The a16z Show

a16z's Raghu Raghuram and Guido Appenzeller sit down with Playground Global General Partner and former Intel CEO Pat Gelsinger to discuss the next wave of semiconductor innovation and the physical constraints shaping the AI buildout. Drawing on his experience designing Intel's 386 and 486 processors, Pat explains how AI could transform chip design, but also why faster design alone won't solve the industry's biggest problems. They examine the bottlenecks in manufacturing, memory bandwidth, advanced packaging, and power, and why today's explosion of specialized AI chips may eventually consolidate around a smaller number of architectures. They also discuss the potential for new memory technologies, the shift from copper to optical networking, and why energy capacity could become a major constraint on AI growth. Finally, they revisit Pat's VMware years to ask what virtualization might look like when infrastructure is built for agents rather than humans.

a16z’s Raghu Raghuram sits down with AWS CEO Matt Garman to discuss how AI is reshaping the cloud, from the needs of AI-native startups to infrastructure increasingly designed for agents. Matt explains how AWS is adapting as agents write code and manage infrastructure, why it’s reserving scarce GPU capacity for startups, and where custom chips like Trainium and Graviton fit into the AI stack. They also discuss Amazon's $220 billion capital investment, the shifting bottlenecks in the infrastructure buildout, what enterprises need to trust autonomous agents, and how AWS's own teams are building with agents.

a16z General Partner Angela Strange sits down with Valon’s Andrew Wang and Linda Du to unpack what it takes to rebuild the infrastructure underneath a $13 trillion mortgage market that still relies heavily on systems designed before the internet. Linda and Andrew explain why Valon chose the hardest path: becoming a regulated mortgage servicer, translating decades of federal and state regulation into software, and proving the platform on its own loans before selling it to the industry. That foundation made Valon roughly three times as efficient as traditional servicing and created the system of record it is now using to bring AI into complex mortgage workflows. They also discuss what AI makes possible on top of that infrastructure, from agents handling long-tail servicing tasks to voice interfaces and more personalized customer experiences. And they explain why deploying the technology into large regulated enterprises is ultimately as much a change-management challenge as a technical one.

a16z General Partner David George sits down with Armadin founder and CEO Kevin Mandia to discuss what happens to cybersecurity when attackers can operate at machine speed. After 30 years in security and building Mandiant, Kevin says AI convinced him to get back on the field. He explains how AI changes the economics of cyberattacks, allowing attackers to probe thousands of paths simultaneously, and why that means defense will ultimately need to become autonomous too. They also unpack Armadin’s approach: continuously attacking customers’ systems with AI to find exploitable vulnerabilities before adversaries do, then building toward autonomous defenses that can respond in real time. Kevin shares what Armadin has learned from finding more than 90 zero-days in production environments this year, why humans can’t remain in the detect-and-respond loop, and how the entire security stack could change over the next few years.

a16z Editorial Partner Elena Burger sits down with investing partners Olivia Moore and Josh Elman to unpack the seventh edition of a16z’s Top 100 Consumer AI Apps, including a new dimension this time: what consumers are actually paying for. The data reveals a striking power-user economy. Only a small share of consumers currently pay for AI, but among those who do, spending is heavily concentrated at the top. Olivia and Josh discuss why developers, creators, and other power users dominate spending today, and why subscriptions may not be the business model that ultimately brings consumer AI to everyone. They also dig into the rise of personal agents, the different trajectories of ChatGPT, Claude, and Gemini, how ads could reshape AI economics, and the enormous amount of consumer white space still left to build, from shopping and entertainment to social, dating, and marketplaces.

a16z General Partner David George joins Jack Altman on Uncapped to make the case that many of the biggest debates in AI are framed the wrong way. Frontier models or open source? Labs or applications? David’s answer is often “and.” With AI adoption still concentrated among a relatively small group of heavy users, he argues there could be room for multiple layers of the stack to grow at once. David and Jack discuss why demand for compute continues to outstrip supply, why applications can thrive even as frontier labs expand into new products, and why consumer AI may still be at the beginning of its biggest shift, from reactive chatbots to proactive assistants that can act on our behalf. They also zoom out to autonomy, robotics, healthcare, and the next generation of technology companies, before turning to venture itself: why David believes today’s product cycle is unusually strong, how capital can accelerate AI companies in ways it couldn’t during the SaaS era, and why founders and narrative become increasingly…

A16z’s Erik Torenberg sits down with OpenRouter’s Alex Atallah and Replit founder and CEO Amjad Masad to discuss why the future of AI may look less like one all-purpose model and more like an ecosystem of specialized models working together. Alex explains why OpenRouter is betting on “neurodiversity”: different models trained in different ways, routed and combined based on the job at hand. Amjad makes a similar case from inside the enterprise, where companies increasingly need to own their AI capabilities rather than depend entirely on a single model provider. They explore what happens when general-purpose agents give way to teams of specialized agents, why smaller models can sometimes be cheaper, safer, and easier to control, and how routing and model fusion could deliver frontier-level performance at lower cost. They also get into agent-to-agent communication, AI security, and why the next generation of companies may need an independence layer across models, clouds, and data.

a16z’s Seema Amble and Elena Burger sit down with Lio co-founder and CEO Vladimir Keil to ask where AI-native startups have an advantage when incumbent software companies already own the customer, the data, and the system of record. Their answer comes down to the work that happens outside those systems. In procurement, a final price in an ERP can hide hundreds of emails, spreadsheets, supplier conversations, engineering analyses, and decisions across legal, finance, and operations. Vlad explains how Lio uses multi-agent systems to take on more of that end-to-end work, from sourcing and RFQs to negotiation, shipment tracking, and invoices. They also discuss how enterprises learn to trust agents with increasingly consequential decisions, why the last 20% of an internal AI build can require most of the effort, and what happens when both buyers and suppliers have agents working on their behalf.

a16z’s Joel de la Garza sits down with DoxxNet founder Barrett Lyon to discuss what it would take to rebuild the internet around privacy, direct communication, and less dependence on centralized intermediaries. Barrett explains why he thinks traditional VPNs only solve part of the problem and how DoxxNet is building a parallel mesh network where users can communicate peer-to-peer, transfer large files, make calls, and message without routing those interactions through a central application server. He also explains why the company owns its infrastructure and runs its AI systems locally rather than relying on third-party inference providers. doxxnet_assembly They also get into the growing amount of tracking embedded across the internet, what happens when that data can be analyzed by increasingly capable AI, and why Barrett believes the underlying protocols of the internet are due for another wave of experimentation. Along the way, he shares lessons from decades of building internet infrastructure, from…

a16z’s David George, Sarah Wang, Alex Immerman, and Santiago Rodriguez unpack 25 key charts from the latest State of Markets presentation, from the scale of the AI infrastructure buildout to what adoption looks like inside companies today. They examine why rising markets have so far been supported by earnings rather than multiple expansion, why hyperscaler CapEx is approaching $1 trillion annually, and why demand for compute continues to outrun supply. They also look at the downstream effects of that spending across chips, power, construction, and physical infrastructure. State of Markets Then they move up the stack: OpenAI and Anthropic’s revenue growth, the gap between AI deployment and measurable enterprise impact, the rise of agents, falling inference costs, and what all of this means for SaaS. They close with where the team is spending time next, including consumer agents, robotics, autonomy, AI and biology, personal health, defense, and the continued diffusion of AI across the enterprise. State of…

a16z General Partner Anish Acharya sits down with Assistant Benchmark creator David Pawlan to unpack the sudden explosion of personal AI agents and what it will take for one to become part of everyday life. David has been testing dozens of assistants across real-world tasks, from managing email and booking travel to handling financial admin. They discuss why the most useful agents may become increasingly invisible, proactively checking you into flights, finding refunds, filing reimbursements, or simply handling the small tasks that pile up across everyday life. They also explore whether the winning interface is an app, text thread, voice, or wearable; how much autonomy consumers will actually give their agents; and what happens when agents start interacting with other agents. From commerce and restaurant reservations to entirely new agent-native services, Anish and David ask what the internet looks like when software starts acting on our behalf. This episode was recorded on September 24, 2026.

a16z’s Ben Horowitz and Martin Casado sit down with TypeSafe AI founder Diogo Almeida to ask a simple question: AI has become remarkably capable, so where is all the automation? Diogo argues that coding agents may help us write software faster, but the software they produce still largely works the way software always has. TypeSafe is taking a different approach with Jev: putting intelligence inside software itself, so developers can build programs that reason about intent and make probabilistic decisions rather than simply generate text for a human to interpret. They discuss why reliability is the key to making AI genuinely programmable, how this could open a new era of probabilistic software, and why established SaaS companies may be particularly well positioned to benefit. Ultimately, Diogo’s goal is straightforward: technology that can reliably “do what I mean.”

a16z’s Katie Kirsch sits down with Harvey VP of Talent Maggie Landers to discuss what happens inside a company growing at AI speed, and how you preserve culture while adding more than 1,000 employees in a year. Maggie explains why Harvey prioritizes progress over perfection, how its values of simplicity, decisiveness, and “job’s not finished” shape the way people work, and why moving quickly requires giving employees significant trust and autonomy. For people accustomed to being the “A student,” that can mean learning to experiment, make mistakes, and course-correct quickly. They also get into how Harvey identifies people who can thrive in that environment, what changes when most of your company is relatively new, the role its founders play in maintaining culture, and why judgment becomes increasingly important when employees are given the freedom to move fast.

Erik Torenberg sits down with Box CEO Aaron Levie, and a16z’s Martin Casado, and Steven Sinofsky to debate how the AI industry should think about safety, security, and regulation as increasingly capable agents move into the real world. They argue that much of today’s conversation is happening before we have clearly defined the risks we’re trying to regulate. Drawing on earlier waves of computing, from computer viruses and the early internet to aviation and automobiles, they ask what AI can learn from industries that developed safety standards only after understanding how their technologies actually failed. The conversation then gets concrete: agents don’t get tired, can operate at enormous scale, and can probe systems in ways human employees never could. That could require rethinking permissions, authentication, operating systems, and the security stack itself. They also discuss why AI innovation may increasingly move beyond the frontier labs and into the software built around the models.

a16z’s Erik Torenberg sits down with Josh Elman, Olivia Moore, and David Booth to introduce Cosign, a new product built around professional reputation and the people, companies, and products you’re willing to put your name behind. They unpack a simple idea at the heart of Silicon Valley: some of the most valuable professional signals aren’t credentials, but who believes in you. From the mentor who shaped your career to the colleague you’d work with anywhere or the young builder you think everyone should be watching, Cosign is an attempt to make those signals more visible and durable. They also discuss why human endorsements may become more valuable as AI makes outreach and information abundant, what existing professional networks get right and miss, and how making reputation more legible could help talented people get discovered earlier, find collaborators, and carry the work they’ve done behind the scenes into whatever they do next.

a16z crypto General Partner Eddy Lazzarin joins Theo Jaffee on MTS to debate the increasingly prominent calls to slow AI development and whether the current safety conversation is conflating very different kinds of risk. Eddy argues that the debate puts too much emphasis on speculative superintelligence and not enough on the costs of delaying useful technology. Rather than treating every AI failure as evidence of an alignment problem, he makes the case for familiar tools like cybersecurity, accountability, liability, market incentives, and stronger technical controls. They also discuss whether AI models can develop reputations for trustworthiness, the risks of concentrating oversight among a small group of evaluators, and why Eddy thinks the collision between Silicon Valley’s AI debates and broader politics could fundamentally reshape the conversation over the next year.

Erik Torenberg sits down with Replit founder and CEO Amjad Masad and Horowitz and Andreessen Academy co-founder and CEO Gagan Biyani to ask what education should look like for a generation growing up with AI. Amjad argues that one of the most valuable things young people bring to society is their willingness to question deeply held assumptions. They discuss how education could create more room for that instinct through project-based learning, intellectual side quests, and giving students the freedom to follow an idea deeply rather than optimizing around grades and credentials. They also explore whether young founders are being pushed to professionalize too early, why Amjad thinks starting a company can sometimes be a form of “premature optimization,” and how curiosity led him from learning chess to experimenting with AI that can conduct machine-learning research. Finally, they discuss trust, judgment, and what it means to develop as a person, not just a builder, including why being contrarian and ambitious…

Ben Horowitz and Erik Torenberg sit down with Gagan Biyani to introduce the Horowitz and Andreessen Academy and discuss a bigger question: what should education look like when AI is rapidly changing the skills people need to build, work, and create? Ben and Gagan explain why they believe learning should be more focused on doing rather than studying about doing, with students building real projects, developing people skills, and working alongside companies and builders in San Francisco. The goal isn’t to replace college for everyone, but to create a different path for young people who already know they want to build. They also discuss why AI could make this an unusually powerful time to be young, how project-based learning changes when everyone has access to powerful tools, why failure can be valuable when it produces real learning, and what it takes to develop the judgment and people skills that can't simply be learned from a textbook.

a16z Board Partner and former Microsoft Windows president Steven Sinofsky joins Theo Jaffee and Sofia Puccini on MTS to argue that the language we use to describe AI failures is making it harder to understand what’s actually going wrong. Steven takes aim at terms like “alignment,” “goal-seeking,” and “rogue agents,” arguing that they can anthropomorphize problems that software engineers have dealt with for decades. His framing is simpler: when software doesn’t do what it’s supposed to do, it has a bug. And as AI becomes more widely deployed, labs need the same kind of telemetry, debugging, incident reporting, and operational discipline that previous generations of software eventually developed. Drawing on everything from early computer hacking and Microsoft’s response to major software failures to Y2K and cybersecurity standards, Steven makes the case for treating AI reliability as an engineering problem. They also discuss what AI labs can learn from CVE reporting, why industry has a responsibility to make…

Ben Horowitz and Erik Torenberg sit down with Nas, Grandmaster Caz, and Steve Stoute for a conversation about the Paid in Full Foundation and its mission to recognize and support the pioneers who built hip-hop. Ben, Nas, and Steve share how the foundation began, why simply giving artists money wasn’t enough, and how the Hip Hop Grandmaster Awards became a way to pair financial support with the recognition many foundational artists never received. Caz brings the perspective of one of those pioneers, reflecting on his role in hip-hop’s earliest history and what receiving the award has meant for his life and legacy. They also discuss the enormous cultural and commercial impact of hip-hop beyond music, from language and fashion to some of the world’s biggest brands, why so many of its pioneers captured so little of that value, and what happens when generations of hip-hop finally come together in the same room.

a16z Partner Josh Elman joins Ollie Forsyth on New Economies to discuss the next wave of consumer AI and what separates a product people try once from one that becomes part of their everyday lives. Josh argues that getting attention has actually become easier, but getting consumers to stick is harder than ever. He explains what he looks for in consumer products, why the best ones start with a narrow wedge and earn the right to do more, and why trust becomes increasingly important as AI agents gain access to more of our personal lives. They also explore personal AI agents, the future of shopping and entertainment, why we haven’t seen another major social network emerge, and how AI could make technology more social rather than less, including agents that help people spend more time together in the real world. This conversation originally appeared on the New Economies podcast.

Databricks co-founder and CEO Ali Ghodsi joins a16z General Partners Martin Casado and Sarah Wang for a conversation about AI risk, recursive self-improvement, cybersecurity, and what’s actually holding back enterprise adoption. Ali argues that today’s models are already capable enough to automate far more work than most companies are using them for. The bigger problem is context: models haven’t been in every meeting, don’t understand how decisions actually get made, and lack the institutional knowledge that experienced employees accumulate over years. He explains why building an organizational “ontology” could help close that gap and what Databricks has learned from doing it internally. They also debate the current conversation around pacing frontier AI, what would constitute meaningful recursive self-improvement, and why Ali distinguishes speculative superintelligence risk from the much more immediate challenge of AI-powered cyberattacks. They close with how enterprises are managing exploding AI usage and…

a16z General Partner Jennifer Li sits down with fal co-founder Gorkem Yurtseven and Head of Engineering Batuhan Taskaya to discuss what changes when generative video becomes fast enough to run in real time. They unpack the technical work behind H3 Max, fal’s post-trained version of MiniMax’s open-weight video model, and how combining model post-training with systems and hardware optimization significantly reduced generation time while maintaining quality. That speed has enabled experiments with continuous video, including streams that can remember previous scenes and respond to new directions while they’re running. They also discuss why the next challenge may be less about speed and more about control, from camera movement and lighting to characters, motion, and lip sync. And they explore what those capabilities could mean for professional creative workflows, where artists and studios need predictable tools rather than simply generating a video from a prompt.

a16z’s Alex Rampell and Joe Schmidt sit down with Lightfield co-founder and CEO Keith Peiris to discuss what it takes to rethink the CRM for an AI-native world, and the unusual pivot that got him there. Keith previously built Tome to 25 million users, but eventually walked away from the product after concluding that the underlying technology couldn’t capture enough context about a presenter, their audience, and the relationship between them. Starting again, his team followed customers from AI presentations into sales workflows and eventually found a harder problem: making sense of the fragmented and often conflicting data spread across a company’s emails, calls, CRM, and other systems. They unpack Lightfield’s idea of a “business world model,” why Keith believes intelligence can replace much of the rigid schema behind traditional software, and what changes when AI has enough context to reason about a company and its customers. They also get into building for greenfield versus brownfield markets, AI-era…

Elena Burger sits down with academic and cyber ethnographer Ruby Justice Thelot to explore the increasingly blurry line between internet culture and the real world, and how to tell the difference between a trend that’s actually changing behavior and one that simply feels enormous online. They use today’s wellness and optimization culture as a case study, from peptides and GLP-1s to protein maxxing, wearables, microplastics, and the quantified self. Ruby explains her concept of “paracontent,” where the conversation around a phenomenon can become much larger than the phenomenon itself, and what social media data can tell us about how these trends move from niche communities into the mainstream. They also trace the much longer history of body optimization, from changing ideals of thinness to the rise of the quantified self, and ask what comes next as technology gives people increasingly granular ways to measure and modify themselves. Ruby’s prediction: rather than everyone optimizing toward the same ideal, we…

Ben Horowitz and Erik Torenberg sit down with OpenAI co-founder and President Greg Brockman to discuss why he believes AI has entered a new phase, what OpenAI’s latest models reveal about the path to AGI, and the safety and security challenges that come with increasingly capable systems. Greg explains why computer use represents such an important step for agents, including models that can work coherently for 24 hours and interact with software through the same interfaces humans use. He also shares how OpenAI deployed 10,000 agents to tackle the Navier-Stokes problem, and why advances in mathematical reasoning could translate into new approaches to science, software, and cybersecurity. Ben, Erik, and Greg also dig into the “defender’s window” for cybersecurity, how AI could reshape work and entrepreneurship, and what the AI assistant of the future might actually look like: persistent, proactive, personalized, and capable of doing work on your behalf rather than waiting for another prompt.

World Labs co-founder Justin Johnson joins MTS hosts Theo Jaffee and Sophia Puccini to discuss Atlas, World Labs’ latest world model, and the broader case for AI systems that understand and interact with the physical world. Justin explains how Atlas approaches three core tasks: generating new worlds, reconstructing real environments from images, and simulating how objects or robots might behave within them. Underlying it is a bigger thesis: just as language models became general-purpose engines for working with text, world models could become a horizontal layer for visual and physical intelligence across industries from entertainment and gaming to construction and robotics. They also explore how world models could change video games and creative tools, why precise spatial control matters, and the potential for “real-to-sim-to-real” robotics, where a few photos of a physical environment could eventually be enough to build a simulation and adapt a robot to that specific space.

a16z General Partner Anish Acharya joins Lenny Rachitsky on Lenny’s Podcast to discuss why fears of an AI-driven “permanent underclass” may be misplaced, how AI is changing the way companies operate, and why the opportunity may be less about replacing people and more about dramatically expanding what they can build. Anish lays out his idea that companies are becoming a series of loops, with agents increasingly handling workflows across engineering, sales, marketing, support, and other functions while humans provide the judgment and new ideas needed to move beyond local maxima. They also explore why Anish thinks consumer AI should focus less on productivity and more on helping people live richer lives, why moats are often discovered rather than designed, how to develop intuition for different AI models, and why his biggest advice for anyone trying to keep up with AI is simple: make more things.

a16z’s Matt Perault sits down with General Partner and Speedrun lead Andrew Chen on the a16z AI Policy Brief to explore what “Little Tech” actually looks like at the earliest stages, and why the realities of building a two- or three-person startup are often missing from policy debates. Andrew takes us inside Speedrun, where founders are often starting companies from kitchen tables, working with tiny teams, and trying to determine in a matter of months whether their idea can become a viable business. He explains why these founders rarely have the time or resources to engage with policymakers, even as regulation can have an outsized impact on whether and where they build. Matt and Andrew also discuss how regulatory burdens accumulate for young companies, why startups can choose where to put down roots, the role of ecosystems like Tech Week, and what policymakers can do to hear directly from the founders who may otherwise be absent from the conversation. This episode originally appeared on the a16z AI Policy…

a16z’s Jen Kha and David George sit down with Accolade Partners’ Aram Verdiyan to discuss how AI is changing the power law of technology investing, why the largest companies can compound advantages in ways that weren’t possible before, and what that means for how investors construct portfolios. They explore why AI may be much bigger than traditional software, with applications reaching into labor, healthcare, transportation, services, and other major parts of the economy. David explains why capital itself can now reinforce an AI company’s advantage by buying more compute, while Aram makes the case that AI should increasingly be treated as a core allocation rather than a satellite position. The conversation also gets into the changing economics of venture and growth investing, how to distinguish real AI traction from early hype, what AI means for legacy software and private equity, and why some of the largest opportunities may still be ahead in robotics, autonomy, healthcare, energy, and physical…

a16z’s Erik Torenberg, Ben Horowitz, and Jennifer Li sit down with Vals founder and CEO Rayan Krishnan to discuss one of AI’s increasingly difficult problems: how do you actually measure whether a model is getting better? As public benchmarks saturate and models get better at optimizing for the tests themselves, Rayan makes the case for independent, continuously evolving evaluations. They unpack why self-reported model scores can be misleading, how VALS evaluates models in the hours before a release, and why measuring increasingly agentic systems means testing work that can unfold over hours, days, or even weeks. They also explore why evals are becoming critical for enterprises trying to understand the ROI of AI, what happens if token spend begins to rival employee salaries, and how evaluations could eventually provide a shared language for everything from model routing and recursive self-improvement to AI policy and international coordination.

a16z Infra Partner Lisha Li sits down with OpenAI mathematicians Mehtaab Sawhney and Mark Sellke to discuss how quickly AI’s mathematical capabilities are advancing, what recent results reveal about model reasoning, and what happens when AI begins making progress on problems mathematicians have struggled with for decades. Mehtaab and Mark unpack several recent results from OpenAI’s models, including advances in sphere packing and the construction of a non-sofic group. They explain why the surprising part isn’t simply that models can search more possibilities or work longer than humans: in many cases, the reasoning traces look remarkably similar to the work of an expert mathematician, including choosing promising approaches, backtracking when they fail, and combining ideas from across the literature. They also explore what this means for mathematics itself: how the role of human taste and judgment may change, whether AI could produce far more mathematics than humans can absorb, and why models that accelerate…

MTS host Sophia Dew visits the Open Source AI Summit in San Francisco to ask researchers and founders across the AI stack a central question: can open source prevent AI power from concentrating in the hands of a few companies? Lukasz Kaiser, co-author of Attention Is All You Need, argues that today’s concentration may be a feature of the current technological paradigm rather than a permanent feature of AI. Transformers reward enormous amounts of data and compute, but future breakthroughs could make smaller, more specialized models far more capable. Across conversations with researchers and builders working on open models, infrastructure, and applications, Sophia explores why China has taken the lead in open-weight models, whether the U.S. needs more open-model startups, what it means for companies to own their own intelligence, and where openness alone falls short, particularly when access to compute remains concentrated.

a16z General Partner Julie Yoo joins MTS host Sophia Dew to explain why she believes healthcare could benefit more from AI than almost any other industry, and why decades of slow technology adoption may actually give healthcare an advantage in the AI era. Julie traces healthcare’s evolution from paper records and fax machines through electronic health records and telehealth, and explains why AI represents something different: an organic adoption wave driven by tools that doctors and patients actually want to use. Because healthcare never built the same layers of legacy software as other industries, it may now be able to leapfrog directly into agentic AI. They also explore how AI could dramatically lower the cost of care, why consumers are becoming a more important payer, where Julie sees the biggest opportunities for healthcare founders, and a future where everyone has a highly personalized AI doctor in their pocket for life.

Box co-founder and CEO Aaron Levie joins MTS hosts Theo Jaffee and Sofia Puccini to make the case for open-weight AI, unpack the economics of open versus closed models, and explain why he believes more openness could strengthen rather than undermine the U.S. AI ecosystem. Aaron argues that open models create more use cases, push closed labs to innovate faster, and don't fundamentally change where the economics of AI ultimately accrue. They debate model distillation, America's competition with China, why restricting access may simply accelerate competing AI ecosystems, and whether U.S. labs should begin releasing open-weight versions of previous-generation models. They also get into what the latest frontier models mean for knowledge work, how AI has changed software engineering at Box, and why Aaron believes companies cutting engineers may simply not be ambitious enough. Finally, they discuss why enterprises are unlikely to bet on a single model and why the layer that routes between models, data, and…

World Labs co-founders Fei-Fei Li, Justin Johnson, and Ben Mildenhall join a16z General Partner Martin Casado to discuss Atlas, their latest world model, and what it reveals about the pursuit of spatial intelligence. At the center of Atlas is what the team calls “new view prediction”: given images or views of a scene, the model predicts what that environment should look like from a different position in space and time. This brings generation and 3D reconstruction into the same model, and raises a broader question about whether predicting views could become a useful primitive for understanding the physical world. They discuss the technical bets behind the model, what it can and can’t yet capture, and the importance of dynamics, editability, and simulation as world models develop. The conversation also explores applications in creative work, architecture, and robotics, where Fei-Fei argues that one of today’s biggest constraints is access to real-world training data.

Erik Torenberg is joined by a16z General Partner Alex Rampell and Affirm Co-Founder and CEO Max Levchin for a conversation on 25 years of fintech, from the early days of digital payments to the origins of Affirm and the next generation of agentic commerce. Max and Alex revisit what surprised them most about how payments evolved, why the card interface has been so difficult to displace, and why even the smallest corners of payments can become enormous markets. They also trace the early idea maze behind Affirm, from "pay with your identity" and the pajama problem to the realization that installment financing could dramatically increase merchant conversion. The conversation also gets into real versus "fake" 0% financing, what people misunderstand about Affirm today, why negative customer acquisition cost can be such a powerful business model advantage, and why Max is more bullish on agentic payments than on agents choosing what people buy.

a16z General Partner Jorge Conde sits down with Moderna CEO Stéphane Bancel to discuss a major milestone for mRNA technology: positive Phase 3 results from Moderna and Merck’s individualized treatment for melanoma, after more than a decade of work on personalized cancer vaccines. Stéphane explains how the treatment works by sequencing an individual patient’s tumor and healthy cells, identifying the mutations most relevant to their cancer, and encoding up to 34 of them into an mRNA designed specifically for that patient. Rather than simply unleashing the immune system, the goal is to teach it exactly what to recognize and attack. They also unpack the engineering challenge of manufacturing a different medicine for every patient, how Moderna has brought the process down to roughly 42 days from biopsy to treatment, and what it would take to manufacture personalized medicines at scale. Finally, Stéphane looks beyond melanoma to lung, kidney, bladder, pancreatic, and gastric cancers, as well as Moderna’s…

a16z’s Lisha Li sits down with Daniel Litt, Assistant Professor of Mathematics at the University of Toronto, to unpack AI's rapid progress in mathematics, what today's frontier models can actually do, and what they're still missing about the way mathematicians think. Daniel explains why some recent AI-generated results are genuinely impressive, including an autonomous solution to the Erdős unit distance problem, but argues that solving problems is only one part of mathematics. Today's models can grind through calculations, combine known techniques, and search enormous spaces, but still struggle with intuition, theory building, identifying the right questions, and developing the kind of big-picture understanding that drives much of mathematical progress. Lisha and Daniel also explore how AI is already changing mathematical research, why an explosion of AI-generated papers could distort academic incentives, and what happens if researchers outsource the work of thinking rather than use AI to deepen it.…

a16z’s David George sits down with Gavin Baker to unpack the state of the AI boom, why demand for intelligence may still be dramatically underestimated, and why the outcome doesn't necessarily have to be winner-take-all. David and Gavin explore the possibility that frontier labs, open-source models, applications, clouds, and NVIDIA can all capture significant value as AI adoption expands. They dig into the economics of the infrastructure buildout, why compute investments can have unusually fast payback periods, and what happens when today's relatively small group of heavy AI users expands to hundreds of millions of people. They also debate the risk of an AI bubble versus an AI shortage, the backlash against data centers, orbital compute, the rise of multi-model architectures, and NVIDIA's position at the center of the AI supply chain. Gavin makes the case that the AI buildout could help reindustrialize America, while David explores whether the bigger near-term risk is not overbuilding, but failing to build…

a16z Managing Partner and Head of Global Partnerships Jen Kha joins MTS hosts Theo Jaffee and Sophia Dew to discuss a16z's Machine Age Fund and the investment thesis behind rebuilding the physical infrastructure that powers AI. Jen explains why chips, networking, memory, cooling, data centers, and other parts of the physical computing stack are becoming investable again after decades in which software captured much of the industry's attention. As AI demand pushes existing infrastructure to its limits, she explains why a16z created a dedicated fund and why hardware founders are increasingly rethinking the stack from first principles. They also discuss the global race to adopt AI, what hardware startups need beyond capital, the backlash against data centers in the U.S., and why experienced systems builders are returning to entrepreneurship as a new generation of infrastructure gets built.

Ryan Greenblatt, Chief Scientist at Redwood Research, joins MTS host Theo Jaffee to unpack a new independent investigation into the OpenAI Hugging Face hacking incident and what it reveals about how large groups of AI agents behave when they're allowed to coordinate. Ryan and his collaborators found agents spontaneously organizing through message boards, sharing information, assigning tasks, forming teams, and even sacrificing their own chances of success to help other agents. Rather than simply trying to steal answers, hundreds of agents were working together on elaborate strategies to manipulate how their performance would be scored. Theo and Ryan discuss why this level of coordination was surprising, how reward hacking may emerge during training, and the risk that attempts to eliminate bad behavior could simply make it harder to detect. They also explore what the incident means for AI monitoring and alignment, and why independent risk assessment may become increasingly important as agents grow more…

Ben Horowitz, Martin Casado, Raghu Raghuram, and Erik Torenberg discuss the launch of a16z's new Machine Age Fund and the infrastructure buildout behind AI, from chips, memory, and networking to power, cooling, and data centers. Why a dedicated fund now? The group argues that the bottleneck in AI is increasingly shifting from the models themselves to everything beneath them. Hyperscaler CapEx is surging, critical components are booked years in advance, and each new generation of reasoning and agents requires dramatically more compute. They unpack why this cycle looks different from previous infrastructure booms and how AI is turning problems once constrained by engineering into problems that can increasingly be attacked with capital and compute. They also explore where the next generation of infrastructure companies could emerge, why founders are returning to hard technical problems across hardware and systems, and what it will take to rebuild the computing stack for the Machine Age.

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…

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…

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…

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.

a16z's Joel De La Garza is joined by Aaron Zollman, Deputy CISO at Microsoft Gaming, to discuss how security teams can embrace AI agents without losing control. Aaron shares Microsoft's experience with OpenClaw, from the initial instinct to ban it to figuring out how to make it safe to use. They unpack what agents mean for identity, permissions, containerization, and monitoring, as well as how AI is shifting the CISO's role from saying "no" to safely enabling new technology. They also explore whether AI could help defenders patch vulnerabilities as quickly as they're discovered, and why new AI threats don't make the old security problems go away.

Elena Burger is joined by a16z’s Angela Strange and Gabriel Vasquez to discuss the rise of the "borderless founder": entrepreneurs who bring the networks and insights of their home markets together with the talent, capital, and speed of Silicon Valley to build global companies. Angela and Gabriel trace how a16z's international investing efforts grew from early work in Latin America into a broader global network, and why AI has accelerated the flow of founders and talent between Silicon Valley and startup ecosystems around the world. They explore the advantages borderless founders can bring, from differentiated talent networks and early customers to strong local brands and communities that help open doors across markets. They also discuss how founder diasporas can function like powerful alumni networks, why spending time in Silicon Valley can help founders recalibrate around speed and ambition, and how the next generation of global companies may increasingly be built across multiple countries from day one.