Training Data Podcast

Workday CEO and Sequoia partner Carl Eschenbach explains how the company is evolving its platform to handle both human and AI workers. He shares Workdays three-pronged approach to monetizing AI through seat-based pricing, role-based agents and consumption-based API access. Eschenbach discusses why domain-specific agents with curated data will be more valuable than general-purpose models in the enterprise, and how Workday is helping enterprises navigate the transition to an AI-powered workplace while maintaining human connection.

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

After pioneering reinforcement learning breakthroughs at DeepMind with Capture the Flag and AlphaStar, Max Jaderberg aims to revolutionize drug discovery with AI as Chief AI Officer of Isomorphic Labs, which was spun out of DeepMind. He discusses how AlphaFold 3's diffusion-based architecture enables unprecedented understanding of molecular interactions, and why we're approaching a "Move 37 moment" in AI-powered drug design where models will surpass human intuition. Max shares his vision for general AI models that can solve all diseases, and the importance of developing agents that can learn to search through the whole potential design space.

Hosted by Stephanie Zhan, Sequoia capital

Mentioned in this episode: 

Playing Atari with Deep Reinforcement Learning: Seminal 2013 paper on Reinforcement Learning 



Capture the Flag: 2019 DeepMind paper on the emergence of cooperative agents



AlphaStar: 2019 DeepMind paper on attaining grandmaster level in StarCraft II using multi-agent RL

AlphaFold Server: Web interface for AlphaFold 3 model for non-commercial academic use

Former Outreach CEO Manny Medina discusses his new company Paid, which provides billing, pricing and margin management tools for AI companies. He explains why traditional SaaS pricing models don’t work for AI businesses, and breaks down emerging approaches like outcome-based and agent-based pricing. Manny shares why he believes focused AI applications targeting specific workflows will win over broad platforms, while emphasizing that AI companies need better tools to understand their unit economics and capture more value.

Hosted by Pat Grady and Lauren Reeder, Sequoia Capital

Mentioned in this episode:

CPQ: Configure, Price, Quote

Invent and Wander: Book by Jeff Bezos and Walter Isaacson

Foundations of Statistical Natural Language Processing: 1999 book by Chris Manning and Hinrich Schütze that Manny cites as a piece of AI content every AI founder should read. (still in print, companion site here)

The fox and the hedgehog: 

Quandri 

XBOW

HappyRobot 

Owl

Crosby

Patrick Hsu, co-founder of Arc Institute, discusses the opportunities for AI in biology beyond just drug development, and how Evo 2, their new biology foundation model, is enabling a broad ecosystem of applications. Evo 2 was trained on a vast dataset of genomic data to learn evolutionary patterns that would have taken years to find; as a result, the model can be used for applications from identifying mutations that cause disease to designing new molecular and even genome scale biological systems.

Hosted by Josephine Chen and Pat Grady, Sequoia Capital

Mentioned in this episode:

Sequence modeling and design from molecular to genome scale with Evo: Public pre-print of original Evo paper

Genome modeling and design across all domains of life with Evo 2: Public pre-print of Evo 2 paper

ClinVar: NIH database of the genes that are known to cause disease, and mutations in those genes causally associated with disease state

Sequence Read Archive: Massive NIH database of gene sequencing data 

Machines of Loving Grace: Daria Amodei essay that Patrick cites on how AI could transform the world for the better

Arc Virtual Cell Atlas: Arc’s first step toward assembling, curating and generating large-scale cellular data from AI-driven biological discovery (among many other tools)

Protein Data Bank (PDB): a global archive of 3D structural information of biomolecules used by DeepMind to train AlphaFold

OpenAI Deep Research: The one AI app Patrick uses daily

Amjad Masad set out more than a decade ago to pursue the dream of unleashing 1B software creators around the world. With millions of Replit users pre-ChatGPT, that vision was already becoming a reality. Turbocharged by LLMs, the vision of enabling anyone to code—from 12-year-olds in India to knowledge workers in the U.S.—seems less and less radical. In this episode, Amjad explains how an explosion in the developer population could change the economy, society and more. He also discusses his early days programming in Jordan, his unique management approach and what AI will mean for the global economy.

Hosted by David Cahn and Sonya Huang, Sequoia Capital 

Mentioned in this episode:

On the Naturalness of Software: 2012 paper on applying NLP to code  Attention Is All You Need: Seminal 2017 paper on transformers I Am a Strange Loop: 2007 follow up to Douglas Hofstadter’s 1979 classic Gödel, Escher, Bach that explores how self-referential systems can describe minds On Lisp: Paul Graham’s 1993 book on the original programming language of AI