Scott Wu

The core shift in AI product strategy is moving from simple text completion to autonomous agents capable of multi-step reasoning and real-world interaction.

"I think the big shift that we really felt we would see is moving from kind of this text to text model to an actual autonomous system that can make decisions, that can interact with the real world, tha..."

AI product strategy must account for the evolving user experience as model capabilities improve and the need to handle messy, real-world edge cases.

"I think the product experience itself is going to change every single time. And then obviously there, there's all of the practicality of just getting it out there in the world. And so folks obviously..."

Modern AI development is shifting toward high-compute Reinforcement Learning (RL) where models learn from automated feedback loops.

"I think this new paradigm which we've gotten into over this last year or year and a half is really high compute RL, which is a very different paradigm, right, which is basically the ability to go and..."

Value in building AI applications comes from teaching models the specific idiosyncrasies and tool-chains of a professional domain rather than just increasing general intelligence.

"I think a lot of what we see actually and what we spend our time on is less so, obviously, we don't our own models or things like that. It's less so increasing the base IQ of a model, for example, and..."

The future of engineering culture involves shifting the human role from manual coding to high-level architectural oversight and specification.

"One of the ways that we've kind of thought about Devin in building Devin is really allowing engineers to go from bricklayer to architect, so to speak. A lot of it is just getting to the point where yo..."

A high-leverage engineering culture uses AI to allow a small team to produce the output of a much larger organization.

"Our whole team is only like 15 engineers a year. We use a ton of Devin when we're building Devin. Most folks on the team are definitely working with up to five Devins at once, and so Devin merges like..."

Identify and pursue exceptional talent based on raw performance in interviews rather than traditional credentials or seniority.

"We had a candidate who came and interviewed, he was a junior at MIT... we gave him an interview, and he did way better than almost any of the full-time candidates that we had ever talked to. And so, w..."

Closing top candidates requires going beyond the offer to help them navigate the personal and professional complexities of joining your team.

"And so, we actually worked with him and basically hand wrote all of his rejection responses to each of the other companies, and worked with him on it to say, here's how you should say it in a way that..."

AI agents can serve as a low-friction knowledge base for new hires, allowing them to ask 'dumb' questions without social pressure.

"One of the fun use cases that we've seen actually with folks is they'll often actually get Devin's help to onboard new engineers on the team. When you're new and you're joining, there's obviously a lo..."

Effective use of AI agents requires breaking down broad problems into specific, well-defined tasks with clear success criteria.

"The way that we often describe it is, I think, Devin is best when it is working on tasks that are well-defined. One way to put it is, you want to be giving Devin tasks, not problems."

Extreme shipping velocity is achieved by setting monthly milestones that move from prototype to launch to first customers in rapid succession.

"Everyone says we go fast, but it's like, yeah, we had a hackathon in November, we had another hackathon in December, we started the company officially in January, we got the prototypes out to initial..."