Google Titans and the End of Digital Amnesia: What SEOs Need to Understand

The Glass Ceiling of Current Transformers

First, we need to understand why ChatGPT or Claude eventually "hallucinates" or loses the thread on very long documents or multiple user iterations. Current models operate on an attention architecture that has a major flaw: its computational cost increases quadratically. If you double the length of the text to analyze, you quadruple the power needed to process it.

This creates a paradoxical situation where models have access to immense context windows, sometimes several million tokens (as with Google Gemini), but struggle to intelligently exploit this information. They behave like a student who would reread their entire textbook before each exam question, without ever storing anything in long-term memory. This brute force attentional method is not only energy-intensive, but it dilutes signal relevance. For an SEO professional, this means that today, the deep content of a massive site is often skimmed over or poorly connected by LLMs, due to the inability to structure coherent memory over time.

Titans: Transforming Reading into Learning

The Titans architecture changes this dynamic by introducing a concept that every educator knows well: we don't retain everything, we only retain what makes an impression on us. Instead of simply extending the reading window, Google has equipped this model with a neural memory module capable of learning in real-time, during inference.

Concretely, imagine the model has two distinct brains that collaborate constantly to process your content. The first brain handles the short term via the classic attention mechanism, processing immediate information with high precision. The second brain is a deep memory module that compresses and encodes past history. The difference from current systems is that this module updates its own neural weights as it reads. It doesn't just store your text in a vector database (as in a classic RAG), it modifies its own internal structure to assimilate knowledge. It's the difference between noting information on a disposable sticky note and integrating it into your understanding of the world.

"Surprise" as a New Relevance Criterion

The sorting mechanism of this long-term memory is fascinating for anyone working on content quality. Titans uses a "surprise" metric based on gradient calculation to decide what it should memorize or forget. Concretely, the system constantly evaluates its ability to predict the next information. If the content is generic, predictable, or repetitive (basically, if it has already stored all the information in its prior knowledge), the gradient is low: the model considers it already "knows" this and doesn't waste memory resources. However, if information contradicts its expectations or brings an unprecedented nuance, the gradient explodes. This "surprise" forces the model to open its gates and update its long-term memory.

In this respect, Titans strangely resembles the philosophy Google has advocated for several years, notably through its E-E-A-T concept. Classic SEO content, filled with platitudes and semantic filler, will be mathematically ignored by Titans' memory, just as it tends today to be pushed far from Google's first pages, when not simply ignored. This is the path LLMs seem to be taking, and it's the path that every content creator should follow in my opinion. Because in the future, only content providing real informational density, a cognitive "surprise" for the machine, will earn the right to reside in AI's persistent memory.

MIRAS and the Rise of Persistent Agents

Beyond the technical architecture, Google proposes with MIRAS a theoretical framework that unifies these new approaches. The goal is to create agents capable of remembering you, your site, and your interactions over years, without having to reread everything each time.

The architecture theoretically allows infinite memory because it doesn't store raw data, but their compressed abstraction. This opens the door to SEO agents and personal assistants that don't start from scratch at each session. By integrating this logic into its ranking algorithms or AI Overviews, the notion of content "freshness" changes nature for Google. It's no longer just about being recent, but about being able to modify the model's memory state by providing the unexpected through new information, a new angle, a different perspective.

We're heading toward an ecosystem where visibility will depend on the memorial salience of your brand. The only important question will then be: "Is my content surprising enough to force the AI to reconfigure its neural networks to include me?". Real expertise and unique angles will then no longer be just recommendable, but technically indispensable.

Resources

• Titans + MIRAS: Helping AI have long-term memory
• Titans: Learning to Memorize at Test Time