Training Data

Clay is leveraging AI to help go-to-market teams unleash creativity and be more effective in their work, powering custom workflows for everything from targeted outreach to personalized landing pages. It’s one of the fastest growing AI-native applications, with over 4,500 customers and 100,000 users. Founder and CEO Kareem Amin describes Clay’s technology, and its approach to balancing imagination and automation in order to help its customers achieve new levels of go-to-market success. 

Hosted by: Alfred Lin, Sequoia Capital

Can GenAI allow us to connect our imagination to what we see on our screens? Decart’s Dean Leitersdorf believes it can.

In this episode, Dean Leitersdorf breaks down how Decart is pushing the boundaries of compute in order to create AI-generated consumer experiences, from fully playable video games to immersive worlds. From achieving real-time video inference on existing hardware to building a fully vertically integrated stack, Dean explains why solving fundamental limitations rather than specific problems could lead to the next trillion-dollar company.

Hosted by: Sonya Huang and Shaun Maguire, Sequoia Capital

00:00 Introduction

03:22 About Oasis

05:25 Solving a problem vs overcoming a limitation

08:42 The role of game engines

11:15 How video real-time inference works

14:10 World model vs pixel representation

17:17 Vertical integration

34:20 Building a moat

41:35 The future of consumer entertainment

43:17 Rapid fire questions

Years before co-founding Glean, Arvind was an early Google employee who helped design the search algorithm. Today, Glean is building search and work assistants inside the enterprise, which is arguably an even harder problem. One of the reasons enterprise search is so difficult is that each individual at the company has different permissions and access to different documents and information, meaning that every search needs to be fully personalized. Solving this difficult ingestion and ranking problem also unlocks a key problem for AI: feeding the right context into LLMs to make them useful for your enterprise context. Arvind and his team are harnessing generative AI to synthesize, make connections, and turbo-change knowledge work. Hear Arvind’s vision for what kind of work we’ll do when work AI assistants reach their potential. 

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital 

00:00 - Introduction

08:35 - Search rankings 

11:30 - Retrieval-Augmented Generation

15:52 - Where enterprise search meets RAG

19:13 - How is Glean changing work? 

26:08 - Agentic reasoning 

31:18 - Act 2: application platform 

33:36 - Developers building on Glean 

35:54 - 5 years into the future 

38:48 - Advice for founders

In recent years there’s been an influx of theoretical physicists into the leading AI labs. Do they have unique capabilities suited to studying large models or is it just herd behavior? To find out, we talked to our former AI Fellow (and now OpenAI researcher) Dan Roberts.

Roberts, co-author of The Principles of Deep Learning Theory, is at the forefront of research that applies the tools of theoretical physics to another type of large complex system, deep neural networks. Dan believes that DLLs, and eventually LLMs, are interpretable in the same way a large collection of atoms is—at the system level. He also thinks that emphasis on scaling laws will balance with new ideas and architectures over time as scaling asymptotes economically.

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital 

Mentioned in this episode:

AI Math Olympiad: Dan is on the prize committee

NotebookLM from Google Labs has become the breakout viral AI product of the year. The feature that catapulted it to viral fame is Audio Overview, which generates eerily realistic two-host podcast audio from any input you upload—written doc, audio or video file, or even a PDF. But to describe NotebookLM as a “podcast generator” is to vastly undersell it. The real magic of the product is in offering multi-modal dimensions to explore your own content in new ways—with context that’s surprisingly additive. 200-page training manuals become synthesized into digestible chapters, turned into a 10-minute podcast—or both—and shared with the sales team, just to cite one example. Raiza Martin and Jason Speilman join us to discuss how the magic happens, and what’s next for source-grounded AI.

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

All of us as consumers have felt the magic of ChatGPT—but also the occasional errors and hallucinations that make off-the-shelf language models problematic for business use cases with no tolerance for errors. Case in point: A model deployed to help create a summary for this episode stated that Sridhar Ramaswamy previously led PyTorch at Meta. He did not. He spent years running Google’s ads business and now serves as CEO of Snowflake, which he describes as the data cloud for the AI era.

Ramaswamy discusses how smart systems design helped Snowflake create reliable "talk-to-your-data" applications with over 90% accuracy, compared to around 45% for out-of-the-box solutions using off the shelf LLMs. He describes Snowflake's commitment to making reliable AI simple for their customers, turning complex software engineering projects into straightforward tasks. 

Finally, he stresses that even as frontier models progress, there is significant value to be unlocked from current models by applying them more effectively across various domains.

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

Mentioned in this episode: 

Cortex Analyst: Snowflake’s talk-to-your-data API

Document AI: Snowflake feature that extracts in structured information from documents

Combining LLMs with AlphaGo-style deep reinforcement learning has been a holy grail for many leading AI labs, and with o1 (aka Strawberry) we are seeing the most general merging of the two modes to date. o1 is admittedly better at math than essay writing, but it has already achieved SOTA on a number of math, coding and reasoning benchmarks.

Deep RL legend and now OpenAI researcher Noam Brown and teammates Ilge Akkaya and Hunter Lightman discuss the ah-ha moments on the way to the release of o1, how it uses chains of thought and backtracking to think through problems, the discovery of strong test-time compute scaling laws and what to expect as the model gets better. 

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital 

Mentioned in this episode:

00:00 - Introduction

01:33 - Conviction in o1

04:24 - How o1 works

05:04 - What is reasoning?

07:02 - Lessons from gameplay

09:14 - Generation vs verification

10:31 - What is surprising about o1 so far

11:37 - The trough of disillusionment

14:03 - Applying deep RL

14:45 - o1’s AlphaGo moment?

17:38 - A-ha moments

21:10 - Why is o1 good at STEM?

24:10 - Capabilities vs usefulness

25:29 - Defining AGI

26:13 - The importance of reasoning

28:39 - Chain of thought

30:41 - Implication of inference-time scaling laws

35:10 - Bottlenecks to scaling test-time compute

38:46 - Biggest misunderstanding about o1?

41:13 - o1-mini

42:15 - How should founders think about o1?

Adding code to LLM training data is a known method of improving a model’s reasoning skills. But wouldn’t math, the basis of all reasoning, be even better? Up until recently, there just wasn’t enough usable data that describes mathematics to make this feasible.

A few years ago, Vlad Tenev (also founder of Robinhood) and Tudor Achim noticed the rise of the community around an esoteric programming language called Lean that was gaining traction among mathematicians. The combination of that and the past decade’s rise of autoregressive models capable of fast, flexible learning made them think the time was now and they founded Harmonic. Their mission is both lofty—mathematical superintelligence—and imminently practical, verifying all safety-critical software.

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital 

Mentioned in this episode:

00:00 - Introduction

01:42 - Math is reasoning

06:16 - Studying with the world's greatest living mathematician

10:18 - What does the math community think of AI math?

15:11 - Recursive self-improvement

18:31 - What is Lean?

21:05 - Why now?

22:46 - Synthetic data is the fuel for the model

27:29 - How fast will your model get better?

29:45 - Exploring the frontiers of human knowledge

34:11 - Lightning round

AI researcher Jim Fan has had a charmed career. He was OpenAI’s first intern before he did his PhD at Stanford with “godmother of AI,” Fei-Fei Li. He graduated into a research scientist position at Nvidia and now leads its Embodied AI “GEAR” group. The lab’s current work spans foundation models for humanoid robots to agents for virtual worlds.

Jim describes a three-pronged data strategy for robotics, combining internet-scale data, simulation data and real world robot data. He believes that in the next few years it will be possible to create a “foundation agent” that can generalize across skills, embodiments and realities—both physical and virtual. He also supports Jensen Huang’s idea that “Everything that moves will eventually be autonomous.”

Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital

Mentioned in this episode:

00:00 Introduction

01:35 Jim’s journey to embodied intelligence

04:53 The GEAR Group

07:32 Three kinds of data for robotics

10:32 A GPT-3 moment for robotics

16:05 Choosing the humanoid robot form factor

19:37 Specialized generalists

21:59 GR00T gets its own chip

23:35 Eureka and Issac Sim

25:23 Why now for robotics?

28:53 Exploring virtual worlds

36:28 Implications for games

39:13 Is the virtual world in service of the physical world?

42:10 Alternative architectures to Transformers

44:15 Lightning round

There’s a new archetype in Silicon Valley, the AI researcher turned founder. Instead of tinkering in a garage they write papers that earn them the right to collaborate with cutting-edge labs until they break out and start their own.

This is the story of wunderkind Eric Steinberger, the founder and CEO of Magic.dev. Eric came to programming through his obsession with AI and caught the attention of DeepMind researchers as a high school student. In 2022 he realized that AGI was closer than he had previously thought and started Magic to automate the software engineering necessary to get there. Among his counterintuitive ideas are the need to train proprietary large models, that value will not accrue in the application layer and that the best agents will manage themselves. Eric also talks about Magic’s recent 100M token context window model and the HashHop eval they’re open sourcing.

Hosted by: Sonya Huang, Sequoia Capital

Mentioned in this episode:

00:00 - Introduction

01:39 - Vienna-born wunderkind

04:56 - Working with Noam Brown

8:00 - “I can do two things. I cannot do three.”

10:37 - AGI to-do list

13:27 - Advice for young researchers

20:35 - Reading every paper voraciously

23:06 - The army of Noams

26:46 - The leaps still needed in research

29:59 - What is Magic?

36:12 - Competing against the 800-pound gorillas

38:21 - Ideal team size for researchers

40:10 - AI that feels like a colleague

44:30 - Lightning round

47:50 - Bonus round: 200M token context announcement