The story of how diffusion models create—and what they leave behind

I’m excited to share my new piece, How AI Generates: From Noise to Form.

Diffusion models are behind much of today’s generative AI—image synthesis, text-to-art, even video. But how do they actually work? And what are their limits?

My goal in this essay is to give readers a way in—to make the core ideas approachable while still keeping the essentials intact. Along the way, I walk through:

• Why diffusion models corrupt data step by step, then learn to reverse the damage
• How generation reduces to a series of prediction tasks
• Why prompts guide but cannot fully constrain creations
• And what glitches like the six-fingered hand reveal about the gap between surface and meaning

I start with a scene from the US Open to set the stage, but the heart of the piece is about how these models generate—and what they leave behind.

Subscribe here (it’s free!) and read: https://nisheethvishnoi.substack.com/

If it resonates, I’d love for you to share it with others who might enjoy exploring the mechanics of generation.