The first time I hit my token limit, I realized AI development isn’t just about coding—it’s about becoming an efficiency expert.

It’s August 15th, and my premium request usage sits at 94%. My limits refresh September 1st. I recently hit a token limit error on a single request because I asked Copilot to refactor a 1,600-line file. It consumed my context, analyzed my entire solution, and then… error. Token limit exceeded.

This is the reality behind those “10x productivity” claims you see on LinkedIn.

My AI Evolution: From Skeptic to Strategic User

I started simple—treating AI like an advanced Stack Overflow. Got a specific question? Ask AI. This evolved into brainstorming sessions for bigger architectural decisions. I was always critical, crafting restrictive prompts to avoid the usual AI hallucinations.

Then came the agents. And that changed everything for me as a .NET developer.

When AI Gets It Right: The FluentAssertions Success

Let me show you where AI truly shines. I recently had Copilot replace FluentAssertions with standard XUnit assertions across a project. Not just a mechanical find-and-replace, but intelligent conversion that:

  • Maintained test readability
  • Preserved assertion intent
  • Updated XML documentation
  • Added comments explaining complex assertions

A task that would have taken me half a day was done in minutes, with quality I couldn’t have maintained manually across hundreds of test methods. This is AI at its best: handling repetitive tasks that require consistent quality and adding value through documentation.

When AI Gets It Wrong: The Discord Bot Reality Check

But then there’s the other side. I asked Copilot to add a simple conversation feature to my Discord bot. What I got was:

  • One massive file with dozens of DTO records
  • Static helper classes everywhere
  • Magic values hardcoded instead of database configuration
  • Complex abstractions for what should have been simple functionality

I wanted to start small and iterate. AI wanted to build enterprise architecture for a hobby project. As Oskar Dudycz recently wrote, working with AI often means becoming “the world’s most annoying micromanager”—watching over every keystroke, eventually grabbing the keyboard yourself.

To make it work, I had to be incredibly specific about constraints, approach, and even file organization. But here’s the kicker: the more detailed your prompts become, the more you’re programming in Markdown instead of code.

The Token Economics Reality

This brings us to the uncomfortable truth about AI development: it’s not free, and it’s not always faster.

Those token limits aren’t just technical constraints—they’re forcing us to become strategic about when and how we use AI. Soon, every developer will face the same choice I’m facing:

  1. Ask for more tokens (passing costs to employers/clients)
  2. Learn to spend tokens effectively

The developers who master the second approach will thrive. We’re not just learning to code anymore—we’re learning to be efficiency experts.

The Hidden Cost: Code Review Becomes Everything

Here’s what nobody talks about in those productivity claims: the time you used to spend writing code is now spent validating AI output. And this isn’t trivial.

Reading and reviewing AI-generated code requires intense focus. You need to understand not just what the code does, but whether it follows your project’s patterns, handles edge cases properly, and maintains the architectural decisions you’ve made. Some developers can scan through AI output quickly and spot issues; others need to trace through every line methodically.

This validation skill is becoming as important as coding itself. The developers who can rapidly assess AI output quality—catching subtle bugs, architectural mismatches, or performance issues—will be the ones who actually see productivity gains. Those who struggle with code review will find themselves slower with AI than without it.

It’s a fundamentally different skill set, and it’s not equally distributed across our profession.

As a senior developer, I’ve found this transition easier because I already spend significant time reviewing my team’s pull requests. The skills translate directly: pattern recognition, spotting architectural inconsistencies, catching edge cases. But here’s the bonus—working with AI has actually made me a better code reviewer, and vice versa. Junior developers who embrace AI validation are becoming much stronger PR reviewers too.

It’s a virtuous cycle that benefits the entire team.

The Documentation Advantage: AI’s Secret Multiplier

Here’s where I’ve found the real AI superpower: documentation. As a documentation addict, I discovered that well-documented code creates a virtuous cycle with AI:

  1. I write code
  2. AI generates comprehensive documentation
  3. When I later ask AI to refactor or add features, it reads that documentation for better context
  4. AI automatically updates both code AND documentation

This isn’t just helpful for other developers—it’s essential for AI itself. Good documentation gives AI the context it needs to make better decisions, reducing those token-burning back-and-forth sessions.

What This Means for .NET Developers

We’re not becoming obsolete—we’re evolving. The classic developer who writes everything from scratch is giving way to something new: solution architects who wield AI effectively.

But here’s the plot twist: the skills that make you effective with AI are the same ones that always made great developers:

  • Strong fundamentals (so you can review AI output critically)
  • Understanding best practices (so you can guide AI toward good solutions)
  • Staying current with frameworks (so you can catch when AI suggests outdated approaches)
  • Clear communication (now essential for prompt engineering)

These weren’t optional before AI, and they’re absolutely critical now.

The Path Forward

Those who adapt will thrive. Not because they can prompt AI better, but because they understand when to use it, when to ignore it, and how to review its output critically.

The time we used to spend writing boilerplate is now spent on architecture decisions, code review, and—crucially—mentoring junior developers who need to learn both traditional development AND AI collaboration.

This isn’t a phase like blockchain or IoT hype. This is a fundamental shift in how we build software. But at its core, it’s amplifying what we already knew: good developers with strong fundamentals will always find ways to be more effective.

The tools have changed. The principles haven’t.

What’s your experience with AI development? Have you hit the token wall yet? I’d love to hear how you’re adapting to this new reality.