Tencent Raises $4.7 Billion in Global Rush of AI Funding

Tencent Holdings Ltd. raised nearly $4.7 billion from the sale of long-dated dollar and yuan bonds, marking the Chinese technology and gaming giant’s largest debt offering since 2020 as companies worldwide tap capital markets to fund artificial-intelligence investments.

The company sold a $2.45 billion offering of dollar-denominated bonds and priced a 15 billion yuan ($2.21 billion) offshore note sale, according to people familiar with the matter who asked not to be identified. The dollar notes had combined orders of more than $9.2 billion during syndication, said one of the people.

Proceeds from the transactions will mainly go toward debt refinancing and general corporate purposes such as developing AI products and services, the people said.

Tencent didn’t immediately respond to a request for comment.

The firm is joining a record flow of bond sales by tech heavyweights as they tap investors for capital spending that is geared toward AI efforts. Amazon.com Inc. earlier this week sold C$14 billion ($10 billion) of notes, putting its total since the start of last year at more than $82 billion.

Tencent’s sale also follows Beijing’s recent decision to allow some top insurers to buy so-called dim sum bonds via an expanded southbound investment program, stoking domestic appetite for yuan-denominated notes issued outside the mainland. Tuesday’s deal is the company’s second dim sum deal, after its first one in 2025.

Meanwhile, this is Tencent’s first dollar bond sale in five years.

Tencent has $3.5 billion of debt including bilateral loans maturing this year and $2.8 billion due next year, according to people who dialed into a deal roadshow on Monday, citing company managers.

The firm last month reported its slowest revenue growth in six quarters, underscoring the urgency of its pivot to AI. Tencent vowed to at least double AI investments to more than 36 billion yuan  this year, but it trails peers like ByteDance Ltd. and Alibaba Group Holding Ltd. in both user adoption and advances in developing large language models.