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… its assumed average jet fuel price was USD 85 per barrel for the second half, and even though it was 83% hedged against Brent crude oil until the end of June, the company said it is still exposed to movements in the "crack spread," the price difference between crude oil and jet fuel.
Air New Zealand said its jet fuel price assumption "is no longer appropriate," and the "crisis is expected to meaningfully affect second-half earnings."
The carrier also said it has made "initial fare adjustments" and may need to make further changes to pricing, its network and schedules.
Apart from the Middle East strain, Cathay reported a loss at HK Express before net finance charges and taxation of HKD 996 million (USD 127.2 million) for 2025, worsening from HKD 204 million (USD 26.1 million) a year earlier. CEO Lam described the amount as "substantial" and attributed it partly to short-term factors, including new routes that "will take time to mature." The disclosure document also said that some aircraft were "grounded due to industrywide Pratt &amp; Whitney engine issues that continued without improvement."
A bigger problem was shifting customer behavior, especially last summer, when many travelers shunned Japan over an unfounded rumor that a major earthquake was about to hit the country. Lam said this had a "rather big impact" on traffic, as HK Express relies heavily on Japanese routes.
Lam said that this is no longer a factor and that the budget carrier's traffic has rebounded, but refrained from making any predictions about a return to the black.
This article first appeared on <a href="https://asia.nikkei.com/business/transportation/cathay-logs-higher-profit-as-iran-war-jolts-airlines-with-sudden-shifts">Nikkei Asia</a>. It has been republished here as part of 36Kr’s ongoing partnership with <a href="https://www.kr-asia.com/nikkei-teams-up-with-36kr-in-asia-tech-news-coverage/">Nikkei</a>.
Note: HKD figures are converted to USD at rates of HKD 7.83 = USD 1 based on estimates as of March 16, 2026, unless otherwise stated. USD conversions are presented for ease of reference and may not fully match prevailing exchange rates.

Cathay logs higher profit as Iran war jolts airlines with "sudden shifts"

Transportation

Travel

Mobility

Hong Kong

China

… conflict."
This article first appeared on <a href="https://asia.nikkei.com/business/transportation/china-s-24bn-global-port-push-reveals-supply-chain-security-goals">Nikkei Asia</a>. It has been republished here as part of 36Kr’s ongoing partnership with <a href="https://www.kr-asia.com/nikkei-teams-up-with-36kr-in-asia-tech-news-coverage/">Nikkei</a>.

China's global port push reveals supply chain security goals

Logistics

Latin America

In a segment where many startups were once expected to be flattened by rivals with deeper pockets once interest gathered, AIsphere is now trying to tell a different story. The company behind the video generation platform PixVerse said it has reached unicorn status after closing a Series C funding round, the company announced on March 13.
The round was led by CDH Investments, with participation from Antler, EnvisionX Capital, iGlobe Partners, Lion X Ventures, UOB Venture Management, and 3W Fund, alongside other investors. The amount raised and the company's latest valuation were not disclosed.
The claim lands with more weight than a typical funding announcement because PixVerse had already been framed as a business with a sturdier commercial base than much of the hype surrounding generative <a href="/industry/ai">artificial intelligence</a>. When 36Kr covered the company's <a href="https://kr-asia.com/aisphere-touts-pixverse-as-the-canva-for-video-generation-and-lands-the-funding-to-prove-it">Series B round</a> in September 2025, founder and CEO Wang Changhu said subscription revenue from PixVerse's products was already covering most of its costs. In that context, the new unicorn framing suggests PixVerse’s rise is driven not only by investor enthusiasm for AI, but also by revenue-backed growth.
PixVerse reported more than 100 million users across 175 countries as of last September. The new funding will be used to accelerate the company's global expansion from a newly established office in Singapore. PixVerse joins a growing group of Chinese-founded companies that have used the city-state as an international base, including <a href="/company/bytedance">ByteDance</a>, <a href="/company/shein">Shein,</a> and Mihoyo, which operates globally as Hoyoverse. The Singapore office will support enterprise operations, sales, partnerships, and global market development, with a particular focus on North America and Asia.
That expansion reflects a broader shift in how PixVerse is positioning itself. The company is no longer presenting the product solely as a consumer-focused video app built around trends. Instead, it is attempting to build a case across several fronts following the latest fundraise, including consumer scale, enterprise adoption, and technical development.
In its announcement, PixVerse linked the Series C round to the January 2026 launch of R1, which it described as the first “real-time video generation model,” as well as the continued rollout of its V5.6 model. According to Artificial Analysis benchmarks as of March 13, V5.6 ranked fifth globally in text-to-video generation and fourth in image-to-video generation.
Wang's description of the company's ambition offers a clearer view of the product strategy than the funding language. PixVerse, he said, is “reconstructing how people interact with content.” The statement reflects the company's long-held view that AI-driven video generation is not only about reducing the cost of existing production workflows, but also about enabling new forms of content that can be generated, modified, and experienced with far less friction than traditional methods.
More broadly, the AI video generation market is beginning to sort itself into more durable categories. For a while, discussions centered on whether startups could survive once large tech companies entered the field. Now, the more important question is which companies can translate model quality into products people return to, businesses pay for, and creators build routines around.
PixVerse is one of several Chinese companies attempting to make that case, alongside products such as <a href="/company/kuaishou">Kuaishou’s</a> Kling AI, <a href="/company/minimax">MiniMax’s</a> Hailuo AI, and Shengshu’s Vidu.

PixVerse hits unicorn valuation with new Series C funding

Venture Capital

Artificial Intelligence

Deep Tech

Singapore

Southeast Asia

<a href="/company/li-auto">Li Auto’s</a> fourth-quarter results for 2025 suggest stabilization after a difficult year, but not a full recovery.
The Chinese <a href="/industry/ev">electric vehicle</a> maker returned to a slim quarterly profit in the December period. It posted net income of RMB 20.2 million (USD 2.9 million) after a third-quarter loss, while vehicle margin improved to 16.8% from 15.5%. Free cash flow also turned positive at RMB 2.5 billion (USD 363.3 million), compared with negative RMB 8.9 billion (USD 1.3 billion) in the previous quarter.
The year-on-year picture remained weak. Revenue fell 35% to RMB 28.8 billion (USD 4.2 billion), vehicle sales declined 36.1% to RMB 27.3 billion (USD 4 billion), and the company remained in the red with an operating loss of RMB 442.6 million (USD 64.3 million).
That makes the quarter more useful as evidence of operational control than of restored earnings power. Li Auto did show it could recover from a weak third quarter without sacrificing gross margin entirely, with gross margin rising to 17.8% from 16.3% in Q3. The comparison with last year remains more important. Gross margin was 20.3% in the fourth quarter of 2024, and Q4 2025 operating performance remained well below that prior-year base.
The company’s own disclosures suggest caution when interpreting the quarter-on-quarter improvement. Li Auto said the sequential rise in vehicle margin was mainly due to the estimated Li Mega recall cost recorded in the third quarter, partly offset by a lower average selling price caused by product mix changes after the start of Li i6 deliveries. In other words, part of the rebound reflected a cleaner comparison base rather than a clear improvement in underlying demand or pricing.
Expenses provided some support. Q4 operating expenses fell 1.3% from the prior quarter, helped by a 4.4% decline in selling, general, and administrative expenses to RMB 2.6 billion (USD 377.8 million). R&amp;D spending remained high at RMB 3 billion (USD 436 million) as Li Auto continued investing in <a href="/industry/ai">artificial intelligence</a> and product development. That mix aligns with management’s current message: the company is trying to preserve technology spending while tightening its sales and operating model.
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The transcript adds a clearer frame for 2026. Management is positioning the year around three levers: tighter store-level execution in its direct sales network, a new L-series cycle led by the all-new Li L9, and a steadier ramp in battery EV models including the i6, i8, and the upcoming i9. Founder and CEO Li Xiang said the company is still targeting 20% year-on-year sales growth in 2026, even as he described the period as the most competitive year yet in China’s high-end automotive market.
That ambition still runs ahead of the near-term numbers. Li Auto guided first-quarter 2026 deliveries of 85,000–90,000 vehicles and revenue of RMB 20.4–21.6 billion (USD 3.0 billion–3.1 billion), both below the level recorded a year earlier. Management is clearly asking investors to look beyond the current trough toward a new product cycle. The guidance, however, does not yet show a clear path toward recovery.
The balance sheet gives Li Auto room to make that argument. The company ended 2025 with RMB 101.2 billion (USD 14.7 billion) in cash, and Q4 operating cash flow was positive at RMB 3.5 billion (USD 508.6 million). That cushion matters because Li Auto is not attempting to cut its way back to growth. Management said 2026 R&amp;D spending should remain around RMB 12 billion (USD 1.7 billion), with roughly half tied to AI-related initiatives including chips, computing power, and autonomous driving.
Still, the full-year numbers show how much ground the company must recover. Total revenue fell 22.3% in 2025 to RMB 112.3 billion (USD 16.3 billion). Operating margin turned negative 0.5% from positive 4.9% in 2024. Net cash from operations swung to an outflow of RMB 8.6 billion (USD 1.2 billion) from an inflow of RMB 15.9 billion (USD 2.3 billion) a year earlier, and free cash flow dropped to negative RMB 12.8 billion (USD 1.9 billion) from positive RMB 8.2 billion (USD 1.2 billion).
The result leaves Li Auto with a credible stabilization story, but not yet a clear turnaround. The fourth quarter showed that margins and cash flow can recover when comparisons become cleaner and execution improves. What it did not yet show is that the company has rebuilt the earnings base it had a year earlier, or that the expected 2026 product cycle is already visible in reported growth.
Note: RMB figures are converted to USD at rates of RMB 6.88 = USD 1 based on estimates as of March 13, 2026, unless otherwise stated. USD conversions are presented for ease of reference and may not fully match prevailing exchange rates.

Li Auto ends 2025 on a steady note, but turnaround remains in progress

East Asia

Electric Vehicles

Dreame's entry into the chip race is becoming increasingly multifaceted. In addition to the previously reported <a href="https://kr-asia.com/dreame-backed-nxmind-enters-commercialization-with-tianqiong-chips-targets-orbital-computing">Tianqiong chip series</a>, NXMind, a chipmaker incubated by Dreame, outlined several additional chip development programs at this year's Appliance &amp; Electronics World Expo.
Fu Haiyang, head of NXMind, told 36Kr that the results of these five chip initiatives are intended not only for Dreame's internal use but may eventually be offered to external partners.
According to information shared at the launch event, one initiative involves a smartphone system-on-chip (SoC) called Chixiao 01, designed with an emphasis on improved graphics performance. The processor will support full-scene ray tracing at 120 frames per second at 1.5K resolution.
The Chixiao 01 will also feature a proprietary neural processing unit architecture capable of delivering <a href="/industry/ai">artificial intelligence</a> computing power of up to 200 TOPS (tera operations per second). By comparison, Qualcomm's latest flagship processor, the Snapdragon 8 Elite, offers around 80 TOPS of AI computing performance.
NXMind is also developing an integrated autonomous driving chip designed for Level 4 self-driving systems. The company aims to deliver computing power of 2,000 TOPS per chip, roughly three times the capability of most mainstream smart driving chips currently on the market. That target would position the product in direct competition with Nvidia's flagship autonomous driving platform, the Drive Thor series.
According to the company, the chip will integrate Dreame's technological capabilities in areas such as computer vision and LiDAR (light detection and ranging), and will be manufactured using a two-nanometer process node.
NXMind is simultaneously developing a processor for personal computers. The chip is expected to deliver computing power of 1.5 petaflops and will feature a unified memory architecture that supports multi-device interconnection and networking.
These efforts join the Tianqiong chip series, which is designed to support robotic vacuum cleaner control. According to Fu, Tianqiong chips will use a heterogeneous computing platform that combines multi-core CPUs, dedicated NPUs, and an independent microcontroller unit. The chip will be manufactured using a 22-nanometer process and is expected to debut in Dreame's robotic vacuum cleaners scheduled for release in the second half of this year, enabling the machines to navigate more complex environments with greater precision in obstacle detection and avoidance.
As previously reported, NXMind is also developing the Yaotai series of space computing modules designed for deployment in computing satellites. The company aims to achieve industry-leading reliability, lifespan, performance, and stability.
The long-term goal is to build a space-based supercomputing cluster consisting of up to two million computing satellites, roughly twice the size of Elon Musk's SpaceX Starlink constellation.
whereRelatedArticle
Despite the scale of its ambitions, however, sources told 36Kr that among the five chip categories announced, only the general <a href="/industry/robotics">robotics</a> chip has so far entered the tapeout and mass production stage.
Another person familiar with the matter told 36Kr that Fu, the head of NXMind, does not have a prior background in the semiconductor sector, and Dreame's chip team currently numbers only a few hundred people.
Chipmaking is a capital-intensive and highly competitive business. NXMind is targeting several areas simultaneously, including robotics SoCs, smartphone processors, autonomous driving chips, and space-based computing infrastructure, in an attempt to provide foundational computing power across industries such as robotics, smartphones, automotive, and aerospace.
At the same time, these efforts are intended to support Dreame's broader product ecosystem.
One advantage for Dreame lies in its downstream presence. Its established robotics hardware products and ecosystem provide an existing market for some of these chips, while its core business continues to generate relatively stable cash flow.
Even so, overtaking global incumbents across five vastly different semiconductor segments at the same time remains an uphill challenge for most companies to achieve alone.
KrASIA features translated and adapted content that was originally published by 36Kr. This <a href="https://36kr.com/p/3719316885304961">article</a> was written by Qiu Xiaofen for 36Kr.

Dreame unveils Chixiao 01 smartphone processor among five new chip initiatives

Dreame

Semiconductors

StairMed, a brain-computer interface (BCI) developer, has secured RMB 500 million (USD 72.7 million) in a funding round led by <a href="/company/alibaba">Alibaba Group</a>, with participation from SDIC Unity Capital. Existing investors including <a href="/company/tencent">Tencent</a>, FountainBridge Capital, OrbiMed, Oriza Seed, Qiming Venture Partners, Lilly Asia Ventures, Source Code Capital, and SSCI Leading Fund also joined the round. Springhill Fund served as financial adviser.
With this deal, StairMed has raised a total of RMB 1.1 billion (USD 159.9 million) over the past year.
Last December, the company unveiled its second-generation high-throughput wireless invasive BCI system, WRS02, which increases electrode channels to 256. Beyond restoring motor control, the system is also designed to explore language reconstruction as a potential future application.
By early this year, using a self-developed surgical robot, StairMed completed clinical implantation of its 256-channel BCI system and validated brain-controlled interaction functions. The company plans to begin multicenter registration clinical trials later in 2026, aiming to enroll and implant approximately 40 patients within the year.
The year 2026 could mark an important stage for BCI development. While technical systems are approaching real-world deployment, policy developments are also providing momentum. Last year, China’s National Healthcare Security Administration released guidance aimed at establishing pricing frameworks for neurological medical services. The guidance includes BCI-related procedures as independently billable medical services, signaling institutional support for integrating BCI technologies into mainstream healthcare.
In a previous interview, Zhao Zhengtuo, co-founder of StairMed, said the clinical value of BCI-based therapies could be verified within the next three to five years, particularly in improving patients’ quality of life and ability to work.
“In five to ten years, [BCIs] will begin to show potential in consumer health,” Zhao said. “Because BCIs enable direct communication between humans and machines, they could redefine human-computer interaction and dramatically enhance efficiency.”
For BCI platforms, however, technological limits extend beyond hardware. Ultra flexible neural electrodes, integrated system design, and clinical validation are all important, but the depth of neuroscience research and understanding of the brain ultimately determines how far these systems can advance.
whereRelatedArticle
In that process, data may become a decisive factor.
“The company that accumulates the largest and richest brain datasets will be best positioned to define the next wave of applications and technologies,” Zhao said. Once large volumes of data are collected from a single brain region performing specific tasks, researchers could train what he described as a “foundation model” for brain data, similar to how large <a href="/industry/ai">artificial intelligence</a> models are trained today.
Such a model could then be applied to new patients, potentially improving the initial decoding performance and brain control capabilities of their BCI systems.
The participation of technology companies such as Alibaba could accelerate that effort. Capabilities in multimodal large models, computing infrastructure, intelligent hardware, and ecosystem development could eventually combine with StairMed’s hardware and clinical translation experience, potentially supporting the development of next-generation BCI systems and related applications.
From the beginning, StairMed envisioned BCI technology eventually reaching consumer-grade applications. The company chose to start in healthcare because it provides both a necessary regulatory pathway and an area with urgent real-world demand, offering a practical environment for refining the technology.
“Our hope is not just to restore motor or sensory function, but also to push the boundaries of human control,” said Li Xue, another co-founder of StairMed. “In the past, we controlled limbs with our brains. In the future, we might control external devices via BCIs, potentially surpassing normal human limits.”
Early results already suggest that possibility. Paralyzed patients implanted with BCI devices have reportedly shown advantages in specific tasks, such as cursor control speed, after training.
Looking ahead, the convergence of BCIs, AI, and advanced hardware could allow people to control intelligent agents and external devices with complex intent through thought alone.
In StairMed’s long-term vision, the brain could connect with multiple external systems through BCIs, bringing the concept of human–machine integration closer to practical use.
KrASIA features translated and adapted content that was originally published by 36Kr. This <a href="https://36kr.com/p/3720572708665989">article</a> was written by Hai Ruojing for 36Kr.
Note: RMB figures are converted to USD at rates of RMB 6.88 = USD 1 based on estimates as of March 13, 2026, unless otherwise stated. USD conversions are presented for ease of reference and may not fully match prevailing exchange rates.

Alibaba leads RMB 500 million investment in BCI startup StairMed

Biotechnology

Health Tech

… consumption patterns are undergoing structural change. Spending is increasingly driven by self-reward, quality upgrades, and emotional or lifestyle experiences rather than purely functional needs.
These emerging consumption patterns have fueled economic activity while creating new supply chains. Supporting such companies in the capital market could diversify the industrial structure of China’s stock market and allow investors to participate in the growth of new consumer trends.
<h3>A broader exit channel could reshape expectations</h3>
For investors, reforms could have far-reaching implications for both venture capital and public market valuations.
Historically, venture capital firms have been cautious about backing emerging consumer brands because even successful companies in these sectors often struggled to qualify for A-share listings due to unstable earnings or unconventional business models.
A more flexible ChiNext listing framework would provide clearer expectations for exit opportunities. This could encourage investors to back earlier-stage startups and smaller companies that may not yet be profitable but demonstrate strong growth potential.
Over time, such a shift could stimulate greater capital inflows into emerging consumer sectors and create a cycle linking industrial innovation with capital formation.
Another factor shaping the discussion is valuation.
Historically, consumer companies listed in the A-share market have traded at higher valuation multiples than their counterparts in Hong Kong.
At present, consumer stocks in the mainland market often trade at price-to-earnings ratios above 30 times, compared with roughly 18 times for consumer companies in Hong Kong.
For example, Tsingtao Brewery trades at roughly 18 times earnings in the mainland market, while China Resources Breweries trades at about 13 times in Hong Kong. Beingmate trades at around 46 times earnings in the mainland market, whereas Feihe, known internationally as Firmus, trades at under 12 times in Hong Kong.
Several mainland-listed liquor companies command even higher valuations. Yanghe trades at roughly 37 times earnings, Shuijingfang around 32 times, and Shede Spirits about 111 times.
Part of this divergence reflects differences in market structure. Hong Kong’s market includes a larger share of discretionary consumer companies, including retail, food service, and apparel businesses, which tend to experience larger valuation swings during economic cycles. By contrast, the mainland market has historically been dominated by staple consumer companies whose valuations are generally more stable.
These valuation gaps suggest that if emerging consumer companies gain a pathway back to the mainland market, their potential market capitalization could expand significantly. That prospect could enhance shareholder returns and attract long-term institutional capital.
For the A-share market itself, the impact could also be structural.
For years, China’s consumer sector in the mainland stock market has been dominated by traditional blue chip companies such as liquor makers and home appliance manufacturers, leaving the valuation framework relatively static.
The arrival of new-generation consumer companies could reshape that landscape. As more consumer brands listed in Hong Kong potentially return to the mainland market, the valuation structure of China’s consumer sector may gradually evolve.
Companies with durable competitive advantages and sustainable growth prospects would likely command more stable valuations, while speculative stocks driven primarily by hype could see their premiums fade. Over time, that shift could move China’s consumer equity market toward a more disciplined and sustainable pricing framework.
KrASIA features translated and adapted content that was originally published by 36Kr. This <a href="https://36kr.com/p/3718189771863427">article</a> was written by Wang Hanyu for 36Kr.
Note: RMB figures are converted to USD at rates of RMB 6.88 = USD 1 based on estimates as of March 12, 2026, unless otherwise stated. USD conversions are presented for ease of reference and may not fully match prevailing exchange rates.

China signals ChiNext reforms to tilt listings toward consumer companies

NXMind, a company within <a href="/company/dreame">Dreame's</a> ecosystem, officially unveiled its Tianqiong chip series on March 11. It said the chips have entered mass production and will soon be deployed across Dreame's robotics product lineup.
Demand for computing power is expanding at a pace that exceeds the historical trajectory of Moore's Law amid the <a href="/industry/ai">artificial intelligence</a> boom. According to a <a href="https://openai.com/index/ai-and-compute/">2018 analysis by OpenAI</a>, compute used in the largest AI training runs has doubled every 3.4 months since 2012, increasing by more than 300,000-fold over that period. As large models continue to scale in parameter size, and edge intelligence evolves from basic perception to more complex decision-making, computing power has emerged as a central constraint in the next phase of AI competition.
At the same time, global computing supply faces structural bottlenecks. Developers are approaching the physical limits associated with Moore's Law, slowing improvements in transistor density. Capacity for advanced semiconductor manufacturing processes also remains constrained, creating uncertainty in chip supply. In addition, terrestrial data centers face limits related to energy consumption targets, cooling efficiency, and land availability, making it difficult to sustain the pace at which AI computing demand is expanding.
According to a <a href="https://www.iea.org/reports/electricity-2024">2024 report</a> by the International Energy Agency, global data center electricity consumption could exceed 1,000 terawatt-hours by 2026, roughly equivalent to Japan's annual electricity usage. Rising energy demand, cooling constraints, and site availability have become three major factors limiting the expansion of computing infrastructure.
As a result, competition in the semiconductor sector is undergoing a structural shift. Edge chips are evolving from single-purpose components into system-level intelligent hubs, with highly integrated system-on-chip designs becoming key enablers of embodied intelligence. At the same time, the physical deployment of computing infrastructure is expanding beyond Earth's surface toward architectures that integrate space, air, and ground networks. Computing nodes in low Earth orbit (LEO) are emerging as a new frontier, with global technology companies exploring deployments in this domain.
Wu Zhongze, former vice minister of China's Ministry of Science and Technology, said at this year's Appliance &amp; Electronics World Expo that chipmakers are quietly reshaping how the world operates. In recent years, China has promoted deeper integration across innovation chains, industrial supply chains, capital networks, and talent pipelines within the chip and computing sectors to support long-term industry development.
From a broader perspective, the rise of edge intelligence is redefining chip design paradigms. As embodied intelligence and humanoid robots move from laboratory research toward industrial deployment, chips are no longer just computing components. Instead, they serve as the backbone connecting perception, understanding, decision-making, and execution.
Robots operating in complex and dynamic environments must respond in real time, placing strict requirements on latency, power consumption, and reliability. Traditional general-purpose chip architectures often struggle to balance performance and efficiency under these conditions. This has accelerated the development of specialized system-on-chip architectures, where heterogeneous computing units integrate perception fusion, decision planning, and motion control within a single chip. The result is an edge intelligence system capable of operating locally on the device.
NXMind's Tianqiong chip series reflects this shift. The chip features a heterogeneous computing platform composed of a multi-core CPU, a dedicated neural processing unit, and an independent microcontroller unit.
NXMind said the chip will be deployed by Dreame to support functions such as LiDAR (light detection and ranging), AI-driven vision fusion, and binocular obstacle avoidance algorithms. These capabilities are intended to improve navigation and obstacle avoidance in complex home environments.
The company's differentiation strategy builds on Dreame's existing technology base. Rather than developing a chip platform independently from the ground up, NXMind draws on Dreame's accumulated algorithm expertise, supply chain ecosystem, and operational data generated from product shipments. According to the company, this enables a collaborative design approach that links chip architecture with algorithms.
Scenario-defined chips have increasingly become a strategy for semiconductor companies seeking to establish competitive barriers.
Meanwhile, the structure of computing infrastructure is evolving. As ground-based data centers encounter physical and environmental constraints, space-based computing deployments are being explored as an alternative approach.
LEO provides a vacuum environment that naturally supports heat dissipation, along with continuous access to solar energy. In theory, this could enable higher-density computing deployments with lower cooling costs. Space-based computing nodes could also provide onboard processing for global LEO satellite internet systems, space-based remote sensing data, and deep space exploration missions, reducing delays associated with transmitting data back to ground stations.
According to 36Kr, NXMind's first Yaotai space computing module is scheduled to launch into orbit in March. The launch is intended to begin construction of a supercomputing center in LEO and initiate preliminary verification of an in-orbit computing network. The move also suggests that NXMind is transitioning from an R&amp;D phase toward early commercialization.
NXMind's current business portfolio spans smartphone processors, autonomous driving chips, robotics chips, space-based computing technology, and personal AI computers. Together, these initiatives form a broad product ecosystem.
This trajectory reflects a wider shift across the AI sector. Computing power is expanding from centralized cloud infrastructure toward distributed edge environments, while also extending into integrated architectures that connect space, air, and ground systems.
KrASIA features translated and adapted content that was originally published by 36Kr. This <a href="https://36kr.com/p/3718486572037507">article</a> was written by Huang Nan for 36Kr.

Dreame-backed NXMind enters commercialization with Tianqiong chips, targets orbital computing

<a href="/company/tencent">Tencent</a>, long known for its measured and cautious style, has begun making a series of assertive moves in <a href="/industry/ai">artificial intelligence</a>.
On March 6, it launched a promotional campaign offering free OpenClaw installation sessions assisted by its employees, drawing thousands of people to queue outside its office building. On March 9, it unveiled WorkBuddy, a general AI agent. A day later, on March 10, it began internal testing of QClaw, an AI assistant. These moves appear to be only the beginning.
36Kr has learned that Tencent's WeChat is experimenting with developing its own independent AI model. The model has already completed foundational capability development and received an internal codename, with plans for a public rollout later this year.
In the near term, the model will be integrated into WeChat's mini program ecosystem, allowing developers to build various AI agents. Over the longer term, WeChat aims to explore applications that embed AI more deeply into its social ecosystem. For example, by drawing on users' long-term behavioral data within WeChat, the company is exploring how AI could improve efficiency and enhance the user experience inside the app.
When DeepSeek emerged a year ago, Tencent was among the fastest companies to respond. It integrated Yuanbao with <a href="/company/deepseek">DeepSeek</a>, quickly driving a surge in daily active users. For some time afterward, however, Tencent remained relatively quiet in the AI agent race. It was not until Doubao's daily active users surpassed 100 million later in the year that Yuanbao began responding more aggressively, backed by Tencent's financial resources.
A report from QuestMobile shows that after the Lunar New Year campaign ended, Yuanbao's daily active users largely fell back to pre-event levels, failing to sustain the momentum created during the promotion period.
Sources familiar with the matter described several themes discussed during a WeChat executive meeting in December 2025, when the company reviewed its AI strategy.
First, WeChat must possess built-in AI tools that do not rely on third-party systems.
Second, WeChat essentially consists of three components: person-to-person social interaction, information acquisition, and efficiency tools. Social relationships themselves cannot be replaced by AI, meaning AI's role can only emerge in the information and productivity layers.
Earlier, during Tencent's third-quarter 2025 earnings call, president Martin Lau outlined a broader vision. In his view, WeChat's ultimate blueprint is to launch an AI agent of its own.
“WeChat's ecosystem includes a communications and social network that allows agents to understand users' needs, intentions, and interests,” Lau said. “It also has a content ecosystem, […] a mini program ecosystem that covers most use cases on the internet, a commercial ecosystem that allows people to purchase goods, and a payment ecosystem that enables transactions almost instantly. Taken together, this could become the user's ideal assistant, one that understands needs and can complete tasks within the ecosystem.”
WeChat already holds a unique advantage over Qwen and Doubao. It is the only app with more than 1.4 billion monthly active users, offering a natural pathway to adoption and usage. More importantly, that position is supported by two scarce assets: vast social networks and a mini program ecosystem that spans e-commerce, transportation, local services, and government services.
If WeChat's AI agent can connect both the social graph and the mini program service system, it could orchestrate these capabilities across its existing user base. For rivals still spending heavily to acquire users, this could pose a significant competitive challenge.
For the same reason, however, building an AI agent within WeChat may prove more complex.
The key capability of an AI agent is understanding user behavior and context. Yet the foundation of any social platform is trust. Users are willing to chat, discuss work, and exchange private information on WeChat because they assume their data is secure. Once AI becomes involved, users may grow uneasy about privacy.
According to an unnamed source cited by 36Kr, one of WeChat's central considerations during development has been ensuring that users can approach AI as a tool without feeling overly monitored, since access to personal or corporate data, often sensitive, may be required to power these capabilities. As a result, each step of development is being approached cautiously.
Since the AI wave began, Tencent's deepest internal concern has revolved around a single question: could a new breakout product emerge that might disrupt WeChat, much like how WeChat once replaced phone contact books and legacy messaging platforms?
For now, however, that moment of disruption has yet to arrive. Regardless of how technology evolves, fundamental human needs, including connection and belonging, are unlikely to disappear. In that sense, AI still cannot replace real human relationships. On that front, WeChat continues to hold an advantage that is difficult to displace.
WeChat's decision to invest in its own model therefore serves both as defense and reinforcement. It also reflects Tencent's longstanding product philosophy: the first product released is not necessarily the final centerpiece.
KrASIA features translated and adapted content that was originally published by 36Kr. This <a href="https://www.36kr.com/p/3717835616335497">article</a> was written by Xiao Sijia for 36Kr.

WeChat explores building in-house AI model as Tencent ramps up AI push

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