As CTO of Block, Dhanji Prasanna has overseen a dramatic enterprise AI transformation, with engineers saving 8-10 hours a week through AI automation. Block’s open-source agent goose connects to existing enterprise tools through MCP, enabling everyone from engineers to sales teams to build custom applications without coding. Dhanji shares how Block reorganized from business unit silos to functional teams to accelerate AI adoption, why they chose to open-source their most valuable AI tool and why he believes swarms of smaller AI models will outperform monolithic LLMs.

Hosted by: Sonya Huang and Roelof Botha, Sequoia Capital

Mentioned in the episode:

goose: Block’s open-source, general-purpose AI agent used across the company to orchestrate workflows via tools and APIs. 

Model Context Protocol (MCP): Open protocol (spearheaded by Anthropic) for connecting AI agents to tools; goose was an early adopter and helped shape.

bitchat: Decentralized chat app written by Jack Dorsey

Swarm intelligence: Research direction Dhanji highlights for AI’s future where many agents (geese) collaborate to build complex software beyond a single-agent copilot.

Travelling Salesman Problem: Classic optimization problem cited by Dhanji in the context of a non-technical user of goose solving a practical optimization task.

Amara’s Law: The idea, originated by futurist Roy Amara in 1978, that we overestimate tech impact short term and underestimate long term.

00:00 Introduction

01:48 AI: Friend or Foe?

03:13 Block's Journey with AI and Technology

04:47 Block's Diverse Product Range

07:04 Driving AI at Block

14:28 The Evolution of Goose

27:45 Integrating Goose with Existing Systems

28:23 Goose's Learning and Recipe Feature

29:41 Tool Use and LLM Providers

31:40 Impact of AI on Developer Productivity

34:37 Block's Commitment to Open Source

39:09 Future of AI and Swarm Intelligence

43:05 Remote Work at Block

45:15 Vibe Coding and AI in Development

48:43 Making Goose More Accessible

51:28 Generative AI in Customer-Facing Products

54:09 Design and Engineering at Block

55:38 Predictions for the Future of AI

Elias Torres has been building AI systems since 1999, from chatbots at IBM to co-founding Drift and now Agency. He believes businesses are caught in an expectation mismatch—demanding AI while rejecting it due to imperfection anxiety. Drawing from his experience scaling HubSpot, Elias explains why human-led customer experience doesn’t scale and how Agency is building AI-first solutions that work autonomously. His contrarian approach focuses on the back-end customer experience rather than front-end AI SDRs, aiming to “deprogram the entire business world” from inefficient human-dependent processes.

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

Mentioned in the episode:

Lookery: David Cancel’s first startup that Elias joined after IBM; shut down in 2009

Performable: Elias and David’s second startup, acquired by HubSpot in 2011

Drift: Elias and David Cancel’s third startup, merged with Salesloft in 2024

Klaviyo: B2C CRM company started by Andrew Bialecki after working with Elias at HubSpot

Secret: Short-lived anonymous messaging app that inspired one of Drift’s early iterations

Tatajuba: Kitesurfing destination in Jericoacoara, Brazil where Elias (briefly) considered retirement

00:00 Introduction

Thomas Wolf, co-founder and Chief Science Officer of Hugging Face, explains how his company is applying the same community-driven approach that made transformers accessible to everyone to the emerging field of robotics. Thomas discusses LeRobot, Hugging Face's ambitious project to democratize robotics through open-source tools, datasets, and affordable hardware. He shares his vision for turning millions of software developers into roboticists, the challenges of data scarcity in robotics versus language models, and why he believes we're at the same inflection point for physical AI that we were for LLMs just a few years ago.

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

Ryan Daniels and John Sarihan are reimagining legal services by building Crosby, an AI-powered law firm that focuses on contract negotiations to start. Rather than building legal software, they've structured their company as an actual law firm with lawyers and AI engineers working side-by-side to automate human negotiations. They've eliminated billable hours in favor of per-document pricing, achieving contract turnaround times under an hour. Ryan and John explain why the law firm structure enables faster innovation cycles, how they're using AI to predict negotiation outcomes, and their vision for agents that can simulate entire contract negotiations between parties.

Hosted by Josephine Chen, Sequoia Capital

Mentioned in this episode:

Data processing agreement (DPA): GDPR-mandated contract between controllers and processors. Crosby handles DPAs as part of B2B contracting.

Credence good: Economic term for services whose quality is hard to judge even after consumption. Used to explain why legal buyers value lawyers-in-the-loop and malpractice coverage.

When the AI wave hit, n8n founder Jan Oberhauser faced a critical choice: become irrelevant or become indispensable. He chose the latter, transforming n8n from a simple workflow tool into a comprehensive AI automation platform that lets users connect any LLM to any application. The result? Four times the revenue growth in eight months compared to the previous six years. Jan explains how n8n’s “connect everything to anything” philosophy, combined with a thriving open source community, positioned the company to ride the AI automation wave while avoiding vendor lock-in that plagues enterprise software.

Hosted by George Robson and Pat Grady, Sequoia Capital

Mentioned in this episode:

Before ChatGPT made AI mainstream, John Noronha was building Gamma with a simple insight: everyone hates making slides but needs visual communication for high-stakes ideas. His background at Optimizely proved crucial as Gamma became a testing laboratory for AI models, running hundreds of experiments to discover that Claude excels at creative taste, Gemini wins on cost efficiency, and reasoning models actually hurt creativity. John explains how solving their own blank page problem inadvertently solved it for millions of users, turning a near-failing startup into a cash flow positive platform with 250 million presentations created. He discusses competing with PowerPoint's 500 million users while expanding beyond slides into documents, websites and visual storytelling.

Hosted by Sonya Huang, Sequoia Capital

Dara Ladjevardian, founder and CEO of Delphi, is creating digital minds that allow people to scale their thoughts and availability without replacing human connection. Inspired by Ray Kurzweil’s theory of mind as a hierarchy of pattern recognizers, Dara built an adaptive temporal knowledge graph that captures how people think and reason. From helping CEOs train new hires to enabling coaches to monetize their expertise 24/7, Delphi represents a new form of conversational media. Dara explains why authentic human representation matters, how digital minds actually increase desire for real human connection, and why he believes 2026 will be the tipping point for adoption for digital minds.

Hosted by Sonya Huang and Jess Lee, Sequoia Capital

Mentioned in this episode:

Vercel CEO Guillermo Rauch has spent years obsessing over reducing the friction between having an idea and getting it online. Now with AI, he's achieving something even more ambitious: making software creation accessible to anyone with a keyboard. Guillermo explains how v0 has grown to 3 million users by focusing on reliability and quality, why ChatGPT has become their fastest-growing customer acquisition channel, and how AI is enabling “virtual coworkers” across design, development, and marketing. He shares his contrarian view that the future belongs to ephemeral, generated-on-demand applications rather than traditional installed software, and why he believes we're on the cusp of the biggest transformation to the web in its history.

Hosted by Sonya Huang and Pat Grady, Sequoia Capital

In just two months, a scrappy three-person team at OpenAI sprinted to fulfill what the entire AI field has been chasing for years—gold-level performance on the International Mathematical Olympiad problems. Alex Wei, Sheryl Hsu and Noam Brown discuss their unique approach using general-purpose reinforcement learning techniques on hard-to-verify tasks rather than formal verification tools. The model showed surprising self-awareness by admitting it couldn’t solve problem six, and revealed the humbling gap between solving competition problems and genuine mathematical research breakthroughs.

Hosted by Sonya Huang, Sequoia Capital

Isa Fulford, Casey Chu, and Edward Sun from OpenAI's ChatGPT agent team reveal how they combined Deep Research and Operator into a single, powerful AI agent that can perform complex, multi-step tasks lasting up to an hour. By giving the model access to a virtual computer with text browsing, visual browsing, terminal access, and API integrations—all with shared state—they've created what may be the first truly embodied AI assistant. The team discusses their reinforcement learning approach, safety mitigations for real-world actions, and how small teams can build transformative AI products through close research-applied collaboration.

Hosted by Sonya Huang and Lauren Reeder, Sequoia Capital