On June 1, Anthropic confidentially filed draft paperwork for an initial public offering with the Securities and Exchange Commission, setting up a listing that could arrive as soon as this autumn and rank among the largest AI offerings ever attempted. The company, maker of the Claude family of models, said the number of shares and the price have not yet been set, and that any offering will depend on market conditions once the SEC completes its review.
The numbers behind the filing
The filing follows a financing round in which Anthropic raised about 65 billion dollars at a 965 billion dollar valuation, including the new investment. The company has told investors it expects to post 10.9 billion dollars in revenue for the second quarter, more than double the prior three months, and that its annualised run rate revenue will surpass 50 billion dollars by the end of next month. Those figures, if borne out, describe one of the fastest commercial ramps the software industry has seen.
Overtaking OpenAI
The 965 billion dollar mark is notable because it lifts Anthropic above OpenAI's valuation for the first time, reordering a rivalry that has defined the current AI race. OpenAI has itself moved toward a public listing, reportedly filing confidentially at a target near 850 billion dollars. Two of the field's defining labs racing to Wall Street within weeks of each other underlines how investors are now pricing AI as infrastructure rather than experiment.
What it means for work
For the labour market the relevance is less about share prices than about what the capital funds. The hundreds of billions flowing into AI labs and their cloud partners are paying for the data centres, chips and models behind the agents and copilots now appearing in white collar workflows. The same buildout has coincided with profitable technology firms trimming staff to help cover the bills, a trade that ties Wall Street appetite directly to hiring decisions on the ground.
A public Anthropic would also face new disclosure obligations, putting hard numbers on revenue, costs and customer concentration that the private AI economy has so far kept opaque. For workers and policymakers trying to gauge how fast the technology is really spreading, a published prospectus from one of its leaders may prove more informative than any forecast.
