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How Block is becoming the most AI-native enterprise in the world | Dhanji R. Prasanna

Overview

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Highlights

Transcript

Chapters

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How Block is becoming the most AI-native enterprise in the world | Dhanji R. Prasanna

Lenny's Podcast: Product | Career | Growth

Oct 26
Lenny's Podcast: Product | Career | Growth

Lenny's Podcast: Product | Career | Growth

Oct 26
OverviewShownoteHighlightsTranscriptChaptersPins

Shownote

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 becom...

Highlights

In this conversation, Dhanji R. Prasanna, CTO of Block, shares insights from leading one of the most AI-native large companies today. From the creation of an internal AI agent that’s reshaping workflows to the organizational shifts that have amplified its impact, he reveals how AI is redefining productivity across engineering and non-technical teams alike.
07:33
Writing the AI manifesto directly led to becoming CTO
10:51
Struggling companies should view themselves as technology-building entities.
12:06
AI-native teams use Cursor to build software with little manual coding
15:25
Engineering teams using Goose save 8–10 hours per week
20:18
Current AI efficiency is the worst it will ever be, pointing to future gains
28:11
Goose watches engineers' screens and anticipates tasks like opening PRs
35:20
Goose succeeds on about 60% of well-described features without human intervention
37:42
Humans are needed to anchor AIs and prevent them from going off-script.
50:57
Functional org structure is more impactful for productivity than AI adoption
53:45
Use AI tools like Goose daily to understand how your workflow can change
55:15
Goose used AppleScript to collate therapy receipts from multiple forms into one Apple Notes entry.
58:01
Goose is free to download and use across Mac, Windows, and Linux, with monetization limited to LLM token usage.
59:59
Organizational structure shapes system design through Conway's Law
1:01:56
YouTube succeeded with poor code because it focused on user needs
1:04:56
Engineers should be free to experiment if the foundation is stable
1:08:07
Start small and narrow your scope to build useful products
1:13:36
Failures like Google Wave and Google+ taught valuable lessons that led to Cash App's success
1:21:49
Problems seem trivial in hindsight; don't let fear of change hold you back

Chapters

Introduction to Dhanji
00:00
The AI manifesto: convincing Jack Dorsey
05:26
Transforming into a more AI-native company
07:33
How engineering teams work differently today
12:05
Goose: Block’s open-source AI agent
15:24
Measuring AI productivity gains across teams
20:18
What Goose is and how it works
21:38
The future of AI in engineering and productivity
32:15
The importance of human taste
37:42
Building vs. buying software
40:10
How AI is changing hiring and team structure
44:08
The importance of using AI tools yourself before deploying them
53:45
How Goose helped solve a personal problem with receipts
55:13
What makes Goose unique
58:01
What Dhanji wishes he knew before becoming CTO
59:57
Counterintuitive lessons in product development
1:01:49
Why controlled chaos can be good for engineering teams
1:04:56
Core leadership lessons
1:08:07
Failure corner
1:13:36
Lightning round and final thoughts
1:15:50

Transcript

Lenny Rachitsky: There's a lot of talk about productivity gains through AI. There's this camp of people that are like, so overhyped, nothing's working, nobody's actually adopting this at scale. Dhanji R. Prasanna: When you see a significant amount of game...