What happens when AI writes 90% of your code, and 60 million people rely on it?

That's the reality for engineers shipping with tools like Claude Code, OpenAI Codex, and Cursor every day. The output is faster and often better, but it raises a harder question:
How do you move at that speed without shipping the bug that breaks production?

In this episode of Agents After Dark, Matt Doughty sits down with Matthew Blode, who builds Link Apps at Linktree and is an OpenAI Codex ambassador, to get specific about writing, reviewing, and shipping AI-generated code at scale.

Together they discuss:

• Why Matthew now writes over 90% of his code with AI yet writes less code than ever, and outputs more
• How vibe coding actually works in production: plan mode, phased to-dos, and layered review across multiple coding agents plus human code review
• The invoicing bug that reached production when an AI hallucinated a dependency upgrade, and the QA that would have caught it
• How risk tolerance shifts from a startup MVP to a platform with 60 million users, using feature flags, internal dogfooding, and AI code reviewers in GitHub
• Why AI evals still need humans in the loop ("who watches the watchman"), and the observability tooling Linktree leans on
• Delivering AI features users actually want, including the right to opt out of AI entirely, and the data sovereignty question that raises
• The Pixar storyboard method for moving from proof of concept to production without burning money on dead-end demos

Whether you are a founder shipping an AI-native MVP or an engineer delivering features to millions, this is a practical look at agentic coding in production: how to keep velocity high and quality intact when the machine writes most of the code.

About Matthew
Matthew Blode builds Link Apps at Linktree and is an OpenAI Codex ambassador, deep in the AI coding and Codex community. He has built and sold two startups, including Fingertip, an AI-powered website builder, and brings a hands-on perspective on agentic coding, code quality, and shipping AI features safely at scale.

About Prefactor
Prefactor helps enterprises trust AI in production.
As organisations deploy more AI agents, maintaining visibility into performance, risk, and operational quality becomes increasingly difficult.
Prefactor gives engineering, product, and security teams a single platform to monitor AI systems, evaluate outcomes, identify risks, and take action when things go wrong.

Learn more at prefactor.ai