Investors Bet Big on AI as Core Hardware Infrastructure

From artificial intelligence (AI)-driven chip design and autonomous vehicles to enterprise customer service, real estate transactions and investment banking, startups this week raised billions in capital as investors doubled down on AI as core operating infrastructure.

Ricursive Intelligence raised $300 million in a Series A at a $4 billion valuation, a rare level of capital for a company at such an early stage.

Ricursive is building AI systems that assist in the design of semiconductors themselves, using machine learning to optimize layouts, performance characteristics and power efficiency.

As AI models grow larger and more specialized, demand is shifting toward custom silicon rather than general-purpose chips. Ricursive’s value proposition is speed and iteration: enabling chipmakers to shorten design cycles that traditionally take years, while squeezing more performance out of each generation of hardware.

The size of the round signals investor belief that gains in AI will increasingly depend on how quickly new chips can be designed and manufactured.

Waabi announced $1 billion in new funding commitments, combining equity and strategic capital to scale autonomous trucking and robotaxi deployments. Waabi uses a simulation-first approach, training models in detailed virtual environments before deploying them in the real world. The company said that autonomy requires reasoning systems that can generalize across scenarios, not just memorize edge cases. With logistics costs under pressure and driver shortages persisting, autonomous trucking represents a clear commercial entry point for physical AI.

Enterprise AI As Operating Infrastructure

Another cluster of announcements showed how enterprise AI is shifting from experimentation into systems that sit at the heart of customer experience and operational workflows.

Decagon announced a Series D round valuing the company at $4.5 billion, less than a year after its previous financing. Decagon touts its platform as an AI concierge capable of handling complex customer interactions end-to-end, including problem resolution, follow-ups and escalation. Unlike traditional chatbots, Decagon’s system is designed to operate autonomously within enterprise guardrails, reducing the need for human intervention while maintaining service quality. The rapid valuation jump reflects how customer support has become a proving ground for agentic AI, where labor costs are high and measurable return on investment is clear.

Gyde announced that it raised $60 million in financing led by Lightspeed. Gyde is targeting broker-led industries such as insurance, health and wealth management, where professionals spend significant time on documentation, compliance and administrative tasks. Its platform uses AI to automate those workflows while keeping brokers in control of advisory decisions. The company’s pitch aligns with a broader enterprise narrative: AI as a productivity layer that expands the capacity of skilled professionals rather than replacing them outright.

In real estate, Propy raised $100 million to scale AI-driven transaction infrastructure. Propy plans to acquire and modernize title and escrow companies, embedding automation and AI agents into processes that remain heavily manual. Closing a home purchase can involve dozens of steps, multiple intermediaries and long delays. By automating document handling and compliance checks, Propy aims to reduce friction, shorten closing timelines and lower costs. The funding reflects investor confidence in applying AI to legacy industries where inefficiency is structural rather than cyclical.

Rogo raised $75 million in a Series C and announced an expansion into Europe, opening a London office to serve global financial institutions. Rogo is building AI-assisted tools for investment bankers, focused on research, modeling and deal execution. Investment banking remains one of the most labor-intensive white-collar professions, with long hours spent synthesizing data and preparing analyses. Rogo’s value proposition is efficiency at the margin: reducing time spent on repetitive tasks while improving the speed and quality of insights delivered to clients.

The expansion into Europe suggests confidence that AI-driven finance tools can cross regulatory and market boundaries. More broadly, the deal reflects a shift in FinTech toward enterprise and institutional users, where even incremental productivity gains can justify significant software spend.