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- 5 Nov 2026
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Join us as we train our neural nets on the theme of the century: AI. Sonya Huang, Pat Grady and more Sequoia Capital partners host conversations with leading AI builders and researchers to ask critical questions and develop a deeper understanding of the evolving technologies—and their implications for technology, business and society. The content of this podcast does not constitute investment advice, an offer to provide investment advisory services, or an offer to sell or solicitation of an offer to buy an interest in any investment fund.
Episodios 114
A Model For Everyone: How Ronak From Trajectory Is Building Products For Continual Learning
Today’s agents are stuck in what Ronak Malde calls “pseudo continual learning”: intelligent, but updated in offline batches, like a paper road map you can’t reroute mid-trip. Ronak, co-founder of Trajectory …
Notas del episodio
Today’s agents are stuck in what Ronak Malde calls “pseudo continual learning”: intelligent, but updated in offline batches, like a paper road map you can’t reroute mid-trip. Ronak, co-founder of Trajectory, explains why closing that loop is the next learning paradigm, and why training your own model, which used to take a dedicated research team, months of work and hundreds of millions in compute, is now something a company can do in a few clicks with their own data. He details two research bets they are making available to everyone with their product: 1) a variant of GRPO that removes the need for a simulated environment by learning from single real-world rollouts instead of 16 replays of the same task, and 1) a meta-grader that increased data efficiency by 100x. Ronak explains how Harvey trained a model six times cheaper than frontier, improving an open-source baseline by over 4x on Harvey's own legal benchmark. He defines the difference between continual learning and recursive self-improvement and predicts product builders will become gardeners, setting trellises for products that grow themselves.
Hosted by Pat Grady and Lauren Reeder, Sequoia Capital
0:00 – Introduction
0:45 – Pseudo continual learning: paper maps vs Google Maps
2:48 – Do we need new architectures? Rich Sutton, David Silver, and the missing layer
4:33 – From a $100M consulting model to a product: lessons from SWE-1 at Windsurf
7:36 – Harvey: 6x cheaper than frontier, 4% to 18% on its own benchmark
8:55 – Why "Trajectory": the unit of work is a task, not a token
10:13 – The primitives of continual learning: traces, replayable environments, reward
12:56 – Group Size 1 and the meta-grader: training without an environment or a labeled reward
16:16 – The platform: train, eval and deploy in one click, and who it's for
18:58 – Rogo and intelligence density
20:14 – What's next: curiosity, and why continual learning beats RSI on alignment
24:19 – Research and product under one roof, and the product builder as gardener
27:26 – Why work at Trajectory, and the optimistic case: from 1% to 99%
European Defense Sovereignty Starts in the Factory: Stark’s Uwe Horstmann and Johannes Schaback
Two years after its founding, Stark has roughly 700 employees and strike drones in the field in Ukraine. CEO Uwe Horstmann and CTO Johannes Schaback argue that the real lesson of that war is not any single …
Notas del episodio
Two years after its founding, Stark has roughly 700 employees and strike drones in the field in Ukraine. CEO Uwe Horstmann and CTO Johannes Schaback argue that the real lesson of that war is not any single weapon: the innovation ecosystem behind it is the wonder weapon. They explain why Stark designs every system for mass manufacturing from day one, runs 10 to 15 programs like a venture portfolio, and views the staff sergeant who operates a system as just as important as the general who buys it. Johannes recounts the publicly reported failed trial that became a forging moment, as engineers worked around the clock at a freezing test range until they landed three consecutive hits. They discuss NATO’s principles of responsible AI use and the hard-coded constraints in Stark’s autonomy stack. And Uwe lays out his next big idea: defense contracts structured like cloud contracts with surge capacity built in, so production itself becomes deterrence.
Hosted by Pat Grady and Luciana Lixandru, Sequoia Capital
Chapters
0:00 – Introduction
1:03 – Billions in contracts in 18 months: Stark's four-part playbook
2:28 – The staff sergeant matters as much as the general, and wood beats plastic
7:42 – Why Europe is ready for a neo prime, and why procurement reform will last
11:45 – Startup DNA, venture-style programs, and "as complex as a washing machine"
16:27 – Born out of fire: the failed trial and the round-the-clock comeback
21:13 – Where Stark finds talent, and why defense is no longer off-limits
23:48 – Anatomy of a strike, from the wooden crate to the kill zone
27:22 – Vertical integration, multiple domains, and shifting left on manufacturing
32:53 – After the Hugging Face hack: guardrails on autonomous weapons
35:56 – Matching the primes on R&D and scaling to 700 people in two years
39:24 – Deterrence by contract, and Europe's next chapter with the US
Inside the Robot Data Factory: Mecka's Josh Gao on Sensorizing Humans to Train Robots
Robotics never got the internet's gift of free training data, so someone has to manufacture it. Josh Gao, founder and CEO of Mecka AI, runs one of the largest robot data factories, and he explains what that …
Notas del episodio
Robotics never got the internet's gift of free training data, so someone has to manufacture it. Josh Gao, founder and CEO of Mecka AI, runs one of the largest robot data factories, and he explains what that actually involves: custom baseball caps and tactile gloves because nothing off the shelf captures force, 100-gigabit upload lines to move hundreds of petabytes, and custom quality gates. Josh lays out the training recipe the industry is converging on (roughly a million hours of egocentric human data, tens of thousands of hours of robot data, and a few hours of in-domain teleop ) and why tactile and simulation data still have to enter the "data pot of soup." He argues the field is still orders of magnitude behind Tesla's FSD, which spent a decade on two degrees of freedom while a robot hand has dozens. His take: there won't be a single GPT-3 moment for robotics, but a steady unlock of commercial tasks, starting with constrained industrial work and ending, he hopes, with robots mining asteroids.
Hosted by Sonya Huang, Sequoia Capital
0:00 – Introduction
1:46 – What Mecka is: pixels, actions and logic
2:45 – Is robot data economically feasible without the internet?
4:15 – Teaching robots about carbon life
5:27 – Cloud kitchens, stablecoins and the path to robotics
7:03 – Entering robotics with a fresh lens
8:45 – What egocentric data is and why Mecka bet on it
10:09 – The first pilot: aunties and uncles with a mobile app
10:42 – Force data and tactile gloves
11:55 – Inside the operations of a data factory
14:41 – Heat-mapping the taxonomy of human work
15:42 – Quality gates, fraud and the McDonald’s POV videos
17:44 – Earning a margin as a data intermediary
19:33 – Millions of hours vs. Tesla’s decade
20:55 – Why it isn’t binary: unlocking commercial tasks along the way
22:02 – Home data is plentiful; home robots are the hardest
23:24 – Useful robots can be deployed today
24:35 – When robotics gets an FSD-style flywheel
26:42 – Deployment diffusion and the modern systems integrator
28:21 – Favorite vertical robot categories; mining an asteroid
30:08 – The hardware slate: Waymo-fying people
30:55 – EgoVerse
32:04 – The data pot of soup: the converged training mix
33:54 – Move 37 for robotics: AlphaGo and Federer
36:02 – World models and the training mix
36:58 – Will scaling the current mix get us to the promised land?
39:05 – Will the AGI labs steamroll robotics?
40:50 – Sci-fi roots and the Kawasaki Corleo
42:44 – How many robots in a decade?
David Silver and Lasse Espeholt of Ineffable Intelligence: A Superlearner for the Age of Experience
David Silver spent two years building AlphaGo on human data. When he removed human data altogether building AlphaGo Zero, the AI surpassed AlphaGo in under two days. He left DeepMind to run that same bet in …
Notas del episodio
David Silver spent two years building AlphaGo on human data. When he removed human data altogether building AlphaGo Zero, the AI surpassed AlphaGo in under two days. He left DeepMind to run that same bet in pursuit of superintelligence. David and Lasse Espeholt, co-founders of Ineffable Intelligence, explain their plan to build a “superlearner”—an agent that starts knowing nothing and discovers science, mathematics, and language for itself by direct experience instead of through a pre-training run. Silver argues that human-language reasoning is a ceiling, not a foundation, that alignment via chain-of-thought monitoring is already reaching its limits, and that the science of how superintelligences coexist should be worked out in simulation before anything touches the real world. Espeholt describes the GPU cluster and the lineage from IMPALA and SEED RL that makes the experiment possible. Silver’s forecast: strong evidence within a year, superintelligence in three.
Hosted by Sonya Huang, Sequoia Capital
0:00 – Introduction
1:09 – What held true from Go to today, and what the LLM era changed
3:35 – Why leave DeepMind to start a new lab
5:58 – Why "Ineffable" (and the secret second reason)
7:03 – Lasse's path: scaling RL at DeepMind and Google Brain
9:31 – Defining a super learner and first contact
12:35 – Super learners vs. LLMs that make discoveries
13:59 – Day to day: the RL stack, crucibles and human-journey milestones
16:13 – Don't breadcrumb the objective: simple rewards, emergent complexity
21:16 – What a crucible looks like
24:14 – Go: one agent is boring, two is infinite
25:41 – Will agents rediscover language?
27:22 – Removing human priors: AlphaGo to AlphaZero
32:05 – Keeping human bias out of the crucibles
33:35 – Confidence, timing and super forecasting
36:24 – Is compute the driver?
38:13 – The ceiling on simulation and Rich Sutton's objection
43:44 – Alignment and the science of superintelligence
47:45 – Monitoring agents that don't think in English
51:01 – Pacing the frontier
52:54 – The compute setup: Vera Rubin on GCP
55:18 – Training, inference, simulation and RL scaling laws
57:40 – IMPALA and SEED RL
1:00:58 – 20 people and the compute-per-person ratio
1:04:49 – A research-led culture
1:05:47 – Predictions
Google's AI Infrastructure Chief, Amin Vahdat, on the Physics & Economics of Frontier AI
At 100,000-accelerator scale, something fails multiple times an hour, which is why Google's Chief Technologist for AI Infrastructure Amin Vahdat thinks FLOPS is a vanity metric. The metric that matters is what …
Notas del episodio
At 100,000-accelerator scale, something fails multiple times an hour, which is why Google's Chief Technologist for AI Infrastructure Amin Vahdat thinks FLOPS is a vanity metric. The metric that matters is what he calls goodput: the useful work a workload actually delivers through real failures. Amin walks through the calculus that split the TPU line into 8i and 8t for the first time, why the TPU's core primitives haven't changed since v1, and how Google and DeepMind co-design in the same rooms, intercepting chip architectures mid-flight before tape-out. He explains why long-horizon agents are sending demand for CPUs and storage through the roof alongside accelerators, how optical circuit switches reroute light to a spare rack in milliseconds, and why Google would rather wait on a utility than build its own gigawatt. We also cover orbital data centers and the multi-megawatt rack of 2036.
Hosted by Sonya Huang, Sequoia Capital
0:00 – Introduction
1:47 – What makes a data center an AI data center
5:30 – Goodput, not FLOPS: holding yourself accountable when something fails every hour
11:52 – Doubling token capacity every six months, and where the gains actually come from
15:32 – The TPU bet: from a contrarian call in 2013 to splitting 8i and 8t
23:30 – The case for and against co-design
26:11 – Shoulder to shoulder with DeepMind: intercepting chips mid-flight
34:16 – Long-horizon agents change the shape of the data center
37:50 – Optical circuit switching and the state of networking
42:35 – Power is the binding constraint: utilities, gigawatts, and sizing a data center
49:23 – Training vs. serving clusters, seven-year-old TPUs, and open standards
58:29 – Orbital data centers and the supercomputer of 2036
Box's Aaron Levie: On Reinventing Yourself in the AI Age and Enterprise Diffusion
Starting a company is hard. Reinventing your company for AI as a public company with quarterly earnings results is even harder. Aaron Levie has pulled off the transition with Box and offers hard-won advice for …
Notas del episodio
Starting a company is hard. Reinventing your company for AI as a public company with quarterly earnings results is even harder. Aaron Levie has pulled off the transition with Box and offers hard-won advice for founders. The cofounder and CEO of Box argues the value isn't only in the model; it's in the bridge from a model's raw capability to the actual workflow inside a bank, a law firm, or a pharma company. That's the case for the application layer, and Box is building it: an agent harness tuned so tightly to its own file system, permissions, and search that it beats handing the raw API to Claude or ChatGPT on both accuracy and latency. Aaron explains why token subsidies from the labs can't last, why you want a model-agnostic company routing your tokens rather than the one selling them, and why coding diffused fast while the rest of knowledge work won't. (There's no "give us your GitHub" for a sales rep.) His prediction: within five years, 90% of enterprise tokens go to work no human user ever initiated.
Hosted by Sonya Huang, Sequoia Capital
0:00 – Introduction
1:55 – Are application companies the hottest neolabs?
6:56 – Will the labs move up the stack?
12:34 – Box and betting the company on AI
16:50 – Hero use cases: reading a million contracts and long-running agents
18:42 – Work slop: why AI code is embraced but AI content isn't
24:08 – Building Box's agentic harness and the evals that matter
27:23 – The state of the model race
29:25 – Open-weight model adoption in the enterprise
32:34 – Memory, continual learning, and what belongs in the weights
37:29 – Box Labs and systems of record in a world of agents
44:55 – Will chat be the dominant UI for enterprise AI?
48:00 – Why coding diffused fast and the rest of knowledge work hasn't
54:31 – Staying wired in, making a company AI-first, and what it takes to win
Making Cities Awesome: Peregrine’s Nick Noone & Ben Rudolph
Most public safety technology companies grow by collecting more data. Peregrine inverted the model: no sensors, no new data, a business built on connecting the data and information cities already own …
Notas del episodio
Most public safety technology companies grow by collecting more data. Peregrine inverted the model: no sensors, no new data, a business built on connecting the data and information cities already own. Co-founders Nick Noone and Ben Rudolph received more than two dozen no's before San Pablo PD let them in the door in February 2018. Today, Peregrine powers law enforcement, emergency medical services, fire and rescue, and other services in more than 400 cities and communities globally. Nick and Ben explain their north star for data sovereignty, and discuss how Peregrine's philosophy and privacy-first approach to data access and ownership preserves individual privacy and cities' sovereignty. They walk through how AI and long-horizon agents are being deployed: a cold case agent that reproduced an exoneration detectives had reached by hand, a Wisconsin county that placed a suspect using cell records buried in 300GB of evidence, identifying threats to a synagogue, root-causing an escalation in weather-related incidents, and more.
00:00 Introduction
02:07 What Forward Deployed Engineering Means
03:58 What Silicon Valley Gets Wrong
05:23 UNHCR, Dimagi And Downstream Data Problems
08:25 Why Cities, Why Safety
10:45 Two Dozen Nos And San Pablo PD
14:19 Building Through Defund The Police
18:16 The Inversion Of The Collection Model
21:20 Data Ownership And Governance
22:57 From Nice Search To Deep Analysis
29:59 Agents Writing The Integrations
31:45 The Cold Case Agent
35:02 The Anti-Network-Effect Proposition
38:40 Facial Recognition And Hard Decisions
40:48 Technology For The Underdogs
42:54 Trusting The Individual Contributor
48:50 Ten Thousand Cities
Parallel’s Parag Agrawal: Building a New Web for AI Agents
Parag Agrawal is making a bet that goes against two decades of web search: agents will query the web a thousand times more than humans ever have, and the infrastructure built around human clicks is wrong for …
Notas del episodio
Parag Agrawal is making a bet that goes against two decades of web search: agents will query the web a thousand times more than humans ever have, and the infrastructure built around human clicks is wrong for them. The former Twitter CEO, now founder and CEO of Parallel Web Systems, explains why Parallel treats human click data as a bug and trains on agent feedback instead. He unpacks the counterintuitive choice to ship a search agent before a search engine, building an index incrementally, and how the new Turbo product cut agentic search to 200 milliseconds. But the problem Parag keeps returning to is economic: the ad-supported internet collapses when agents show up instead of people. His fix draws on Shapley values to pay content owners for the value their pages provide agents, with real dollars reaching publishers, he predicts, within 12 to 24 months.
Hosted by Sonya Huang and Andrew Reed, Sequoia Capital
00:00 Introduction 03:25 What Is Web Search 05:17 Why Start a New Index 07:52 Search Agents First 10:17 Not a Neolab 13:14 Agents vs Google Search 19:38 Inside the Search Stack 28:59 Search Multipliers With Agents 30:21 Meeting Prep Agent Workflows 31:46 Quality Cost Latency And Turbo 32:42 Are Agents Overtaking Humans 34:28 Ads Model Meets Agent Web 37:20 New Incentives For Content 40:48 Shapley Values Attribution 47:46 Parallel Web And Future Vision
Rich Sutton and Khurram Javed: Why AI Models Stop Learning, and How to Start It Again
Rich Sutton, who helped pioneer reinforcement learning and wrote the seminal AI essay The Bitter Lesson, has now cofounded Oak Lab with his former student Khurram Javed. Their goal: to build agents that …
Notas del episodio
Rich Sutton, who helped pioneer reinforcement learning and wrote the seminal AI essay The Bitter Lesson, has now cofounded Oak Lab with his former student Khurram Javed. Their goal: to build agents that continuously learn from their own experience rather than from us. Rich doesn't think he holds a radical view: "I'm not weird. The field is weird." He says all learning is continual, and the field is the one that needed a new name for it. Rich and Khurram argue synthetic data is "a big mistake." Their "big world hypothesis" is that the world is massively more complex than any agent or simulator, so approximations have to be updated continuously rather than frozen at deployment. Rich calls LLMs an unanticipated scientific breakthrough, but says they represent roughly a quarter of intelligence. He says catastrophic forgetting is "totally curable" with the ideas behind their continual backprop algorithm. Khurram explains why the frontier labs can't follow: they sit in a local minimum where a new paradigm gets worse before it gets better. Their target, five to ten years out, is a trillion-parameter mind that keeps learning, stays coherent, and runs on 20 watts.
Hosted by Sonya Huang and Alfred Lin, Sequoia Capital
00:00 Introduction 02:10 An AI winter, a cancer diagnosis, and the move to Alberta 07:07 Writing "The Bitter Lesson," and what people get wrong 09:53 Are LLMs a positive or a negative example of it? 11:03 Synthetic data is "just a big mistake," and the Big World Hypothesis 18:01 AlphaGo, human priors, and why prior knowledge and learning should be friends 22:37 "Their weights never change": do LLM assistants actually learn? 26:09 Babies, squirrels, and why no animal learns by supervised learning 32:02 Rockets, imagination, and where paradigm shifts come from 36:42 The Alberta Plan and its 12 steps 38:53 Catastrophic forgetting and the cure 43:43 Oak's biggest ambition: a self-maintaining mind 47:56 Why the big labs are stuck in a local minimum 49:13 If everything goes right: LLMs, many minds, and hiring
Chai Discovery's Bitter Lesson: Drug Design Is Another Scaling Problem
Most people treat biology as a bespoke, messy science. Josh Meier and Matt McPartlon, co-founders of Chai Discovery, treat it as an engineering problem. They make the case that drug design obeys the bitter …
Notas del episodio
Most people treat biology as a bespoke, messy science. Josh Meier and Matt McPartlon, co-founders of Chai Discovery, treat it as an engineering problem. They make the case that drug design obeys the bitter lesson: scale data, models, and compute, and the model can learn what a hand-built pipeline simply couldn't capture. The results are concrete: Chai-2 pushed de novo antibody design from a sub 0.1% hit rate to 16%, turning a needle-in-a-haystack search into something more like designing a key to fit a lock. Josh argues, counterintuitively, that biology is more verifiable than code, and explains why the goal should be more lab experiments, not fewer. Their bet: a design suite that collapses drug discovery from nine months to nine days, and arms the pharma industry rather than competing with it.
Hosted by Pat Grady and Sonali Singh, Sequoia Capital
00:00 Introduction 01:52 From Discovery to Design 03:25 Protein AI Breakthroughs Timeline 06:04 Why Start in 2024 10:13 Diffusion Models Intuition 11:41 Building the Avengers Team 15:22 Hit Rates and Scaling Laws 25:01 Molecular CAD Vision 25:24 Faster Design Loops 26:32 Future Drug Discovery 28:37 Platform Business Model 31:14 Partnering Reality Check 33:44 Data Flywheel Explained 37:16 Staying Ahead at Scale 39:44 Culture and What's Next
Building the Automated AGI Lab: Core Automation's Jerry Tworek and Rohan Anil
Jerry Tworek led reasoning at OpenAI, convinced that scaling reinforcement learning was the path to AGI. Rohan Anil co-led Gemini pre-training and built the Shampoo optimizer. Now they've teamed up at Core …
Notas del episodio
Jerry Tworek led reasoning at OpenAI, convinced that scaling reinforcement learning was the path to AGI. Rohan Anil co-led Gemini pre-training and built the Shampoo optimizer. Now they've teamed up at Core Automation on a contrarian premise: the transformer has carried us as far as it can, and the bottleneck to smarter systems is no longer scale — it's the architecture itself. The missing capability is continual learning, models that adapt at test time, which transformers can't do. In-context learning taps out fast (Codex needs compacting after ~20 minutes) and fine-tuning invites catastrophic forgetting. Rohan argues pre-training and RL should be optimized end-to-end, and that transformers spend computation inefficiently. They lay out why the largest labs won't chase alternatives while locked in the coding-agent race, and why building the world's most automated lab starts with automating kernel generation—the one place frontier models still lose to a high-taste human.
Hosted by Sonya Huang and Pat Grady, Sequoia Capital
Factory's Matan Grinberg: The Coming ‘Dark Factory’ Where Software Builds Itself
Factory started building fully autonomous coding agents in April 2023, two years before enterprises were ready. Matan Grinberg now says this is indistinguishable from being wrong. The Factory co-founder and CEO …
Notas del episodio
Factory started building fully autonomous coding agents in April 2023, two years before enterprises were ready. Matan Grinberg now says this is indistinguishable from being wrong. The Factory co-founder and CEO explains how the company survived its "journey in the desert," including the decision to hand nearly all of its revenue back to customers when the product wasn't making developers obsessed. Matan makes the contrarian technical case that a model-agnostic harness beats the model-and-harness co-design that labs like OpenAI and Anthropic favor, because exposing a harness to many models keeps it from overfitting to any single one. He argues open-weight models like GLM will capture the majority of tokens by staying one generation behind the frontier at a fraction of the cost, and that CIOs will soon justify every incremental token the way they justify headcount. Looking ahead, he predicts 90% of coding tokens will run asynchronously—the "dark factory" where software builds itself.
Hosted by Sonya Huang and Pat Grady, Sequoia Capital
Anthropic's Katelyn Lesse & Angela Jiang: Building an Ecosystem, not a Walled Garden
Katelyn Lesse and Angela Jiang lead the team building Anthropic's developer platform - the layer that both outside builders and Anthropic's own products run on top of. Angela frames the platform as a …
Notas del episodio
Katelyn Lesse and Angela Jiang lead the team building Anthropic's developer platform - the layer that both outside builders and Anthropic's own products run on top of. Angela frames the platform as a three-layer stack: knowledge, execution, and coordination. She argues the real leverage is what’s at the top: "strategies," or meta-harnesses that give each token a different job, from advising to executing to reflecting to memory. On the question of open ecosystem vs. walled garden, they say they aren't precious about owning the stack. Katelyn points to Anthropic's self-hosted sandboxes with partners like Modal, Vercel, and Cloudflare. Whether the work runs on Anthropic's infrastructure or someone else's, what really matters to them is that the architecture is sound. The deeper bet is standards: they hand skills and MCP to the whole industry, build connectors on the MCP spec, and help agents (Claude and non-Claude) work together. The one place they stay closed is model routing: they argue harnesses should be tuned to a model family, so they're designing for Claude rather than routing across models. Angela's frame for the ecosystem bet is electricity: transformative only because everyone could plug in, and no company wired it alone.
Hosted by Sonya Huang and Lauren Reeder, Sequoia Capital00:00 Introduction
01:49 Two North Stars
02:27 External Builders And Primitives
03:54 What To Externalize
06:00 From Messages To Agents
08:19 Managed Agents Adoption
09:07 Three Layer Cake
10:22 Execution Harnesses Explained
11:09 Coordination Strategies Roadmap
12:13 Ecosystem Standards And Safety
15:39 Open Ecosystem Not Walled
17:12 Vertical Products And Form Factors
22:26 Claude Tag Under The Hood
26:04 Harness Best Practices
38:13 Token Costs And Whats Next
Inside Zipline's Autonomous System: 140M Miles, Zero Incidents
The largest commercial autonomous system on earth isn't a robotaxi fleet — it's Zipline, which has flown 140 million autonomous miles with zero safety incidents. Co-founder Keller Rinaudo Cliffton and Eric …
Notas del episodio
The largest commercial autonomous system on earth isn't a robotaxi fleet — it's Zipline, which has flown 140 million autonomous miles with zero safety incidents. Co-founder Keller Rinaudo Cliffton and Eric Watson, who leads systems engineering and safety, explain why the drone itself is only 15% of the solution. The rest spans inventory management, air traffic integration, and engineering systems such as a dual flight computer failover protocol that recently saved a delivery mid-flight. They trace Zipline's path from launching blood delivery in Rwanda in 2016 (when drone delivery was illegal in the US) to a 51% reduction in maternal mortality in that country, a $550 million commercial diplomacy partnership with the State Department, and a cost curve that fell from $300 per delivery to $12. Zipline is now racing toward a million deliveries a day, and a quiet inflection point when autonomous delivery becomes cheaper than sending a car.
Hosted by Alfred Lin and Pat Grady, Sequoia Capital
Why Hardware-Software Co-Design Is AI's Real 100x: Dylan Patel of SemiAnalysis
Dylan Patel, founder of SemiAnalysis, argues the biggest gains in AI don't come from faster chips, they come from software-hardware co-design. Optimizing the model, the kernels, and the silicon together turns a …
Notas del episodio
Dylan Patel, founder of SemiAnalysis, argues the biggest gains in AI don't come from faster chips, they come from software-hardware co-design. Optimizing the model, the kernels, and the silicon together turns a 2x here and a 2x there into 100x. He explains why DeepSeek's experts were shaped for Nvidia's Hopper (and why TPUs struggle to run it), why OpenAI's sparser models and Anthropic's denser ones pull them toward different hardware, and why the so-called CUDA moat was never really about CUDA. Dylan breaks down InferenceX, his living benchmark that runs the latest models on over $50M of donated hardware daily, tracking a roughly 60x annual drop in cost per unit of quality. He makes the case that inference will be a bigger market than oil, that the compute crunch persists because models expand the value of useful work faster than compute grows, and why Jensen Huang is bankrolling neoclouds to engineer a multipolar world.
Hosted by Shaun Maguire and Sonya Huang, Sequoia Capital
Memory and Continual Learning: Engram's Dan Biderman and Jessy Lin
Dan Biderman and Jessy Lin, co-founders of Engram, are building a neolab around memory and continual learning, which they call two sides of the same coin. Their contrarian premise: instead of stuffing …
Notas del episodio
Dan Biderman and Jessy Lin, co-founders of Engram, are building a neolab around memory and continual learning, which they call two sides of the same coin. Their contrarian premise: instead of stuffing ever-larger prompts into the context window or bolting on RAG, bake a team's knowledge directly into the model's weights, so it knows your company the way an employee of several years does.
The payoff: matching or beating frontier models while consuming up to 100x fewer tokens. Working with partners like Microsoft, Notion, and Harvey, the team draws on roots in computational neuroscience and state-space architectures to attack what they see as the real bottleneck in AI — not raw intelligence, but memory and continual learning. In contrast to the frontier labs' race toward one ever-bigger model and AGI, Dan and Jessy imagine a world where everyone has their own model — privately trained, always learning, and good at the things you actually care about. The real ChatGPT moment for memory, they argue, is the day your model feels like an intern that genuinely got smarter overnight.
Hosted by Sonya Huang and Shaun Maguire, Sequoia Capital
Simulating Humans at Scale: Simile's Joon Sung Park
The race to build superintelligence is producing models that keep getting better at objective problems, but not at behaving like actual people. Joon Sung Park, founder and CEO of Simile and creator of …
Notas del episodio
The race to build superintelligence is producing models that keep getting better at objective problems, but not at behaving like actual people. Joon Sung Park, founder and CEO of Simile and creator of Stanford's "Smallville" generative agents study, argues that simulating human society requires a fundamentally different kind of model. He frames today's frontier models as the "CPU of intelligence"—rational, superhuman at problems with right answers—and Simile as creating the "GPU of intelligence," built to encode the diversity of people's values, preferences, and tastes. It simulated 1,000 Americans and predicted their behavior 85% as accurately as people reproduce their own answers. CVS uses it for concept testing; some customers simulate their own earnings calls. Joon's larger bet: a "CERN of human society" that could one day model bank runs, climate cooperation, or the early signals of a collapsing democracy.
Hosted by Sonya Huang, Sequoia Capital
Google DeepMind's Logan Kilpatrick: Why the Model Eats the Harness
The entire startup ecosystem is racing to build agent harnesses. Logan Kilpatrick, who leads Google AI Studio and the Gemini API, argues that scramble has a roughly 12-month shelf life. Models will absorb the …
Notas del episodio
The entire startup ecosystem is racing to build agent harnesses. Logan Kilpatrick, who leads Google AI Studio and the Gemini API, argues that scramble has a roughly 12-month shelf life. Models will absorb the scaffolding and run it natively, so the edge moves elsewhere. Google's own bet runs in parallel: a single agent harness, born from the Windsurf team and now called Antigravity, has become the connective tissue across search, the Gemini app, Cloud, and AI Studio — the role Gemini-the-model used to play. Logan makes the case that coding already feels like narrow superintelligence, and that "jagged" vertical superintelligence (in math, finance, and science) will arrive well before AGI. He argues Google's real goal is maximizing outcomes for users, not eyeball time. He unpacks Omni, the single model built to replace multiple separate systems Google once trained for text, audio, music, image, and video. His throughline: AI is an accelerant for human ambition, not a substitute for it.
Hosted by Sonya Huang, Sequoia Capital
LIVE: Jensen Huang on Building the Dynamo of the Intelligence Age
Jensen Huang, founder and CEO of NVIDIA, makes the case that computing is undergoing its biggest shift in 60 years: from retrieval, where data centers store files we look up, to generation, where every word …
Notas del episodio
Jensen Huang, founder and CEO of NVIDIA, makes the case that computing is undergoing its biggest shift in 60 years: from retrieval, where data centers store files we look up, to generation, where every word, image, and video is produced in real time and customized for whoever is asking. He explains why NVIDIA's AI factories are the dynamos of this era: machines that take in electrons and send out tokens of intelligence, just as Siemens' dynamo once turned motion into electricity. Jensen frames intelligence as the third force to "cocoon" the planet after electricity and the internet. He describes the five-layer cake of AI investment—energy, chips, infrastructure, models, applications—and dismantles the fear that AI will erase jobs, using radiology and software engineering to show how automation raised labor demand instead of killing it. His bottom line: you won't lose your job to AI, but you might lose it to someone who uses AI.
Hosted by Konstantine Buhler, Sequoia Capital
Knowing What Your Customers Want, All the Time: Listen Labs' Alfred Wahlforss
Alfred Wahlforss, co-founder and CEO of Listen Labs, is building an AI agent that interviews your customers at a scale no focus group ever could—thousands of voice conversations at once, drawn from an audience …
Notas del episodio
Alfred Wahlforss, co-founder and CEO of Listen Labs, is building an AI agent that interviews your customers at a scale no focus group ever could—thousands of voice conversations at once, drawn from an audience of 30 million people. A year after launch, Listen serves hundreds of Fortune 100s to Startups including Microsoft, Google, NBC Universal, P&G, Anthropic, Cursor, and Cognition. Alfred explains the counterintuitive finding underneath it all: people are often more honest with an AI than a human interviewer, opening up to a non-judgmental entity that costs less and never makes them feel rushed. He walks through why interview transcripts—not credit card data or behavioral logs—turn out to be the richest fuel for predicting how customers will behave, how Listen back-tests its simulations to know which questions it can and can't answer, and why 80% of the company's engineering goes into building the right audience. As AGI makes building trivial, Alfred argues the scarce resource becomes knowing what to build. That's the loop Listen wants to own.
How Cursor Trained Composer on Fireworks: Distributed Infrastructure for High-Performance RL
Cursor's Federico Cassano and Fireworks' Dmytro Dzhulgakov explain how they collaborated to build Composer as a specialized foundation model. The core insight: models have finite capacity in their weights, and …
Notas del episodio
Cursor's Federico Cassano and Fireworks' Dmytro Dzhulgakov explain how they collaborated to build Composer as a specialized foundation model. The core insight: models have finite capacity in their weights, and allocating all those bits to the singular task of software engineering in Cursor frees the model to be both better at the task and far more efficient at inference. Rather than start from pre-training and work up, they took an unconventional top-down approach — mid-training and RL on top of an open-source base to get a useful model into users' hands fast, then specializing the model around real Cursor usage. With Fireworks providing distributed infrastructure, Composer delivers frontier-class coding performance with the speed of a much smaller model.
Hosted by Sonya Huang, Sequoia Capital
Rebuilding IT From the Ground Up for the AI Age: Serval's Jake Stauch
Jake Stauch, founder and CEO of Serval, is building a ServiceNow for the AI era. His most contrarian bet is that the product should look like boring old enterprise software, but with unlimited intelligence …
Notas del episodio
Jake Stauch, founder and CEO of Serval, is building a ServiceNow for the AI era. His most contrarian bet is that the product should look like boring old enterprise software, but with unlimited intelligence. Serval's architecture splits work between two agents: an admin agent that uses code generation to spin up workflows from natural language, and a help desk agent that can only act through the tools admins explicitly approve. Jake explains why his team uses OpenAI models for end-user interaction and Anthropic models for code generation, why new model releases sometimes have to be rolled back when prompt tuning breaks, and why he's not worried the foundation labs will come downmarket. He also makes the case for "fewer, better" hiring as the only durable moat in a world where products may need to be rebuilt every six months.
Hosted by Pat Grady, Sequoia Capital
Suno's Mikey Shulman: Everyone Can Make Music Now
Most music platforms assume you're a listener. On Suno, 90% of daily users make something. Founder and CEO Mikey Shulman explains why that flips the model: the act of creating IS the entertainment, with closer …
Notas del episodio
Most music platforms assume you're a listener. On Suno, 90% of daily users make something. Founder and CEO Mikey Shulman explains why that flips the model: the act of creating IS the entertainment, with closer parallels to gaming and Claude Code than to Spotify. He breaks down the technical bets that got them here — modeling raw sound waves instead of encoding music theory, choosing autoregression over diffusion to prioritize full songs over crisp clips, and why music isn't a scale problem the way LLMs are. He also shares why partnering with Warner matters more than disrupting the record labels, what a truly interactive Coachella might look like, and why he thinks the digital music experience is finally due for its first real change in 25 years.
Hosted by Sonya Huang, Sequoia Capital
ElevenLabs' Mati Staniszewski: How Voice Becomes the Interface for Everything
Mati Staniszewski, co-founder and CEO of ElevenLabs, joins Sequoia partner Andrew Reed at AI Ascent 2026 to talk about how a four-year-old company built a frontier audio AI business with just over 400 people …
Notas del episodio
Mati Staniszewski, co-founder and CEO of ElevenLabs, joins Sequoia partner Andrew Reed at AI Ascent 2026 to talk about how a four-year-old company built a frontier audio AI business with just over 400 people and over $400M in revenue. He explains why audio was overlooked in 2022 when the rest of AI was chasing text and images, why ElevenLabs chose to monetize from day one rather than raise indefinitely, and why he believes voice will be the primary interface for agents, robots, and the next generation of computing. Also: why emotional intelligence is the next frontier in voice, and what happens when one voice agent realizes it's talking to another.
Anthropic's Boris Cherny: Coding's Printing Press Moment
Boris Cherny, creator of Claude Code at Anthropic, joins Sequoia partner Lauren Reeder at AI Ascent 2026 to talk about where coding goes from here. He explains why he hasn't written a line of code in 2026, why …
Notas del episodio
Boris Cherny, creator of Claude Code at Anthropic, joins Sequoia partner Lauren Reeder at AI Ascent 2026 to talk about where coding goes from here. He explains why he hasn't written a line of code in 2026, why he now ships dozens of PRs a day from his phone, and why he believes coding is effectively solved — at least for the code he writes. Also: why loops are the future, why he thinks Claude Code itself may be 100 lines of code a year from now, and why the invention of the printing press is the right analogy for what's about to happen to software.
Waymo's Dmitri Dolgov: 20 Million Rides and the Road to Full Autonomy
Dmitri Dolgov, co-CEO of Waymo, joins Sequoia partner Konstantine Buhler at AI Ascent 2026 to talk about the 20-year arc from the DARPA Grand Challenge to fully autonomous service in eleven cities and counting …
Notas del episodio
Dmitri Dolgov, co-CEO of Waymo, joins Sequoia partner Konstantine Buhler at AI Ascent 2026 to talk about the 20-year arc from the DARPA Grand Challenge to fully autonomous service in eleven cities and counting. He explains how Waymo persisted through every AV hype cycle by treating safety as the non-negotiable foundation, why exponential scaling is finally here (10 of Waymo's 20 million autonomous rides have happened in the last seven months), and how the Waymo Foundation Model — a multimodal world action model that powers the driver, the simulator, and the critic — actually works under the hood. Also: why Waymo is now 13x safer than human drivers, and the moment a Waymo detected a pedestrian behind a city bus by reading the LiDAR returns of their feet.
OpenAI's Greg Brockman: Why Human Attention Is the New Bottleneck
Greg Brockman, co-founder and president of OpenAI, joins Sequoia partner Alfred Lin at AI Ascent 2026 for a conversation that spans the full OpenAI stack. He explains why the company will never have enough …
Notas del episodio
Greg Brockman, co-founder and president of OpenAI, joins Sequoia partner Alfred Lin at AI Ascent 2026 for a conversation that spans the full OpenAI stack. He explains why the company will never have enough compute, why he believes we're 80% of the way to AGI, and why the agentic coding tools that wrote 20% of your code last December are now writing 80% of it. Also: why human attention is becoming the scarcest resource in AI-augmented work, and what it might be like to one day run an organization of 100,000 agents.
Demis Hassabis on Building DeepMind, AlphaFold, and the Final Stretch to AGI
Demis Hassabis, co-founder and CEO of Google DeepMind and 2024 Nobel laureate in chemistry for AlphaFold, joins Sequoia partner Konstantine Buhler at AI Ascent 2026 for a wide-ranging conversation about the …
Notas del episodio
Demis Hassabis, co-founder and CEO of Google DeepMind and 2024 Nobel laureate in chemistry for AlphaFold, joins Sequoia partner Konstantine Buhler at AI Ascent 2026 for a wide-ranging conversation about the path to AGI and what comes after. He explains why he believes AGI is achievable by 2030, why drug discovery could collapse from ten years to days, and why we should think of information, not matter or energy, as the most fundamental substance in the universe. Also: what Einstein would tell us about the limits of today's models, and why the next year or two will be critical for humanity.
Andrej Karpathy: From Vibe Coding to Agentic Engineering
Andrej Karpathy (co-founder of OpenAI, former head of AI at Tesla, and now founder of Eureka Labs) talks with Sequoia partner Stephanie Zhan at AI Ascent 2026 about what's changed in the year since he coined …
Notas del episodio
Andrej Karpathy (co-founder of OpenAI, former head of AI at Tesla, and now founder of Eureka Labs) talks with Sequoia partner Stephanie Zhan at AI Ascent 2026 about what's changed in the year since he coined "vibe coding." He explains why he's never felt more behind as a programmer, why agentic engineering is the more serious discipline taking shape on top of vibe coding, and why we should think of LLMs not as animals but as ghosts: jagged, statistical, summoned entities that require a new kind of taste and judgment to direct. He also touches on Software 3.0, the limits of verifiability, and why you can outsource your thinking but never your understanding.
From SEO to Agent-Led Growth: Profound’s James Cadwallader
James Cadwallader, co-founder and CEO of Profound, makes the case that we are living through the biggest platform shift in marketing history. The front door of the internet hasn't changed, but the visitor …
Notas del episodio
James Cadwallader, co-founder and CEO of Profound, makes the case that we are living through the biggest platform shift in marketing history. The front door of the internet hasn't changed, but the visitor walking through it has. Where consumers once clicked blue links, AI agents now crawl the web on their behalf, synthesizing answers and steering purchase decisions at scale.
James explains why Gemini, ChatGPT, and Claude all recommend brands differently, why mapping AI visibility onto traditional SEO is the wrong instinct, and why the real imperative is to equip a superintelligent agent with original insight it couldn't find anywhere else. He also digs into the dead internet theory – the possibility that human browsing could largely cease within three years – how AI advertising may become the most effective form the world has ever seen, and why agent-led marketing isn't just automation of the old work, but an entirely new capability.
Hosted by Sonya Huang, Sequoia Capital
Aquí están los 40 episodios más recientes de 114. Los anteriores están en el feed del podcast.
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