Miami-based AI startup Subquadratic emerged from stealth last month claiming to have solved a fundamental mathematical bottleneck that has held back large language models for nearly a decade. Initial reactions from the research community were skeptical, with technical details appearing sparse relative to the scale of the claim. The company has since begun releasing more substantive supporting evidence, and MIT Technology Review investigates whether it can back up its assertions.
A newly surfaced HRM model trained at the strikingly low cost of $1,500 has gone viral in AI circles after drawing strong recommendations from HuggingFace CEO Clem Delangue and backing from a team affiliated with Turing Award laureate Yoshua Bengio. The story underscores a growing industry fascination with cost-efficient AI training. Its rapid spread signals that the community sees it as evidence that meaningful model development no longer requires million-dollar compute budgets.