Some unexpected effects of using AI in software engineering

 

AI in software engineering hasn’t just made teams faster. It has changed behavior, incentives, and failure modes in ways most orgs didn’t anticipate. Some of these effects are beneficial; many are subtle and dangerous if unacknowledged.

Here are the unexpected ones that actually matter.

1. Code quality variance increases, not decreases

AI raises the average productivity but also widens the spread between good and bad outcomes.

• Good engineers use AI to explore, refactor, and reason
• Weak engineers use AI to ship code they don’t understand

Result:

• More code gets written
• Less code is truly owned
• Debugging costs shift downstream

Velocity goes up. Maintainability becomes bimodal.

2. Architectural debt accelerates faster than technical debt

AI is excellent at:

• Local correctness
• Pattern completion
• Incremental changes

It is bad at:

• Global coherence
• Long-term architectural intent
• Saying “don’t build this at all”

Teams discover later that:

• Interfaces multiplied
• Invariants drifted
• Systems “work” but feel brittle

AI doesn’t resist bad architecture. Humans must.

3. Junior engineers skip the struggle phase and pay later

AI short-circuits:

• Syntax errors
• Boilerplate learning
• Trial-and-error discovery

This feels great until engineers face:

• Production incidents
• Non-obvious race conditions
• Emergent system behavior

The missing piece isn’t knowledge; it’s intuition built through friction.

Without deliberate training design, AI produces engineers who can assemble systems but can’t reason about them under stress.

4. Review culture collapses unless explicitly redesigned

Traditional code review assumed:

• Humans wrote the code
• Reviewers could infer intent
• Mistakes were personal, not systemic

With AI:

• Intent is unclear
• Code looks “reasonable” even when wrong
• Reviewers hesitate to challenge the generated output

Many teams experience:

• Rubber-stamp approvals
• Superficial stylistic feedback
• Deep logic errors are slipping through

Code review must shift from syntax policing → assumption and invariant checking.

5. Documentation paradox: more code, less explanation

AI generates:

• Code faster than humans can explain
• Implementations without rationale

Unless enforced, teams end up with:

• Working systems
• No record of why decisions were made
• Fragile onboarding and change processes

Ironically, AI increases the value of human-written design docs, but teams often produce fewer of them.

6. Debugging becomes harder even as coding gets easier

AI handles happy paths well.

But when things break:

• The codebase is larger
• Fewer people understand it end-to-end
• Errors span generated and human-written logic

Engineers report:

• Longer time-to-root-cause
• More “I didn’t write this” moments
• Higher cognitive load during incidents

The work shifts from writing code to interpreting behavior.

7. Engineers optimize for prompting skill, not system understanding

Unexpected career effect:

• Some engineers become prompt specialists
• Others deepen system intuition

The risk:

• Prompt fluency can mask shallow understanding
• Teams reward speed over comprehension

Over time, this creates fragile organizations that are fast in normal times and slow during crises.

8. Organizational bottlenecks move, not disappear

AI removes coding as a constraint.

New bottlenecks appear in:

• Requirements clarity
• Decision-making
• Testing strategy
• Deployment and ownership

Teams discover the uncomfortable truth:

Coding was never the hard part.

AI exposes organizational dysfunction faster than it fixes it.

9. “Good enough” becomes the default, and excellence becomes rarer

Because AI produces plausible solutions quickly:

• Teams stop pushing for elegance
• Refactoring gets deprioritized
• “It works” beats “it’s right”

Excellence now requires intentional resistance to convenience.

The meta-effect (this is the real one)

AI doesn’t replace engineering skill.
It amplifies whatever skill or lack of it already exists.

In strong teams, AI compounds leverage.
In weak teams, it compounds chaos.

Practical takeaway for leaders and senior engineers

If you don’t explicitly redesign:

• Training
• Review standards
• Ownership models
• Architectural governance

AI will quietly degrade your engineering culture while making you feel productive.

Used well, AI turns engineers into system thinkers.
Used lazily, it turns teams into code factories with no intuition.