Citigroup lifts AI market view to over $4 trillion on enterprise adoption

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Reuters

Tue, April 28, 2026 astatine 4:35 AM CDT 1 min read

April 28 (Reuters) - Citigroup raised its planetary artificial quality marketplace forecast, citing faster-than-expected endeavor adoption of artificial quality tools ‌for coding and automation, with companies specified arsenic Anthropic showing ‌strong gross growth.

The Wall Street brokerage, successful an April 27 note, expects the ​global AI marketplace to scope much than $4.2 trillion by 2030, with astir $1.9 trillion of that tied to endeavor AI.

Citi antecedently forecast the planetary AI marketplace to beryllium worthy much than $3.5 trillion, with astir $1.2 trillion ‌driven by endeavor AI.

Here are ⁠key points from Citi's enactment connected Anthropic:

• Enterprise request and gross are being driven by Claude models ⁠and Claude Code, portion Mythos represents imaginable aboriginal benefits alternatively than near-term monetisation.

• Anthropic is "the person successful endeavor AI," owed to beardown traction ​in commercialized ​uses specified arsenic bundle improvement ​and task‑automating, agentic workflows.

• Early ‌and sustained absorption connected endeavor customers has fixed the steadfast a structural advantage, adjacent arsenic it navigates rising compute costs, capableness constraints and intensifying contention from rival AI labs.

• About 80% of Anthropic's gross comes from endeavor customers, reflecting a deliberate displacement distant from ‌consumer-first AI strategies.

• Anthropic's annualised gross tally ​rate has surged past $30 cardinal by ​April, 1 of the ​fastest maturation trajectories successful tech history.

• Company has signed ‌major computing‑capacity deals, including up to $40 ​billion from Google ​earlier this week and arsenic overmuch arsenic $25 cardinal from Amazon.

• Competition is tightening arsenic OpenAI, Google and others propulsion deeper ​into endeavor markets, ‌shifting the conflict toward workflow integration and reliability alternatively than ​AI exemplary benchmarks.

(Reporting by Rashika Singh and Kanishka Ajmera ​in Bengaluru; Editing by Harikrishnan Nair)

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