Does the code produced with AI belong to me?

Yes. It’s full-code that you own, with no lock-in. AI changes the way code gets written, not who owns it.

Is an AI-assisted developer less reliable?

Quite the opposite, if they’re experienced. Unreviewed generated code often carries invisible defects, so we treat any AI-produced code as third-party code to verify. Reliability comes from the discipline of verification, not from the tool.

Can I skip a developer and code alone with AI?

For a throwaway prototype, sometimes. For a real product with users, money or data, the risk is letting mistakes you can’t see slip through: that’s where project owners lose money. The value of an experienced developer doesn’t disappear, it shifts toward judgement, architecture and verification.

Will AI make developers obsolete?

No. It shifts the value: less time writing routine code, more time on the right problem, architecture and security. The data bears this out: most AI projects fail for lack of that judgement, not for lack of models.

AI-assisted developer (Claude Code): what it changes for your project

What is an AI-assisted developer?

It’s an experienced developer steering an AI agent (like Claude Code) as a co-pilot: the agent generates code, suggests tests, reviews and documents, under the direction of someone who decides the architecture and validates every step. AI changes the speed of execution, not the nature of the craft. And you get real code that you own, with no platform dependency.

What AI genuinely speeds up

Used well, AI saves real time on concrete tasks: prototyping and iteration, test coverage, documentation, picking up an existing codebase. What these tasks have in common: they’re things a developer already knows how to do; AI simply does them faster. That’s the key to using it well: automate workflows you’ve already proven, don’t improvise expertise.

What AI doesn’t replace

This is where a project succeeds or fails, and the share of that work AI can take on today is far from the main one. What stays decisive, and human:

Understanding the right problem before writing a single line.
Adapting the project to the project owner and their socio-economic context.
The dozens of small subtleties that let a product find its market.
The security review: unreviewed generated code often carries invisible defects, and we treat any AI-produced code as third-party code to verify.

The numbers back up this caution. According to MIT’s 2025 report on enterprise AI, 95% of generative-AI pilots produce no measurable return: the obstacle isn’t the quality of the models, but the integration and judgement around them. Even large companies have reversed course: Klarna, after replacing some 700 agents with AI, brought humans back when quality dropped, its CEO conceding he’d “focused too much on efficiency and cost.”

The “do-it-yourself with AI” trap

AI marketing repeats a trap we already saw with no-code: the idea that a single tool is enough to run a solid project. In reality, a project owner who tries to do everything alone with AI, without expertise, risks letting fatal mistakes slip through, losing money, or even losing the trust of their first customers. Using AI to replace a skill you don’t have is the most expensive mistake there is: the AI line item often ends up costing more than a human expert.

There’s an economic trap too. AI tools are currently sold below their real cost: the big labs post massive operating losses, and the price you pay is propped up in part by investors racing to capture the market. It isn’t sustainable, and providers have already started capping and re-pricing their “unlimited” plans. Building a product’s business model on today’s price for AI tokens would be reckless.

How we use AI

We’re neither technophobes nor tech-worshippers: we use AI every day, as a force multiplier on top of expertise, never in its place. In practice, we use it to speed up what we already know how to do, we automate proven workflows, and we review every line as unverified code. You get the best of both: the speed of AI and the reliability of an experienced human eye, on code that you own. That’s our definition of the hybrid.

A project where AI should speed things up without breaking anything? Let’s talk.