TikTok owner ByteDance has secured the second-largest dollar loan deal in Asia this year, raising a $29.6 billion syndicated loan with proceeds earmarked for the data centers supporting the company's costly expansion into AI.

Bank demand for the loan far exceeded what ByteDance originally set out to raise, pushing the company past its $20 billion target to roughly $30 billion. Citigroup and JPMorgan coordinated the deal, which carries a three-year term with an option to extend to five. The transaction is not yet finalized, as banks are still confirming their allocations — a step that will show how exposure is spread across the lending group when the final tally is published.

Roughly triple its 2024 loan

The new facility is nearly triple the loan ByteDance raised two years ago. In 2024, the company pulled in roughly $10.8 billion from about 20 lenders in what was then the largest dollar corporate loan in Asia outside Japan. As recently as June, ByteDance was in early talks over what would have been a record $20 billion loan facility; the final figure surpassed that mark by almost half.

The only Asian borrower that has raised more from a loan facility in 2026 is SoftBank, which arranged a $40 billion bridge facility in March to back its OpenAI stake. The two deals differ, however: SoftBank's is a bridge against an equity position, set to be repaid once longer-term financing comes in, while ByteDance is borrowing against a business that already generates cash. Banks are effectively treating TikTok and Douyin revenue as the security behind the ByteDance loan.

For a privately held company like ByteDance, syndicated bank loans are a natural financing route: banks can underwrite against private financials without the public disclosure a bond issuance would invite, which helps explain why the firm taps loan markets rather than selling debt to institutional investors.

A run at the AI frontier

The stated purpose of the borrowing is general corporate purposes, which for ByteDance right now means compute — and a lot of it. The company is looking at capital spending of as much as $70 billion a year on AI infrastructure, a level that would place it alongside the American hyperscalers.

The budget stretches further than that number suggests, because the compute costs more to assemble. US export controls cap ByteDance's access to Nvidia's top chips, so the AI expansion leans on custom silicon built on Arm and RISC-V designs, Qualcomm inference parts, and domestic Chinese suppliers. Assembling the same amount of compute that way becomes more expensive over the long run, and part of the spend still flows to a rival: ByteDance pays more than $1 billion a year to run OpenAI's models through Microsoft Azure even as it funds the homegrown hardware meant to end that reliance.

The company is also said to be training a ten-trillion-parameter model and enlarging its data center cluster in Inner Mongolia, a scale of development that operating cash flow alone cannot cover.

Numbers only the banks can see

The $70 billion capex plan sits sharply against the 160 billion yuan ($22.7 billion) that ByteDance had earlier claimed to budget for 2026, and accounts differ on whether the larger figure is a firm decision or a scenario still under review. The company has not addressed these figures publicly: it remains privately held, files no financial statements, and reveals its capital spending mainly through the banks that handle its funds. That opacity means outside observers will gauge the scale of ByteDance's AI buildout indirectly — through loan sizes, chip orders, and data center construction rather than quarterly disclosures.

Big Tech's AI-related debt has now passed $350 billion as firms fund data centers with borrowed money instead of their own earnings, and a deal of this size moves a large chunk of that borrowing outside the US. If the allocations close near the reported figure, ByteDance's facility will stand as a benchmark for how far lenders are willing to stretch for AI infrastructure credits backed by operating cash flow rather than hard assets.