Training Data

On Training Data, we learn from innovators pushing forward the frontier of AI’s capabilities. Today we’re bringing you something different. It’s the story of a company currently implementing AI at scale in the enterprise, and how it was built from a bootstrapped idea in the pre-AI era to a 150 billion dollar market cap giant. 

It’s the Season 2 premiere of Sequoia’s other podcast, Crucible Moments, where we hear from the founders and leaders of some legendary companies about the crossroads and inflection points that shaped their journeys. In this episode, you’ll hear from Fred Luddy and Frank Slootman about building and scaling ServiceNow. Listen to Crucible Moments wherever you get your podcasts or go to:

Spotify: https://open.spotify.com/show/40bWCUSan0boCn0GZJNpPn

Apple: https://podcasts.apple.com/us/podcast/crucible-moments/id1705282398

Hosted by: Roelof Botha, Sequoia Capital

Transcript: https://www.sequoiacap.com/podcast/crucible-moments-servicenow/

Customer service is hands down the first killer app of generative AI for businesses. The reasons are simple: the costs of existing solutions are so high, the satisfaction so low and the margin for ROI so wide. But trusting your interactions with customers to hallucination-prone LLMs can be daunting.

Enter Sierra. Co-founder Clay Bavor walks us through the sophisticated engineering challenges his team solved along the way to delivering AI agents for all aspects of the customer experience that are delightful, safe and reliable—and being deployed widely by Sierra’s customers. The Company’s AgentOS enables businesses to create branded AI agents to interact with customers, follow nuanced policies and even handle customer retention and upsell. Clay describes how companies can capture their brand voice, values and internal processes to create AI agents that truly represent the business.

Hosted by: Ravi Gupta and Pat Grady, Sequoia Capital

Mentioned in this episode:

• Bret Taylor: co-founder of Sierra
• Towards a Human-like Open-Domain Chatbot: 2020 Google paper that introduced Meena, a predecessor of ChatGPT (followed by LaMDA in 2021)
• PaLM: Scaling Language Modeling with Pathways: 2022 Google paper about their unreleased 540B parameter transformer model (GPT-3, at the time, had 175B) 
• Avocado chair: Images generated by OpenAI’s DALL·E model in 2022
• Large Language Models Understand and Can be Enhanced by Emotional Stimuli: 2023 Microsoft paper on how models like GPT-4 can be manipulated into providing better results
• 𝛕-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains: 2024 paper authored by Sierra research team, led by Karthik Narasimhan (co-author of the 2022 ReACT paper and the 2023 Reflexion paper)

00:00:00 Introduction

00:01:21 Clay’s background

00:03:20 Google before the ChatGPT moment

00:07:31 What is Sierra?

00:12:03 What’s possible now that wasn’t possible 18 months ago?

00:17:11 AgentOS

00:23:45 The solution to many problems with AI is more AI

00:28:37 𝛕-bench

00:33:19 Engineering task vs research task

00:37:27 What tasks can you trust an agent with now?

00:43:21 What metrics will move?

00:46:22 The reality of deploying AI to customers today

00:53:33 The experience manager

01:03:54 Outcome-based pricing

01:05:55 Lightning Round

After AlphaGo beat Lee Sedol, a young mechanical engineer at Google thought of another game reinforcement learning could win: energy optimization at data centers. Jim Gao convinced his bosses at the Google data center team to let him work with the DeepMind team to try. The initial pilot resulted in a 40% energy savings and led he and his co-founders to start Phaidra to turn this technology into a product.

Jim discusses the challenges of AI readiness in industrial settings and how we have to build on top of the control systems of the 70s and 80s to achieve the promise of the Fourth Industrial Revolution. He believes this new world of self-learning systems and self-improving infrastructure is a key factor in addressing global climate change.

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital 

Mentioned in this episode:

• Mustafa Suleyman: Co-founder of DeepMind and Inflection AI and currently CEO of Microsoft AI, known to his friends as “Moose”
• Joe Kava: Google VP of data centers who Jim sent his initial email to pitching the idea that would eventually become Phaidra
• Constrained optimization: the class of problem that reinforcement learning can be applied to in real world systems 
• Vedavyas Panneershelvam: co-founder and CTO of Phaidra; one of the original engineers on the AlphaGo project
• Katie Hoffman: co-founder, President and COO of Phaidra 
• Demis Hassabis: CEO of DeepMind

In the first wave of the generative AI revolution, startups and enterprises built on top of the best closed-source models available, mostly from OpenAI. The AI customer journey moves from training to inference, and as these first products find PMF, many are hitting a wall on latency and cost.

Fireworks Founder and CEO Lin Qiao led the PyTorch team at Meta that rebuilt the whole stack to meet the complex needs of the world’s largest B2C company. Meta moved PyTorch to its own non-profit foundation in 2022 and Lin started Fireworks with the mission to compress the timeframe of training and inference and democratize access to GenAI beyond the hyperscalers to let a diversity of AI applications thrive.

Lin predicts when open and closed source models will converge and reveals her goal to build simple API access to the totality of knowledge.

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital 

Mentioned in this episode:

• Pytorch: the leading framework for building deep learning models, originated at Meta and now part of the Linux Foundation umbrella
• Caffe2 and ONNX: ML frameworks Meta used that PyTorch eventually replaced
• Conservation of complexity: the idea that that every computer application has inherent complexity that cannot be reduced but merely moved between the backend and frontend, originated by Xerox PARC researcher Larry Tesler 
• Mixture of Experts: a class of transformer models that route requests between different subsets of a model based on use case
• Fathom: a product the Fireworks team uses for video conference summarization 
• LMSYS Chatbot Arena: crowdsourced open platform for LLM evals hosted on Hugging Face

 00:00 - Introduction

02:01 - What is Fireworks?

02:48 - Leading Pytorch

05:01 - What do researchers like about PyTorch?

07:50 - How Fireworks compares to open source

10:38 - Simplicity scales

12:51 - From training to inference

17:46 - Will open and closed source converge?

22:18 - Can you match OpenAI on the Fireworks stack?

26:53 - What is your vision for the Fireworks platform?

31:17 - Competition for Nvidia?

32:47 - Are returns to scale starting to slow down?

34:28 - Competition

36:32 - Lightning round

GithHub invented collaborative coding and in the process changed how open source projects, startups and eventually enterprises write code. GitHub Copilot is the first blockbuster product built on top of OpenAI’s GPT models. It now accounts for more than 40 percent of GitHub revenue growth for an annual revenue run rate of $2 billion. Copilot itself is already a larger business than all of GitHub was when Microsoft acquired it in 2018.

We talk to CEO Thomas Dohmke about how a small team at GitHub built on top of GPT-3 and quickly created a product that developers love—and can’t live without. Thomas describes how the product has grown from simple autocomplete to a fully featured workspace for enterprise teams. He also believes that tools like Copilot will bring the power of coding to a billion developers by 2030.

Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital 

Mentioned in this episode:

• Nat Friedman: Former Microsoft VP (and now investor) who came up with the idea that Microsoft should buy GitHub
• Oege de Moor: Github developer (and now founder of XBOW) who came up with the idea of using GPT-3 for code and went on to create Copilot
• Alex Graveley: principal engineer and Chief Architect for Copilot (now CEO of Minion.ai) who came up with the name Copilot (because his boss, Nat Firedman, is an amateur pilot)
• Productivity Assessment of Neural Code Completion: Original GitHub research paper on the impact of Copilot on Developer productivity
• Escaping a room in Minecraft with an AI-powered NPC: Recent Minecraft AI assistant demo from Microsoft
• With AI, anyone can be a coder now: TED2024 talk by Thomas Dohmke
• JFrog: The software supply chain platform that GitHub just partnered with

00:00:00 - Introduction

00:01:18 - Getting started with code

00:03:43 - Microsoft’s acquisition of GitHub

00:11:40 - Evolving Copilot beyond autocomplete

00:14:18 - In hindsight, you can always move faster

00:15:56 - Building on top of OpenAI

00:20:21 - The latest metrics

00:22:11 - The surprise of Copilot’s impact

00:25:11 - Teaching kids to code in the age of Copilot

00:26:38 - The momentum mindset

00:29:46 - Agents vs Copilots

00:32:06 - The Roadmap

00:37:31 - Making maintaining software easier

00:38:48 - The creative new world

00:42:38 - The AI 10x software engineer

00:45:12 - Creativity and systems engineering in AI

00:48:55 - What about COBOL?

00:50:23 - Will GitHub build its own models?

00:57:19 - Rapid incubation at GitHub Next

00:59:21 - The future of AI?

01:03:18 - Advice for founders

01:05:08 - Lightning round

As head of Product Management for Generative AI at Meta, Joe Spisak leads the team behind Llama, which just released the new 3.1 405B model. We spoke with Joe just two days after the model’s release to ask what’s new, what it enables, and how Meta sees the role of open source in the AI ecosystem.

Joe shares that where Llama 3.1 405B really focused is on pushing scale (it was trained on 15 trillion tokens using 16,000 GPUs) and he’s excited about the zero-shot tool use it will enable, as well as its role in distillation and generating synthetic data to teach smaller models. He tells us why he thinks even frontier models will ultimately commoditize—and why that’s a good thing for the startup ecosystem.

Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital 

Mentioned in this episode: 

Llama 3.1 405B paper

Open Source AI Is the Way Forward: Mark Zuckerberg essay released with Llama 3.1.

Mistral Large 2

The Bitter Lesson by Rich Sutton

00:00 Introduction

01:28 The Llama 3.1 405B launch

05:02 The open source license

07:01 What's in it for Meta?

10:19 Why not open source?

11:16 Will frontier models commoditize?

12:41 What about startups?

16:29 The Mistral team

19:36 Are all frontier strategies comparable?

22:38 Is model development becoming more like software development?

26:34 Agentic reasoning

29:09 What future levers will unlock reasoning?

31:20 Will coding and math lead to unlocks?

33:09 Small models

34:08 7X more data

37:36 Are we going to hit a wall?

39:49 Lightning round

In February, Sebastian Siemiatkowski boldly announced that Klarna’s new OpenAI-powered assistant handled two thirds of the Swedish fintech’s customer service chats in its first month. Not only were customer satisfaction metrics better, but by replacing 700 full-time contractors the bottom line impact is projected to be $40M. Since then, every company we talk to wants to know, “How do we get the Klarna customer support thing?”

Co-founder and CEO Sebastian Siemiatkowski tells us how the Klarna team shipped this new product in record time—and how embracing AI internally with an experimental mindset is transforming the company. He discusses how AI development is proliferating inside the company, from customer support to marketing to internal knowledge to customer-facing experiences. 

Sebastian also reflects on the impacts of AI on employment, society, and the arts while encouraging lawmakers to be open minded about the benefits.

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital 

Mentioned in this episode:

DeepL: Language translation app that Sebastian says makes 10,000 translators in Brussels redundant

The Klarna brand: The offbeat optimism that the company is now augmenting with AI

Neo4j: The graph database management system that Klarna is using to build Kiki, their internal knowledge base

00:00 Introduction

01:57 Klarna’s business

03:00 Pitching OpenAI

08:51 How we built this

10:46 Will Klara ever completely replace its CS team with AI?

14:22 The benefits

17:25 If you had a policy magic wand…

21:12 What jobs will be most affected by AI?

23:58 How about marketing?

27:55 How creative are LLMs?

30:11 Klarna’s knowledge graph, Kiki

33:10 Reducing the number of enterprise systems

35:24 Build vs buy?

39:59 What’s next for Klarna with AI?

48:48 Lightning round

LLMs are democratizing digital intelligence, but we’re all waiting for AI agents to take this to the next level by planning tasks and executing actions to actually transform the way we work and live our lives. 

Yet despite incredible hype around AI agents, we’re still far from that “tipping point” with best in class models today. As one measure: coding agents are now scoring in the high-teens % on the SWE-bench benchmark for resolving GitHub issues, which far exceeds the previous unassisted baseline of 2% and the assisted baseline of 5%, but we’ve still got a long way to go.

Why is that? What do we need to truly unlock agentic capability for LLMs? What can we learn from researchers who have built both the most powerful agents in the world, like AlphaGo, and the most powerful LLMs in the world? 

To find out, we’re talking to Misha Laskin, former research scientist at DeepMind. Misha is embarking on his vision to build the best agent models by bringing the search capabilities of RL together with LLMs at his new company, Reflection AI. He and his cofounder Ioannis Antonoglou, co-creator of AlphaGo and AlphaZero and RLHF lead for Gemini, are leveraging their unique insights to train the most reliable models for developers building agentic workflows.

Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital 

00:00 Introduction

01:11 Leaving Russia, discovering science

10:01 Getting into AI with Ioannis Antonoglou

15:54 Reflection AI and agents

25:41 The current state of Ai agents

29:17 AlphaGo, AlphaZero and Gemini

32:58 LLMs don’t have a ground truth reward

37:53 The importance of post-training

44:12 Task categories for agents

45:54 Attracting talent

50:52 How far away are capable agents?

56:01 Lightning round

Mentioned: 

• The Feynman Lectures on Physics: The classic text that got Misha interested in science.
• Mastering the game of Go with deep neural networks and tree search: The original 2016 AlphaGo paper.
• Mastering the game of Go without human knowledge: 2017 AlphaGo Zero paper
• Scaling Laws for Reward Model Overoptimization: OpenAI paper on how reward models can be gamed at all scales for all algorithms.
• Mapping the Mind of a Large Language Model: Article about Anthropic mechanistic interpretability paper that identifies how millions of concepts are represented inside Claude Sonnet
• Pieter Abeel: Berkeley professor and founder of Covariant who Misha studied with
• A2C and A3C: Advantage Actor Critic and Asynchronous Advantage Actor Critic, the two algorithms developed by Misha’s manager at DeepMind, Volodymyr Mnih, that defined reinforcement learning and deep reinforcement learning

The current LLM era is the result of scaling the size of models in successive waves (and the compute to train them). It is also the result of better-than-Moore’s-Law price vs performance ratios in each new generation of Nvidia GPUs. The largest platform companies are continuing to invest in scaling as the prime driver of AI innovation.

Are they right, or will marginal returns level off soon, leaving hyperscalers with too much hardware and too few customer use cases? To find out, we talk to Microsoft CTO Kevin Scott who has led their AI strategy for the past seven years. Scott describes himself as a “short-term pessimist, long-term optimist” and he sees the scaling trend as durable for the industry and critical for the establishment of Microsoft’s AI platform.

Scott believes there will be a shift across the compute ecosystem from training to inference as the frontier models continue to improve, serving wider and more reliable use cases. He also discusses the coming business models for training data, and even what ad units might look like for autonomous agents.

Hosted by: Pat Grady and Bill Coughran, Sequoia Capital

Mentioned:

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, the 2018 Google paper that convinced Kevin that Microsoft wasn’t moving fast enough on AI. 

Dennard scaling: The scaling law that describes the proportional relationship between transistor size and power use; has not held since 2012 and is often confused with Moore’s Law.

Textbooks Are All You Need: Microsoft paper that introduces a new large language model for code, phi-1, that achieves smaller size by using higher quality “textbook” data.

GPQA and MMLU: Benchmarks for reasoning

Copilot: Microsoft product line of GPT consumer assistants from general productivity to design, vacation planning, cooking and fitness.

Devin: Autonomous AI code agent from Cognition Labs that Microsoft recently announced a partnership with.

Ray Solomonoff: Participant in the 1956 Dartmouth Summer Research Project on Artificial Intelligence that named the field; Kevin admires his prescience about the importance of probabilistic methods decades before anyone else.

00:00 - Introduction

01:20 - Kevin’s backstory

06:56 - The role of PhDs in AI engineering

09:56 - Microsoft’s AI strategy

12:40 - Highlights and lowlights

16:28 - Accelerating investments

18:38 - The OpenAI partnership

22:46 - Soon inference will dwarf training

27:56 - Will the demand/supply balance change?

30:51 - Business models for data

36:54 - The value function

39:58 - Copilots

44:47 - The 98/2 rule

49:34 - Solving zero-sum games

57:13 - Lightning round

As impressive as LLMs are, the growing consensus is that language, scale and compute won’t get us to AGI. Although many AI benchmarks have quickly achieved human-level performance, there is one eval that has barely budged since it was created in 2019.

Google researcher François Chollet wrote a paper that year defining intelligence as skill-acquisition efficiency—the ability to learn new skills as humans do, from a small number of examples. To make it testable he proposed a new benchmark, the Abstraction and Reasoning Corpus (ARC), designed to be easy for humans, but hard for AI. Notably, it doesn’t rely on language.

Zapier co-founder Mike Knoop read Chollet’s paper as the LLM wave was rising. He worked quickly to integrate generative AI into Zapier’s product, but kept coming back to the lack of progress on the ARC benchmark. In June, Knoop and Chollet launched the ARC Prize, a public competition offering more than $1M to beat and open-source a solution to the ARC-AGI eval.

In this episode Mike talks about the new ideas required to solve ARC, shares updates from the first two weeks of the competition, and shares why he’s excited for AGI systems that can innovate alongside humans.

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital 

Mentioned:

• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models: The 2019 paper that first caught Mike’s attention about the capabilities of LLMs
• On the Measure of Intelligence: 2019 paper by Google researcher François Chollet that introduced the ARC benchmark, which remains unbeaten
• ARC Prize 2024: The $1M+ competition Mike and François have launched to drive interest in solving the ARC-AGI eval
• Sequence to Sequence Learning with Neural Networks: Ilya Sutskever paper from 2014 that influenced the direction of machine translation with deep neural networks.
• Etched: Luke Miles on LessWrong wrote about the first ASIC chip that accelerates transformers on silicon
• Kaggle: The leading data science competition platform and online community, acquired by Google in 2017
• Lab42: Swiss AU lab that hosted ARCathon precursor to ARC Prize
• Jack Cole: Researcher on team that was #1 on the leaderboard for ARCathon
• Ryan Greenblatt: Researcher with current high score (50%) on ARC public leaderboard

(00:00) Introduction

(01:51) AI at Zapier

(08:31) What is ARC AGI?

(13:25) What does it mean to efficiently acquire a new skill?

(19:03) What approaches will succeed?

(21:11) A little bit of a different shape

(25:59) The role of code generation and program synthesis

(29:11) What types of people are working on this?

(31:45) Trying to prove you wrong

(34:50) Where are the big labs?

(38:21) The world post-AGI

(42:51) When will we cross 85% on ARC AGI?

(46:12) Will LLMs be part of the solution?

(50:13) Lightning round

Archimedes said that with a large enough lever, you can move the world. For decades, software engineering has been that lever. And now, AI is compounding that lever. How will we use AI to apply 100 or 1000x leverage to the greatest lever to move the world?

Matan Grinberg and Eno Reyes, co-founders of Factory, have chosen to do things differently than many of their peers in this white-hot space. They sell a fleet of “Droids,” purpose-built dev agents which accomplish different tasks in the software development lifecycle (like code review, testing, pull requests or writing code). Rather than training their own foundation model, their approach is to build something useful for engineering orgs today on top of the rapidly improving models, aligning with the developer and evolving with them. 

Matan and Eno are optimistic about the effects of autonomy in software development and on building a company in the application layer. Their advice to founders, “The only way you can win is by executing faster and being more obsessed.”

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital 

Mentioned: 

• Juan Maldacena, Institute for Advanced Study, string theorist that Matan cold called as an undergrad
• SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering, small-model open-source software engineering agent
• SWE-bench: Can Language Models Resolve Real-World GitHub Issues?, an evaluation framework for GitHub issues
• Monte Carlo tree search, a 2006 algorithm for solving decision making in games (and used in AlphaGo)
• Language agent tree search, a framework for LLM planning, acting and reasoning
• The Bitter Lesson, Rich Sutton’s essay on scaling in search and learning 
• Code churn, time to merge, cycle time, metrics Factory thinks are important to eng orgs

Transcript: https://www.sequoiacap.com/podcast/training-data-factory/

00:00 Introduction

01:36 Personal backgrounds

10:54 The compound lever

12:41 What is Factory? 

16:29 Cognitive architectures 

21:13 800 engineers at OpenAI are working on my margins 

24:00 Jeff Dean doesn't understand your code base

25:40 Individual dev productivity vs system-wide optimization 

30:04 Results: Factory in action 

32:54 Learnings along the way 

35:36 Fully autonomous Jeff Deans

37:56 Beacons of the upcoming age

40:04 How far are we? 

43:02 Competition 

45:32 Lightning round

49:34 Bonus round: Factory's SWE-bench results

Last year, AutoGPT and Baby AGI captured our imaginations—agents quickly became the buzzword of the day…and then things went quiet. AutoGPT and Baby AGI may have marked a peak in the hype cycle, but this year has seen a wave of agentic breakouts on the product side, from Klarna’s customer support AI to Cognition’s Devin, etc.

Harrison Chase of LangChain is focused on enabling the orchestration layer for agents. In this conversation, he explains what’s changed that’s allowing agents to improve performance and find traction. 

Harrison shares what he’s optimistic about, where he sees promise for agents vs. what he thinks will be trained into models themselves, and discusses novel kinds of UX that he imagines might transform how we experience agents in the future.     

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

Mentioned: 

• ReAct: Synergizing Reasoning and Acting in Language Models, the first cognitive architecture for agents
• SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering, small-model open-source software engineering agent from researchers at Princeton
• Devin, autonomous software engineering from Cognition
• V0: Generative UI agent from Vercel
• GPT Researcher, a research agent 
• Language Model Cascades: 2022 paper by Google Brain and now OpenAI researcher David Dohan that was influential for Harrison in developing LangChain

Transcript: https://www.sequoiacap.com/podcast/training-data-harrison-chase/

00:00 Introduction

01:21 What are agents? 

05:00 What is LangChain’s role in the agent ecosystem?

11:13 What is a cognitive architecture? 

13:20 Is bespoke and hard coded the way the world is going, or a stop gap?

18:48 Focus on what makes your beer taste better

20:37 So what? 

22:20 Where are agents getting traction?

25:35 Reflection, chain of thought, other techniques?

30:42 UX can influence the effectiveness of the architecture

35:30 What’s out of scope?

38:04 Fine tuning vs prompting?

42:17 Existing observability tools for LLMs vs needing a new architecture/approach

45:38 Lightning round

Join us as we train our neural nets on the theme of the century: AI. Sequoia Capital partners Sonya Huang and Pat Grady 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.