What senior engineers do differently with AI to maximize impact

In 2025, I made over 10,000 contributions to Formation’s codebase with the help of AI.

 AI hasn’t killed the Coding Machine — it’s multiplied it. 

The longer we work with these tools, and the more conversations we have, the more obvious the split becomes: senior engineers aren’t using AI the same way everyone else is. 

For senior engineers, AI is leverage.

For myself and other senior engineers, AI is not flattening experience. It’s amplifying it. The engineers with the most architectural awareness, the strongest product instincts, and the clearest standards are the ones extracting the most value. 

They’re using AI to shift how they work entirely.

Parallel work and delegating the right tasks

A senior engineer used to tackle quick fixes one at a time. Even the fastest contributors were bound by the limits of sequential work. But now, those same engineers are queuing multiple tasks, each handled by an AI assistant, and stepping away to focus on other work.

The result is compound throughput. 

While AI works through a series of scoped issues, the engineer is reviewing PRs, unblocking a teammate, or investigating a more complex bug. When they return, the outputs are ready for inspection.

It’s not that AI is writing better code. It’s that it can follow instructions reliably enough to execute a known plan while the human brain is busy elsewhere. That alone changes what an hour of engineering time can produce.

Separately, AI has changed how engineers debug. It’s become a fast, competent partner for investigative work, especially in codebases with years of formatting quirks or obscure documentation. Tasks that used to take hours, like scouring Slack threads, combing through historical commits, or guessing at undocumented behavior, can now be narrowed down in minutes. In many cases, the model knows more or retrieves answers faster than a human could on their own.

Taken together, shifts like these have unlocked a different rhythm of work for senior engineers. Not necessarily louder or flashier, but undeniably faster.

Taste and judgment: the senior differentiator AI can’t replicate

What sets senior engineers apart isn’t just speed or output. It’s taste.

AI can generate components, rewrite logic, and offer ten different implementations of the same idea. But it can’t understand the system-level consequences of those choices. It doesn’t know when a seemingly small change will quietly fracture a shared pattern or introduce an experience inconsistency that shows up two releases later. That kind of judgment only comes from working through enough real systems to understand how easily they drift out of alignment.

For example, a junior engineer might be asked to move a button and go straight to adjusting the layout, without realizing how many other parts of the codebase are touched by that one decision. 

A senior engineer, on the other hand, knows instinctively that moving the button isn’t just a UI tweak. They’ve seen it before. They know that small changes in a shared container can impact other features or quietly break something in another place.

A junior engineer might ask AI to move the button. A senior engineer might ask AI to surface every layout that uses the same structure, identify shared styles, or flag dependencies across services. AI becomes a force multiplier if you already understand the system. It can’t replace judgment, and it certainly can’t develop taste (yet). But it can compress the work needed to validate assumptions, check for edge cases, and scan across a codebase.

The instinct to ask better questions — questions rooted in an understanding of architecture and tradeoffs — is what sets a senior engineer apart. And at least for right now, that sense of judgment, the ability to zoom out, weigh tradeoffs, and choose deliberately, is the most important thing AI can’t do for you.

Senior engineers should be the ones pushing AI hardest — strategically

If the world is moving toward more AI-assisted development (and it is), then the engineers who understand their systems best should be the ones leading the experimentation. 

Senior engineers are uniquely positioned to push the boundaries of what these tools can do. 

They have the technical judgment to know when it’s safe to over-rely, when it’s time to step in, and how to course-correct if something breaks. And they know which parts of the system are stable enough to withstand a little chaos.

This often looks like giving AI a bigger role on greenfield projects. They might ask it to scaffold entire features, spin up tests, or refactor large swaths of boilerplate, all in the name of speed and exploration. 

But unlike a junior engineer who might take the output at face value, a senior engineer reviews everything with a critical eye. They care about cohesion. They care about whether the generated code actually integrates well with the broader architecture, whether it introduces tech debt, or whether it breaks established design patterns.

If the AI suggests a new implementation that technically works but doesn’t align with the established pattern across the app, the senior engineer doesn’t just ship what they’ve been given. They might reuse an existing modal structure or update the prompt to align with team conventions. The goal isn’t just working code. It’s working code that fits.

That balance, moving quickly with AI while maintaining quality and structure, is the new bar. And senior engineers are the ones who can meet it.

Senior engineers know where to draw the line

As AI tools become more powerful, they also become more capable of causing harm, especially in production environments. 

Senior engineers know how to get value from these tools, and they also know when to pause, inspect the output, and question what’s missing. They’re constantly evaluating tradeoffs: What can the model reliably do? Where do I need to step in and ensure we’re not opening up risk?

Security is one of the biggest considerations. AI can accelerate repetitive work or help scaffold new features, but it won’t always flag that a suggested change touches a critical system, introduces an injection vulnerability, or violates internal policy. 

In many orgs, AI is being used in environments where a single misstep could compromise sensitive information or violate user trust. Without deliberate checks in place, it’s easy to overlook edge cases or miss early warning signs.

The best engineers design systems that make safety easier. They build in linting rules, tests, and process guardrails to catch issues early. They make sure prompts aren’t exposing confidential data or encouraging models to hallucinate unsafe behaviors. They don’t hand over control; they create boundaries.

What this means for the future 

For senior engineers, this shift brings both responsibility and opportunity. AI expands the surface area of what a single engineer can touch — more parallel work, more system-level decisions, more cross-functional impact. It gives experienced engineers new leverage, allowing them to explore ideas faster, investigate problems more deeply, and deliver at a scale that would have been unrealistic even a few years ago. 

But it also raises the bar for stewardship. Senior engineers will be the ones who design guardrails, recognize when a model’s output carries hidden risks, and ensure the foundations of their systems remain coherent as AI accelerates development.

With the labor-market data showing early signs of a senior/junior divide, the imperative for senior engineers to lead becomes even stronger — not just in productivity, but in shaping how AI is integrated responsibly across the org.

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