Factory's Matan Grinberg: The Coming ‘Dark Factory’ Where Software Builds Itself
Factory started building fully autonomous coding agents in April 2023, two years before enterprises were ready. Matan Grinberg now says this is indistinguishable from being wrong. The Factory co-founder and CEO explains how the company survived its "journey in the desert," including the decision to hand nearly all of its revenue back to customers when the product wasn't making developers obsessed. Matan makes the contrarian technical case that a model-agnostic harness beats the model-and-harness co-design that labs like OpenAI and Anthropic favor, because exposing a harness to many models keeps it from overfitting to any single one. He argues open-weight models like GLM will capture the majority of tokens by staying one generation behind the frontier at a fraction of the cost, and that CIOs will soon justify every incremental token the way they justify headcount. Looking ahead, he predicts 90% of coding tokens will run asynchronously—the "dark factory" where software builds itself.
Hosted by Sonya Huang and Pat Grady, Sequoia Capital
20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory
Matan Grinberg is the Founder and CEO @ Factory, an AI research lab, bringing autonomy to software engineering. Matan has raised over $220M for the company from the likes of Sequoia, Khosla, NEA, Evantic and 20VC. Last round valued the company at a whopping $1.5BN.
AGENDA:
00:00 – Why AI Means Everyone Will Become a Builder
04:55 – Will AI Finally Break the 200-Year GDP Growth Ceiling?
06:45 – The Rise of the 100x Engineer & Load-Bearing Talent
08:00 – The New Executive Job: Allocating Tokens Like Capital
10:35 – Kirkland's $500M AI Bet: Brilliant or Delusional?
12:45 – The AI Value War: Models vs Applications vs Infrastructure
18:45 – Token Maxing, AI Hangovers & The Coming ROI Reckoning
22:00 – Why AI Spend Could Soon Exceed Developer Salaries
24:00 – Open Source Can Already Replace 80–90% of Frontier Model Work
28:00 – What Makes a Great Engineer in the Age of Agents?
35:00 – Jobs That Will Disappear First Because of AI
40:00 – Why Matan Isn't Worried About AI Taking Jobs Long-Term
46:00 – From String Theory to Startup Founder: The Sequoia Origin Story
52:00 – The Meeting That Led to Sequoia's First Check
58:00 – Why America's Lack of Frontier Open Models Is Embarrassing
1:08:00 – What Matan Looks for in Every New Employee
1:12:00 – Why Elite Companies Will Treat Employees Like NBA Athletes
1:16:00 – The Most Important Prediction Matan Has Changed His Mind On
First Time Founders with Ed Elson – This Physicist Is Building AI Droids
Ed speaks with Matan Grinberg, co-founder and CEO of Factory, an AI company focused on bringing autonomy to software engineering. They discuss the long-term future of AI, the role of regulation, and whether or not he’s concerned about an AI bubble.
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The AI Coding Factory
We are joined by Eno Reyes and Matan Grinberg, the co-founders of Factory.ai. They are building droids for autonomous software engineering, handling everything from code generation to incident response for production outages. After raising a $15M Series A from Sequoia, they just released their product in GA!
https://factory.ai/
https://x.com/latentspacepod
Full Video Episode
Timestamps
00:00 Introductions 00:35 Meeting at Langchain Hackathon 04:02 Building Factory despite early model limitations 06:56 What is Factory AI? 08:55 Delegation vs Collaboration in AI Development Tools 10:06 Naming Origins of 'Factory' and 'Droids' 12:17 Defining Droids: Agent vs Workflow 14:34 Live Demo17:37 Enterprise Context and Tool Integration in Droids 20:26 Prompting, Clarification, and Agent Communication 22:28 Project Understanding and Proactive Context Gathering 24:10 Why SWE-Bench Is Dead 28:47 Model Fine-tuning and Generalization Challenges 31:07 Why Factory is Browser-Based, Not IDE-Based 33:51 Test-Driven Development and Agent Verification 36:17 Retrieval vs Large Context Windows for Cost Efficiency 38:02 Enterprise Metrics: Code Churn and ROI 40:48 Executing Large Refactors and Migrations with Droids 45:25 Model Speed, Parallelism, and Delegation Bottlenecks 50:11 Observability Challenges and Semantic Telemetry 53:44 Hiring55:19 Factory's design and branding approach 58:34 Closing Thoughts and Future of AI-Native Development
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Factory’s Matan Grinberg and Eno Reyes Unleash the Droids on Software Development
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
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