Stephan Schmidt - September 29, 2025

You Generate It, You Own It: Navigating AI-Owned and Human-Owned Code

A CTO's guide to managing code ownership in the age of AI

As CTO, how do you migrate your organization and code to AI? Your CEO is pushing hard and you need to come up with a strategy to make AI work – giving every developer Cursor and Claude Code isn’t enough. And it doesn’t help that most AI projects do not show any return on investment.

The future is clear: in some years, all code is generated and maintained by AI. Or there is no code at all, the AI just does things.

How do we get from here to there?

Your tech strategy needs to distinguish between human-owned and AI-owned code. While both are code, they are two totally different things.

What do I mean with human-owned and AI-owned code? AI-owned code is code where developers don’t take a look at it. It’s like in the old days, code generated by a framework or tool. Like protobuf handling code that was generated by a tool. This is where you want to go.

Human-owned code is where developers look at the code and understand it. While they do use Claude Code to generate code, or Cursor to generate some code in the IDE, they still own the code. Think of this like an elaborate version of autocomplete.

AI-owned code works great for MVPs and prototypes, but what to do with existing code bases? How can you introduce AI-owned code?

1. Declare every module or microservice you have as either AI-owned or human-owned.
2. Add all necessary guardrails for AI-owned code, like architecture documents, screenshots, domain models, coding style, security requirements. Make them available to the AI at code generation time – e.g. by putting this in the repository.
3. Developers work without these explicit guardrails, AI does not.
4. AI generates bad code if you’re code is bad. Refactor code to make it great before you let the AI work on it, otherwise this will be a disaster. In an MVP or prototype, after letting the AI write some code, refactor it to make it great - then let the AI work on it again (e.g. show how it should do error handling).
5. Refactor code to make it AI-friendly; this is different from human-friendly. There might be fewer functions, longer files, deeply nested code or duplicated code. There might be more NIH code and fewer frameworks. Make the code accessible to the AI.
6. Over time, migrate modules from human-owned to AI-owned until all modules are AI-owned.

AI-owned modules

What to do for modules that are owned by AIs:

• Agentic coding (e.g. Claude Code, Cursor CLI)

• Works easiest for MVPs and prototypes.

• AI-owned modules need different rules and guardrails than human-owned modules.

• AI guardrails are architecture documents, screenshots, domain models, coding style, security requirements. All of these need to be documented in the project so the AI will do the right thing. Developers work without, AI does not.

• What do you need to confidently deploy AI-owned modules to production? You need to be able to answer this question to introduce AI-owned modules to production. “Just ship!” is not a valid answer.

• Tip: Read the test summary of the AI module – like in the old days, take a look at the manual test list and decide: If all of that works, I’m confident we can deploy.

Human-owned modules

What to do for modules that are owned by humans:

• Agentic (e.g. Claude Code, Cursor CLI) and Augmented coding (e.g. Cursor) is used to generate the code.

• All code generated by AI here is owned by the developer who told the AI to produce it.

• In a crisis, “the AI wrote this” is not a valid defense. You generate it, you own it.

• Developers need to understand the code, review the code, test the code. It’s your code, just like with autocomplete before (“The IDE autocompleted the line, I didn’t write it” was never an excuse).

This needs to be at the heart of your AI strategy.

FAQ: AI Code Ownership

Q: What is the difference between AI-owned and human-owned code? A: AI-owned code is generated and maintained by AI systems with minimal human oversight—think of it like code generated by frameworks or tools in the past. Human-owned code, even if AI-assisted, is still reviewed, understood, and ultimately the responsibility of a developer.

Q: Why does it matter who “owns” the code? A: Ownership determines responsibility. For human-owned code, developers are accountable for quality, security, and maintainability. For AI-owned code, organizations must set up guardrails and processes to ensure the AI produces reliable and safe code.

Q: How do I decide which modules should be AI-owned? A: Start by evaluating which modules are less business-critical, have well-defined requirements, or are suitable for rapid prototyping. Over time, as confidence in AI-generated code grows, more modules can be migrated.

Q: What guardrails should I put in place for AI-owned code? A: Provide clear architecture documents, domain models, coding standards, and security requirements. Make these resources available to the AI during code generation to ensure consistency and compliance.

Q: Can I trust AI-owned code in production? A: Only if you have robust testing, monitoring, and review processes in place. Always review test summaries and ensure that the AI’s output meets your organization’s quality and security standards before deploying.

Q: What happens if something goes wrong with AI-generated code? A: For human-owned code, the developer is responsible. For AI-owned code, responsibility shifts to the organization and its processes. Make sure you have clear accountability and incident response plans for both scenarios.

Q: How should developers approach AI-assisted coding? A: Treat AI as a powerful assistant, but remember: if you generate it, you own it. Always review, test, and understand the code before merging or deploying.

Q: Will AI replace developers? A: Not in the foreseeable future. AI will change how developers work, but human oversight, creativity, and responsibility remain essential—especially for critical systems and business logic.