The real AI bottleneck isn't models—it's governance, cost control, and sovereignty.

AI infrastructure costs have eclipsed all prior cloud spending, yet enterprise FinOps tooling remains immature at connecting token consumption to business outcomes. The panel consensus is that cost visibility is still lacking, making it difficult for engineering leaders to justify or optimize AI spend without falling back on problematic metrics like lines of code. This

creates a tension between rapid AI adoption and financial accountability that tech leads must resolve. Governance and compliance around AI agents are emerging as bigger blockers than model capability. Teams are hitting problems with MCP tool exposure bypassing existing IAM controls, and platform teams struggle to keep pace with decentralized agent experiments. The episode suggests

the real bottleneck is workflow design and access governance, not the sophistication of frontier models. Standardization efforts like the Agentic AI Foundation are early but signal a needed shift. Platform engineering teams are pivoting from infrastructure automation to AI-native enablement, but the panel questions whether managed services like Bedrock are a cost-effective long-term solution or

a first step with hidden complexity. Digital sovereignty trends, especially in the EU, are driving some enterprises back on-premise, though true European cloud alternatives remain limited in service maturity compared to hyperscalers. Overhyped predictions about replacing junior or senior engineers are fading, replaced by more nuanced discussions about human-in-the-loop design for agents. The panel warns