How Professional Developers Use AI (and What It Means for Your Price)

How developers use AI in 2026 is less dramatic than the headlines suggest: the AI drafts routine code, suggests tests and explains unfamiliar code, while the developer makes the decisions, reviews every change and owns the result. That saves real time on repetitive work, but not on what usually drives the cost of a project: working out what to build, choosing the right design and making sure it holds up in production. For you as a client, that means some tasks get cheaper, but whole projects rarely shrink as much as the hype implies.

I am a freelance developer based in Denmark, so I have a stake in this topic. This breakdown shows the work behind the price.

The short answer: where AI helps and where it doesn't

My rule of thumb: the easier it is to check whether a result is correct, the more AI can help. The more a mistake costs, and the later it shows up, the less you should hand over to a tool.

This post is about how developers use AI on client work. If you're building with tools like Lovable or Cursor yourself, that's a different question, and I cover it in my guide to taking a vibe-coded app to production.

The three ways developers use AI tools

AI is now standard kit. Google's 2025 DORA research, based on nearly 5,000 technology professionals, found that 90% use AI at work. In the 2025 Stack Overflow Developer Survey, 51% of professional developers said they use AI tools every day. But "I use AI" can mean three quite different things.

Autocomplete in the editor

As the developer types, a tool like GitHub Copilot in VS Code suggests the next few lines. It saves keystrokes, and the developer sees every line before accepting it, so the risk is low.

Chat as a second opinion

The developer asks a language model such as ChatGPT or Claude anything from how an API works to why a query fails. It's faster than digging through documentation and forums, but the answers need checking, because they can sound convincing and still be wrong.

Agents that work on their own

Agentic tools like Claude Code, Cursor or Copilot's agent mode can read the whole project, edit several files, run the tests and fix their own mistakes. This is where the biggest time savings are possible, and also where it's easiest to lose track of what changed. In the same Stack Overflow survey, 31% of developers used AI agents (14% daily), while 38% didn't use them at all and had no plans to.

A professional AI workflow in six steps

The tools change every few months, but the workflow that holds up on client projects tends to look like this. It's also the one I recommend.

1. Understand the problem before the first prompt

The most important work happens before any code: what should the feature do, who uses it, and what happens when something goes wrong? AI is useful for surfacing edge cases, but the decisions get made with you. A precise task produces better code, whether a person or a model writes it.

2. Break the work into small, well-defined pieces

AI performs best with a narrow task and the right context: which files are involved and what must not change. "Build user management" gets you a mess. "Add a phone number field to the profile page, validate it and show it in the admin panel" gets you something that can be reviewed in a few minutes.

3. Review every suggestion like a colleague's code

Treat AI output as code from a fast but inexperienced colleague. It gets read, understood and corrected before it goes into the project. In my view, code the developer can't explain has no place in a client project. That single rule is what separates AI-assisted development from vibe coding, where nobody reads the code.

4. Test what actually matters

AI is good at writing tests, but it's just as happy to write tests that confirm the code does what it does, even when that's wrong. So the developer decides what gets tested: payments, permissions, calculations and the flows your users depend on. Automated checks that run on every change catch much of the rest.

5. Keep client data and secrets out of prompts

Passwords, API keys and real customer data don't belong in a prompt. If AI tools are used on your project, they should run on a business plan where your code isn't used to train models, and you should know about it. For EU companies, GDPR applies whenever personal data reaches an AI provider, which I cover in my guide to GDPR and LLM APIs.

6. Commit small changes to a repository you own

Small, clearly described commits make it easy to see what changed and to roll back if something breaks. The code should live in a repository that belongs to you, so another developer can take over.

Does AI make software development cheaper?

Short answer: partly, but by less than most people think. The research points in two directions, and the gap between them is telling.

In a controlled experiment run by GitHub, 95 developers were given the same narrow task: write an HTTP server in JavaScript. The group using Copilot finished 55% faster. The task was well defined and started from scratch, which is exactly where AI shines. Keep in mind that GitHub sells the tool it was measuring.

In 2025, the research group METR had 16 experienced developers work through 246 real issues in large open source projects they knew well. With AI tools, the work took 19% longer, even though the developers believed afterwards that they'd been about 20% faster. The researchers are clear that this doesn't apply to all software work, and the tools have improved since.

My take: AI is fast when a task is narrow and easy to verify. On a real project with existing code, business rules and production concerns, much of the time goes into understanding, deciding and checking. That's where AI helps least.

A back-of-the-envelope example

Picture a €25,000 project where a third of the hours go into writing code. The rest goes into requirements, solution design, testing, review, meetings and launch. If coding takes 30% less time, the total drops by about 10%, or roughly €2,500. That's a real saving, but nowhere near half price. The numbers are purely illustrative, since the split varies a lot between projects.

What AI means for your quote

How much of that saving reaches you depends on how you're billed. I compare the two models in more depth in fixed price vs hourly rate.

On an hourly or day rate

If you pay by the hour or by the day, you benefit directly when routine work gets faster. The catch is that an invoice doesn't show whether the hours were well spent. Ask how the work was done, not only how long it took.

On a fixed price

Here the developer's efficiency is already priced into the quote. A developer who uses AI well can offer a lower price or more scope for the same budget. So compare quotes on what's included: testing, code review, documentation and handover.

When a quote looks too cheap

If one quote is far below the others, AI may be used to skip steps rather than speed them up. You'll notice later, as bugs, security holes or code that's expensive to build on. More code isn't an asset either, because every line has to be maintained. My breakdown of what AI-generated code costs to maintain shows how that bill grows over a year.

Where experience still makes the difference

AI has made writing code cheaper. It hasn't made mistakes cheaper. When code arrives faster, the scarce skill is judging it.

The Stack Overflow numbers back this up. The top frustration, named by 66% of developers, is AI solutions that are almost right, but not quite. Close to half (45%) say debugging AI-generated code takes longer, and only 3% highly trust the accuracy of AI output.

Almost right is the expensive kind of wrong

Code that clearly fails gets caught straight away. Code that works in the demo but charges the wrong VAT in one edge case, or lets one user see another user's data, gets caught when it costs you. Knowing where to look takes experience, and I've collected the usual suspects in my list of common security issues in vibe-coded apps.

Architecture and the data model

How data fits together, where the business logic lives and how the system will grow are decisions that are hard to undo. AI will happily propose a design, but it doesn't know your business, your plans for next year or your hosting budget. A wrong call here usually costs more than everything AI saves on typing.

AI amplifies what's already there

The DORA research describes AI as an amplifier of a team's existing strengths and weaknesses. AI adoption was linked to higher delivery throughput, but also to less stable delivery. In practice, AI in the hands of a developer without good habits mostly produces more bugs, faster.

When you don't need a developer yet

I make a living building software, so weigh this accordingly: AI and off-the-shelf products mean you can handle more on your own. You probably don't need a developer yet if

• you want to show customers an idea with a clickable prototype before investing,
• an existing SaaS product covers most of what you need and you can live with the gaps,
• you need a small script or spreadsheet that only you will use,
• the job is a one-off data analysis that never needs to run again.

A developer becomes worth it when other people log in, pay, store personal data or rely on the system staying up. That's also where using AI responsibly pays off, because the mistakes land on someone other than you.

Next steps

AI has changed how software gets written, but not what a good project needs: clear requirements, considered decisions, testing and someone who stands behind the result. A developer using AI is neither a seal of quality nor a red flag. What matters is how, and these questions will tell you in a few minutes.

Good answers are specific and a little boring. Be wary if the answer is that AI handles everything, or if nobody can explain how the code gets checked. And compare quotes on what's included, not just the total.

If you want a developer who uses AI where it saves time and experience where it counts, take a look at the services I offer: websites, custom web apps, SaaS products, integrations and ongoing development of existing systems. Larger builds start with a paid, fixed-price discovery phase, you own the code from day one, and I reply within one business day.

Frequently asked questions

Is AI-generated code worse than code written by a developer?+

Not necessarily, but it's uneven. AI often writes solid code for familiar tasks and surprisingly weak code for anything unusual, and it sounds equally confident either way. Quality therefore depends on who reviews it. Code that an experienced developer has read, tested and adapted isn't worse just because an AI wrote the first draft.

Should my developer tell me they're using AI?+

It's good practice, and you can make it part of the contract. The bigger question isn't whether AI is used, but whether your code and data are sent to a third party, and on what terms. If the project involves confidential information or personal data, agree in writing which tools are allowed. For anything legal, such as GDPR or IP ownership, check with a lawyer.

Will I pay for my developer's AI subscriptions?+

Usually not directly. Coding tool subscriptions are normally part of a developer's overheads, like their editor and laptop, and are built into the hourly rate or fixed price. It's different if your product uses AI itself, for example a chatbot or automated document processing. Then you pay the provider for usage, and that belongs in your running costs.

Can I use AI myself to make my project cheaper?+

Yes, especially before the developer starts. Use AI to write up your requirements, find gaps in them, build a simple prototype and gather sample data. The better prepared you are, the fewer hours go into clarification. If you hand over AI-generated code, set expectations: anything going into production still needs a review, and that takes time too.