JSNation is coming soon, and besides my talk (I’ll drop the link in the comments so I don’t spam you with it for the tenth time 😅), there are also discussion rooms. And somehow, I got invited to two of them.

Now, a normal person would probably stop for a second and think: “Do I even have time for this?”, “Is it worth it?”, “Should I maybe not overcommit myself for once?”. Meanwhile, in classic Sylwia fashion, I replied almost instantly: “Oh, that sounds amazing! Sure, sign me up for everything!” 😎

And that’s how I ended up in a discussion room called “The New Senior Engineer: Builder, Reviewer or Orchestrator?”.

Now, my career advisor, ChatGPT, always tells me: “Sylwia, please get your life together. And if you insist on doing ten things at once, at least reuse the content.” 😅 So instead of coming up with all the conclusions myself, I thought: why not ask the DEV community?

But before I ask the big question — what should a Senior Engineer become in the AI era — I think there’s another, more interesting one first: how are you actually using AI at work?

Not in conference demos. Not in viral Twitter threads. Not in “my AI agent rewrote Kubernetes during lunch” stories. I mean real work. Real projects. Real teams.

Of course, feel free to jump straight into the comments (you know I love talking with you all ❤️), but first, a few observations from my side.

AI Above Everything

At least that’s the image the internet gives us. Conference titles. Newsletter headlines. LinkedIn prophets.

Matteo Collina opens a 100k-line PR for Node.js and people panic.

Someone rewrites an entire React application to Svelte in two weeks. Hundreds of thousands of files.

The creator of Bun rewrites it from Zig to Rust in one evening, while casually mentioning he also went on a date that night. (Am I the only one getting weird associations here? 😅)

Armies of agents replacing development teams. Agents opening PRs for other agents to review. Everything automated. And somewhere in the middle of all this, the Senior Engineer becomes some kind of AI shepherd, occasionally checking whether the robots are heading straight into a cliff.

Honestly, it’s both fascinating and mildly terrifying. Sometimes it makes you wonder whether we should all reconsider our career choices and maybe sign up for hairdressing school before the robots learn that too 😅

But then I stop for a second and think: I actually work in this industry. I know a lot of developers. And real life often looks… very different.

From Excitement to Cost Optimization

A friend of mine works at a huge tech corporation. One of those companies you definitely know — and probably either love or hate 😄

Of course they started using AI tools very early, including Copilot. But things really escalated once they got proper coding agents — I think Claude Code.

At first, the company was completely mesmerized. They bought the most expensive plans possible and encouraged people to use AI aggressively. If someone hit token limits, management basically said: “Don’t worry, we’ll buy more. It’s revolutionary!”

My friend happened to be building a new application from scratch and honestly — he loved it. The amount of code generated was absurd. Normally, building something like that would take months with an entire team. Now? A few days and huge chunks of the system already existed.

And because he’s genuinely an excellent developer, he became very good at noticing the exact moment Claude started going completely off the rails. Interestingly, this often happened around 5 PM. Apparently the AI wanted to clock out too 😅

But after a few months, the excitement slowly started fading. Turns out that while AI sometimes makes development dramatically faster, it definitely doesn’t always.

And then came the second surprise: the company actually calculated how much all this AI usage was costing. Suddenly everyone discovered that — surprise, surprise — unlimited AI agents aren’t exactly cheap 😄

So now there are discussions about limits, optimization, and reducing token usage. At this rate, maybe hiring interns will eventually become the cheaper option again 😂

And honestly, we’re already seeing this trend more and more. Wasn’t it Meta that introduced some kind of “tokenmaxxing” culture where people were rewarded for using fewer tokens?

And Finally, My Own Story

Now let’s move to my world.

A massive international institution. An enterprise ship that takes three years to turn right. A place where privacy is treated almost like religion. So naturally, people were extremely skeptical about LLMs for a long time.

But eventually AI arrived there too, which honestly makes me think these tools are now basically everywhere 😄

So: do coding agents massively accelerate development in enterprise legacy systems?

Well… that’s where things become complicated.

Sure, there are tasks where AI is genuinely useful. Simple bugs. Small features. Boilerplate work. But on some tasks? It completely collapses.

The agent reads library code. It crawls through the application. It searches half the repository. And still somehow understands absolutely nothing 😅

Sometimes I literally have to tell it: “Maybe check that weird file written by a junior developer seven years ago.” Or: “Our UI library has some very specific legacy quirks, maybe investigate that direction.”

And honestly? After 2.5 years in this project, I’m simply faster than the AI agent in many debugging scenarios.

Not because I’m smarter. Not because AI is useless. But because enterprise systems accumulate context, history, weird decisions, tribal knowledge, hidden dependencies and architectural scars over many years. And I have that context. The AI usually doesn’t.

And I honestly doubt my project is unique here. A huge percentage of software running the world today is enterprise legacy that survived far longer than anyone originally planned — and is still actively evolving 😄

So maybe, somehow, I’ll actually survive as a programmer until retirement after all. And maybe I won’t need to learn hairdressing.

Which is probably good news for humanity, because I’d be terrible at it 😅

But now I’m genuinely curious: what does AI usage actually look like in your work?

Million-line AI PRs? Daily battles with legacy systems? Token optimization? Or maybe something completely different?

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