The Hidden Costs of AI-Assisted Development
AI has changed how I write software, and I think the costs are easy to miss: ownership uncertainty, skill atrophy, and accountability that stays with the engineer.
AI has changed how I write software. I can design a system, specify its behavior and constraints, and have an agent implement a large part of it in far less time.
I also think there are costs that are easy to overlook. This is my position as an engineer who uses these tools daily, not a case against them.
Cost one: ownership uncertainty
The question is not whether an AI company owns part of your product. There is no established rule that says it does.
The real question is what rights and risks attach to generated code. Copyrightability, provider terms, indemnification, training-data provenance, and open-source license contamination are all open or varied. For a company building a proprietary product, uncertainty is itself a risk.
I am not a lawyer and this is not legal advice. My practical response is to keep the architecture and the core of the system under my control, and treat the AI as a tool rather than a party to the work.
Cost two: skill atrophy
Understanding a solution and generating one are different skills. I can recognize a good cursor-based pagination design, but designing one means reasoning through tie breakers, deleted records, indexes, and concurrent inserts.
The better the agent gets, the fewer of those repetitions I get. Over time that can slide from “I know how to solve this and I told the agent” to “this looks plausible, ship it.”
The counter I am adopting is to periodically implement something substantial myself without an agent. It works like strength training: I do not need to carry every box, but I want to be able to lift one when the forklift breaks.
Cost three: accountability
An agent cannot own a production incident. It cannot explain a failure to a customer or be responsible for an architectural decision.
The engineer still owns the result. AI changes who does the typing without changing who is accountable.
The verification problem
If an agent makes me several times faster at producing code, the number of things that could be wrong grows too. My capacity to review does not grow at the same rate.
More throughput with the same verification capacity is not more engineering capacity. That is why I invest in tests, fixed contracts, and small reviewable changes, as I describe in Why I Write Tests Before AI Writes Code.
What I do about it
- I keep architecture and the data model human-owned.
- I write tests that define behavior before code is generated.
- I review every change before it is committed.
- I stop the agent from inventing new patterns on its own.
- I set aside time to implement important pieces without an agent.
Takeaway
Implementation has become cheaper. Architecture, verification, judgment, and accountability have not.
The engineering process has to change around that fact, instead of simply maximizing generated code. If you want a developer who works this way, see the custom web applications service.