Training Data

Opening — Why this matters now Search is no longer a feature. It’s a capability moat. Over the past year, “deep research agents” quietly evolved from novelty demos into decision-making infrastructure. Models are no longer judged by how well they answer, but by how well they search, verify, and synthesize across the web. And yet, despite all the noise about model architectures, one inconvenient truth remains: the best-performing search agents are still controlled by a handful of companies—not because of better models, but because of better data pipelines. ...

Opening — Why this matters now Large language models are getting better at everything—reasoning, coding, writing, even pretending to think. Yet beneath the polished surface lies an old, uncomfortable question: are these models learning, or are they remembering? The distinction used to be academic. It no longer is. As models scale, so does the risk that they silently memorize fragments of their training data—code snippets, proprietary text, personal information—then reproduce them when prompted. Recent research forces us to confront this problem directly, not with hand-waving assurances, but with careful isolation of where memorization lives inside a model. ...