Tight Inapproximability for Welfare-Maximizing Autobidding Equilibria
A new paper reveals a massive flaw in how AI agents bid and compete.
Deep Dive
A new computer science paper proves it's computationally impossible to find efficient equilibria in AI-driven autobidding markets. The research shows that finding an autobidding equilibrium that approximates the welfare-optimal one within a factor of 2 - ε is NP-hard. This means the systems used by major ad platforms are fundamentally limited, with no efficient solution possible, significantly strengthening previous APX-hardness results and revealing deep intractability in algorithmic game theory.
Why It Matters
This exposes a core limitation in trillion-dollar ad markets and AI agent economies, forcing a rethink of automated competition.