Recent developments in litigation, enforcement, and public policy reflect growing scrutiny of how algorithmic tools can facilitate coordination and personalize prices. Courts are increasingly allowing claims to proceed where competitors pool non-public pricing or other competitively sensitive data and follow software recommendations at high rates, while distinguishing those allegations from the mere use of common software. Enforcers are pairing litigation with settlements that restrict the use and sharing of competitors’ sensitive data, and legislatures are targeting algorithmic rent-setting, although the focus is on tools which rely on non-public data. Regulators and policymakers are also focusing on AI agents, personalized pricing, and the potential for tacit coordination even without direct collusive intent.