In recent years, the speed at which development teams operate has increased dramatically.

When problems arise, people now ask AI first before asking colleagues,

and they quickly try out unfamiliar technologies based on AI-summarized explanations.

In terms of work progress alone, it's incomparable to the past.

However, something is quietly disappearing amidst this accelerated flow.

It is the sharing of experiences that used to flow naturally within the team.

The Team's Safety Net Created by Direct and Indirect Experience

There are broadly two ways we learn while working.

Direct experience, where I learn by facing and overcoming problems myself,

and indirect experience, where I learn in advance through the trial and error, documents, and case studies of colleagues and seniors.

Indirect experience, in particular,

is like a safety device that warns us of risks we haven't yet encountered.

It's a structure where one person's mistakes and learnings reduce the risk for the entire team.

But nowadays, this flow of experience is being disrupted.

Questions are mostly directed to AI, not to colleagues.

A Moment When a Single Experience Prevented a Major Accident

A few years ago, when the Anti-Money Laundering system was first introduced in Korea,

I participated in the database design for a project.

At the time, I suggested using the resident registration number as the primary key.

I thought it was a unique identifier issued by the government, so it would be unlikely to have duplicates

and would be good to use as an identifier.

Then, a developer quietly said,

"There are quite a few duplicate resident registration numbers."

It was the first time I had heard that.

If this hadn't been said,

we would have built the core structure of the entire system on a flawed foundation.

It wasn't my direct experience,

but a single piece of indirect experience from a colleague's previous project

that prevented a major risk.

Moments like these protect the team's quality.

And this kind of indirect experience sharing used to happen naturally during the collaboration process.

But Now This Process is Gradually Disappearing

Now, most questions go to AI.

Because AI provides quick answers.

However, those answers lack contextual understanding of the field.

They don't contain accumulated experience such as

why a certain design failed,

what data is an exception,

or how policy or regulatory changes made a difference.

As a result,

the number of quickly built features increases,

but the number of accurately built features decreases.

The team's overall understanding becomes shallower,

and risks grow in unseen places.

How Aline.team Views the Problem

This is precisely what we've been continuously contemplating while building Aline.team.

In the AI era, speed has already become sufficient,

but the context of collaboration and the flow of experience are weakening.

What disappears first when development teams operate is the context of execution, such as

why a certain task was necessary,

in what flow it occurred,

where bottlenecks arise,

and what risk signals appeared.

Aline.team focuses on re-exposing this context

based on actual development activity data.

Who is doing what task,

what connections the tasks have,

where the speed slows down,

what patterns foreshadow risks.

When this flow becomes clear,

indirect experiences that have naturally disappeared within the team are revived.

It's a way for data to fill the gaps in experience.

Conclusion

Thanks to AI, we are moving much faster than before.

However, it is the experience accumulated by people that ultimately guides the team's direction.

Speed without experience,

while seemingly efficient, is actually dangerous.

Especially in the AI era,

the value of experience within the team is increasing,

and creating a structure where that experience can flow

is the core goal we are focusing on at Aline.team right now.