Tencent Says AI Spending Is Yielding Results
Chinese tech giant Tencent says its artificial intelligence (AI) investments have begun paying off.
In releasing its quarterly earnings on Wednesday (May 14), the company’s CEO noted that its AI capabilities had “contributed tangibly” to its gaming and advertising businesses. Revenue was up 13% year over year to 180 billion yuan ($25 billion).
“We also stepped up our spending on new AI opportunities, such as the Yuanbao application and AI in Weixin,” CEO Ma Huateng said, referring to the company’s super app.
“We believe the operating leverage from our existing high-quality revenue streams will help absorb the additional costs associated with these AI-related investments and contribute to healthy financial performance during this investment phase,” Ma added. “We expect these strategic AI investments will create value for users and society, and generate substantial incremental returns for us over the longer term.”
A report on the earnings by the Wall Street Journal (WSJ) cites analysis from Citi calling the results “a stronger-than-expected print.”
It also pointed to analysts from Morgan Stanley, who argued that ongoing AI advertising tech improvements could make Tencent’s ad venue growth more solid than its peers. That is largely down to the valuable user data it can glean from Wexin, and its international cousin, WeChat, which had 1.4 billion monthly active users as of the end of March.
The report also noted that worries about American tariffs have curbed a recent rally in Chinese tech stocks sparked by the arrival of domestic AI startups such as DeepSeek.
However, Tencent has something of an edge, the WSJ added, in that it makes most of its revenue in China. The company’s stock is up 25% this year, although stricter U.S. controls on AI chips could hinder its plans to ramp up its AI efforts.
In other AI news, PYMNTS wrote Wednesday about the rise of large transaction models (LTMs), a new type of generative AI model adapted to financial crime and positioned to revolutionize security, efficiency and operational effectiveness in financial services.
“For years, payments players and banks have employed traditional machine learning (ML) models to enhance transaction processing and improve user experiences by boosting conversion rates and reducing fraud,” PYMNTS wrote. “These approaches, built on clearly defined features like BIN numbers, ZIP codes, and payment methods, have delivered substantial gains, but their conventional approach has intrinsic limitations.”
Cognizant of these constraints, businesses are increasingly taking a radically different route, the report added: transformer-based models, known for their transformative impact in the field of natural language processing (NLP).
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