A startup claims it broke through a bottleneck that’s holding back LLMs
TL;DR
Miami-based Subquadratic came out of stealth in May with a big claim: it says it solved a mathematical bottleneck in LLMs, where attention becomes expensive because tokens are compared pair by pair in long contexts. The first announcement was light on detail, so skepticism was justified. A real breakthrough here would be larger than a normal model release and needs more than a polished pitch.
Nauti's Take
This is the kind of AI story that deserves attention, not instant belief. Subquadratic is aiming at a real bottleneck, not a cosmetic feature layer.
The story still has a heavy startup-PR smell: huge claim, limited public detail, and a lot riding on expert validation. Worth watching, but trust should wait for independent tests.
Briefingshow
If the approach holds up, long-context models could become much cheaper and faster. That matters beyond larger chat windows: agents, coding tools, and research systems all suffer when they need to keep many documents in view. The catch is that a mathematical result is not yet a scalable product.
Sources