Saying "I don't know" is a sign of seniority for me.
07.03.2026 16:02
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Saying "I don't know" is a sign of seniority for me.
Just asking for a friend :)
Happy Friday and have a great weekend!
The best engineering leaders won’t “know more.” They’ll know what to ignore. AI will flood your org with options, dashboards, opinions, and half-baked ideas. Your job becomes subtraction: fewer priorities, fewer meetings, fewer “nice-to-haves.” Clarity will be the rarest skill.
I can tell a lot about engineer's seniority by the way they approach solving problems.
Are they really taking care of the problem?
Or do they just accept the first solution they come across and leave it for others to deal with?
The next era of leadership is less motivation, more environment design. You don’t “inspire” deep work. You remove friction. You set fewer goals. You kill recurring meetings. You make quality the default. Culture is just the shape of the calendar.
AI makes output cheap, impact becomes rarer. So, anyone can "ship more code", while business impact becomes that much more important.
Software engineering is becoming position-less.
Titles are becoming less important. Impact is becoming everything.
Waiting for some pokemon AI tools to put it in the list :)
Happy Friday and have a great weekend!
The ultimate leadership flex is making yourself unnecessary. Not by disappearing but by building a team that can decide, ship, and work well without you. If everything needs your approval, you’re not leading. You’re the bottleneck.
Yes! Being AI-native goes beyond simply using AI tools. It means continuously identifying and eliminating bottlenecks across the entire software engineering lifecycle.
That means questioning everything.
I've shared more info in this article: newsletter.eng-leadership.com/p/how-to-bui...
When AI makes output cheap, impact becomes rare. Anyone can ship more code. The real multiplier is the person who helps the whole team ship better code, focusing on the right problems, with fewer failures.
To learn more, read this Engineering Leadership article: newsletter.eng-leadership.com/p/how-to-bu...
That means optimizing how decisions get made: clear ownership, clear escalation paths, and minimal friction.
As a leader, it’s more important than ever to stay close to the technical details. At the same time, it’s critical to empower your team to make decisions.
4. Stay close to technical details
Create space for sharing: internal hackathons, demo sessions, and dedicated channels where people can exchange tips and workflows.
Do you already have people who are excited about AI and noticeably more productive? If so, help them spread what they’re learning.
3. Look for people who are excited about AI
Where this breaks down is when leadership has a very rigid idea of how AI should be used and forces that approach onto teams.
Give teams a generous, sometimes unlimited, token budget and the freedom to experiment. Encourage people to explore without pressure or fixed expectations about what success should look like.
2. Let engineers experiment
Instead, look for the real bottlenecks, as more code won’t solve the problem. The bottleneck might be code review, planning, or system design, especially as engineers become much more productive.
1. Don't focus too much on code generation and metrics like lines of code produced by AI
Advice for engineering leaders building AI-native engineering teams.
Being able to understand users, ask the right questions, and decide which problems are actually worth solving matters more than ever.
Being able to understand users, ask the right questions, and decide which problems are actually worth solving matters more than ever.
As a leader, when you trust your people and empower them, in many situations, you’ll be surprised by what kind of amazing things they’ll come up with.
As a leader, when you trust your people and empower them, in many situations, you’ll be surprised by what kind of amazing things they’ll come up with.
As a leader, when you trust your people and empower them, in many situations, you’ll be surprised by what kind of amazing things they’ll come up with.
What AI-native engineering teams do well is:
Identifying where work slows down and applying AI to remove those bottlenecks.