Cloud Startup Lambda Nets $1.5 Billion as AI Infrastructure Remains a Focus

A new round of artificial intelligence (AI) fundraising this week shows continued investor interest in companies building large-scale platforms across infrastructure, automation and healthcare. From cloud computing to model-training centers investors remain keen on supporting ventures that address the growing demand for advanced computing resources, reliable infrastructure and real-world applications.

Lambda Raises More Than $1.5 Billion for AI Infrastructure

Lambda secured more than $1.5 billion to expand its AI cloud business. The company is shifting its strategy by building and owning its own data centers rather than renting capacity from larger cloud providers. Lambda said it plans to grow its footprint of GPU clusters and invest in the physical infrastructure needed to support customers building large models and agent-driven applications.

The round reflects a growing belief that AI infrastructure itself has become a competitive advantage. Companies training advanced models often face long wait times and rising costs when relying on shared cloud platforms. Lambda’s approach aims to give customers predictable access to high-end computing resources. Investors see this model as supporting stronger long-term margins as demand for new AI systems continues to rise.

Luma AI Expands With Giant Series C and Saudi Backing

Luma AI closed a $900 million Series C round led by HUMAIN, the Saudi-backed initiative focused on large-scale AI development. Luma plans to support a 2-gigawatt supercluster in Saudi Arabia to train the company’s next generation of multimodal models. The company is known for Ray3, a model that handles video and image generation, and for Dream Machine, a tool that has grown in popularity among designers and creative teams.

The new investment marks a shift for Luma as it moves from entertainment and digital content into areas that require deeper real-world understanding, such as robotics, simulation and industrial design. The involvement of Saudi capital highlights how national investors are positioning themselves in the global AI race by building their own large-scale infrastructure. For Luma, the partnership gives the company access to reliable computing power at a moment when demand for advanced training capacity continues to exceed supply across the industry.

NestAI Lands Funding and Partnership to Accelerate Physical AI

In Europe, NestAI announced a $100 million raise alongside a new partnership with Nokia. The company focuses on what it calls physical AI, which refers to AI systems that operate in real environments rather than on screens. NestAI develops software for unmanned vehicles, autonomous inspections and command platforms used in logistics, infrastructure and industrial facilities.

Nokia plans to integrate its secure connectivity and sensing capabilities into NestAI’s systems, creating a joint approach that links advanced networking with autonomous operations. For customers, the appeal is the promise of automation that can handle tasks like facility monitoring, site inspections or logistics routing with fewer delays and fewer manual steps. NestAI’s raise suggests that real-world autonomy is moving from pilot programs into broader commercial use as companies seek ways to streamline operations and reduce routine on-site labor.

Function Reaches New Valuation as It Launches Medical Intelligence Lab

Function Health, a U.S.-based medical intelligence company, announced a new valuation of $2.5 billion as it launched Function Lab, a research program aimed at improving clinical decision support. The company develops AI tools that help physicians evaluate patient data, analyze imaging and plan care. Function said the new capital will allow it to build more advanced reasoning systems that support care teams across multiple specialties.

The company positions its platform to reduce administrative burden and improve diagnostic accuracy. Health systems have shown growing interest in technology that can help clinicians navigate rising patient volumes, expanding documentation requirements and complex medical histories. Function’s focus on clinically aligned models reflects an industry shift toward AI tools that must be reliable, traceable and safe for use in regulated environments.

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