SubQ Says It's Fixed AI's Biggest Cost Problem. But Does It?

A Miami startup called Subquadratic claims to have fixed the math problem that makes long-context AI permanently expensive. Their model SubQ uses a new attention mechanism called SSA. An independent evaluation from Appen (signed attestations, hardware-validated FLOP counts) came back stronger than their own launch numbers. But the technical paper is still forthcoming, a 17-point research-to-production gap on MRCR v2 is unexplained, and the graveyard of prior attempts is long. Did they actually do it this time? ───────────────────────────── Chapters: ───────────────────────────── 0:00 The Current State of AI 1:26 Subquadratic's Bet 2:22 What SubQ built 5:56 The Appen Evaluation 7:29 Historical Pattern 8:55 SubQ could change the industry 10:12 Open Questions, no answers yet 11:45 Outro ───────────────────────────── SOURCES ───────────────────────────── SubQ's Website- https://subq.ai/how-ssa-makes-long-co... Appen - https://appen.com/whitepapers/benchma... #SubQ #AIArchitecture #LLM #IndieHacker #DevTools