Do AI Detectors Actually Work? Arena Conversations With Pangram CEO Max Spero

Can you tell whether a piece of writing came from a human, ChatGPT, Claude or Gemini? Peter Gostev talks with Max Spero, CEO of Pangram Labs and one of TIME’s 100 most influential people in AI, about how AI text detection works—and why it takes more than spotting em dashes. We go inside Pangram’s training process: synthetic mirrors, model-family fingerprints, AI-assisted writing, image detection and robustness against AI humanizers. Max also explains how the team chooses base models, finds difficult training examples and builds the data and infrastructure to keep up with new AI models. Pangram is hiring: https://jobs.ashbyhq.com/pangramlabs Visualize style markers: https://www.pangram.com/pangram-space 00:00 Meet Max Spero and why he built Pangram 01:11 Pangram’s approach: deep learning vs perplexity 02:52 Training data and synthetic mirrors 05:27 Identifying ChatGPT, Claude and Gemini 09:05 New models and signals beyond em dashes 12:15 Edited and AI-assisted writing 14:51 AI images, video and deepfakes 17:37 How Pangram trains its image detector 22:33 Model size, scaling and difficult examples 26:48 Humanizers and adversarial robustness 28:22 Choosing the right base model 33:53 Data quality, training pipelines and retraining 38:40 The promise and limits of small classifiers 43:11 What model fingerprints reveal in the wild 47:21 Hiring engineers and researchers at Pangram #arenaai #aidetector #llm