A Stargate for Data

TLDR Dev · 2 weeks ago ● 3 sources

AI labs face a shift from compute-constrained to data-constrained development, with training demand exceeding available public internet data by 2030. The article projects data spending will exceed $100 billion annually by 2030, up from roughly $7 billion currently, as labs license private datasets and fund human experts to generate training data. This transition will make proprietary data a major competitive moat, reshape which companies succeed, and require coordinated national-scale data collection efforts comparable to compute infrastructure projects.