The Wrap | 21 - 27 Feb 2026
A weekly digest of what mattered in Asia’s tech stack
Editor’s Note: Another AI heavy week. The biggest moves came from incumbents setting the terms on price, capacity, and where workloads can run. Across Asia, the questions are growing sharper: how cheap can AI get, how quickly can compute expand, and what constraints are already showing up in power, networks, and governance.
Alibaba Bundles China’s Top Models into A Low-Cost Coding Tool
Alibaba is pushing into AI coding tools by selling a subscription that lets users switch between several Chinese models, including Qwen 3.5 plus models from Zhipu, Moonshot, and MiniMax. The pricing is deliberately mass-market: the lite tier is 7.9 yuan (about US$1.15) for the first month, then 40 yuan (about US$5.82) thereafter. The pro tier starts at 39.9 yuan (about US$5.81) and goes up to 200 yuan (about US$29.11).
By bundling multiple models behind one workflow and one price point, Alibaba is trying to make AI-assisted coding a default habit, not a special purchase. For operators, this is the practical version of ‘priced by incumbents: AI becomes a line item you can roll out widely, but it also increases the need for controls, because cheap access makes it easier for usage to sprawl across teams without clear governance.
Signals To Watch:
Whether other clouds copy the bundle model (multi-model switching, one subscription).
Whether enterprises start treating coding assistants like a standard seat licence.
China Models Take The Lead As MiniMax And Moonshot Top Token Usage
Chinese open-source models from MiniMax and Moonshot topped OpenRouter’s latest token-usage ranking, after a wave of new releases pushed Chinese models ahead of U.S. labs on the platform. MiniMax’s M2.5 (launched about two weeks ago) ranked first with 4.55 trillion tokens, while Moonshot’s Kimi K2.5 (released last month) ranked second with 4.02 trillion tokens. MiniMax, Moonshot, and DeepSeek together made up nearly two thirds of total token usage among the top five models.
Token usage on a neutral hosting platform tells you where developers are actually spending time and money, which tends to shape tooling, integrations, and what shows up on enterprise shortlists next. It also tightens the set of “default” choices, which is helpful for speed but risky for resilience. If a small group of models becomes the default across teams, you inherit concentration risk: pricing power shifts to the winners, and you need a clear plan for governance, support, and switching costs before dependency hardens.
Signal To Watch:
Whether enterprise software vendors start supporting these models by default.
Whether pricing tightens as usage concentrates and capacity gets scarce.
Whether buyers start requiring stronger operational guarantees: audit logs, incident response, uptime commitments, and clear data boundaries.
Malaysia Freezes New Non-AI Data Centers
Malaysia has stopped approving new data centers that are not related to AI because of rising electricity and water demands. Prime Minister Anwar Ibrahim said Malaysia’s current energy supply is sufficient for the next one to two years, but the country will need more sources longer term, including the ASEAN Power Grid. The policy has effectively been in place for about 1.5 to 2 years.
For operators, this is the hard constraint behind AI scale in Southeast Asia. Compute is starting to be treated like a limited resource that gets allocated. That changes rollout planning, contract terms, and where you place workloads. It also shifts leverage: when capacity is scarce or gated, vendors and landlords set the rules.
Signals To Watch:
Clearer definitions of what qualifies as “AI-related” for approvals.
Power and water requirements showing up as stricter conditions in contracts and permits.
More workloads designed to run across multiple countries from day one.
SK Hynix Plans A US$15 Billion Expansion Of Chip Production In Yongin
SK Hynix said it will invest 21.6 trillion won (about US$15.07 billion) to build new semiconductor production lines in Yongin by 2030. For operators, this is a reminder that AI budgets are being shaped by the supply chain. The biggest costs in AI systems are still hardware and availability, especially for memory-heavy configurations. Announcements like this do not help your next purchase cycle, but they do matter for 12–36 month planning because they are one of the clearest signals on whether component shortages and pricing pressure might ease, or whether AI infrastructure stays expensive and capacity-constrained.
Signals To Watch:
Whether lead times improve for AI-grade components and systems.
Whether pricing pressure eases or just moves to a different bottleneck.
More large capex commitments across the Korea–Taiwan–Japan semiconductor supply web.
Japan’s DOCOMO Uses Its Mobile Network Servers To Run AI Apps
NTT DOCOMO, Japan’s largest mobile operator, ran an AI application on the same general-purpose servers it already uses to run its virtualized mobile network (vRAN), inside its commercial network.
For operators, this is a shift in where AI can live. If telcos turn spare network compute into a managed AI layer, AI execution starts to sit alongside connectivity, not just inside hyperscaler clouds. That can help for latency-sensitive use cases, distributed operations, and situations where you want tighter control over where data is processed. The trade-off is dependency: you are buying into a telco platform, with telco tooling and telco terms. If this becomes a real product, buyers will need clear performance guarantees and a practical exit path.
Signals To Watch:
Whether telcos productize this with pricing.
What workloads it can support in production.
Portability: whether the setup can move across telcos/vendors without major rework.
Takeaway
This week’s signals point in the same direction: AI is getting packaged into low-cost seats, usage is concentrating around a few models, hardware investment is ramping, and networks are being positioned as a place to run workloads. The operator move is to make governance practical: set budget guardrails, standardize logging and approvals, and design for at least one credible fallback option from the start.
Sources
The Edge Singapore: Alibaba pushes deeper into AI coding tools with low-cost access
SCMP: China’s MiniMax, Moonshot top AI token use ranking, ending year of US dominance
Malay Mail: Malaysia freezes new non-AI data centres over power and water concerns, says Anwar
Evertiq: SK hynix to Invest $15 billion in Yongin Semiconductor Cluster
The Fast Mode: DOCOMO Demos AI Applications Running Directly on CPUs in Commercial vRAN Network