Why this matters

Language models are built on yesterday’s internet. They rely on static training data and begin drifting out of date the moment they launch. Search integrations try to fill the gap, but they’re a patchwork solution: reactive, surface-level, and disconnected from how the model reasons.

The result is a powerful system with a blind spot for the most important point in time there is: the present moment. As AI becomes woven into products, institutions, and decision-making tools, that blind spot becomes a critical constraint on what this era-defining technology can achieve.

At Break The Web, our work focuses on resolving that real-time knowledge gap and keeping AI in sync with the world as it moves.

Towards a living model of the world

For the last five years, we’ve been developing CT-X, a data engine built to understand the internet as a living system. It runs continuously: ingesting web data in real time, clustering it, structuring it, and transforming it into a clean, easily navigable knowledge graph of current events.

It’s not search. It’s not RAG in the traditional sense. It’s a precomputed world model that updates all day long and can be woven directly into how AI understands reality.

On top of CT-X, we’ve built the Live Language Model, a platform that fuses an LLM with a continuously updating understanding of the world. Rather than looking things up online after a query, it draws on an offline, highly structured knowledge base of evolving facts, events, entities, and relationships—an architecture it can reason over as naturally as its own memory.

How our work shows up today

Our work takes two forms, both central to our broader mission.

We build products ourselves: Break The Web, The Pulse Report, Question of the Day, Political Attention Scoreboard, and a growing collection of real-time experiments. These projects let us explore the frontier, pressure-test the LiveLM in the wild, and demonstrate what’s possible when AI has a living model of the world.

We also power products built by others, including MasterClass, Glance Inmobi, and Unreasonable Group. Licensing CT-X and the Live Language Model is the long arc of our plan: making real-time knowledge an invisible layer that strengthens the tools people already use every day.

These pathways (building and licensing) advance the same goal: ensuring that the AI systems people increasingly rely on have the most up-to-date understanding of the world possible.

Where we're headed

As we open the full LiveLM knowledge graph to developers and organizations, the line between an “AI response” and a “world-aware system” starts to blur. It marks the beginning of a new external-memory architecture, where AI systems draw on a structured, continuously updated view of reality rather than fixed training data or scattered web results.

In this architecture, reasoning and knowledge are separate. Models remain stable and reliable, while an external memory evolves alongside the world and can be accessed effortlessly whenever the moment calls for it.

This enables any AI-powered system—chatbots, voice assistants, search tools, prediction markets, news products, education workflows, and more—to deliver experiences that meet the world where it actually happens: right now.