Block CTO Dhanji Prasanna: Building the AI-First Enterprise with Goose, their Open Source Agent
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
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Why Businesses Are Rejecting the AI They’ve Asked For: Agency CEO Elias Torres
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
01:50 AI and Customer Expectations
03:36 Managing Emails with AI
07:21 Elias' Personal Journey
11:27 Early Career
14:28 Joining HubSpot and Scaling Challenges
16:31 Hiring Exceptional Talent
18:53 Founding Drift
20:27 Pivoting to Success with Drift
21:41 Drift's Chatbot Innovation
22:09 Challenges and Limitations of Drift
22:37 The Struggle with Customer Knowledge
23:09 Scaling Challenges and Lessons Learned
25:58 Rediscovering Purpose Post-Drift
28:55 The Birth of Agency
29:42 AI's Role in Customer Experience
35:13 Building a Sustainable Business Model
37:06 The Vision for Agency
38:22 Challenges and Opportunities with AI
41:22 Deprogramming and Embracing Change
43:23 Optimism for the AI Future
44:15 Closing Thoughts
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Building the "App Store" for Robots: Hugging Face's Thomas Wolf on Physical AI
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
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Deal Velocity, Not Billable Hours: How Crosby Uses AI to Redefine Legal Contracting
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.
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n8n CEO Jan Oberhauser on Building the Universal AI Automation Layer
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:
Model Context Protocol (MCP): Open protocol that lets AI models safely use external tools and data that is used extensively by n8n for orchestration.
Vector database: A database optimized for storing and searching embeddings. These “vector stores” can pair with LLMs for retrieval-augmented workflows.
Granola: AI productivity tool mentioned by Jan as a recent favorite.
Her: A film that Jan says, “a few years ago, it was sci fi, and it’s now suddenly this thing that is just around the corner.”
Join us as we train our neural nets on the theme of the century: AI. Sonya Huang, Pat Grady and more Sequoia Capital partners 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.
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