AI adoption in software development is accelerating fast, and naturally, clients want to know where we stand.
Over the past few months, we’ve received more questions from partners wondering:
“Are you using AI to speed things up?”
“Why is delivery slower when AI is supposed to help?”
“Is Onix behind?”
Let’s address that — openly and with data.
The Promise vs Reality of AI-Powered Coding
Undoubtedly, AI tools like GitHub Copilot, Cursor, and Claude have changed how code is written. Many developers feel faster when using them. However, recent studies reveal a more complex reality, especially for experienced teams working on production-level software.
A 2025 randomized trial by METR, a nonprofit backed by Open Philanthropy, tested AI-assisted development in real-world conditions. Sixteen seasoned open-source developers completed 246 coding tasks using tools like Cursor Pro and Claude 3.5/3.7. Surprisingly, developers with AI assistance were 19% slower on average.
Even more interesting: the same developers thought they were working faster. In reality, much of their time was spent reviewing AI output, rewriting buggy suggestions, and adjusting misleading completions. Only 44% of AI-generated code was accepted, and over 9% of dev time went to cleaning it up.
Why This Happens — And Why It Matters
At Onix, we’ve observed similar patterns. AI offers a genuine speed boost for junior developers or early MVPs. But the lift isn’t automatic for senior engineers working on security-sensitive or legacy systems.
Here’s what slows teams down:
- Time spent prompting and tweaking AI-generated suggestions
- Verifying unfamiliar package imports or hallucinated code
- Ensuring outputs follow our architecture and style guides
- Cleaning up technical debt introduced by quick AI patches
In short, AI can write code. But production software is more than writing, reading, testing, debugging, and maintaining. And that’s where shortcuts can cost more than they save.
As TechRadar notes in its coverage of the METR study:
“Developers may perceive a productivity boost, but the reality includes more time spent reviewing and correcting AI output — not less.” (TechRadar)
Where Onix Stands — and Why
We’re committed to integrating AI tools responsibly. That means:
- Using AI to support developers, not replace them
- Prioritizing security, code quality, and long-term maintainability
- Ensuring clients receive stable, scalable, production-grade solutions
This also means not forcing AI where it doesn’t fit. Some of our developers have been quick to adapt AI into their workflow. Others take more time — and we support that. Because cutting corners on learning is not an option when building serious systems.
As a development partner, our job is to deliver value, not velocity for its own sake.
How We’re Moving Forward
To use AI effectively without compromising delivery standards, we’re taking a structured approach:
- ✅ Piloting tools like Cursor and GitHub Copilot with junior teams and isolated features
- ✅ Implementing automated code scanning and peer review gates for all AI-assisted code
- ✅ Rolling out internal training on prompt engineering, AI debugging, and code validation
- ✅ Collecting internal metrics to track whether AI actually improves time-to-value and quality
We’re also coordinating this strategy with our tech leads, who are directly involved in validating the process, tools, and impact, team-wide.
Final Thoughts: AI Is a Tool, Not a Shortcut
We understand the pressure to “go faster with AI.” But we won’t trade quality, stability, or security for short-term hype.
Instead, we’re investing in the right AI integrations that help our team deliver better work while meeting our clients’ high expectations.
In the words of MIT Sloan:
“Generative AI offers large boosts for lower-skill tasks — but the gains flatten at the high-skill end. For senior devs, the impact is more nuanced.” (MIT Sloan)
That’s precisely what we’re navigating today.
We’re learning, adapting, and always putting our clients first.
AI in Software Development at Onix: Progress, Challenges, and Why Quality Comes First was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.