Eridu just raised over $200 million in Series A funding—an unusually large round that signals serious investor conviction that AI’s explosive growth has created a fundamental infrastructure crisis. The networking startup, led by serial entrepreneur Drew Perkins, emerged from stealth with backing from legendary investor John Doerr at Kleiner Perkins to tackle what the company calls the “network wall” throttling AI data centers.

The funding round’s size reflects both the capital intensity of hardware development and recognition that current data center networking wasn’t designed for AI’s massive communication demands. As training clusters scale to thousands of GPUs and models grow larger, the networks connecting them have become critical bottlenecks that billions in AI infrastructure investment can’t solve with software alone.

Lyzr AI’s valuation just jumped 500% in five months, reaching $250 million in a $14.5 million Series A+ round led by Accenture. This isn’t just another AI funding story. It’s evidence that enterprises are rejecting vertically integrated AI agent platforms for infrastructure they can control.

The New York-based startup builds infrastructure for deploying AI agents on enterprise systems while keeping critical data within company boundaries rather than sending it to external cloud platforms. Revenue has grown over 300% each of the past two quarters, with profitability expected by April.

Sutherland launched FinAI Hub on March 6, featuring 90+ domain-trained AI agents purpose-built for banking and financial services operations. The enterprise platform directly targets financial institutions’ pilot-to-production crisis, where AI initiatives struggle to scale beyond experimental phases into core banking operations.

This launch reflects a broader industry shift toward specialized agentic AI infrastructure for regulated sectors. As financial institutions accelerate AI adoption, the gap between promising pilots and production deployment has become a critical bottleneck, with most initiatives failing to achieve enterprise scale across legacy systems and compliance frameworks.