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- 🔆 Beyond Auto-Complete: Intro to Text Diffusion
🔆 Beyond Auto-Complete: Intro to Text Diffusion
The latest advancements in text generation, automated risk assessments, and data center realities.
Emma G Moller & Allegra Guinan
June 05, 2025
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🗞️ Issue 73 // ⏱️ Read Time: 7 min
In this week's newsletter
What we’re talking about: The shift from autoregressive to text diffusion models - a new generation of AI that Google and other major players are already using.
How it’s relevant: This transition impacts how AI generates text, promising faster, more coherent outputs that could transform human-AI collaboration.
Why it matters: Text diffusion marks a significant evolution from models that have dominated text generation for years. Understanding these technical foundations is crucial for leaders shaping the future of technology, as it opens doors for innovative applications and strategic competitive advantages.
Hello 👋
Today, we're walking you through a technical change that's quietly reshaping how AI generates text. We'll start with the evolution of text generation and the mechanics behind it, then examine why this transition matters for speed and quality, and finish with the business implications you need to understand as a leader.
The Evolution of AI Text: From Predictors to Polishers
For years, AI text generation has been ruled by ‘autoregressive models’. Like your phone's predictive text, these models suggest the next word, then the next, building sentences sequentially. This is how ChatGPT and similar models operate. They are incredibly powerful, but have limitations in speed and global coherence.
Text Diffusion models take a radically different approach. Instead of predicting one word at a time, they start with "noise" (imagine a blurry image) and iteratively refine it into clear, perfectly formed text. They don't predict; they denoise and refine.
This concept comes from image diffusion models that revolutionized AI art. While diffusion research dates back to 2015, applying it to complex text generation is recent. In early 2025, Inception Labs introduced Mercury, the first commercial-scale diffusion language model, followed by Google DeepMind's adoption at I/O '25.