AI Coding Bugs: Why AI Demos Work But Fail in Production

“It worked when I demoed it” is one of the most common things founders say right before something breaks with real users. The general reason for that gap — a demo covers a narrow set of scenarios, production covers everything — is well understood at this point. What’s more useful is a breakdown of the specific kinds of AI coding bugs behind that gap, so you can check for each one by name instead of treating “production bugs” as one undifferentiated risk. For the broader explanation of why the gap exists in the first place, see why AI-generated code often breaks in production — this post picks up from there with the actual bug categories.

Bug Category 1: Race Conditions

A race condition happens when two actions touching the same data happen close together in time, and the outcome depends on which finishes first — two clicks on a “submit” button, two requests updating the same record, two processes reading a value before either has written the update. A demo, where one person clicks one thing at a time, essentially never triggers this. Production, with multiple real users acting concurrently, triggers it constantly. AI-generated code frequently doesn’t guard against concurrent access unless the prompt specifically asked for it, because a single-user demo never surfaces the need.

Bug Category 2: Hallucinated or Misused APIs

An AI assistant can generate a call to a library method, endpoint, or parameter that doesn’t exist, is deprecated, or behaves differently from what the code assumes. This kind of bug frequently passes a visual demo check — the screen renders, nothing obviously crashes — and only fails once that exact code path actually executes against real data or a real edge case in the underlying service.

Bug Category 3: Missing Edge-Case Branches

This is the most common category: the AI implements the case described in the prompt and leaves everything adjacent to it unhandled — an empty field, a value at the boundary of what’s allowed, two contradictory inputs at once. A demo follows the expected path by construction; real users, in aggregate, reliably find the paths that weren’t described.

Bug Category 4: Silent Failures

Some AI-generated error handling catches a problem and does nothing visible about it — an operation that doesn’t complete but shows no error. In a demo, you already know what result to expect, so a silent failure is easy to miss. In production, a silent failure can run for weeks, quietly losing data or frustrating users before anyone notices a pattern.

Bug Category 5: Assumptions That Break at Scale

Code that’s fast and correct with ten sample records can be slow or simply wrong with ten thousand real ones — a query that scans a whole table, or logic that assumes a list will always be short enough to hold in memory. A demo, by definition, runs on small data. These bugs are specifically invisible until real volume exists.

Bug Category 6: Inconsistent State Across Features

When a codebase is built through many separate AI prompts, the same kind of data can end up validated, formatted, or stored differently in two different features. Individually harmless, this creates bugs exactly where two inconsistent parts of the system have to interact — a case a single-feature demo never exercises, because demos are usually walked through one feature at a time.

Bug Category 7: Timing and Sequencing Assumptions

Some AI-generated flows assume steps will always happen in a fixed order — a payment confirms before an order record is created, a file finishes uploading before its metadata is saved. In a demo, actions happen slowly and deliberately, one at a time, so the assumed order always holds. In production, a slow network, a retried request, or an impatient user clicking ahead can cause steps to arrive out of the assumed sequence, and code that never questioned the ordering has no path for handling it.

Why Naming These Categories Matters More Than a General Warning

It’s common advice to hear “test your AI-generated code thoroughly before launch,” but that advice alone doesn’t tell you what to actually go looking for. Naming each bug category — race conditions, hallucinated APIs, missing edge cases, silent failures, scale assumptions, inconsistent state, and sequencing assumptions — turns a vague instruction into seven concrete, testable questions you can ask about your own codebase, one at a time, and check off as you go.

The Bug Taxonomy at a Glance

Bug category	What triggers it in production	Why a demo misses it
Race conditions	Concurrent real users acting on the same data	Demos are single-user, one action at a time
Hallucinated APIs	The exact code path actually executing against a real service	Visual check alone doesn’t exercise the call
Missing edge cases	Unexpected or boundary input from real users	Demos follow the expected path by construction
Silent failures	An operation failing with no visible error	The demoer already knows the expected result
Scale assumptions	Realistic data volume and load	Demos run on small sample data
Inconsistent state	Two features interacting with the same data type	Demos usually walk through one feature at a time
Sequencing assumptions	Steps arriving out of the expected order	Demos proceed slowly, one deliberate step at a time

Using This as a Checklist, Not Just an Explanation

The value of naming these categories is that you can check for each one specifically, rather than doing a generic “does it feel okay” pass. Debugging AI-generated code before a production launch turns this taxonomy into a step-by-step process you can run against your own build, and AI-generated code problems: what founders need to know before launching puts these bug categories in the wider context of security, cost, and maintainability risk beyond bugs alone.

The Takeaway

“Demo works, production fails” isn’t one problem — it’s several distinct, nameable bug categories, each triggered by a condition demos structurally don’t produce: concurrency, real data volume, unexpected input, or a code path actually executing for the first time. Naming them is what turns a vague worry about AI-generated code into a specific, checkable list.

Frequently Asked Questions

What kinds of AI coding bugs are most likely to survive a demo but fail in production?

Race conditions, hallucinated API calls, missing edge-case handling, and silent failures are the most common categories — all of them tend to require conditions (real concurrency, real scale, unusual input) that a single-person demo doesn't naturally produce.

What is a race condition, in plain terms?

A race condition happens when two actions that touch the same data run close together in time, and the result depends on which one finishes first — something a demo, where only one person clicks at a time, almost never triggers.

What does a hallucinated API look like in practice?

The AI generates a call to a library method, endpoint, or parameter that doesn't actually exist or behaves differently from what was assumed. It can pass a quick visual check and only fail when the exact code path actually executes with real data.

Are these bug categories specific to any one AI coding tool?

No — the same categories show up across chat-based builders, editor-integrated assistants, and terminal-based coding agents, because the underlying cause is the same in all of them: code generated in response to a specific, narrow prompt.

How is this different from a general 'AI code breaks in production' explanation?

This is a categorized bug taxonomy, not a general explanation of the phenomenon — it's meant to be used as a checklist against a specific codebase, working through each bug category one at a time rather than treating the gap as one undifferentiated risk.