An announcement of the artificial intelligence (AI) startup’s joint venture with Blackstone, Goldman Sachs and other Wall Street companies could come as soon as Monday (May 4), The Wall Street Journal (WSJ) reported, citing sources familiar with the matter.
Anthropic, Blackstone and Hellman & Friedman are leading the deal and are each expected to invest around $300 million, the sources added, with Goldman Sachs contributing $150 million. In all, the partners are expected to commit $1.5 billion, the sources said.
According to the report, the investors want to launch a company that serves as a consulting service for Anthropic and helps businesses — including the ones in private equity firms’ portfolios — embed AI into their operations.
The WSJ also notes that OpenAI has been in discussions to start a similar effort as the two rival AI companies focus on selling their offerings to businesses. Anthropic has established itself as the dominant player in the enterprise market, the report added, with revenues surging recently due to the popularity of its coding tool.
Meanwhile, PYMNTS wrote last week about the issues facing enterprise buyers as they try to incorporate AI into their operations.
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“Traditional software costs tracked headcount,” that report said. “AI costs track activity. A single employee can generate thousands of AI interactions in a day. Another may trigger none. An automated process can run continuously without anyone watching the bill.”
As covered here, enterprise AI invoices have begun to resemble utility bills more than software subscriptions, with charges based on model activity, not employee count.
“Finance teams built around stable annual renewals now manage a cost structure with no prior reference point,” PYMNTS added.
Costs can also compound further down. For every dollar spent on AI models, companies spend between $5 and $10 on integration, compliance and monitoring.
Research from PYMNTS Intelligence shows that more than 8 in 10 CFOs at large companies are either using or considering using AI, with AI pricing models continuing to evolve as adoption of the technology scales.
“The pricing pressure has a structural cause,” PYMNTS added. “Building and running frontier AI models requires enormous amounts of computing infrastructure. That cost compounds as usage rises. Model makers are not yet profitable at scale and usage-based pricing is one mechanism for closing that gap as adoption grows.”
