Citi Projects $4 Trillion AI Market Fueled by Enterprise Growth

Citigroup has reportedly increased its global AI market forecast amid rising enterprise adoption.

The banking giant now expects the worldwide artificial intelligence (AI) market to exceed $4.2 trillion by 2030, Reuters reported Tuesday (April 28), citing a Citi research note.

Citi said nearly half that total — $1.9 trillion — would be related to enterprise AI. The bank’s earlier forecast had put the global AI market at $3.5 trillion, with around $1.2 trillion driven by enterprise AI, Reuters added.

According to the report, the research note makes a number of points about Anthropic, the AI startup that has become a leader in the enterprise space. Citi said the company’s enterprise demand and revenue are being driven by Claude models and Claude Code, while Anthropic’s Mythos represents potential future benefits rather than a short-term source of revenue.

The report said 80% of Anthropic’s revenue is from its enterprise clients, showing a deliberate pivot away from consumer-focused AI strategies. Citi also says Anthropic’s annualized revenue run rate had surpassed $30 billion by April, marking one of the fastest growth trajectories in the history of the tech world.

While the company has inked major computing‑capacity deals, including up to $40 billion from Google earlier this week and as much as $25 billion from Amazon, it is facing increasing competition from the likes of OpenAI and Google, the report said.

In related news, PYMNTS wrote earlier this week about the way enterprise AI was altering long-standing corporate billing models by “pricing AI in granular units such as tokens, compute cycles and API calls.”

The report uses the example of companies like Adobe, OpenAI, Anthropic, Salesforce and Hubspot offering usage-based and outcome-based pricing.

“And from outcome-based models to engineering teams ‘token-maxxing,’ the disconnect between engineering velocity and financial visibility is becoming harder for CFOs to ignore,” PYMNTS wrote. “A surge in internal experimentation, a new product feature or even a poorly optimized prompt can cause costs to spike in ways that are difficult to anticipate. The unit economics are precise, but the aggregate behavior is not.”

With that in mind, the report continued, decisions about which model to use are no longer just technical, but carry financial implications that need to be understood and managed.