Level 1: AI-Resistant – Not Using AI? Here's How to Evaluate If You Should

You don't use AI tools in your development workflow. Maybe you tried them and weren't impressed. Maybe your context doesn't allow it. Maybe you're skeptical of the hype.

This is a completely valid position. Many excellent developers produce outstanding work without AI assistance.

But it's also worth periodically evaluating whether specific AI capabilities might save you time on specific tasks — not because you're "falling behind," but because tools evolve and your needs change.

Why Developers Choose Not to Use AI

Let's start by acknowledging legitimate reasons for not using AI tools:

Technical & Security Reasons

• Working with classified or proprietary code

• Regulatory compliance requirements (HIPAA, financial services)

• Customer data that can't leave your infrastructure

• Air-gapped or restricted environments

Quality & Learning Concerns

• Need to understand every line of code you write

• Teaching or learning fundamentals

• Maintaining specific code style/patterns

• Avoiding subtle bugs AI often introduces

Practical Considerations

• Current workflow is already optimized

• Cost of API access doesn't justify benefits

• Team doesn't support AI-assisted code

• Debugging AI output takes longer than writing from scratch

If any of these apply to you, not using AI might be the right choice.

Signs AI Might Be Worth Exploring

Consider experimenting with AI if you:

• Spend significant time on repetitive, well-defined tasks

• Often look up syntax or boilerplate patterns

• Write lots of similar tests or documentation

• Need to quickly prototype ideas

• Work with unfamiliar languages occasionally

Three Honest Experiments (With Real Tracking)

If you're curious whether AI could help, try these experiments. Track the actual time, not the theoretical time.

Experiment Setup

For each test:

1. Do the task manually first, timing yourself

2. Try it with AI, timing the entire process

3. Compare quality and time honestly

4. Note any issues or corrections needed

Test 1: Writing Tests for a Function

Why this test: Test generation is where AI often provides genuine value.

Manual approach: Write comprehensive tests for one of your functions

• Time to write: _____ minutes

• Coverage achieved: _____

• Confidence level: _____

AI approach:

"Write Jest tests for this function: [paste your function]
Cover: normal cases, edge cases, error handling"

• Time to prompt and generate: _____ minutes

• Time to review and fix: _____ minutes

• Coverage achieved: _____

• Confidence level: _____

Honest assessment: Did AI save time after accounting for review? Was the coverage better or worse?

Test 2: Generating Boilerplate

Why this test: Boilerplate is tedious but has clear patterns.

Pick one:

• A React component with standard props

• A REST API endpoint with error handling

• A database migration script

• A configuration file

Track:

• Manual time (including looking up syntax): _____

• AI time (including prompt, review, fixes): _____

• Which approach you'd use next time: _____

Test 3: Understanding Unfamiliar Code

Why this test: AI can be helpful for explanation, not just generation.

Find a piece of code in your codebase you don't fully understand.

Manual approach: Research and understand it yourself

• Time spent: _____

• Resources used: _____

• Confidence in understanding: _____

AI approach: Ask for explanation

"Explain what this code does and why: [paste code]"

• Time to get useful explanation: _____

• Accuracy of explanation: _____

• Helped understanding? Yes/No

Evaluating Your Results

After running these experiments, you'll have data instead of assumptions.

AI might be worth adopting if

• It consistently saved time (including review)

• Output quality was acceptable

• The cognitive overhead was manageable

• It helped with tasks you dislike

AI probably isn't worth it if

• Review and correction ate up any time savings

• You spent more time crafting prompts than coding

• Output quality was consistently poor

• It disrupted your flow state

Alternative Productivity Tools to Consider

Before adopting AI, consider whether these traditional tools might better serve your needs:

For Boilerplate

• Snippet managers (TextExpander, VS Code snippets)

• Template generators (Yeoman, Plop)

• Your own script library

For Code Quality

• Linters and formatters

• Static analysis tools

• Pair programming or code reviews

For Learning

• Official documentation

• Stack Overflow (still excellent for specific issues)

• Team knowledge sharing

For Testing

• Test generators specific to your framework

• Property-based testing tools

• Coverage analysis tools

These tools are often more predictable, reliable, and appropriate than AI for specific use cases.

If You Decide to Try AI

Should your experiments show genuine value, here's a pragmatic adoption approach:

1. Start with one specific use case where AI clearly saved time

2. Use it consistently for a week for just that task

3. Keep using your normal workflow for everything else

4. Evaluate after a week whether it's actually helping

5. Only expand if the first use case proves valuable

If You Decide Not to Use AI

That's perfectly fine. You might revisit this decision in a year as tools evolve, or you might not.

You're not:

• Falling behind

• Missing out

• Being resistant

• Hurting your career

You are:

• Making an informed decision based on your context

• Prioritizing what works for your specific needs

• Maintaining a workflow that serves you well

The Reality Check

The tech industry often presents AI adoption as inevitable and necessary. It's neither. It's a tool that helps some developers with some tasks in some contexts.

Many highly productive developers use AI extensively. Many equally productive developers don't use it at all. Both groups are making rational choices based on their specific situations.

Moving Forward

Whether you adopt AI or not, what matters is:

• You've evaluated it based on data, not hype

• Your decision fits your context

• You remain open to re-evaluation as needs change

• You don't feel pressured either way

The best workflow is the one that helps you ship quality code efficiently — with or without AI assistance.

Note: This article is part of a series on AI adoption patterns. Your pattern of not using AI is as valid as any other if it serves your needs.

Explore all 7 levels in the AI-Native Scorecard →