How Block is becoming the most AI-native enterprise in the world | Dhanji R. Prasanna
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What is the AI manifesto Dhanji wrote for Jack Dorsey and how did it spark Block’s transformation?
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. When you see a significant amount of games, we find engineering teams that are very, very AI forward are recording about eight to ten hours save per week. Whenever I hear a stat like this, I think an important element is this is the worst it will ever be. This is now the baseline. The truth is the value is changing every day. Need to ride that wave along with it. There's a story I heard you share on a different podcast where there's an engineer who has goose watch him. He'll be talking to a colleague.
Gun Slack or an email, and they'll be discussing some feature that they think is useful to implement. Now, a few hours later, he'll find that Goose has already tried to build that feature and opened a PR for it on Git. What level of engineer is most benefiting from these tools? What's been surprising and really amazing? The non-technical people using AI agents and programming tools to build things, the people that are able to embrace it to optimize for their particular workday and their particular set of tasks are really showing them
Most impact from these tools. How do you think things will look in a couple years in terms of how engineers work that's different from today?
All these LLMs are sitting idle overnight and on weekends while humans aren't there. Like, there's no need for that. They should be working all the time. They should be trying to
build in anticipation of what we want. What's maybe the most counterintuitive lesson you've learned about building products or building teams? A lot of engineers think that coach.
Quality is important to building a successful product. The two have nothing to do with each other.
Today, my guest is Donji Prasana, Donji's chief technology officer at Block, where he oversees a team of over 3,500 people. With Donji's leadership, Block has become one of the most AI-native large companies in the world and has basically achieved what many Engine product leaders are trying to achieve within their companies. In our conversation, we chat about their internal open source agent called Goose, that, by their measure, is saving employees. On average, 8 to 10 hours a week of work time, and that number is going up. How AI specifically making their teams more productive, and the teams that are benefiting most? Interestingly, it's not the engineering team. What it took to shift the culture to be very AI-oriented, the very boring change they made internally that boosted productivity even more than any AI tool.
Also, lessons from building Google Wave and And Google Plus and Cash App and so much more. This episode is for anyone curious to see what a highly AI-forward, technology-driven, large company looks like and can act like. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. It helps tremendously. Also, if you become an annual subscriber of my newsletter, you get a year free of 16 incredible products. Including Devon, Replit, Lovable, Bolt, N8N, Linear, Superhuman, D Script, Whisperflow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, ChatBRD, and Mobbin. Head on over to Lenny's Newsletter.com and click Product Pass. With that, I bring you Danji Prasana after a short word from our sponsors.
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Chapters
8 chapters
1
What is the AI manifesto Dhanji wrote for Jack Dorsey and how did it spark Block’s transformation?
0:00–9:55
2
How did Block reorganize from a GM structure to a functional organization to boost AI adoption?
9:55–21:38
3
What is Goose and how does it save employees 8‑10 hours per week?
21:38–33:05
4
How does Block measure AI‑driven productivity gains across technical and non‑technical teams?
33:05–45:24
5
Why are non‑engineering teams seeing the biggest impact from AI tools?
45:24–57:12
6
What counterintuitive lessons did Dhanji learn about product building and team culture?
57:12–1:06:43
7
How does leadership’s daily use of AI tools drive company‑wide adoption?
1:06:43–1:17:20
8
What practical tips did Dhanji share for experimenting with AI agents like Goose?
1:17:20–1:26:37
Speakers
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