Anthropic Raises $30B Series G at $380B Post-Money Valuation

What happened

Anthropic announced on February 12, 2026 that it raised $30 billion in Series G funding at a $380 billion post-money valuation. The round was led by GIC and Coatue, with D. E. Shaw Ventures, Dragoneer, Founders Fund, ICONIQ, and MGX listed as co-leads. Anthropic also stated that part of previously announced investments from Microsoft and NVIDIA is included in this financing package.

The company framed the raise as fuel for three priorities: frontier research, enterprise product development, and infrastructure buildout. In the same announcement, Anthropic tied the fundraise to rising demand for Claude across enterprise software, coding, and agentic workflows.

Key figures disclosed by Anthropic

Run-rate revenue: $14 billion

Growth pace: over 10x annually in each of the past three years

Customers spending over $100,000 annually: up 7x year-over-year

Customers spending over $1 million annually: from about 12 two years ago to more than 500

Fortune 10 customers using Claude: 8

Claude Code run-rate revenue: over $2.5 billion

Why this matters

This is not just a large funding headline. It is a signal that enterprise AI demand is moving from pilot budgets into core operating budgets. The metrics Anthropic published suggest that model providers are increasingly being judged not only on benchmark performance, but also on reliability, security posture, deployment tooling, and integration depth across business systems.

The emphasis on Claude Code and agentic use cases is also notable. It implies that monetization in 2026 is shifting from pure chat usage toward workflow automation tied to measurable business outputs, especially in software engineering and analytics-heavy teams.

What to watch next

The central question is execution. Markets will track whether this capital translates into sustained product velocity, stronger production reliability, and continued customer expansion at high spending tiers. Infrastructure deployment speed and margin discipline will likely become as important as model quality in the next competitive phase.