AI in China
Huawei’s AI-Centric Network Vision: Six Imperatives for the Next Decade; Critical Questions for IEEE Techblog Community
The Case for AI-Native Networks:
At MWC Shanghai 2026 [1.], David Wang, Deputy Chairman of the Board and Rotating Chairman of Huawei, outlined a strategic roadmap for AI-native mobile networks, positioning artificial intelligence as the cornerstone of industry growth over the next decade.
Note 1. MWC Shanghai 2026 was held June 24–26, 2026 at the Shanghai New International Expo Center (SNIEC), with Huawei showcasing products and solutions in Hall N1.
Over the past 40 years, innovation in mobile technology from each generation to the next has been key to the industry’s success. “With each generation, we have pushed the limits of spectral efficiency and performance,” said Wang. “Network architecture has gradually flattened, with new application scenarios and services emerging left and right. This has consistently expanded the boundaries of communications, helping carriers translate network capabilities into commercial value,” he added.
Huawei argues that traditional telecom infrastructure built around data traffic is no longer sufficient. As the global digital ecosystem transitions toward real-time interactions with AI applications and intelligent agents, mobile and transport networks must be completely redesigned to support both communication and computing. According to Huawei, an AI-native architecture transforms networks from simple communication utilities into revenue-generating engines while helping operators transition to Level-4 and Level-5 network autonomy
Huawei’s Six Strategic Imperatives:
Wang identified six imperatives to guide the industry through the age of intelligence:
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Developing new services and capabilities for future mobile communications systems
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Integrating AI with mobile communications to build three distinct layers of intelligence
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Building network architecture for integrated satellite-ground communications
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Advocating for sustainable and future-oriented spectrum planning and allocation
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Clearly defining the specifications of AI-native core networks
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Exploring new business models and application scenarios for mobile services
Photo Credit: Huawei
Innovations Unveiled: Byte and Token Monetization:
Huawei released a portfolio of innovations targeting both services and infrastructure. On the services side, in collaboration with China’s three major carriers, the company announced advances in 5G-Advanced (5G-A) high-uplink and experience monetization, AI-powered business upgrades, and token monetization.
For infrastructure, Huawei launched the AI-centric target network, designed to enhance carrier competitiveness in byte and token monetization. This architecture comprises three layers:
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Basic Communications Network: A shift from traffic-centric to real-time interaction networking, offering guaranteed connectivity with high uplink and downlink capabilities alongside advanced QoS mechanisms.
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Computing Network: A transition from traffic transport to network-wide compute scheduling and supply, where “connecting to the network is equivalent to accessing compute.”
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AI Computing Infrastructure: High-performance, efficient compute with support for open-source and open ecosystems.
5G-Advanced: 100 Million Users and Beyond:
The global 5G-A (based on 3GPP Release 18) user base has surpassed 100 million. Huawei is now working with network operators worldwide to advance 5G-A experience monetization and integrate it into installed base operations, targeting mid-range and high-end user retention, ARPU growth, and sustainable revenue expansion.
High Uplink Speed: The New Frontier for AI Applications:
High uplink capacity is critical for token monetization. Emerging AI applications—such as multimodal AI glasses for real-time translation and augmented exhibitions—demand uplink speeds of 20 Mbps or higher. In 2026, leading carriers globally are piloting commercial high-uplink services with guaranteed peak speeds, latency, and universal uplink performance.
Upper-6 GHz: The Next Golden Band:
The proliferation of AI agents is expected to drive rapid growth in token services, requiring ultra-broadband networks with high uplink, high reliability, and low latency. Upper-6 GHz (U6 GHz) is positioned as the next-generation golden frequency band for this purpose.
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More than 20 countries and regions have designated U6 GHz for IMT, covering nearly 80% of the global population.
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2026 marks the commercial debut of U6 GHz, with the Middle East expected to deploy the world’s first commercial 5G-A network on U6 GHz.
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Select carriers in Hong Kong and Macao will also initiate commercial U6 GHz deployment.
AI-Native B2C and B2B Services:
Huawei plans to collaborate with carriers in Guangdong, Shanghai, Hebei, and other regions in 2026 to reengineer B2C and B2H services with AI, targeting consumer applications such as smart home assistants, personal communication assistants, and integrated consumer-home services. In the B2B segment, the focus is on AI computing services centered on compute-network integration, unlocking new business growth avenues.
Path to Level-4 Autonomous Networks:
Huawei is advancing AI-native technologies toward Level-4 autonomous networks by developing domain-specific intelligence. In 2026, the company will work with carriers to deploy domain-specific intelligence across wireless and transmission network domains in key regions. This will enable cross-domain synergy in maintenance, optimization, energy efficiency, and user experience, enhancing network quality and enabling differentiated products for high-speed rail, event venues, and campuses.
Critical Questions:
Huawei’s AI-centric network vision positions AI not as an incremental improvement to mobile networks but as a foundational network architecture. That vision raises several critical questions for the IEEE community and IEEE Techblog readers:
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Interoperability: How does Huawei’s AI-centric target network align—or conflict—with AI-RAN Alliance initiatives and O-RAN specifications?
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Vendor Comparison: How does Huawei’s AI-centric target network compare with Ericsson’s cloud RAN strategy and Nokia’s Altiplano/Corteca agentic AI platforms in terms of technical architecture and commercial viability?
- Specifications and Standards: What role will 3GPP and ITU-R play in standardizing AI-native core network specifications, particularly for token monetization and compute-network integration?
- Autonomous Networks: How do Huawei’s domain-specific intelligence approaches compare with vendor-neutral SMO/rApp ecosystems, and what are the implications for multi-vendor interoperability?
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Are carriers adequately prepared for the operational and cultural shifts required to transition from traffic monetization to token monetization?
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How will U.S./European regulatory frameworks (e.g. spectrum policy, AI governance, data sovereignty) shape the deployment of AI-native networks compared to China’s more centralized approach?
- Spectrum Policy: With Upper 6 GHz emerging as a key enabler for AI-driven token services, what are the regulatory and coexistence challenges, particularly in regions yet to designate Upper 6 GHz for IMT 2030? What will WRC 2027 decide?
Conclusions:
Huawei’s roadmap underscores the ICT industry’s rapid shift towards AI token monetization, positioning 5G-Advanced high-uplink, AI-native networks, and Upper 6 GHz spectrum as the foundational pillars for the next decade of growth. The success of this vision depends not only on technological feasibility but also on standards alignment, regulatory support, and carrier willingness to reinvent business models—a complex challenge that warrants close scrutiny from the IEEE technical community.
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References:
https://www.huawei.com/en/news/2026/6/mwcs-ai-byte-token
https://carrier.huawei.com/minisite/mwcs2026/en/
https://www.huawei.com/en/news/2026/6/mwcs-gsma-asac-5g-advanced
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Analysis & Economic Implications of AI adoption in China
Executive Summary:
Visible signs of artificial intelligence adoption in China are everywhere. Consumers interact seamlessly with chatbots, livestream hosts promote algorithmically selected products, and recommendation engines exhibit an almost anticipatory understanding of user preferences. Yet, beyond these consumer-facing applications, a deeper and potentially more consequential transformation is unfolding. Across China’s retail and services sectors, AI is shifting from demand generation to cost optimization. Enterprises are deploying machine learning in logistics, inventory management, customer service, and fulfillment operations to reduce inefficiencies as revenue growth slows and pricing power tightens.
Highlights:
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Chinese companies are increasingly using AI to control operational costs and improve efficiency in a low-growth economic environment.
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AI is being deployed in logistics, inventory management, and customer service to reduce expenses rather than primarily drive demand.
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This shift towards AI for cost reduction is leading to steadier cash flow and improved operating margins for consumer companies.
China’s Consumer Sector: AI Powers Efficiency Over Growth:
As China’s economy adjusts to structural deceleration—marked by subdued household confidence, persistent real-estate overhang, and maturing market saturation—consumer companies face an unfamiliar imperative: prioritize resilience over expansion. With pricing power eroded and cost inflation persistent, traditional growth levers have lost potency. Leading platforms are responding by reorienting AI investments toward operational efficiency, transforming algorithms from engagement engines into margin-defense mechanisms. For investors, this evolution signals a new phase of earnings potential—one where incremental productivity gains could prove more durable than cyclical demand recovery.
“In a low-growth environment, incremental efficiency gains matter more than top-line expansion,” notes Zhao Ming, senior analyst for China internet companies at Hongyuan Capital. “AI has become a strategic lever for margin preservation.”
China’s consumer sector entered 2026 navigating familiar structural headwinds: cautious household sentiment, a fading property-wealth effect, and fierce price competition. Unlike in previous cycles, companies are finding it increasingly difficult to pass rising costs on to consumers. The result has been a strategic realignment. Where past growth phases emphasized volume and engagement, today’s market is rewarding operational discipline. That shift has sharpened the appeal of AI—not as a marketing showcase, but as a core instrument of productivity and cost control.
“In a slower-growth environment, leading Chinese consumer companies are using AI primarily to improve productivity and reduce operating costs rather than to drive incremental demand,” McKinsey said in a recent analysis of AI adoption across China’s retail and services sectors.
From Growth Catalyst to Cost Lever:
The center of gravity for AI investment has shifted from customer-facing innovation to operational optimization. E-commerce platforms and logistics operators have been among the earliest to integrate AI into mission-critical workflows. Demand-forecasting models are helping warehouses fine-tune inventory levels and reduce exposure to slow-moving goods. Routing algorithms are compressing last-mile delivery times and cutting fuel consumption. Automated customer-service systems are deflecting an ever-larger share of inquiries typically handled by human agents.
On their own, each of these applications may appear incremental. Taken together, they represent a meaningful improvement in margin resilience at a time when top-line expansion remains constrained. In an environment where minor percentage-point gains in efficiency can significantly affect earnings quality, AI is emerging as a quiet but potent differentiator.
Logistics as a Testbed for Scalable Efficiency:
The operational impact of AI is most visible in the logistics ecosystem, a sector that remains one of the largest cost centers in China’s consumer economy. Machine-learning systems are now proficient at forecasting order density by neighborhood and time of day, enabling fulfillment centers to position inventory closer to anticipated demand. In dense urban markets, adaptive algorithms continually adjust delivery routes in response to evolving conditions—from traffic and weather to cancellations and reorders—reducing both transit times and redundancy.
For investors, the value proposition is compelling: logistics efficiency scales. Once AI models are trained and stress-tested, they can be deployed across regions at low incremental cost, generating operating leverage even in periods of stagnant demand. Crucially, incumbents benefit from data scale. Years of transaction and delivery records translate into more accurate predictive models, reinforcing competitive moats and raising barriers to entry. This dynamic is reshaping industry structure even as consumer-facing platform features converge toward commoditization.
AI Extends Gains to Physical Retail:
Beyond e-commerce, brick-and-mortar retail—long considered a laggard in China’s digital transformation—is also seeing measurable efficiency dividends. Smart shelving, computer-vision inventory systems, and automated stock monitoring are cutting labor intensity while increasing inventory turnover. Grocery and convenience chains now rely on AI to optimize product assortments at the store level, calibrating selections to localized consumption patterns instead of applying national averages. The effect is twofold: reduced waste and fewer markdowns, both of which have historically weighed on profitability. The outcomes may not register as eye-catching innovation, but they align closely with investor priorities—stabler cash flows and predictable margins.
Labor Efficiency as a Strategic Imperative:
AI-enhanced customer service represents another underappreciated margin driver. Major consumer platforms report that routine customer interactions—order tracking, returns, product troubleshooting—are now predominantly handled through automated systems. This transition is particularly relevant in a labor market where wage growth continues to outpace consumption. Limiting headcount growth while maintaining response times and service quality has become a key operational goal.
“AI doesn’t replace customer service,” says Li Wenyuan, chief technology officer at retail software firm Qimeng Tech. “It filters it, so humans deal only with the expensive problems.” That filtering function is transforming customer operations from cost centers into scalable service platforms, balancing efficiency with user satisfaction.
Economic Implications:
For investors, the impact of China’s second-wave AI adoption will likely manifest less in headline growth metrics and more in incremental financial performance indicators. Key areas to watch include:
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Operating margin expansion driven by process automation
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Reduced fulfillment and logistics costs as a share of revenue
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Improved capital-expenditure efficiency through data-driven asset utilization
The first chapter of China’s AI consumer story was about differentiation—using algorithms to personalize experiences, boost engagement, and drive sales. The next chapter is about discipline. As growth normalizes, companies are deploying AI to do more with less: compress costs, stabilize earnings, and build leaner, more adaptive operating models. In a market where scale alone no longer guarantees profitability, AI has become not just a tool for innovation—but a mechanism for survival.
References:
https://www.barrons.com/articles/china-ai-boom-commerce-warehouses-b1ad55f1
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