Anthropic PBC has selected Morgan Stanley and Goldman Sachs to serve as lead underwriters for its upcoming initial public offering [1, 2].
The move signals a critical escalation in the race between major artificial intelligence developers to capture public markets. By moving toward an IPO, Anthropic aims to secure massive capital for growth and beat its primary rival, OpenAI, to the stock exchange [1, 2].
Reports indicate the company is targeting October 2026 for its public debut [2]. This timeline places the company on a fast track to transition from a private entity to a publicly traded corporation during the final quarter of the year.
Financial projections for the offering are substantial. The company is targeting a valuation of approximately $965 billion [2]. This figure reflects the immense market appetite for generative AI infrastructure, and the perceived value of Anthropic's proprietary models.
Beyond its public offering plans, the company has engaged in high-value infrastructure partnerships. Anthropic has a computing deal with SpaceX valued at $1.25 billion per month [2]. Such agreements highlight the extreme computational costs associated with training and maintaining large-scale AI systems.
Anthropic is based in the U.S. and continues to expand its footprint in the competitive AI landscape [1, 2]. The selection of two of the world's most prominent investment banks suggests a complex and high-stakes financial strategy designed to maximize investor confidence.
“Anthropic aims to secure massive capital for growth and beat its primary rival, OpenAI, to the stock exchange.”
The pursuit of a nearly trillion-dollar valuation indicates that the AI sector is moving from a phase of experimental growth into a phase of massive institutional capitalization. By securing top-tier underwriters and immense computing power from SpaceX, Anthropic is positioning itself not just as a software provider, but as a systemic piece of global digital infrastructure. The race to go public before OpenAI will likely set the benchmark for how the market prices AI companies based on compute costs versus revenue potential.
