Anthropic revelations suggest a much stronger AI negotiating stance for enterprise CIOs

The fact that almost one-fourth of Anthropic’s revenue last year came from just two customers, and that many of its largest customers have not signed long-term contracts, could translate into a much stronger negotiating position for enterprise CIOs.

According to a detailed analysis by Reuters, the company’s confidential prospectus depicted a company that might be suffering from a lack of revenue diversification, with Anthropic, like other AI vendors, needing enterprise revenue more than enterprise CIOs might need that particular AI vendor.

Analysts and consultants said the results of the analysis might be good news for enterprise CIOs, but events still need to play out. 

The key risk factor for Anthropic, according to the prospectus, is the revenue concentration, said Frank Dickson, principal analyst at Dickson Research.

“Nearly a quarter of revenue came from two customers, and Anthropic itself warns that many large clients are not locked into long-term contracts. That cuts both ways,” he said. “CIOs have more leverage than they think, because the vendor needs them more than the pitch deck admits. Negotiate price protection, advance notice before models are retired, and data portability now, while you are the customer they cannot afford to lose.” Or, for that matter, a prospective customer it absolutely needs to attract.

The newly-disclosed information also underscores the AI risks that every enterprise is absorbing, and Dickson said there are clear ways for CIOs to try to minimize that exposure. 

“The CIO who hardwires one model into every workflow has not bought AI. He has bought a dependency,” he warned. “CIOs should build for portability, creating an abstraction layer between applications and models, evaluations you own, and prompts and data you can move. The exit plan is part of the purchase.”

Jack Collier, CMO at io.net, said the revenue numbers suggest that Anthropic’s concentration problems go beyond over-reliance on a small number of clients. 

“Strip away the existential language and the filing reads like a case study in concentration,” he said. “Nearly a quarter of its revenue last year came from two clients, its seven co-founders hold majority voting control over the company’s decisions, and about 80% of its $518 billion infrastructure bill is non-cancellable or payable regardless of usage. If the best-funded lab outside Big Tech can only secure compute on take-or-pay terms, [Anthropic has] almost no leverage at all.”

And, said Scott Bickley, advisory fellow at Info-Tech Research Group, the vast control that a relatively small number of AI vendors hold adds to the AI complexity for enterprise IT executives.

“The problem is that every time Anthropic’s revenues beat expectations, their costs beat them by more. This is a structural feature of a frontier growth model,” he said. “Adding insult to injury is [the fact] that Anthropic and OpenAI now capture 89% of all revenue generated by the 34 leading AI native startups, and this is increasing.”

This, he pointed out, is another reason that enterprise CIOs are going to have to carefully negotiate the future AI landscape.

“At least half of the $2 trillion revenue backlog for Google, Amazon, Microsoft, and Oracle traces back to OpenAI and Anthropic,” Bickley said. “This is a narrow base of customers and price increases should be expected and built into any ROI forecasts.” Thus, he said, CIOs should negotiate for shorter-duration contracts while pushing for flat per-seat pricing where possible for larger organizations.

“This market is extremely fluid,” he noted, “so reserving the ability to switch vendors is key to retaining leverage and optimizing spend in a nimble manner.”

However, Arnal Dayaratna, a research VP at IDC, stressed that open source is most likely to influence, if not outright dictate, the direction of enterprise AI for the next couple of years, putting pressure on companies like Anthropic.

In fact, he said, Anthropic’s long-term viability depends on its ability to demonstrate measurable differentiation from open models on predictability, latency and total cost of ownership at scale.

“The assumption that open models are cheaper than their frontier counterparts is a fallacy in many enterprise contexts,” he noted, “because open models can be significantly more difficult to operationalize at scale. Anthropic needs to make that case clearly and consistently to sustain its pricing and its position.”