Brin and Page’s PageRank showed how structural signals at internet scale predict relevance.
In 1998, Sergey Brin and Larry Page described PageRank: an importance measure based on the hyperlink graph. Google showed that web structure is a massive feature.
Beyond search, the episode taught industry that simple algorithms + planetary data can beat rich heuristics on poor data.
The scale lesson—infrastructure, logs, feedback loops—is the same one that later drove deep learning and LLMs: whoever controls compute and data sets the pace.
Authors, media, and artists sued model providers over works used in pretraining; courts began setting tests.
September 2026 reporting (Business Insider): Meta’s Model Capability Initiative (MCI) sought to collect employee keystrokes and mouse data to train computer-use AI in a TBD unit. An internal revolt followed—flyers, memes, an “Eye of Sauron” petition—then concessions and a halt after an internal data leak.
On September 15, 2026, the OpenAI Foundation announced Public Data for Health / Data for Public Health, with $40 million to UNC Lineberger Comprehensive Cancer Center to generate multimodal data that could improve personalized cancer vaccines, plus support for OpenAdmet. MIT Technology Review and UNC itself detailed the bet: higher-quality biological observations so models can advance medicine.