A strategic, economics-first discussion designed to challenge assumptions, clarify incentives, and help marketers think more clearly about the future architecture of advertising in the AI era.
For more than a decade, marketers have publicly demanded transparency across the digital advertising ecosystem. Transparency in fees, supply chains, measurement, optimization logic, attribution, and media economics. And yet, during that same period, brands have allocated an increasing share of global advertising spend to the least transparent environments in media history.
Why? At the center of this debate is a fundamental economic tension: Do marketers truly value transparency? Or do they value performance more?
As AI accelerates black-box optimization across advertising, the tension becomes even more acute. General-purpose platform algorithms increasingly optimize outcomes without exposing mechanics, causality, or economics to buyers. Some argue this is the inevitable direction of the industry. Others believe the future belongs to brands that own their own optimization layer, proprietary data, and decisioning intelligence.
This session brings together two distinct perspectives to debate one of the defining strategic questions facing modern advertising:
• Is transparency fundamentally at odds with performance?
• Is “black-box optimization” a feature or a bug?
• Will AI further concentrate power inside platforms?
• Or will sophisticated advertisers increasingly own their own models, data, and optimization logic, making transparency an outcome rather than a request?
