Kuaishou’s Kling AI platform generates video from text and still images.
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BEIJING — China’s video-heavy entertainment world has yielded a trove of data for companies — and they’re now ramping up money-making artificial intelligence tools for generating ads and film clips.
TikTok parent ByteDance holds the first and third spots in research firm Artificial Analysis‘ top-ranked text-to-video generative AI models, which were launched in the last two months. Google holds the second and fourth spots, while Beijing-based short video app Kuaishou’s Kling AI ranks fifth.
Despite some consolidation in other parts of the AI industry, “competition in AI video generation models is at an earlier stage, and some Chinese companies have emerged as early leaders in this space,” said Wei Xiong, China internet analyst at UBS Securities.
“We believe AI video generation has the potential to reshape the content industry,” she said, “by enhancing production efficiency, lowering barriers to creation and unlocking new monetization models.”
With such AI tools, users can upload a single image or multiple ones, and direct the AI to generate a video clip based on them. Other tools allow users to enter text, from which the AI will generate the video clip.
More than 20,000 businesses from advertisers to movie animators already use Kling AI for generating video, the Beijing-based company claimed this week during the World AI Conference in Shanghai. The latest version, Kling 2.1, can automatically add relevant sound effects to match the AI-generated video.
It’s not just for users in China.
“Whether it’s user scale or commercial revenue, overseas accounts for the majority,” Zeng Yushen, head of operations at Kling AI, told CNBC in Mandarin, translated by CNBC. She said the company plans to enhance its support for the tool in places such as Japan, South Korea and Europe.
“This is something we’ve observed, AI big models are increasingly globalized,” she said. “People don’t seem to care which country’s product it is.”
Kuaishou claimed Kling AI made over 150 million yuan ($20.83 million) in revenue in the first three months of the year, and that daily advertising spend on generative AI tools was 30 million yuan during that time. The company has yet to announce when it will release second-quarter results. Zeng declined to share Kling AI’s model training costs.
While the reduced production cost implies a “sizeable” market, UBS’ Xiong said, “current model capabilities remain constrained by clip length, motion consistency and controllability.”
Chinese video AI companies also face competition from the U.S., beyond the Trump administration’s restrictions on China’s access to advanced semiconductors needed for training AI models.
However, Kling AI had already launched to the public in June 2024. Users subscribe and buy credits to generate videos.
Vidu, a rival tool from Beijing-based startup Shengshu, launched to global users roughly 12 months ago, and around March this year said it expected annual revenue of $20 million based on user subscription fees.
“Chinese firms tend to attempt to first identify a commercial ‘pain point’ …, areas where companies will pay for services, which has been a challenge for AI applications,” said Paul Triolo, partner and senior vice president for China at advisory firm DGA-Albright Stonebridge Group.
He pointed to how Chinese startup 3DStyle uses generative AI to design new clothing styles and integrate them with internet-connected, automated manufacturing.
U.S. companies have also been applying AI to specific industries, Triolo said, but Chinese businesses are often able to integrate AI more quickly because they face a very competitive environment and can recruit from a “very qualified” local base of software engineers.
‘AI as filmmaker’
Chinese e-commerce giant Alibaba has also stayed on top of the trend by releasing the latest version of its video generation AI model this week called Wan2.2. The company claimed that with the open-source model, users can control lighting, time of day, color tone, camera angle, frame size, composition and focal length.
Open source allows users to download a model for free, and customize, if not commercialize, products with it. Alibaba claimed that since open sourcing the “Wan” model series in February, the models have been downloaded more than 5.4 million times from the Hugging Face platform and a similar one in China called ModelScope.
“The age of AI in film is over. We’ve entered the age of AI as filmmaker,” said Winston Ma, adjunct professor at NYU School of Law. He pointed out that China’s 1.4 billion population has given local companies “enormous” amounts of video-watching data to work with.
“Just like TikTok took the global markets by storm with short videos in the mobile internet age, Chinese AI companies could well lead the Generative AI revolution in visual digital entertainment,” said Ma, author of “The Digital War: How China’s Tech Power Shapes the Future of AI, Blockchain and Cyberspace.”
Avatars and gaming
Chinese companies are also building AI tools for more than just generating videos.
In the past week, Baidu announced that its newest AI-powered digital human technology — which powered sales of $7.65 million during an interactive livestreaming session of over six hours in June — would be released for broader industry use in October.
In 3D visualization, Tencent released its Hunyuan World model for creating digital panoramic images of scenes, generated from text and visual prompts. The visuals use a “mesh” file format which gamer developers can then use to edit specific parts of the image.
“Beyond supporting [Tencent’s] internal development teams, the platform demonstrates Tencent’s ambition to standardize high-fidelity game asset generation and expand its influence across China’s game development landscape,” said Daniel Ahmad, director of research and insights at Niko Partners.
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Niko found that more than half of game development studios in China already use AI for content generation and reducing development time and costs.
But game development reflects broader challenges in using AI at scale for generating videos and graphics.
“While interest in AI is high,” Ahmad said, “we’ve already seen some backlash to games that have poorly implemented the technology.”
Fixed-income markets recorded significant re-pricing during the week ending July 25, 2026, as a convergence of strong labor market metrics and surging energy costs drove U.S. Treasury yields higher across all maturities. The benchmark 10-year Treasury yield climbed toward 4.70%, reaching its highest point in several months. Institutional bond investors rapidly adjusted portfolio durations as expectations for near-term interest rate cuts by the Federal Reserve faded in response to inflation concerns.
The upward shift in sovereign yields reflects a broader fundamental reassessment of global monetary policy. Earlier in the quarter, money markets had priced in a series of rate reductions designed to support economic activity. However, with initial jobless claims falling to 187,000 and crude oil breaching $100 per barrel, fixed-income traders are pricing in a ‘higher-for-longer’ interest rate environment. The inversion between short-term Treasury bills and long-term bonds narrowed, indicating a shift toward term premium expansion.
Rising Treasury yields present both challenges and opportunities for institutional wealth managers. While commercial lenders and mortgage origination volumes face headwinds from elevated borrowing costs, fixed-income investors are locking in attractive real yields on high-quality sovereign and investment-grade corporate bonds. Institutional debt issuers, conversely, are recalibrating their capital structures, opting for shorter-term refinancing instruments or private credit facilities to avoid committing to elevated long-term coupon rates.
Navigating the current bond market landscape demands strict duration management and credit selection. Wealth advisors recommend maintaining flexible fixed-income allocations, combining short-duration Treasuries with inflation-protected securities (TIPS) to shield capital against potential energy-driven inflation spikes while earning dependable nominal income.
Private credit markets reached a pivotal milestone during the week ending July 25, 2026, as non-bank direct lending consortiums captured a record share of middle-market corporate debt originations. With commercial banks maintaining conservative credit standards and public bond yields remaining elevated, corporate borrowers are increasingly turning to private fund managers for customized capital solutions. This expansion marks a permanent structural shift in enterprise finance, establishing private credit as a primary pillar of institutional corporate liquidity.
The acceleration of private credit deals is driven by speed, deal certainty, and flexible terms. Unlike traditional syndicated bank loans that require lengthy underwriting, credit rating approvals, and public roadshows, private direct lenders can structure tailored financing packages within days. Middle-market firms facing upcoming debt maturities are utilizing private debt facilities to execute recapitalizations, strategic acquisitions, and growth capital deployments without risking execution delay in public markets.
However, financial regulators and central bank supervisors are scrutinizing the sector’s rapid growth. Supervisory agencies are evaluating potential systemic risks associated with non-bank leverage, valuation transparency, and liquidity mismatches during economic downturns. Despite regulatory interest, major pension funds, insurance firms, and sovereign wealth entities continue to expand capital allocations to private credit funds, attracted by reliable floating-rate yields that outperform public fixed-income benchmarks.
As private credit matures into a dominant asset class, corporate chief financial officers must evaluate non-bank lenders alongside traditional banking relationships. Direct lending partnerships provide valuable balance sheet resilience, enabling companies to secure flexible financing terms even during periods of public market turbulence.
The landscape of institutional debt markets is undergoing a profound structural shift on July 21, 2026, as major corporate issuers and commercial banks rapidly accelerate the deployment of tokenized debt instruments. Data published by leading capital market consortiums indicates that primary issuances of digital commercial paper and tokenized corporate bonds have reached record volumes this month. By moving legacy debt origination, underwriting, and secondary distribution onto permissioned distributed ledgers, corporate treasurers are unlocking unprecedented operational flexibility and instantaneous cross-border liquidity.
The adoption of tokenized debt is fundamentally altering how enterprise balance sheets manage short-term liquidity needs. Traditional corporate bond settlement cycles historically required multi-day clearing processes involving numerous intermediaries, custodial entities, and clearinghouses. Through programmable smart contracts on distributed ledgers, issuers can now execute atomic settlement—enabling continuous, 24/7 access to institutional capital pools. This instantaneous clearing mechanism drastically reduces counterparty risk, eliminates costly settlement friction, and allows treasury teams to dynamically optimize working capital in real time.
A major catalyst driving this institutional migration is the establishment of comprehensive digital asset regulatory frameworks across major financial hubs. Clear legal guidelines regarding ledger-based securities ownership have provided institutional compliance officers with the regulatory confidence necessary to transition multi-billion-dollar liquidity facilities onto digital platforms. Furthermore, the integration of automated regulatory reporting directly into token smart contracts simplifies ongoing compliance audits, ensuring that secondary market trades automatically enforce investor accreditation limits and tax withholding requirements.
For chief financial officers and institutional portfolio managers, tokenized debt represents a fundamental evolution in fixed-income strategy. Companies that embrace ledger-based debt structures gain direct access to a broader, global base of digital-native institutional investors while substantially reducing borrowing overhead. As ledger interoperability continues to improve across global exchanges, tokenized debt is poised to become the standard infrastructure for global corporate finance.