One thing became very clear to me over the last year.
AI tools are moving faster than any tech stack I’ve seen in my career.
Faster than frameworks.
Faster than cloud services.
Faster than anything we’ve dealt with before.
And that creates a trap.
A lot of teams are debating which AI tool to use.
Very few are seriously and continuously reviewing how they build software.
Whenever I share how we operate as an AI-native engineering team, people almost always ask the same question:
“How do you keep quality high if AI is writing more code and feedback loops are so much faster?”
It’s a valid concern.
And it usually points to a deeper misunderstanding.
Quality has never come from slowing down.
Quality comes from strong systems.
AI does not lower quality by default.
Weak processes do.
Strong teams don’t lose quality with faster loops.
They expose quality problems faster and fix them earlier.
Our engineers operate at a 10x level.
Not because of hero behavior.
But because the system they operate in multiplies good decisions and reduces bad ones.
We’ve intentionally kept the team lean while growing at a high rate.
That only works if you’re relentless about reviewing and evolving how work gets done.
Our product development process is never finished.
We constantly challenge:
how we plan and prioritize
how decisions are made
where AI adds leverage and where it adds noise
what slows teams down
what creates hidden risk
AI is not something we “add” at the coding phase.
It shows up before code exists.
It helps engineers reason about problems, explore alternatives, pressure-test ideas, and converge faster on the right solution.
This is my strong opinion as someone working closely with AI every day:
If AI is only helping you write code faster, you are underusing it.
The real leverage comes from better thinking, not faster typing.
But speed without control is still a failure mode.
As feedback loops accelerate, testing, validation, and review become more important, not less.
That’s the only way fast systems stay reliable.
This acceleration is also reshaping the role of QA.
When feedback loops tighten, quality can’t live at the end of the pipeline anymore.
It moves upstream.
It becomes continuous.
That deserves its own conversation, and I’ll write about it separately.
Many teams get this wrong.
They layer AI on top of an old process and hope productivity magically improves.
That rarely works.
We treat AI as a signal to rethink the entire product development workflow.
What should change?
What should disappear entirely?
What needs to become stricter instead of looser?
AI tooling will keep changing.
The mistake is anchoring your team to tools instead of principles.
We reassess tools frequently.
More importantly, we reassess how work flows through the organization.
Processes are adjustable.
Tools are replaceable.
Principles are not.
I don’t have answers for every question this AI shift is creating.
Anyone who claims they do is guessing.
But I do have one certainty.
The only viable strategy is to learn and adapt as fast as possible.
If you feel comfortable right now, you are probably on the wrong path.
In this industry, comfort is often a lagging indicator.
Top comments (0)