AI can build a marketing mix model in days, not months. But can you trust what it tells you? In this episode of AdExchanger’s Inside the Stack, Madan Bharadwaj, founder and CEO of M-Squared, explains why faster modeling makes human judgment more important. With 20 years in the attribution space, Bharadwaj walks through why no single method is the truth and why causal attribution means triangulating MMM and incrementality testing. He also argues that a go-to-market thesis tells you whether a model is credible, and that a marketing accounting framework gives marketing and finance a shared language for outcomes like contribution margin. And then there’s the story of how a beauty brand’s AI-built model recommended cutting $30 million from its Amazon ad spend, and how SKU-level modeling led to a very different investment thesis and 5–7% top-line growth. Plus: why he thinks the future of measurement is super cognition.