In a TechCrunch video interview, Pangram CEO Max Spero explained why detecting AI-generated content is fundamentally more difficult than answering the binary question of whether something is 'real or fake.'

What Happened

Spero appeared in a TechCrunch AI video discussion focused on the technical challenges facing AI detection tools. He argued that distinguishing AI-generated material from human-created content requires moving beyond simple authenticity verification into more nuanced territory.

"The problem isn't just about determining if something is real or fake," Spero said during the interview. "It's about understanding the statistical fingerprints and subtle patterns that differentiate machine-generated text from human prose, which requires a fundamentally different approach than traditional fact-checking."

Spero also explained why this distinction matters for detection infrastructure: "When you're trying to detect AI content, you're not looking for something that's outright false—you're identifying characteristics that emerge from how these models generate language. The model isn't lying; it's just producing text based on patterns in its training data."

"This makes the detection problem harder because there's no single smoking gun," Spero added. "It's a distribution of signals across vocabulary choice, sentence structure, and stylistic tendencies that you have to evaluate holistically rather than checking against a database of known fakes."

Why It Matters

For developers and platforms building content moderation systems, Spero's framing suggests that AI detection requires a different conceptual approach than traditional fact-checking or authenticity verification. The distinction matters for companies investing in detection infrastructure and for policymakers drafting legislation around synthetic media disclosure requirements.

The Bottom Line

Pangram continues to work on detection technology as the industry grapples with increasingly sophisticated generative AI tools capable of producing realistic text, images, and video at scale.