
MTAR T3D Sessions: Why AI Needs So Much Compute | Better AI 3/6
Why does current AI need so much compute to produce an answer? In episode three of “Better AI,” our six-part series from More Than a Refresh Presents: T3D Sessions, Reza Rassool of Kwaai and JD Drake begin a two-part look at faster AI. They compare current neural networks with biological intelligence, which can learn while thinking and make a fast initial decision without processing every detail first. Reza explains that current AI separates training from inference and passes input through multiple layers before producing a result. The connections between those layers can cause computation to grow quadratically as inputs expand, while larger context windows add further load. He also contrasts human memory with LLM weights, where a specific fact cannot be located and edited directly. The episode closes with Kwaai’s research into the “blink transform,” an approach intended to change the relationships within a neural network so computation grows linearly rather than quadratically as the network increases in size. Learn more about Kwaai and its work at https://kwaai.ai .












