The Year AI started Doing a Tech Lead’s Job is Here

I watched it happen in real time. AI can complete software development work in a few hours that would traditionally take a Tech Lead and four Senior Software Engineers about a year.

A senior engineer I know stuck on a feature for two days. Authentication flow, some edge cases with token refresh, a few tricky race conditions. He was doing what any good engineer does thinking it through, testing, reading docs, asking questions in Slack.

Then he opened an AI coding tool and described the problem out loud, in plain text.

Two hours later, the feature was done. Working tests included.

I’ve been building software for over fifteen years. That moment sat with me for a long time.

Something Real Changed This Year

I’m not talking about autocomplete getting better. I’m not talking about Claude filling in a function body.

I mean AI is now doing what a mid-level tech lead does on a normal Tuesday. Writing implementation plans. Breaking down requirements. Catching integration issues before they happen. Explaining why a particular approach will cause problems down the road.

Not perfectly. But well enough to change how a team works.

Software companies that haven’t noticed this yet are going to feel it hard. Not in five years. By end of 2027, any company still organizing engineering teams the way they did in 2022 is going to struggle to compete with one that hasn’t.

Why The “Software Engineer” Role is Under Real Pressure

Let me be honest about what I mean and what I don’t mean.

AI is not replacing engineers who understand systems. It is replacing the part of the job that involves typing familiar code into familiar patterns. That part is already mostly gone for teams using these tools seriously.

A junior engineer who knows one framework, writes CRUD endpoints all day, and doesn’t understand the system they’re working in that role is shrinking. Not because the person isn’t smart. Because the job they were hired to do can now be done faster with a prompt.

This is uncomfortable to say. But pretending it isn’t happening doesn’t help anyone.

I have a friend who leads engineering at a mid-size company. They froze junior hiring one year ago. Not as a cost cut. Because they found they could cover the same output with fewer, more experienced people and better tooling. They’re not a startup trying to be clever. They just ran the math.

What Goes Up in Value

Here’s what I keep seeing hold its value and gain it.

Someone who can look at a system and say “this will fall apart when traffic triples.” Someone who catches the database design decision that will make data migrations impossible in two years. Someone who can tell a product manager why their requested feature is actually three separate problems, and which one to solve first.

That’s not a skill AI has. AI can reason about patterns it has seen. It is weak at reasoning about the specific, weird, half-documented system your company built over seven years with three different teams and two acquisitions.

Experienced architects and tech leads understand context. They know the history. They know which shortcuts were taken and why. That knowledge is worth more now, not less, because AI tools need someone to direct them well.

Prompting badly gives you bad output fast. Prompting well requires knowing what good looks like.

A New Kind of Engineer is Coming

I’ve started interviewing people called “AI-native engineers.” They are usually younger. They don’t think of AI as a tool they add on top of their workflow. They think of it as part of how they reason through a problem.

They move fast. Sometimes too fast they miss things experienced engineers would catch. But their output per day is genuinely different from what I saw two years ago.

The best ones pair that speed with curiosity about the system underneath. They want to understand why something works, not just get it working. Those people are going to be very good very quickly.

The ones who rely on AI to do their thinking for them not just the typing, but the thinking are going to hit a ceiling. Hard. When the AI is wrong (and it is wrong), you need the foundation to catch it. Without that, you ship the mistake.

What I think Companies Need to Do

Stop hiring for headcount. Start hiring for judgment.

Restructure teams around fewer senior people who can direct AI tools well, review AI output critically, and catch the things AI misses.

Invest in the experienced people you have. An architect who learns to work with these tools is not just slightly more productive. They can multiply the output of a small team in a way that was not possible two years ago.

Stop waiting to see how this plays out. The companies that are figuring this out now are building an advantage that will be hard to close later.

And don’t mistake speed for quality. AI can generate a lot of code fast. Someone still needs to own whether that code is correct, whether it fits the system, and whether it will still work in a year.

One Thing

I genuinely don’t know what happens to the people in the middle of this shift. The engineers with three or four years of experience who built their skills the traditional way and are now watching the ground move.

Some will adapt fast. Some will find the new tools make them much more effective than they were before. Some will struggle.

I don’t have a clean answer for that. Anyone telling you they do is guessing.

What I do know is that software is not done as a profession. It is reorganizing. The work is getting harder to see and easier to do badly at scale.

Experience still matters. Judgment still matters. Understanding a system deeply still matters.

The difference is that now those things matter more than they did before because the easy work is no longer the bottleneck.

SoftwareEngineering #AIInDevelopment #TechLeadership #SoftwareArchitecture #EngineeringCulture