Oege de Moor, the creator of GitHub Copilot, discusses how XBOW’s AI offensive security system matches and even outperforms top human penetration testers, completing security assessments in minutes instead of days. The team’s speed and focus is transforming the niche market of pen testing with an always-on service-as-a-software platform. Oege describes how he is building a large and sustainable business while also creating a product that will “protect all the software in the free world.” XBOW shows how AI is essential for protecting software systems as the amount of AI-generated code increases along with the scale and sophistication of cyber threats.

Hosted by: Konstantine Buhler and Sonya Huang, Sequoia Capital

Mentioned in this episode: 

When ChatGPT ushered in a new paradigm of AI in everyday use, many companies attempted to adapt to the new paradigm by rushing to add chat interfaces to their products. Eric has a different take—he doesn’t think chatbots are the right form factor for everything. He thinks “zero-touch” automation that works invisibly in the background can be more valuable in many cases. He cites self-driving cars as an analogy—or in this case, “self-driving money.” Ramp is a new kind of finance management company for businesses, offering AI-powered financial tools to help companies handle spending and expense processes. We’ll hear why Eric thinks AI that you never see is one of the most powerful instruments for reducing time spent on drudgery and unlocking more time for meaningful work.  

Hosted by: Ravi Gupta and Sonya Huang, Sequoia Capital

Mentioned in this episode:

Founded in early 2023 after spending years at Stripe and OpenAI, Gabriel Hubert and Stanislas Polu started Dust with the view that one model will not rule them all, and that multi-model integration will be key to getting the most value out of AI assistants. In this episode we’ll hear why they believe the proprietary data you have in silos will be key to unlocking the full power of AI, get their perspective on the evolving model landscape, and how AI can augment rather than replace human capabilities.

Hosted by: Konstantine Buhler and Pat Grady, Sequoia Capital

00:00 - Introduction

02:16 - One model will not rule them all

07:15 - Reasoning breakthroughs

11:15 - Trends in AI models

13:32 - The future of the open source ecosystem

16:16 - Model quality and performance

21:44 - “No GPUs before PMF”

27:24 - Dust in action

37:40 - How do you find “the makers”

42:36 - The beliefs Dust lives by

50:03 - Keeping the human in the loop

52:33 - Second time founders

56:15 - Lightning round

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?