YouTube Video
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
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