Bye, Bye Open Ai & Anthropic?..

A tiny Miami startup just made one of the boldest claims in AI history: they've solved the attention bottleneck — the single most expensive problem in modern artificial intelligence. Subquadratic, a 13-person company backed by $29 million in funding, says its new AI architecture can process up to 12 million tokens while using up to 1,000x less compute than traditional Transformer models. If true, this could fundamentally change how AI systems work and potentially make large portions of today's RAG, vector database, and retrieval infrastructure unnecessary. In this video, we break down: • What the attention bottleneck actually is • Why Transformer models become exponentially more expensive with larger context windows • How Subquadratic's SSA (Subquadratic Sparse Attention) architecture works • The difference between dense attention, sparse attention, Mamba, RetNet, RWKV, and DeepSeek's approach • The benchmark results behind SubQ's 12-million-token model • Why some researchers call this the biggest AI breakthrough since Transformers • Why others compare it to Theranos and Magic.dev • Whether RAG, vector databases, and retrieval systems could become obsolete • The risks, skepticism, and missing evidence behind the claims SubQ is promising something extraordinary: AI systems that can read entire codebases, legal contracts, financial filings, technical documentation, and massive knowledge bases all at once — without the crippling computational costs that have defined AI for nearly a decade. But extraordinary claims require extraordinary evidence. Is Subquadratic the company that finally breaks the attention bottleneck? Or is this another long-context promise that won't survive independent verification? Watch until the end for a complete breakdown of the technology, the benchmarks, the skepticism, and what to watch next as independent researchers begin testing the model. What do you think? Is SubQ the real breakthrough, or the next Magic.dev? Let me know in the comments. #AI #ArtificialIntelligence #SubQ #Subquadratic #OpenAI #Claude #GPT55 #RAG #MachineLearning #DeepLearning #LLM #Transformer #GenerativeAI #TechNews #AIResearch #Anthropic #DeepSeek #CodingAI #FutureOfAI #AGI