Archimedes said that with a large enough lever, you can move the world. For decades, software engineering has been that lever. And now, AI is compounding that lever. How will we use AI to apply 100 or 1000x leverage to the greatest lever to move the world?

Matan Grinberg and Eno Reyes, co-founders of Factory, have chosen to do things differently than many of their peers in this white-hot space. They sell a fleet of “Droids,” purpose-built dev agents which accomplish different tasks in the software development lifecycle (like code review, testing, pull requests or writing code). Rather than training their own foundation model, their approach is to build something useful for engineering orgs today on top of the rapidly improving models, aligning with the developer and evolving with them. 

Matan and Eno are optimistic about the effects of autonomy in software development and on building a company in the application layer. Their advice to founders, “The only way you can win is by executing faster and being more obsessed.”

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital 

Mentioned: 

• 
  Juan Maldacena, Institute for Advanced Study, string theorist that Matan cold called as an undergrad


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  SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering, small-model open-source software engineering agent


• 
  SWE-bench: Can Language Models Resolve Real-World GitHub Issues?, an evaluation framework for GitHub issues


• 
  Monte Carlo tree search, a 2006 algorithm for solving decision making in games (and used in AlphaGo)


• 
  Language agent tree search, a framework for LLM planning, acting and reasoning


• 
  The Bitter Lesson, Rich Sutton’s essay on scaling in search and learning 


• 
  Code churn, time to merge, cycle time, metrics Factory thinks are important to eng orgs

Transcript: https://www.sequoiacap.com/podcast/training-data-factory/

00:00 Introduction

01:36 Personal backgrounds

10:54 The compound lever

12:41 What is Factory? 

16:29 Cognitive architectures 

21:13 800 engineers at OpenAI are working on my margins 

24:00 Jeff Dean doesn't understand your code base

25:40 Individual dev productivity vs system-wide optimization 

30:04 Results: Factory in action 

32:54 Learnings along the way 

35:36 Fully autonomous Jeff Deans

37:56 Beacons of the upcoming age

40:04 How far are we? 

43:02 Competition 

45:32 Lightning round

49:34 Bonus round: Factory's SWE-bench results