Most AI systems treat every piece of data as equal. But in reality, not every voice carries the same weight. A panicked Reddit post and a considered expert opinion get counted the same, so teams end up reacting to noise instead of signal. In this session, you’ll see what happens when you model this properly. Through a live demo of MiroFish, a swarm intelligence engine powered by thousands of AI agents, you’ll watch how different audience types respond to the same input and which ones actually predict real-world outcomes. Each agent is calibrated to reflect real demographic and behavioural profiles, allowing you to separate what drives attention from what drives impact. Using the Bad Bunny Super Bowl campaign as a case study, you’ll see the gap play out in real time. While social platforms called it a failure, the system predicted amplification and the results followed: 66M views, a sevenfold jump in streams, and a number one Billboard ranking. This isn’t just about marketing. You’ll leave with a clear framework for: → Distinguishing signal from noise in large-scale datasets → Understanding which audiences actually drive outcomes → Applying multi-agent simulation to product launches, policy decisions, and adoption forecasting → Moving beyond surface-level sentiment to more reliable prediction models The shift is simple: stop listening to everything equally and start modelling what actually matters.