Why are entry-level smartphones losing their market appeal?

On-device AI models like large language models and real-time image generation require significant RAM and NPU compute. The minimum memory threshold for smooth AI experience has jumped from 4GB to 8GB or even 12GB, leaving 1GB-4GB entry devices incapable of delivering modern AI features.

How is AI reshaping competition in the smartphone industry?

AI is restructuring the smartphone tier system. The mid-range segment (2000-4000 yuan) is becoming the new battleground where manufacturers must balance AI capabilities with costs. Entry-level makers face a dilemma: stay cheap and lose AI features, or upgrade AI chips and sacrifice margins. Chipmakers like Qualcomm and MediaTek are also ramping up mid-range AI chips, intensifying industry polarization.

What trends should consumers and investors watch in the smartphone market?

Entry-level phones may regress into basic feature phones or backup devices, while the mid-range segment will see fierce AI experience competition shifting from hardware specs to software optimization and AI ecosystem integration. The market depends on lightweight on-device AI models—if efficient inference can run on lower compute platforms, entry-level may rebound; otherwise mid-range competition will intensify further.

In the Age of AI, Budget Smartphones Are Losing Their Allure

As large language models and AI capabilities increasingly penetrate mid-to-low-end smartphones, consumers are showing a growing preference for flagship or near-flagship devices with larger memory and stronger compute. Budget phone makers face a dilemma: continue a price war with thin margins, or invest in AI-capable chips that drive up costs. TMTPost analysis suggests AI is reshaping the smartphone tier structure, with the mid-range segment emerging as the next battleground.

Background and Context

The smartphone industry is currently undergoing a fundamental structural transformation driven by the integration of artificial intelligence, a shift that is visibly reshaping market dynamics through the rapid contraction of the entry-level segment and the simultaneous rise of the mid-range tier. For the past decade, entry-level smartphones, traditionally defined by price points around one thousand yuan, constituted the largest volume segment globally, relying on a value proposition centered on extreme cost-efficiency and stable basic communication capabilities. However, the migration of generative AI and large language models from cloud servers to local device execution has fundamentally disrupted this long-standing equilibrium. As major manufacturers increasingly position on-device AI as a primary selling point, the technical requirements for running these applications have imposed strict hardware mandates. Smooth local inference of large language models, real-time image generation, and advanced voice assistants now necessitate specific thresholds for Random Access Memory (RAM) and the computational power of dedicated Neural Processing Units (NPU).

Data indicates that the minimum memory threshold required to run current mainstream on-device AI models has quietly jumped from 4GB to 8GB, and in some cases, 12GB. This technical leap effectively marginalizes the 1GB to 4GB memory configurations that previously defined the entry-level market, rendering them incapable of delivering a complete AI experience. Consequently, consumer purchasing preferences are shifting decisively toward flagship or near-flagship devices that offer larger memory capacities and superior computational power. This trend poses a severe challenge to traditional entry-level manufacturers, as their core market share is eroding due to the inability of their hardware to support the new AI-centric feature set that consumers now expect as standard.

Deep Analysis

From a technical and business model perspective, the introduction of AI functionalities has实质上 altered the valuation system of smartphones. In traditional hardware iterations, upgrades focused on CPU clock speeds, screen resolutions, and camera megapixels, where the perceived marginal utility of each upgrade diminished over time. This allowed manufacturers to maintain low-price strategies by cutting costs on non-core components. In the AI era, however, computational power and memory have become the "hard currency" determining product experience. On-device AI is not merely a software overlay but requires deep adaptation of the underlying hardware architecture. For instance, running a 7-billion-parameter large language model locally demands high memory bandwidth and low-latency data transmission, which directly increases the cost proportion of the System on Chip (SoC) and LPDDR memory.

For entry-level smartphone manufacturers, this creates an insurmountable dilemma. If they continue to adhere to low-price strategies, they cannot equip devices with AI-capable chips, causing their products to quickly fall behind in functionality and become inferior substitutes. Conversely, if they invest in upgrading to AI-enabled chips, the inherent business model of entry-level phones—high volume but low unit price—means that even minor increases in hardware costs can lead to a sharp decline in profit margins. In extreme cases, this could result in a scenario where selling a device incurs a loss. This rigidity in cost structure leaves entry-level manufacturers trapped between the need for technological innovation and the imperative of commercial survival, rendering the traditional model of "stacking specs while lowering prices" unsustainable in the AI age.

Industry Impact

This technological shift is having a profound impact on the competitive landscape, with the mid-range market rapidly replacing the entry-level segment as the primary battleground for major manufacturers. For brands like Xiaomi, Realme, and Redmi, which have historically focused on cost-performance ratios, as well as international giants such as Samsung and Motorola, mid-range devices—typically priced between 2,000 and 4,000 yuan—have emerged as the ideal testing ground for demonstrating AI capabilities while balancing costs. These devices usually feature 8GB to 12GB of RAM and mid-range chips equipped with NPUs, allowing them to run mainstream AI applications smoothly while leveraging economies of scale to amortize costs, thereby creating a new competitive advantage.

In contrast, manufacturers that remain entrenched in the entry-level market face immense survival pressures. Brands lacking core technological reserves risk being marginalized or even exiting the mainstream market entirely. Furthermore, this trend is accelerating the divergence among upstream chip manufacturers. Giants like Qualcomm and MediaTek are increasingly launching AI-optimized chips specifically for the mid-range market, such as the Snapdragon 7 series and the Dimensity 8000 series. These specialized components further solidify the technical barriers in the mid-range segment. For consumers, this signifies the end of the era of "low price and low specs." The price ladder for smartphones is becoming steeper, with manufacturers deliberately widening the experiential gap between entry-level and mid-range devices to guide users toward higher-priced tiers.

Outlook

Looking ahead, the stratification of the smartphone market will become increasingly pronounced, with AI capabilities serving as the core metric for distinguishing product tiers. It is anticipated that over the next two to three years, entry-level smartphones will gradually degenerate into pure "feature phones" or "backup devices," retaining only basic calling, messaging, and minimal application functions, while no longer bearing the burden of smart interaction. Simultaneously, the mid-range market will witness intense competition focused on "AI experience." Manufacturers will move beyond simple hardware parameter comparisons, shifting their focus to software optimization, AI ecosystem integration, and personalized service capabilities.

A noteworthy signal in the industry is that some manufacturers may attempt to lower hardware barriers through cloud service subscription models, offering low-cost hardware while charging for cloud-based AI services. However, this approach is constrained by concerns over network latency and data privacy, making it unlikely to become mainstream in the short term. The industry must closely monitor the progress of on-device AI model lightweighting. If efficient inference can be achieved on lower-compute platforms in the future, the entry-level market may see a turnaround. Otherwise, the intense competition in the mid-range market will continue to intensify, accelerating industry consolidation. For investors and practitioners, understanding this transition from "price competition" to "function and compute competition" is crucial for navigating the next growth cycle of the consumer electronics industry.