Gather AI has secured a $40 million Series B led by Smith Point Capital, the firm of former Salesforce co-CEO Keith Block, bringing its total funding to $74 million. The round validates the Carnegie Mellon-founded startup’s Physical AI platform, which uses classical Bayesian techniques—not LLMs—to power autonomous systems that actively map and monitor warehouse operations.

This investment marks a critical inflection point for AI infrastructure, shifting focus from purely digital, text-generating agents to systems that must operate reliably in the physical world. As enterprises seek to scale automation beyond the data center, a new class of Physical AI infrastructure is emerging to solve the critical bottlenecks of real-world deployment across logistics, manufacturing, and field services.

Decagon tripled its valuation to $4.5 billion in six months, raising $250 million in Series D funding led by Index Ventures and Coatue Management. The customer service AI agent platform now processes over 80 million conversations across 100+ enterprise customers including Duolingo, Hertz, Deutsche Telekom, and ClassPass.

The funding milestone signals enterprise confidence in autonomous customer service infrastructure over traditional human-centric support models. While legacy platforms like Salesforce, Intercom, and Zendesk retrofit AI capabilities onto existing architectures, agent-native companies are capturing market share by rebuilding customer operations from the ground up.

Daytona reached $1M forward revenue run rate in under three months. Six weeks later, it doubled.

The speed reflects enterprise urgency around a fundamental infrastructure gap: current cloud platforms were built for production workloads—stateless, immutable systems optimized to run the same code the same way every time. AI agents need the opposite: persistent, stateful environments where they can experiment, branch execution paths, and recover from failures at massive scale.

The Infrastructure Mismatch

Enterprise adoption of AI agents is hitting a hard infrastructure bottleneck. While agents can reason through complex workflows and make decisions, they break when deployment infrastructure treats every workload like a web server.