For people: A fix in one copy must be repeated in all the others; a missed copy becomes a bug.

For AI agents: Agents copy the patterns they find, so duplicates multiply; a change to one copy misses the others, and every copy costs context.

For people: Large files are hard to navigate, mix responsibilities and attract merge conflicts.

For AI agents: Agents read whole files into their context: every change to a large file costs many tokens and crowds out other relevant code.

For people: Long functions do many things at once: they are hard to name, read, test and reuse.

For AI agents: To change one line, an agent reads and often rewrites the whole function: bigger edits, more tokens, more breakage.

For people: Every branch is another path to understand and test; complex logic is where mistakes are made and missed in review.

For AI agents: Nested conditions are where generated changes break edge cases; complex units need more tests and review before an agent's change can be trusted.

For people: When a few people make most changes, the knowledge sits with them: their absence slows everyone, and reviews and onboarding depend on them.

For AI agents: Not in the AI rating by default: an agent reads the code, not people's memory, though it gains when that knowledge is written down.

For people: More code is more to learn, search and keep in mind; onboarding and judging a change's impact slow down as it grows.

For AI agents: An agent sees only a slice at a time: the bigger the code base, the more it searches, and the likelier it misses relevant code or rewrites an existing helper.

For people: The code people look at around a change: the more there is, the slower the change and its review.

For AI agents: Lines read per change drive an agent's token cost and accuracy: the more it reads, the costlier and more error-prone each task.

For people: Complex files concentrate risk: understanding one means keeping many paths in mind.

For AI agents: Much to read and much logic to get right in one place: the costliest combination for an agent.

For people: Tests document the intended behaviour and make changes safe.

For AI agents: Tests let an agent check its own work; without them its mistakes surface later, in review or in production.

For people: When a typical change touches many components, every task needs knowledge of many parts, and the boundaries do not match how the code changes.

For AI agents: Scattered changes make an agent find and load many places per task; each extra component adds context and another place to miss.

For AI agents: Agents copy the patterns they find, so duplicates multiply; a change to one copy misses the others, and every copy costs context.

For people: A fix in one copy must be repeated in all the others; a missed copy becomes a bug.

For AI agents: Agents read whole files into their context: every change to a large file costs many tokens and crowds out other relevant code.

For people: Large files are hard to navigate, mix responsibilities and attract merge conflicts.

For AI agents: Lines read per change drive an agent's token cost and accuracy: the more it reads, the costlier and more error-prone each task.

For people: The code people look at around a change: the more there is, the slower the change and its review.

For AI agents: To change one line, an agent reads and often rewrites the whole function: bigger edits, more tokens, more breakage.

For people: Long functions do many things at once: they are hard to name, read, test and reuse.

For AI agents: An agent sees only a slice at a time: the bigger the code base, the more it searches, and the likelier it misses relevant code or rewrites an existing helper.

For people: More code is more to learn, search and keep in mind; onboarding and judging a change's impact slow down as it grows.

For AI agents: Nested conditions are where generated changes break edge cases; complex units need more tests and review before an agent's change can be trusted.

For people: Every branch is another path to understand and test; complex logic is where mistakes are made and missed in review.

For AI agents: Scattered changes make an agent find and load many places per task; each extra component adds context and another place to miss.

For people: When a typical change touches many components, every task needs knowledge of many parts, and the boundaries do not match how the code changes.

For AI agents: Much to read and much logic to get right in one place: the costliest combination for an agent.

For people: Complex files concentrate risk: understanding one means keeping many paths in mind.

For AI agents: Tests let an agent check its own work; without them its mistakes surface later, in review or in production.

For people: Tests document the intended behaviour and make changes safe.