Training Data

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

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

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

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, 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