How Block Is Becoming the Most AI-Native Enterprise in the World | Dhanji R. Prasanna

Why It Matters

Block’s move demonstrates how centralized, production-ready AI agents can drive measurable time savings and reshape who benefits from automation, offering a blueprint for rivals seeking fast productivity and product-velocity gains. If sustained, the approach could translate into lower costs, faster feature delivery, and a competitive edge for AI-forward enterprises.

Summary

Block has aggressively retooled into one of the most AI-native large companies under CTO Dani Prasanna, who persuaded CEO Jack Dorsey to centralize AI efforts after writing an internal manifesto. The company built an open-source agent called Goose that engineers report saves roughly 8–10 hours per employee per week and continues to increase productivity, with nontechnical teams seeing outsized gains. Prasanna emphasizes running LLM agents continuously to anticipate work, shifting culture and tooling rather than relying solely on traditional engineering practices like pristine code. Those organizational and operational changes have been reinforced across Block’s roughly 3,500-strong tech organization.

Original Description

Dhanji R. Prasanna is the chief technology officer at Block (formerly Square), where he’s managed more than 4,000 engineers over the past two years. Under his leadership, Block has become one of the most AI-native large companies in the world. Before becoming CTO, Dhanji wrote an “AI manifesto” to CEO Jack Dorsey that sparked a company-wide transformation (and his promotion to CTO).

We discuss:

1. How Block’s internal open-source agent, called Goose, is saving employees 8 to 10 hours weekly

2. How the company measures AI productivity gains across technical and non-technical teams

3. Which teams are benefiting most from AI (it’s not engineering)

4. The boring organizational change that boosted productivity even more than AI tools

5. Why code quality has almost nothing to do with product success

6. How to drive AI adoption throughout an organization (hint: leadership needs to use the tools daily)

7. Lessons from building Google Wave, Google+, and other failed products

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Transcript: https://www.lennysnewsletter.com/p/how-block-is-becoming-the-most-ai-native

My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/176082333/my-biggest-takeaways-from-this-conversation

Where to find Dhanji R. Prasanna:

• LinkedIn: https://www.linkedin.com/in/dhanji/

Where to find Lenny:

• Newsletter: https://www.lennysnewsletter.com

• X: https://twitter.com/lennysan

• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/

In this episode, we cover:

(00:00) Introduction to Dhanji

(05:26) The AI manifesto: convincing Jack Dorsey

(07:33) Transforming into a more AI-native company

(12:05) How engineering teams work differently today

(15:24) Goose: Block’s open-source AI agent

(20:18) Measuring AI productivity gains across teams

(21:38) What Goose is and how it works

(32:15) The future of AI in engineering and productivity

(37:42) The importance of human taste

(40:10) Building vs. buying software

(44:08) How AI is changing hiring and team structure

(53:45) The importance of using AI tools yourself before deploying them

(55:13) How Goose helped solve a personal problem with receipts

(58:01) What makes Goose unique

(59:57) What Dhanji wishes he knew before becoming CTO

(01:01:49) Counterintuitive lessons in product development

(01:04:56) Why controlled chaos can be good for engineering teams

(01:08:07) Core leadership lessons

(01:13:36) Failure corner

(01:15:50) Lightning round and final thoughts

Referenced:

• Jack Dorsey on X: https://x.com/jack

• Block: https://block.xyz/

• Square: https://squareup.com/

• Cash App: https://cash.app/

• What is Conway’s Law?: https://www.microsoft.com/en-us/microsoft-365-life-hacks/organization/what-is-conways-law#

• Goose: https://github.com/block/goose

• Gosling: https://github.com/block/goose-mobile

• Salesforce: https://www.salesforce.com/

• Snowflake: https://www.snowflake.com/

• Claude: https://claude.ai/

• Anthropic co-founder on quitting OpenAI, AGI predictions, $100M talent wars, 20% unemployment, and the nightmare scenarios keeping him up at night | Ben Mann: https://www.lennysnewsletter.com/p/anthropic-co-founder-benjamin-mann

• OpenAI: https://openai.com/

• OpenAI’s CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai

• Llama: https://www.llama.com/

• Cursor: https://cursor.com/

• The rise of Cursor: The $300M ARR AI tool that engineers can’t stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell

• Top Gun: https://www.imdb.com/title/tt0092099/

• Lenny’s vibe-coded Lovable app: https://gdoc-images-grab.lovable.app/

• Afterpay: https://github.com/afterpay

• Bitkey: https://bitkey.world/

• Proto: https://github.com/proto-at-block

• Brad Axen on LinkedIn: https://www.linkedin.com/in/bradleyaxen/

• Databricks: https://www.databricks.com/

• Carl Sagan’s quote: https://www.goodreads.com/quotes/32952-if-you-wish-to-make-an-apple-pie-from-scratch

...References continued at: https://www.lennysnewsletter.com/p/how-block-is-becoming-the-most-ai-native

Recommended books:

• The Master and Margarita: https://www.amazon.com/Master-Margarita-Mikhail-Bulgakov/dp/0802130119

• Tennyson Poems: https://www.amazon.com/Tennyson-Poems-Everymans-Library-Pocket/dp/1400041872/

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_For inquiries about sponsoring the podcast, email [e-mail]._

Lenny may be an investor in the companies discussed.