Training Data

Sonya Huang, Pat Grady, Andrew Reed, Lauren Reeder

podcast@sequoiacap.com

For verified host and producer emails, sign up to view.

Booking Overview

Training Data features Sequoia’s partners talking with top AI builders and researchers about how frontier systems are made—and what those choices mean for products, business, and society. For PR pros, it’s a high-credibility platform where founders, CEOs, and core researchers get direct, technical-to-strategic visibility.

Metrics

Episodes: 94

Frequency: Irregular

Rating: 4.4/5.0

Estimated listeners: 1k-10k

Gender skew: Male

Location: USA

Contact Information

podcast@sequoiacap.com

For verified host and producer emails, sign up to view.

Host

Sonya Huang - Sequoia Capital partner focused on technology and entrepreneurship, hosting conversations with leading AI builders and researchers to explore emerging capabilities and their implications for busine...

Pat Grady - Sequoia Capital partner who hosts AI-focused discussions with prominent builders and researchers, emphasizing critical questions about technology direction and real-world impact.

Andrew Reed - Sequoia Capital partner who interviews AI founders and leaders, grounding discussions in product, business strategy, and technical differentiators.

Lauren Reeder - Sequoia Capital partner who hosts AI and frontier-software conversations with researchers and creators, connecting engineering insights to market and product implications.

Booking Intelligence

Booking Requirements

high
Typical Credentials:  
Frontier AI builders (founders/CEOs/co-founders) and senior researchers/technical creators with real deployed products or widely recognized model/tool work; typically backed by meaningful traction, adoption, or technical authorship (e.g., major AI systems, developer tooling, or breakthrough implementations).
Required Achievements:  
Founded or led a leading AI company/product, Created or shipped a notable AI system, model, or developer tool, Demonstrated significant adoption, revenue scale, or platform impact, Public recognition within the AI research/build community (e.g., notable technical contributions)

Recent Guest Discussions

Mikey Shulman - Audio AI Creation, Product Model Design, Technical Choices In Music Generation, Distribution/partnership Strategy

Mati Staniszewski - Voice As An Interface, Audio AI Business Strategy, Product Monetization, Emotional Intelligence In Voice Systems

Boris Cherny - Future Of Coding, Agentic/loop-based Workflows, Automation Of Software Creation, Evolution Of Coding Tools

Recent Topics

Artificial Intelligence, Ai Builders, Machine Learning, Frontier Models, Voice Ai

Episodes

Here's the recent few episodes on
Training Data
:

How Cursor Trained Composer on Fireworks: Distributed Infrastructure for High-Performance RL

May 26, 2026

Cursor's Federico Cassano and Fireworks' Dmytro Dzhulgakov explain how they collaborated to build Composer as a specialized foundation model. The core insight: models have finite capacity in their weights, and allocating all those bits to the singular task of software engineering in Cursor frees the model to be both better at the task and far more efficient at inference. Rather than start from pre-training and work up, they took an unconventional top-down approach — mid-training and RL on top...

Rebuilding IT From the Ground Up for the AI Age: Serval's Jake Stauch

May 19, 2026

Jake Stauch, founder and CEO of Serval, is building a ServiceNow for the AI era. His most contrarian bet is that the product should look like boring old enterprise software, but with unlimited intelligence. Serval's architecture splits work between two agents: an admin agent that uses code generation to spin up workflows from natural language, and a help desk agent that can only act through the tools admins explicitly approve. Jake explains why his team uses OpenAI models for end-user interac...

Suno's Mikey Shulman: Everyone Can Make Music Now

May 13, 2026

Most music platforms assume you're a listener. On Suno, 90% of daily users make something. Founder and CEO Mikey Shulman explains why that flips the  model: the act of creating IS the entertainment, with closer parallels to gaming and Claude Code than to Spotify. He breaks down the technical bets that got them here — modeling raw sound waves instead of encoding music theory, choosing autoregression over diffusion to prioritize full songs over crisp clips, and why music isn't a scale problem t...

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