Visa announced its strongest Q3 earnings in history on July 28: net revenue of $11.5 billion (up 14% year over year), and payment volume rose 10% year over year, breaking through $4 trillion for the first time. That same day, CEO Ryan McInerney sent an internal memo announcing layoffs of about 2,600 positions; in his letter, McInerney said that AI is “accelerating” the evolution of how the entire organization works.
Visa Q3 2026 earnings data: net revenue of $11.5 billion, up 14% year over year
The key figures from Visa’s Q3 2026 earnings are as follows: net revenue of $11.5 billion, up 14% year over year; GAAP net profit of $5.6 billion, with earnings per share of $2.97; payment volume up 10% year over year, breaking through $4 trillion for the first time (a historic milestone for the quarter); and transactions processed at 71.7 billion, up 10% year over year.
Value-added services performed particularly well: this quarter’s revenue was $3.8 billion, up 34% year over year on a constant-currency basis—the fastest-growing segment among Visa’s business lines. After the earnings release, Visa shares briefly rose 2.2% in pre-market trading.
AI productivity data: code submission volume up 80%
During the earnings call, Visa disclosed specific metrics it uses to measure the effectiveness of its AI investment:
Code submissions: For product development teams that integrate AI, submission volume increased by 80%
Requirements definition time: reduced from 30 days to 5 days
Feature development speed: accelerated by more than 65%
Number of AI application deployments: over 150 in the past 12 months
Major product releases: over 300 in the same period
Some product development teams are being reorganized into smaller “agentic squads”: one team is paired with a set of AI tools, while humans provide oversight. McInerney said on the earnings call: “As a leading, large-scale global payments company, we are designing, building, and launching products at a faster pace.”
Layoff memo and resource reallocation: 2,600 technical and product roles
According to an internal memo obtained by Bloomberg, Visa plans to cut about 7% (about 2,600) of its positions, mainly affecting technology and product teams. In the letter, McInerney said AI is accelerating the evolution of how the organization works, and the capital saved will be invested in the most promising opportunities.
Specifically, resources will be redirected to: the stablecoin domain (there are currently more than 160 stablecoin card programs globally, with partners including Rain, Reap, and Bridge; the Visa Stablecoin Platform handles minting, transfers, and management of stablecoins); cross-border and B2B products; and consumer payment solutions. McInerney emphasized maintaining a “multi-currency, multi-chain” strategy, investing in every layer of the stablecoin stack—from blockchain, issuance, and wallets to the coordination layer.
FAQ
What were Visa’s key financial figures for Q3 2026, and how did the stock react?
Visa Q3 2026 net revenue was $11.5 billion (up 14% year over year), GAAP net profit was $5.6 billion, and earnings per share were $2.97; payment volume broke through $4 trillion for the first time, with transactions processed at 71.7 billion; value-added services rose 34% year over year on a constant-currency basis; after the earnings release, Visa shares briefly climbed 2.2% in pre-market trading.
What is the reason for Visa’s layoffs of 2,600 people, and where will the saved resources go?
According to the memo obtained by Bloomberg, Visa plans to cut about 7% (about 2,600) of its positions, mainly affecting technology and product teams; CEO McInerney said AI is accelerating the evolution of organizational work methods, and the capital saved will be reinvested into stablecoins, cross-border payments, B2B products, and consumer payment solutions.
What specific productivity gains did Visa’s AI applications deliver?
The figures disclosed in the earnings call: code submission volume from development teams integrating AI rose 80%, requirements definition time was reduced from 30 days to 5 days, and feature development speed increased by more than 65%; deployments of more than 150 AI applications over the past 12 months, along with more than 300 major product releases; some teams have reorganized into “agentic squads (AI agent squads).”
