SF Compute’s $40 million Series A addresses a $11.76 billion infrastructure bottleneck: AI startups locked into 12-36 month GPU contracts while serving customers with sporadic usage patterns. The San Francisco-based startup has created a marketplace allowing companies to resell unused compute capacity, managing over $100 million in hardware across several thousand GPUs.

The funding round, led by DCVC and Wing Venture Capital with participation from Electric Capital and Alt Capital, values the company at $300 million. This represents a growing recognition that AI infrastructure financing models have created hidden systemic risks across the ecosystem.

Factory downtime costs the global manufacturing industry €1.21 trillion annually—a 319% increase since 2019 as supply chains grow more complex and energy prices rise. Zurich-based Cerrion raised €15.6 million ($18M) Series A led by Creandum to scale AI video agents that transform existing factory cameras into intelligent production monitors.

The funding comes amid escalating pressure on manufacturers to reduce unplanned downtime while managing rising operational costs. Traditional monitoring relies on human operators watching dozens of screens or reactive maintenance after problems occur—creating blind spots that lead to cascading failures.

Vijil secured $17 million in Series A funding to accelerate deployment of its AI agent trust infrastructure platform that addresses the enterprise adoption bottleneck through continuous resilience improvement.

The funding round, led by BrightMind Partners with participation from Mayfield and Gradient, brings the company’s total funding to $23 million and validates growing enterprise demand for trusted AI agent deployment capabilities that reduce time-to-production from months to weeks.

The Trust Bottleneck Crisis

Enterprises struggle to bring AI agents into production because teams lack the expertise, tools, or bandwidth to ensure reliability, security, and governance at scale. This creates a fundamental deployment bottleneck where organizations experiment with agents but can’t scale them across operations.