AI Coding Assistants Are Making Code Look the Same, But Not Think the Same

Seven years of Kaggle contest submissions reveal a curious split personality in how AI coding assistants shape human work: they're flattening the surface of code while leaving the underlying problem-solving intact.

A new study analyzing Kaggle competition entries from 2019 through mid-2026 finds that as AI coding tools became embedded in developer workflows, submissions grew noticeably more similar to one another in literal syntax and structure. Researchers tracked this convergence at two levels: how closely individual submissions within a given contest resembled each other, and how narrow or wide the overall "conceptual span" of approaches was across an entire contest. Both measures point the same direction, toward growing structural sameness.

Two Ways to Measure Sameness

To separate style from substance, the researchers used two distinct analytical lenses. TF-IDF representations captured surface-level syntax, essentially how code is written, token by token. Voyage 3 code embeddings, by contrast, were used to capture something closer to intent, the actual logic and semantics behind a solution regardless of how it's phrased.

The gap between these two measurements is the paper's central finding. Syntactic similarity rose substantially across the dataset. But semantic distance between submissions stayed essentially flat over the same period, and in some contests the range of distinct problem-solving strategies actually widened slightly rather than shrinking. In other words, code is starting to look more alike without necessarily thinking more alike.

The Random Seed 42 Tell

One detail stands out as a small but telling artifact of this dynamic: a marked increase in submissions using the seed value 42 for random number generation. It's a long-running in-joke among programmers, a nod to "The Hitchhiker's Guide to the Galaxy," and its rising frequency in the dataset suggests AI assistants are absorbing and then reinforcing existing cultural conventions in code rather than inventing new habits. It's a small signature of how these tools nudge collective behavior even in details that have nothing to do with correctness or performance.

Why the Distinction Matters

The paper's authors frame their conclusion carefully: AI coding assistants appear to be standardizing implementation details, the boilerplate, naming patterns, and structural choices that make code look similar, without yet collapsing the diversity of strategies people bring to solving a problem. That's a meaningfully different claim than the more alarmist version of "AI is making all code the same," which would imply a narrowing of creative and analytical range, not just of style.

For teams evaluating how coding assistants affect engineering culture, the distinction has practical weight. Homogenized syntax can ease code review and onboarding, but a genuine collapse in problem-solving diversity would be a bigger warning sign, one this dataset does not yet show. Whether that semantic stability holds as these tools grow more capable remains an open question the researchers leave for future contest cycles to answer.