Query the internet at scale

One of the biggest challenges with all AI models is their limited access to real-time information.

Most large language models rely heavily on pretrained datasets—huge collections of text from books, websites, and forums. While this gives them strong language skills, it also means they are frozen in time. Their understanding of the world is limited to the data available at the moment of training. After that, they’re often cut off, unable to learn from anything new unless explicitly updated. While some models offer web search plugins or APIs to patch this gap, their knowledge still leans heavily on a static, limited worldview. This introduces blind spots, especially for dynamic or fast-evolving domains.

The Efficient Compute Frontier

As AI models grow larger and are trained on more data, they usually become more accurate, but only up to a certain point. This point is known as ‘the efficient compute frontier’. Beyond it, any amount of data or even massive increases in computing power won’t lead to significant improvements.

Researchers have found that this pattern repeats across different models and tasks, no matter which architecture or training method is used. It’s part of a broader set of patterns called neural scaling laws, which show how closely performance is tied to data, model size, and computation.

What’s still unclear is whether this performance limit is a hard rule of nature, or just a result of how current AI models were built and trained.