Effective AI detection frames the problem as large-scale authorship identification. By training on paired examples of human versus LLM-generated text on the same prompts, detection models learn to recognize the unique statistical "smell" or stylistic signature of each major AI model.

AI detection can identify text from new LLMs because most models share a common "ancestry." They are either trained on the same foundational corpora, like Common Crawl, or fine-tuned with synthetic data from major models, giving them a detectable shared statistical fingerprint.

While one could fine-tune a custom AI to evade detection, the most capable models are centralized and expensive. This market concentration means most users rely on a few common models (like from OpenAI or Anthropic), making their distinct "fingerprints" easier for detectors like Pangram to identify.

Beyond raw capability, top AI models exhibit distinct personalities. Ethan Mollick describes Anthropic's Claude as a fussy but strong "intellectual writer," ChatGPT as having friendly "conversational" and powerful "logical" modes, and Google's Gemini as a "neurotic" but smart model that can be self-deprecating.

The two leading AI models are diverging. Claude is positioned as an intelligent advisor that provides unbiased, critical feedback ('That's freaking stupid'). In contrast, ChatGPT, with its massive consumer base, is optimizing for engagement and emotional connection, risking a 'pleasing' bias to keep users happy.

Pangram Labs' detector isn't hard-coded. It's a deep learning model trained on millions of examples. For each human text (e.g., a Yelp review), it sees an AI-generated equivalent, learning the subtle, often inarticulable, differences in word choice and structure that separate them.

Anthropic's Claude Opus 5 is described as "neurotic and timid," seeking human approval, while OpenAI's GPT is a "confident BFF" that's direct and pragmatic. These personalities offer a new lens for understanding the models' underlying design philosophies, alignment strategies, and intended use cases.

While the em dash is a known sign of AI writing, a more subtle indicator is "contrastive parallelism"—the "it's not this, it's that" structure. This pattern, likely learned from marketing copy, is frequently used by LLMs but is uncommon in typical human writing.

Top-tier AI models exhibit distinct personality quirks and stylistic preferences, akin to an artist's signature. For example, OpenAI's GPT-5.6 Soul has a noticeable tendency to use 'forest green' in its designs, a recurring 'tell' that users can learn to identify and anticipate in its outputs.