AI can write code, but can you catch its mistakes?

Abhishek Shaji — Sun, 01 Feb 2026 10:17:41 +0000

"100% of my code is written by AI" or "I barely review it anymore."

I've been hearing this a lot from devs recently. AI coding agents are powerful and here to stay - but there's a subtle risk worth being conscious of.

Review ≠ Production

Reading code is not the same mental activity as writing it.

Writing code forces full causal reasoning
Reviewing AI-generated code often becomes pattern matching and surface plausibility checks

Security bugs are rarely obvious syntax errors. They live in assumptions:

"This input can't be attacker-controlled"
"This function is always called after auth"
"This state can't be reached concurrently"
"This service will never be exposed publicly"
"This transaction will either complete or roll back cleanly"

When developers stop constructing solutions themselves, edge-case thinking weakens, threat modeling degrades, and that critical "this feels wrong" intuition fades.

The Real Danger

The danger isn't bad code - it's unexamined code that no human fully reasoned about.

Over time, repeated "looks good, ship it" reviews change how developers think. The edge-case instinct dulls. Threat modeling becomes implicit instead of deliberate. The subtle "something feels off here" intuition built over years of hard-earned mistakes - starts firing less often.

A senior engineer who once spotted race conditions by instinct now trusts that the retry logic "probably handles it."

A security-minded developer stops asking "what if this input is hostile?" because the code appears to validate it.

Nothing breaks immediately. That's what makes this dangerous.

What Actually Goes Wrong

AI doesn't understand intent, business context, or adversarial thinking. It generates plausible implementations - not resilient systems.

That plausibility hides systemic failures:

Transactions that half-fail

Money debited but never credited, inventory decremented but orders never placed. The code looks like it handles errors. It doesn't.

Data that leaks sideways

API responses that include fields the frontend doesn't display but attackers absolutely notice. Logs that quietly capture tokens, passwords, PII.

Money that vanishes into edge cases

Rounding errors that compound, race conditions in payment flows, retry logic that charges twice and refunds never.

State that corrupts silently

Concurrent writes that don't conflict loudly but leave data subtly wrong, discovered weeks later when reconciliation breaks.

These bugs don't surface in happy path testing - they surface later as breaches, incident response, regulatory scrutiny, and emergency rewrites under pressure. Often at 2am. Often with customers already affected.

The Productivity Trap

Companies are pushing AI-assisted development hard right now - and the productivity gains are real. Features ship faster. Backlogs shrink. Quarterly metrics look great.

But here's the math that rarely makes it into the ROI calculation:

A single data breach can cost millions in regulatory fines
Payment processing bugs trigger chargebacks, fraud investigations, and potential loss of merchant accounts
Security incidents bring lawsuits, mandatory audits, and insurance premium spikes
Customer trust, once broken, doesn't recover with a PR statement

The developer hours saved by skipping thorough review can evaporate overnight when legal, compliance, and incident response teams are working around the clock. That feature shipped two weeks early? It might cost two years of litigation.

Productivity gains mean nothing if they're financing future disasters.

What Guardrails Actually Look Like

Organizations encouraging AI-first development need safeguards that match the speed they're pushing for.

Every AI-generated change has a human owner who can explain why it's correct, not just that it works
Threat modeling and failure-mode analysis are mandatory for AI-authored code touching auth, payments, data access, or concurrency
Large AI-generated diffs require architectural review, not just line-by-line approval
"The AI wrote it" is never an acceptable justification for unclear logic or missing assumptions

AI should accelerate implementation - not replace understanding.

This Isn't an Argument Against AI

AI is an extraordinary tool. Used well, it removes boilerplate, speeds up execution, and frees engineers to think at higher levels.

But systems fail at the boundaries: assumptions, edge cases, incentives, and adversarial behavior. Those are precisely the areas where humans must remain fully engaged.

Velocity is powerful.

But it's only valuable if it's sustainable - and sustainability requires engineers who still understand the systems they're shipping.

DEV Community: Abhishek Shaji