Lately, I’ve been reflecting on something:
The question for most developers is no longer "Are you using AI?", but rather "How and why are you using AI?".
I’ve noticed AI tooling becoming increasingly embedded in my daily workflow. At this time last year, my usage of AI was limited to code autocomplete suggestions in my IDE that I would manually validate. Now I am using coding assistants to help ideate, prototype, and refactor my projects.
In just one year, my workflow has changed dramatically.
It's hard for me to now imagine coding without AI tooling as it has been making me more productive. It's the same feeling I got when I got my first smartphone.
AI increasingly feels like my first smartphone: transformative, incredibly useful, and powerful enough that I need intentional habits to avoid overdependence. For example, keeping it outside of my bedroom when I go to sleep and occasionally going on walks without it, just to be alone with my thoughts.
This is the kind of awareness I’m now trying to build around my AI usage: understanding how it shapes my thinking, where it helps, and where it may quietly erode it.
AI isn’t just changing how fast we build. It is also impacting how we think.
Because not all AI usage is equal. Each usage can be mapped to a certain cognitive cost.
Sometimes AI helps us think more clearly and move faster.
But sometimes it quietly replaces parts of our reasoning before we even notice.
I realized that sometimes I wasn’t using AI because I needed leverage, I was using it because I wanted to avoid friction.
That realization pushed me to start thinking less about AI usage in general, and more about AI modes.
Each AI mode carries a cognitive cost
I distinguish 3 groups: Supportive, Mixed, Risky.
Supportive modes:
- Explaining unfamiliar code or architecture
- Exploring tradeoffs
- Critiquing a plan
- Testing assumptions
- Clarifying concepts
These modes use AI to expand your thinking and have a low cognitive cost.
Mixed modes:
- Boilerplate generation
- Refactoring suggestions
- Drafting documentation
These modes use AI to save time but can also compress understanding if used carelessly. These modes have a sizeable cognitive cost.
Risky modes:
- Blindly accepting generated solutions
- Delegating core architecture too early
- Letting AI define implementation before you’ve thought deeply
- Heavy debugging delegation without understanding the root cause
These can feel productive on the surface, but these AI modes can weaken long-term comprehension if used frequently and have a high cognitive cost.
Hands-on reflective practices also affect your total score
When shipping code I also keep a set of reflective exercises that I can make use of at different stages of my workflow. These can reinforce healthy behavior that alleviate some of the cognitive cost from AI modes.
Before:
- Did I attempt this myself first?
- Am I using AI to expand my thinking or bypass it?
During:
- Am I reviewing assumptions deeply?
- Could I explain why this output works?
- What risks or edge cases might AI be skipping?
After:
- Could I explain this solution tomorrow without rereading it?
- Did I preserve ownership?
- Was this leverage or dependency?
Over time, repeated AI habits don’t just affect productivity.
They shape how we think.
Those patterns shape your cognitive archetype
There are 4 main cognitive archetypes:
AI Architect
AI expands thinking without replacing ownership
AI Balancer
Mostly healthy, but mixed-mode creep needs monitoring
Autopilot Builder
Efficiency may be masking weakened comprehension
AI Passenger
AI may be driving too much of the reasoning path
These archetypes aren’t an exact science. They’re simply a framework for raising awareness.
Have you also noticed different "AI modes" in your own daily workflow? If so, what mode are you in the most?
What would be your cognitive archetype and why?
I’d be curious to hear how others are thinking about their own AI usage.
It feels like we’re all trying to figure out and build these habits in real time.
My take: I don’t think the right path is using AI less.
AI clearly offers real leverage, but leverage without awareness can come with hidden costs.
Knowing when to switch modes becomes key.
When should AI be a teacher, critic, accelerator or collaborator?
When should AI support the driver without taking the wheel?
Because faster output is great but only if understanding keeps up.
I built a personal tracker around this framework to better measure my own habits over time.
It helps me score AI modes, monitor dependency drift, and spot patterns in how I work so I can adjust more intentionally over time.
If it sounds useful to you too, I’m sharing it free and would genuinely love feedback: https://javz.gumroad.com/l/ai-thinking-balance-tracker