Over the past year, I’ve been working closely with some of the bigger and more visible AI customers in Microsoft’s ecosystem.
Large platforms. Fast-moving teams. High expectations. High stakes.
On paper, that kind of visibility sounds exciting.
In reality, it comes with a weight that’s hard to explain unless you’ve been there.
Because being close to impact also means being close to consequence.
Visibility changes everything
When you work with smaller teams or early-stage projects, mistakes are usually contained.
You can recover. You can explain. You can iterate.
When you work with high-visibility AI customers, nothing is really small.
A single design decision can affect:
- Thousands of engineers downstream.
- Millions of end users.
- Public narratives around safety, reliability, and trust.
- Strategic bets measured in years, not quarters.
The room feels different.
Conversations slow down.
Words are chosen more carefully.
Silence starts to carry meaning.
Visibility doesn’t just magnify success.
It magnifies uncertainty.
Trade-offs stop being abstract
Earlier in our careers, most trade-offs are technical.
Performance versus simplicity.
Speed versus correctness.
Cost versus convenience.
At this level, trade-offs become organizational. And human.
You start weighing things like:
- Time to market versus long-term trust.
- Capability versus controllability.
- Innovation versus operational risk.
- Transparency versus competitive exposure.
There is rarely a clean answer. Most of the time, there isn’t one.
Only options with different failure modes.
What I’ve learned is that senior decision-making is not really about finding the “right” answer.
It’s about choosing which risk you’re willing to own. And living with it.
Being in rooms you didn’t imagine entering
One of the quiet surprises of this work has been the rooms it puts you in.
GMs.
CVPs.
Distinguished Engineers.
CEOs and CTOs.
People who have built companies, shaped platforms, and influenced entire industries.
What surprised me most was not their intelligence.
It was their restraint.
The best leaders I’ve met ask fewer questions, not more.
They listen longer than they speak.
They are very aware of second-order effects.
And despite their credentials, many of them are still actively learning.
That realization recalibrates your ego very quickly.
The humbling part
I’ve interacted with people from places like Harvard and Stanford.
People whose resumes read like blueprints for success.
What stands out is not pedigree.
It’s clarity of thought.
They are comfortable saying:
- “I don’t know.”
- “What are we missing?”
- “What breaks if this scales ten times?”
That kind of humility is not insecurity.
It’s discipline.
It taught me that intellectual confidence is not about having answers.
It’s about being honest about uncertainty.
Responsibility changes how you show up
When your work touches high-impact AI systems, you stop optimizing for personal brilliance.
You start optimizing for:
- Clarity.
- Repeatability.
- Safety margins.
- The ability for others to reason about your decisions.
You think more about things like:
- How this will be interpreted.
- Who will inherit this system.
- What happens at three in the morning when something goes wrong.
Doing this work as an immigrant, in a language that is not my first, adds another layer of care.
You slow down even more. You double-check yourself. You choose words with intention.
Responsibility has a way of sanding down sharp edges.
It forces you to trade cleverness for reliability.
Learning accelerates under pressure
I’ve learned more in this phase than in years of steady growth.
Not because the problems are always harder, although many of them are, but because the feedback loop is unforgiving.
Ambiguity shows up fast.
Assumptions get challenged immediately.
Hand-waving doesn’t survive contact with scale.
The learning is not linear.
It’s layered.
Technical depth compounds with organizational awareness, communication, and judgment.
You start seeing systems less as code, and more as reflections of people, incentives, and constraints.
A quiet shift in ambition
This experience has changed how I think about growth.
Earlier, growth meant:
- Bigger scope.
- More visibility.
- Harder problems.
Now, growth feels more like:
- Better questions.
- Fewer unnecessary moves.
- Decisions that age well.
Impact is no longer about being the loudest voice in the room.
It’s about making the room calmer after you speak.
Closing thought
Working close to power in the age of AI is not glamorous in the way people imagine.
It’s demanding, humbling, and often uncomfortable.
But it’s also deeply formative.
It teaches you that the real work is not building impressive systems.
It’s building systems people can trust, including the people who will inherit them later.
And that responsibility, more than visibility, is what stays with you.
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