AI-assisted development makes writing code, tests and documentation faster, especially for routine work and prototypes. It does not make software automatically better or cheaper. The research shows that speed gains depend heavily on the task and the developer, that AI-generated code often fails security checks, and that teams adopting AI can ship faster while becoming less stable unless they strengthen review and testing. The winning approach keeps strong engineering discipline and uses AI inside it. Below we compare traditional development with AI used wrong and AI used right, and explain what it means for the cost of your project.
How much faster is AI-assisted development really?
It depends on the task and on who is using it. In a controlled experiment published by researchers at Microsoft Research and GitHub, developers with GitHub Copilot completed a defined task, building an HTTP server in JavaScript, 55.8% faster than developers without it.
Real projects are messier. In a 2025 randomised study by METR, experienced open-source developers working on their own large codebases took 19% longer when they were allowed to use AI tools. Before starting, they expected AI to speed them up by 24%, and afterwards they still believed it had sped them up by 20%. METR has since marked those results as out of date, and its 2026 follow-up with newer tools points to a speedup, while calling its own data weak evidence of the size of that gain.
At the largest scale, AI is already writing most of the code, under human review. Google says that 75% of all new code at Google is now AI-generated and approved by engineers, up from 50% the previous autumn. The key words are "approved by engineers".
How does traditional development compare with AI-assisted development?
| Development task | Traditional way | AI used wrong | AI used right |
|---|---|---|---|
| Prototypes and demos | Days or weeks of setup | A demo shipped to production as it is | AI builds the prototype fast and the team rebuilds what goes live properly |
| New features | Every line written by hand | Large AI changes merged without review | AI drafts small changes and a senior developer reviews each one |
| Tests | Often skipped under deadline pressure | AI tests that only confirm what the code already does | AI drafts tests from the requirements and developers add edge cases |
| Security | Periodic manual checks | Trusting AI code because it compiles | Automatic security scanning on every change plus human review of sensitive code |
| Understanding old code | Weeks of reading | Rewriting what nobody understands | AI explains the code and maps dependencies before anyone changes it |
| Documentation | Rarely up to date | Generic documents nobody checked | AI drafts docs from the code and the team corrects them |
What happens to quality and stability when teams adopt AI?
Google's DORA research team studies thousands of software teams every year. Its 2025 State of AI-assisted Software Development report, based on nearly 5,000 respondents, found that 90% use AI at work and more than 80% say it has increased their productivity. It also found that AI adoption now improves delivery throughput but still increases delivery instability. The report cites its 2024 estimate that every 25% increase in AI adoption came with 1.5% lower delivery throughput and 7.2% higher delivery instability.
Security is the other concern. Veracode's 2026 GenAI Code Security Report, which tested more than 100 models, found that AI-generated code passes security checks only 56% of the time, and only 30% of the time in Java. Code that runs is not the same as code that is safe.
How much do developers trust AI-generated code?
Less than its usage suggests. The Stack Overflow Developer Survey 2026 found that 73% of developers use coding assistants or agents daily, yet only 16% trust AI for most tasks, and 48% trust it only when they can easily validate the answer. In DORA's 2025 report, 30% of respondents reported little or no trust in AI-generated code. The developers closest to the tools are the most careful about them, which is the right instinct.
What does AI mean for the cost of a software project?
It changes where the money goes more than how much is needed.
- Cheaper: prototypes, boilerplate code, first drafts of tests and documentation, and understanding existing code.
- Not cheaper: understanding your business, designing the system, reviewing code, security, testing real scenarios, integrations with your other systems and long-term maintenance.
- More expensive if skipped: software built fast with AI and no review tends to cost more later in bugs, security fixes and rewrites.
Be careful with any offer that promises a full system at a fraction of the usual price "because we use AI". Ask how the code is reviewed, tested and secured. For realistic numbers, see our guides to custom ERP and CRM development cost and mobile app development cost in Egypt.
How should a software team use AI correctly?
- Keep changes small so every AI-assisted change can be reviewed properly.
- Require a senior review for anything touching payments, personal data, permissions or security.
- Run automatic tests and security scanning on every change, whoever or whatever wrote it.
- Never paste secrets, keys, client data or private source code into tools that are not approved for it.
- Check the licences of any code or package the AI suggests.
- Track stability as well as speed: failed deployments, rollbacks and bugs found after release.
- Choose models and tools that fit each task and avoid lock-in. Our guides to small vs large AI models and avoiding AI vendor lock-in explain how.
What should you ask a software company about its use of AI?
- Which AI tools do your developers use, and are they approved for client code?
- How is AI-written code reviewed, and by whom?
- What automated tests and security scans run before release?
- Where does my code and data go when you use AI tools?
- Who owns the code, and how do you check licences?
- How do you measure stability after release?
For the full checklist, read how to choose a software company: 8 criteria.
This article is part of our series The AI-Ready Company. Read the opening article, Traditional vs AI: what really changes and why most companies use AI wrong.
Planning a system, a platform or an app and want the speed of AI with the discipline of real engineering? Tell us what you want to build. Talk to us.
