The a16z Show

a16z is expanding a16z crypto into a16z Frontier, a broader mandate to back founders building at the edges of what's possible. Chris Dixon joins Marc Andreessen, Ben Horowitz, and Erik Torenberg to explain why founders should increasingly ask not “is this too ambitious?” but “is this ambitious enough?” — and how AI could pull decades of technological progress forward across hardware, robotics, entertainment, science, and entirely new forms of media. They also explore the coming internet of AI agents, why open source matters as AI becomes more powerful, and why some of the most important companies of the next decade may combine technologies and business models that previously belonged in separate categories.

AI agents can reason over enormous amounts of information, but much of the world’s actual work is still trapped in PDFs, spreadsheets, scans, and other formats that models struggle to reliably understand. Reducto co-founder and CEO Adit Abraham argues that solving this document layer is essential if agents are going to take on consequential work in healthcare, legal, finance, and insurance. Adit joins a16z General Partner Jennifer Li to explain why traditional OCR breaks down on messy real-world documents, how Reducto combines vision-language models with specialized infrastructure to reach high accuracy without sacrificing cost or latency, and why frontier models can silently drop information on long extraction tasks. Reducto now processes billions of pages each month, including its seven billionth page the morning of the recording. They also discuss Reducto’s R1 model, agents that can review and correct their own document extraction, and the company’s push beyond simply reading files toward letting agents…

More than 300 million people use ChatGPT for health and wellness questions each week. OpenAI’s Karan Singhal thinks that’s only the beginning: the longer-term goal is AI that can help people navigate their health proactively, while making trustworthy medical intelligence dramatically more accessible. Karan joins a16z Partner Jay Rughani to explain why OpenAI trains healthcare expertise directly into its general-purpose frontier models, how it works with hundreds of medical experts to evaluate safety and disagreement, and why the company sees AI as a complement to doctors rather than a replacement for medical care. They also discuss integrations with Epic, AI copilots for clinicians, and what happens as personal agents begin navigating appointments, health records, and other fragmented parts of the healthcare system. Looking further ahead, Karan makes the case that the cost of medical intelligence could continue to fall dramatically, allowing AI to act as a safety net for patients and clinicians, surface…

Why hasn't AI transformed everyday work as quickly as coding? Town CEO Jean-Denis Greze argues the bottleneck isn't intelligence, but making AI useful to people who don't have the time or technical skills to build their own automations. Jean-Denis joins a16z General Partner Alex Rampell to share why he pivoted Town from tax software to a personal AI assistant built around email, calendars, and the workflows that run businesses. They discuss why consumer-grade experiences may win in enterprise AI, how Town discovers and automates tasks users wouldn't think to delegate, and why model neutrality and data ownership matter as AI labs increasingly compete with application companies. They also explore what happens when agents begin acting on our behalf, from managing everyday work to shopping and commerce, and why the next generation of software may need to adapt to each user rather than the other way around.

What changes when an AI mistake doesn’t just hurt a benchmark score, but affects someone’s housing or healthcare? EliseAI Head of Applied Research Mario Martone argues that deploying AI in essential industries requires a very different standard of reliability. Mario joins MTS to explain how EliseAI handles roughly 5 million phone calls a month, why LLMs aren’t the right solution for every problem, and how the team combines language models, traditional machine learning, neural networks, and internally built evaluations to decide what actually works in production. Drawing on 15 years studying theoretical physics, Mario also explains how the scientific method shapes EliseAI’s research culture, from testing new models against its own benchmarks to fine-tuning open-source models and automating increasingly complex workflows across housing and healthcare. And he makes the case that applying AI to the real world can produce problems every bit as intellectually difficult as the ones he encountered studying string…

Why is booking a doctor's appointment still so difficult, and can AI finally change that? Zocdoc CEO Oliver Kharraz argues that the real problem isn't finding doctors, but connecting patients to accurate availability and making appointments bookable wherever they start their search. Oliver joins a16z General Partner Julie Yoo to discuss how Zocdoc spent nearly 20 years building the infrastructure behind online scheduling, and why it's now opening that technology to AI agents, insurers, and other platforms. They explore Zocdoc's partnership with Gemini, why patients shouldn't be locked into a single healthcare platform, and how surfacing last-minute availability can reduce appointment wait times from weeks to days. They also discuss what happens when AI becomes a new front door to healthcare and why Oliver believes the future of Zocdoc extends far beyond its own app.

a16z General Partner Martin Casado sits down with Grepr CEO Jad Naous to ask what comes after AI learns to write code: can it also debug and operate the systems that code creates? Jad shares how AI has transformed engineering inside his 10-person team, cutting some projects from six months to just 10 days. But operating production systems is a harder challenge, with unpredictable workloads, failures, and countless possible states. They discuss whether AI can learn to diagnose problems, test hypotheses, and debug systems like a human engineer. They also explore AI SREs, security, query optimization, and why the best engineers may be gaining the most leverage from coding agents.

a16z General Partner Alex Rampell sits down with VGS Executive Board Member, Terry Angelos, to unpack what happens to payments when AI agents go from recommending what to buy to actually making the purchase. Terry explains the infrastructure required to safely give agents access to payment credentials, why wallets like PayPal, Shop Pay, and Link are competing to become the default payment layer for agents, and how new agentic tokens could preserve the chargebacks, identity checks, and merchant relationships that make today's payment networks work. VGS, which already secures payment credentials across a significant share of e-commerce, is now provisioning millions of these agentic tokens each week. They also discuss what happens when merchants don't want to transact with agents, how delegated authority changes liability when an agent makes a mistake, and why AI could simultaneously increase consumer spending while eliminating the subscriptions, fees, and other forms of economic "breakage" that depend on…

a16z Partner Lisha Li sits down with AI researcher and Trillium founder Nathan Lambert to ask how durable the gap really is between frontier AI labs and open models. Nathan argues that OpenAI and Anthropic have structural advantages in compute, capital, and the ability to pioneer new training paradigms, but open-model labs, particularly in China, have proven remarkably capable of following close behind. They unpack how much of that comes from distillation versus technical talent, why compute remains a major constraint, and how data, RL environments, and post-training are increasingly shaping model progress. They also discuss Nathan’s new nonprofit research lab, Trillium, and its plans to open up parts of AI research that are increasingly difficult to study from the outside. That includes releasing RL environments and documenting how AI is actually being used to automate AI research, with the goal of demystifying recursive self-improvement and giving researchers and policymakers a clearer picture of what…

Could AI break the cryptography protecting everything from crypto wallets and banking to private messages? Recent advances in AI-driven mathematical research have sparked new warnings about the security of today's encryption systems, but how much of the concern is supported by evidence? a16z crypto's Liz Harkavy is joined by Stanford cryptographer Dan Boneh, a16z crypto General Partner Eddy Lazzarin, and Research Partner Justin Thaler to separate what we know from what remains speculation. They explain why modern cryptography relies on mathematical assumptions, what OpenAI's recent results do and don't demonstrate, and how the potential threat from AI differs from quantum computing. They also discuss why existing security practices and cryptographic alternatives offer reasons for confidence, the importance of building systems that can adapt if an assumption fails, and how AI could ultimately help researchers and developers make software more secure.

a16z General Partner Martin Casado sits down with MakerMaker.AI co-founders Dhaval Adjodah and Owen He to unpack recursive self-improvement, or RSI, and separate the increasingly popular term from what it would actually mean for an AI system to improve itself. Dhaval and Owen distinguish true RSI from simply using AI to automate pieces of AI development. Their goal is a closed loop where the system being improved is also doing the improving, across components including models, harnesses, and eventually more of the AI stack. They explain why closing that loop still doesn’t guarantee runaway “takeoff,” and why measuring real progress requires accounting for both capability gains and the resources required to achieve them. They also share what they’re seeing in their own research, including early signs that self-improving systems can increase performance while improving efficiency. And they dig into the practical challenges of proving those gains are real, from variance and reward hacking to the enormous…

Nick Bostrom joins Theo Jaffee and Sofia Puccini on MTS to revisit the ideas about superintelligence and existential risk that he helped bring into the mainstream, now that increasingly capable AI systems actually exist. Nick discusses why reinforcement learning may be pushing models toward more goal-seeking behavior, what recent agent experiments tell us about instrumental convergence, and why he favors preserving the ability to slow frontier development rather than committing now to a broad or indefinite AI pause. He also makes the case that the risks of AI need to be weighed alongside its potential upside, from advances in medicine and longer lifespans to entirely new ways of being human. They also get into whether today's models could already have some form of consciousness, the tradeoffs of open-source AI, whether machines can do original philosophy, mind uploading, and what the simulation hypothesis might mean for the superintelligence we create.

a16z co-founder and General Partner Ben Horowitz joins James Norman on the inaugural episode of Vintage x Vintage for a conversation spanning entrepreneurship, investing, hip-hop, and what changes when AI makes building dramatically easier. Ben reflects on going from Netscape to becoming a first-time CEO, why every founder eventually hits a “we’re fucked, it’s over” moment, and what he learned from building a16z around the needs of entrepreneurs. He explains why great investors should look for world-class strengths rather than an absence of weaknesses, and why expanding the ways we recognize talent can raise the bar rather than lower it. They also look at the current AI cycle, why Ben believes it is bigger than the internet, how AI changes who can become a builder, and why the infrastructure beneath today’s models may need to be rebuilt from the ground up.

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.