application layer, and consumer AI in particular, may be entering a new phase. Anish explains why he believes there will be multiple winners at the model layer, why traditional moats like network effects, scale, and brand still matter, and how companies can choose between frontier and open-weight models depending on the economics of the task. They also explore why models are increasingly specializing, and how applications can combine different types of intelligence to create products that are more valuable than any single model. The conversation then turns to consumer AI: personal agents that can shop and manage your inbox, coding tools enabling a new generation of small businesses, and why Anish thinks we're seeing a renaissance for consumer builders. They also discuss the changing economics of AI software, the rise of "luxury software," and why the biggest risk for today's founders may no longer be thinking too big, but thinking too small.

a16z General Partners Martin Casado and Erik Torenberg are joined by Board Partner Steven Sinofsky to explore what recent breakthroughs in AI and mathematics tell us about where the technology is headed, and whether some of the basic assumptions that have governed computing for decades are starting to break. Martin and Steven debate whether AI's progress in mathematics represents a genuine leap in reasoning or simply a new tool for solving problems at a higher level of abstraction. From the four-color theorem and early computers to graphing calculators and today's models, they trace how new technologies have repeatedly changed which problems humans need to solve themselves, and ask what makes this moment different. The conversation then turns to one of the biggest shifts in AI: problems that were once constrained by engineering talent can increasingly be attacked with capital and compute. They discuss what that means for startups versus incumbents, venture capital, the coming wave of AI applications, and why pouring billions into increasingly capable models may force us to rethink what these systems can ultimately accomplish.

Daisy Wolf and Eva Steinman are joined by Engy Ziedan, co-founder and Chief Scientific Officer of Protege, to discuss why medical AI has a measurement problem, and why scoring well on a benchmark doesn't necessarily mean a model is ready for the hospital. Engy explains why healthcare AI needs independent evaluations that go beyond static exams and measure how models actually perform in real-world clinical workflows. They explore the risks of subtle bias and misalignment, why the same model can rank differently depending on how it's prompted or tested, and what happens as AI becomes more personalized and changes faster than traditional healthcare quality systems can keep up. The conversation also gets into Protege's role as an independent evaluator, how contaminated training data can undermine benchmarks, and why the future of medical AI may require continuous monitoring rather than occasional testing.

Martin Casado joins MTS hosts Theo Jaffee and Sophia Dew to unpack where value is actually accruing in AI, why this technology cycle looks fundamentally different from previous waves, and whether the frontier labs will ultimately capture most of the market. Martin explains why AI has turned venture into a scale-up capital game, where small teams can productively deploy extraordinary amounts of money, and why the relationship between capital, innovation, and growth has never been tighter. He lays out the case both for and against the frontier labs dominating AI, the role of open-source and specialist models, and why applications are increasingly capturing more value. The conversation also explores model routing, AI economics, founder-market fit, and why Martin believes this may be the biggest unlock of wealth he's seen since the 1990s.