As 2026’s second AI wave moves from experimentation into large‑scale deployment, Chinese companies face a strategic fork: anchor their stacks to closed, fast‑to‑deploy platforms and risk long‑term vendor lock‑in, or build on open, interoperable foundations that preserve choice and control. At a recent SUSE media briefing in Shanghai, Peter Lees, SUSE’s Asia‑Pacific vice‑president and head of solutions architecture, framed the year as a turning point in which firms must decide whether to “embrace change or be wholly negated.”
SUSE, a leading independent open‑source software vendor relied upon by more than 60% of Fortune 500 firms, is positioning that choice around infrastructure rather than application features. Lees argues that the immediate convenience of closed “black‑box” AI platforms — rapid deployment and integrated stacks — masks significant downstream costs: price escalation, exclusionary ecosystems and limited upgrade paths that increasingly bind customers to a single supplier.
The tension is acute in China, where enterprises tend to move faster from pilot to production and operate under stricter regulatory constraints. Lees and SUSE Greater China solutions architect director Su Xianyang note that sectors such as finance, healthcare and manufacturing demand data sovereignty, traceability and model explainability, requirements that are hard to reconcile with opaque cloud or proprietary model services.
SUSE’s product response is twofold. First, it has released SLES 16, promoted as the first enterprise Linux tailored for “Agentic AI” use cases — systems of AI agents that act autonomously rather than merely respond. SLES 16 offers a 16‑year support window intended to align with long enterprise lifecycles, letting organisations stabilise a trusted base while layering rapidly changing models and applications on top.
Second, SUSE is pushing an open, containerised architecture for edge and manufacturing scenarios, bundling Rancher and K3s to enable centralised control of thousands of edge devices with zero‑downtime rollouts and one‑click rollbacks. This addresses a practical bottleneck for China’s advanced manufacturers, where traditional upgrades can force hours or days of production stoppage and thus significant revenue loss.
SUSE also emphasises operational tooling that tackles an increasingly painful reality of AI rollouts: runaway compute costs. The company’s Observability suite provides full‑stack visibility into AI services, GPU usage and resource consumption, making inefficient workloads visible and optimisable. Lees says such tools can materially raise GPU utilisation and curb avoidable expenditure, which is particularly important as large models scale and compute becomes the dominant line item in AI projects.
The SUSE executives caution against three common missteps in enterprise AI: launching projects for the sake of being on trend rather than to solve clearly defined business problems; prioritising innovation at the expense of compliance and security; and underestimating the cost dynamics of large‑scale models. Their prescription combines open foundations, long support cycles for stability, and observability to drive both compliance and cost control.
For international readers, the significance is twofold. First, 2026 marks a broader industry re‑rating of where value resides in the AI stack: increasingly at the infrastructure layer rather than in isolated applications. Second, the debate in China—between rapid deployment and sovereign, explainable systems—mirrors global tensions but is amplified by scale and regulation, making China a proving ground for enterprise AI governance models.
If enterprises conclude that protecting future choice is paramount, vendors with open‑source DNA and commitments to long‑term support will gain traction. Conversely, if the short‑term allure of turnkey closed systems remains dominant, many firms may trade flexibility for speed and accept the attendant strategic risks.
Either outcome will reshape procurement patterns for cloud providers, hardware vendors and systems integrators, and will influence how governments and regulators assess acceptable levels of control and transparency in AI systems deployed across critical industries.