The Loop  ·  Issue 017

The Loop

A field journal of the AI frontier — for engineers who ship.

  Lab bench

Experiment №010
filed Apr 21, 2026

game

Filed under

  • #detection
  • #writing
  • #ai-output
  • #game

AI or human?

Twenty short snippets. Half written by models. Classify each. Reveal the tells at the end.

  Primer

Skip if you already know the theory; the interactive is right below.

Twenty short snippets. Half written by humans, half by models. Classify each. Reveal at the end with a one-line tell per snippet. Your best score is saved; the leaderboard is opt-in.

▶  Try it

Loading snippets…

▤  Leaderboard · top 25

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  Notes from the bench

What to watch for, why it matters, and the one thing that usually surprises people.

Tells that usually work

AI tells

  • Hedged symmetry. "While this approach has its merits, it also presents certain challenges…" Whenever a sentence balances two sides with no specificity, suspect a model.
  • Empty superlatives. "Truly transformative", "an exciting new chapter", "the possibilities are endless." Humans reach for superlatives rarely and specifically. Models reach for them as punctuation.
  • Corporate vocabulary. "Leverage synergies", "empower users", "navigate complexities", "sustainable growth" — content-farm vocabulary clusters hard in LLM output.
  • Self-reference. "As an AI, I'm happy to…" — the giveaway, though usually stripped out before snippets go out.

Human tells

  • Specific nouns you wouldn't invent. A time (7:12am), a name (Dave), a cat, a printer. Humans ground their jokes in stuff.
  • Self-deprecation that lands. Good humans are mean to themselves in a way that's not performative. Models trained on safe text usually can't.
  • Grammatical irregularity on purpose. A lowercase "ok", a fragment, a dropped period. Humans break grammar to convey tone. LLM outputs almost always end with a period.
  • Admissions of not-knowing. "nobody knows why", "this is cursed", "refactor later" — the voice of someone who has lived with consequences.

Why any of this matters

The stock question "how do we detect AI-generated text" has no stable answer — the generators keep improving and every tell rotates out eventually. What doesn't rotate is the underlying shape: models are trained to sound like text, but humans write to sound like themselves. The gap is usually specificity.

Also useful: the same pattern-matching you use to spot AI in other people's writing is the pattern-matching you need to avoid it in your own. If your first draft reads like item #8 above ("Whether you're a seasoned professional…"), something is wrong — either a model wrote it or you're imitating one without meaning to.

In a line

A twenty-round classification game over curated tweets, product copy, and code comments, with a one-line tell per snippet at reveal. Best score saved; opt-in leaderboard.

Other experiments

11
  1. Exp 001

    How a sentence becomes tokens

  2. Exp 002

    Temperature and top-p, visibly

  3. Exp 003

    What does this prompt actually cost?

  4. Exp 004

    Tokens per second

  5. Exp 005

    How far should the model think?

  6. Exp 006

    Neural language vs a Markov chain

  7. Exp 007

    What each token looks at

  8. Exp 008

    Words in space

  9. Exp 009

    The injection arena

  10. Exp 011

    Context Tetris

  11. Exp 012

    Magnet flip