Ricursive Intelligence has raised $300 million in Series A funding at a $4 billion valuation, just two months after the company’s formal launch. The round was led by Lightspeed Venture Partners, with participation from DST Global, Nvidia’s NVentures, Felicis Ventures, Sequoia Capital, and others.

The funding milestone reflects investor confidence in what the company calls “recursive AI-hardware co-evolution”—AI systems that design and continuously improve the chips that power them. For an industry where chip design cycles have become a critical bottleneck to AI advancement, Ricursive’s platform promises to collapse months-long design processes into hours while achieving superhuman layout optimization.

Waabi secured $1 billion in total funding this week—including a $750 million Series C round co-led by Khosla Ventures and G2 Venture Partners, plus milestone-based investment tied to its robotaxi partnership with Uber—marking one of the largest funding rounds in Canadian tech history.

The Toronto-based company represents a critical infrastructure shift: while most AI agent development has focused on digital environments, Waabi’s Physical AI platform bridges autonomous agents into real-world deployment at enterprise scale. This funding validates Physical AI as the next major infrastructure category, where agents must navigate safety-critical environments rather than just process digital workflows.

Poetiq’s $45.8 million seed round, announced Wednesday, comes with proof that six former Google DeepMind researchers have cracked a fundamental barrier in AI reasoning—achieving 75% accuracy on the notoriously difficult ARC-AGI-2 benchmark, a 16-percentage-point leap beyond previous state-of-the-art.

The achievement matters now because enterprises are drowning in a $30-40 billion AI investment crisis. An MIT study published in August 2025 found that 95% of organizations are “getting zero return” on their GenAI deployments, with most failures traced to LLMs’ inability to handle real-world reasoning tasks that require more than pattern matching.