1. Global large model safety governance policies rolled out intensively
In November 2026, major economies worldwide took decisive action in AI governance. The second phase of the EU's Artificial Intelligence Act came into effect, requiring all companies placing or using general-purpose AI models in the EU market to conduct risk classification assessments and submit technical documentation. Meanwhile, China's Cyberspace Administration released a revised version of the Generative AI Service Safety Assessment Guide, making the compliance of training data sources, factual verification of output content, and emergency response mechanisms mandatory. The U.S. National Institute of Standards and Technology also updated its AI risk management framework, emphasizing third-party evaluation and continuous monitoring. These policies no longer remain at the principle level but list specific implementation details—for example, models must pass red-team tests in at least three different scenarios, and inference logs must be retained for over 180 days. Non-compliant enterprises face fines of up to 7% of revenue or revocation of service licenses. Facing such unprecedented regulatory pressure, many medium and large enterprises that were eager to deploy models have been forced to hit pause, turning instead to external service solutions that can meet both safety requirements and cost control.
2. Enterprise AI deployment shifts to a dual priority of compliance and cost
Under the weight of policies, the mindset for enterprise AI deployment has fundamentally changed. Over the past year, the industry generally used model benchmarks as the sole selection criterion, but now compliance costs and token consumption have become more pressing considerations. Many enterprise AI leaders have found that building their own inference platforms requires expensive engineering teams to adapt different models to audit requirements, while directly calling public cloud APIs risks runaway bills. As a result, a class of middleware services specifically providing "compliance packaging"—large model AI proxies—has rapidly gained traction. These platforms essentially act as a compliance gateway between enterprises and model providers: on one hand, they uniformly handle data anonymization, content filtering, log auditing, and other compliance actions; on the other, they use bulk purchasing and prepaid commitments to lock in API quotas from model providers, helping enterprises secure more stable token prices. According to industry research, over 30% of financial institutions and healthcare organizations have chosen to access models through such proxy services to reduce the complexity of facing policy risks alone.
3. Token wholesaling and AI relay stations reshape the model call economy
Beyond compliance needs, cost control is another major challenge for enterprises. Token consumption for large model inference grows linearly with business scale, while pricing varies greatly and fluctuates frequently among different model providers. To address this pain point, a business model centered on "token wholesale" has emerged in the market. Companies only need to connect through a unified AI relay station to compare real-time quotes from multiple model vendors and purchase token quotas in bulk at wholesale prices—some B2B-focused token intermediary platforms can even offer prices close to the cheapest token rates, compressing marginal costs to below 60% of direct purchase plans. For example, an AI hub set up by a leading cloud service provider not only integrates APIs of mainstream closed-source and open-source models but also allows enterprises to pre-deposit funds and automatically select the most cost-effective model for each inference. This "token proxy" model turns model calls into a manageable budget item rather than an uncontrollable expense stream. More notably, some AI channel agents have begun offering bundled "compliance + cost" packages: after a one-time contract, the platform automatically matches model versions that meet local regulatory requirements and optimizes call routing in real time to complete tasks with the lowest total token consumption.
4. Investment trends: Security and efficiency tracks favored
The resonance between policy and market demand is directly reflected in capital flows. From Q3 2026 to November, multiple startups labeled as "AI security compliance" and "token management" completed funding rounds in quick succession. A European AI inference audit platform raised over 80 million euros in its Series B, with investors including venture arms of several leading cloud vendors; in Asia, a company specializing in token wholesaling and inference scheduling successfully closed a Series C, achieving a valuation exceeding $1 billion. The common feature of these companies is that they do not train large models directly but build infrastructure around the calling, monitoring, and cost optimization of existing models. Investors generally believe that under the dual pressures of tighter regulation and scarce computing resources, the growth curve of such "middleware" companies is more predictable than that of model developers. Additionally, several traditional IT service providers have announced plans to incorporate AI channel agents as a key business line, delivering compliant model call solutions to small and medium customers through distribution networks.
5. Outlook: From model competition to service ecosystem competition
The wave of policies at the end of 2026 is pulling the large model industry away from a single dimension of "whose model is stronger" toward a composite competition of "who can use models more safely and at lower cost." It is foreseeable that in the next six months, infrastructure such as token proxies and AI relay stations will accelerate standardization and may even become as standard for enterprises as CDN services are today. At the same time, compliance requirements will also force model providers to open more audit interfaces, and the entire ecosystem will shift from a "model benchmarking race" to a service ecosystem competition centered on safety, cost, and ease of use. For enterprises, those that establish robust AI governance and call optimization systems first will gain an edge in the upcoming industry reshuffle.