AI, your code,
and your IP.
I use AI tools to compress engineering work. The questions that follow (what you can do with what I deliver, where client code goes, and who is accountable when something is wrong) have direct answers, and this page gives them.
Yours to use, modify, and deploy
Everything I deliver at handoff is contractually assigned to you: source code, infrastructure definitions, architecture diagrams, documentation, and test suites. You can use, modify, and deploy all of it without restriction. You can hand it to another developer, incorporate it into a product, or open-source it. None of that requires my involvement or permission.
The choice of contractual assignment over copyright framing is deliberate. In January 2025, the U.S. Copyright Office concluded that AI-generated content produced without sufficient human creative control is not eligible for copyright protection, and that prompts alone do not constitute that control. In March 2026, the Supreme Court declined to hear Thaler v. Perlmutter, leaving that position as settled doctrine. What the threshold looks like in practice (whether code produced with AI assistance meets the bar for sufficient human contribution) is still being defined case by case. Rather than build your rights on a claim that may or may not hold, the project agreement assigns everything I deliver to you contractually. That instrument works regardless of how copyright doctrine continues to develop for AI-assisted work.
Confidentiality
I treat client code, architecture decisions, credentials, and business context as confidential by default. Client work doesn't get discussed with other clients. Credentials stay in your systems or in a secrets manager under your control. Architectural details don't go beyond the people working on the project. Formal confidentiality terms are addressed in the project agreement before any work begins.
What tools I use and for what
The primary AI tool is Claude Code. I use it to accelerate code generation, test scaffolding, refactoring, and documentation. Every line of output goes through my review before it's committed. I don't ship AI-generated code without reading it, and I don't accept AI-generated test suites as a substitute for understanding what they're actually testing.
For clients with compliance requirements or security policies that restrict AI tooling, I can work without it. That option is available from the first conversation.
Judgment and accountability stay with me
The engineer who designs your system, writes the code, and delivers the handoff is me, not a model. AI accelerates the work; it doesn't make the decisions. Every architectural choice is mine to defend. Every line of code in a production system is code I've reviewed and am accountable for.
If something in a delivered codebase turns out to be wrong, I fix it. That accountability doesn't get distributed across a toolchain.
Questions to ask any AI-augmented consultant
Before the project starts
- Which AI tools do you use, and are they API-based or consumer products?
- What does the data handling policy say about code submitted to those tools?
- Do you review AI-generated code before committing it, or does it go in directly?
- How is confidentiality handled, and what does the project agreement say about it?
- If your client has a no-AI policy, can you work without it?
- Who is accountable for defects in delivered code?
- Can you explain any piece of the codebase you've shipped?
Ready to talk through a project?
If you have more questions about how AI tooling fits your specific situation, bring them to the discovery call. Nothing is off the table.
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