Introduction: Computing Power Anxiety Ebbs, AI Industry Enters a New Era of "Algorithmic Parity"
In H2 2026, the global AI industry is undergoing a profound reconstruction of its underlying logic. Over the past three years, the capital market's frenzied pursuit of AI was built on "brute-force aesthetics"—computing power is authority, and parameters are barriers. However, with the better-than-expected prosperity of the open-source ecosystem and the maturation of model distillation technology, the training and inference costs of large models are declining faster than Moore's Law. Huagang Zhishi's trend forecast observations reveal that generative AI is accelerating its shift from the first half characterized by "brute-force computing" to the second half centered on "algorithmic parity" and "scenario penetration." This industrial paradigm shift not only breaks the computing power monopoly of tech giants but also brings a historic revaluation singularity to the HK-listed AI application industry chain capable of scenario landing.
1. Computing Power "Arms Race" Cools: Cost Declines Reconstruct Industrial Logic
Looking back at 2024 to 2025, global tech giants' capital expenditures in the AI sector primarily flowed into the procurement of high-end GPU clusters and the construction of ultra-large-scale data centers. At that time, the market believed in the "computing power moat" theory, assuming that whoever mastered the most computing power would win the race toward Artificial General Intelligence (AGI). But entering 2026, this logic is facing substantial falsification.
On one hand, open-source large models have infinitely approached or even partially surpassed closed-source models in performance. Thanks to the collaborative innovation of the global open-source community, model architecture optimizations (such as sparsified attention mechanisms and the deepening of MoE expert mixed architectures) have significantly improved training efficiency under the same parameter scale. On the other hand, the rise of edge-side computing power has broken the sole path dependence on "cloud-side inference." With the leap in built-in NPU computing power of smartphones, PCs, and IoT devices, an increasing number of large model inference tasks are being offloaded to the edge side. This not only drastically cuts bandwidth and inference costs for cloud service providers but also spawns a brand-new edge AI ecosystem.
According to industry model calculations, in Q3 2026, the end-to-end training cost of mainstream 100-billion-parameter large models has dropped by over 60% from its peak, while the latency for edge-side execution of complex multimodal inference has been reduced to the millisecond level. The ebbing of computing power anxiety marks the AI industry's official transition from "infrastructure-driven" to "application-driven."
2. The Era of "Algorithmic Parity": A Window of Counterattack for HK-listed Tech Companies
When computing power is no longer an insurmountable gap, "algorithmic parity" becomes the core industrial trend in H2 2026. "Algorithmic parity" refers to the popularization and homogenization of underlying large model capabilities, making the model itself no longer the core competitive barrier. What truly determines a company's success or failure is the ability to integrate it into specific business scenarios.
This trend is of extraordinary significance to the HK stock market. The HK tech sector has long been dominated by internet platform companies, which suffered from growth bottlenecks at the end of the previous mobile internet dividend cycle. However, it is precisely these internet giants with massive user bases, rich high-frequency interaction scenarios, and deep industry know-how that are ushering in a second growth curve in the era of "algorithmic parity."
Unlike US tech giants' asset-heavy investments in foundational model R&D, HK tech companies lean more toward "pragmatism"—fine-tuning open-source models combined with their own private domain data to build vertical industry large models. This strategy not only keeps capital expenditures controllable but also offers an extremely short monetization path. For example, in high-frequency enterprise SaaS services such as intelligent customer service, precision marketing, code assistance, and content generation, HK tech companies have achieved scaled embedding of AI capabilities, significantly improving gross margins.
3. Edge-Side Inference Explosion: A Davis Double Play for Consumer Electronics and Edge Computing Power
Against the macro backdrop of "algorithmic parity," the explosion of edge-side inference is undoubtedly the most noteworthy industrial inflection point in 2026. With the maturation of large model "slimming" technologies (such as quantization, pruning, and knowledge distillation), 10-billion-parameter-level multimodal models can now run smoothly on high-end smartphones and AR/VR devices.
This technological breakthrough has directly ignited a replacement cycle in consumer electronics. According to the latest industry forecast data, in H2 2026, global shipments of new-generation smart terminals with native edge-side AI running capabilities are expected to double year-on-year. The HK consumer electronics industry chain is thus ushering in a golden window of "rising volume and price": on one hand, the recovery in terminal shipments has driven an increase in overall capacity utilization across the industry chain; on the other hand, AI devices' requirements for higher-spec memory, cooling, and high-frequency signal transmission have lifted the per-unit value of components.
Even more noteworthy is the edge computing chip sector. Stimulated by the surging demand for edge-side AI, domestic edge AI chip manufacturers are experiencing a dual resonance of domestic substitution and demand explosion. Unlike the absolute monopoly in cloud-side training chips, the edge inference chip market landscape has not yet solidified, placing greater emphasis on low power consumption, high energy efficiency ratios, and scenario customization. In the HK market, design companies that laid out edge computing power, AIoT, and smart cockpit chips early are seizing market share with high cost-performance solutions, with their performance and valuations expected to achieve a Davis double play.
4. Panoramic Scan and Investment Forecast of the HK-listed AI Application Industry Chain
Based on the aforementioned trend forecast analysis, Huagang Zhishi believes that in H2 2026, the AI investment mainline in the HK market will undergo a fundamental migration: funds will shift from pure storytelling and concept speculation to AI application companies that can deliver actual commercialization data and demonstrate real profit improvements.
Specifically, the following three sub-sectors deserve investors' close tracking:
- Enterprise SaaS and Vertical Industry Large Models: Vertical SaaS service providers in the HK market with deep industry accumulation are transitioning their business models from "tool subscription" to "pay-for-performance" through AI technology. In vertical fields such as healthcare, legal, and finance, AI empowerment not only significantly reduces clients' labor costs but also improves decision-making efficiency. These companies possess strong customer stickiness and high switching costs, and the growth rate of their Annual Recurring Revenue (ARR) is the core metric for measuring their investment value.
- Edge Computing Power and Domestic AI Chips: With the popularization of edge-side AI devices, the demand for edge inference chips is showing exponential growth. Under the logic of autonomous controllability, the R&D progress of relevant HK-listed semiconductor design companies in the GPU/NPU field is worth attention. Investors should focus on examining their shipment volumes and market shares in high-growth tracks such as smart terminals and autonomous driving.
- Multimodal Content and Digital Marketing: The maturation of multimodal AI technologies such as text-to-image and text-to-video is disrupting the traditional digital marketing and content production landscape. Media and internet companies in the HK market with massive high-quality IP resources and mature traffic monetization channels are utilizing AI technology to drastically reduce content production costs and achieve personalized, one-to-one content delivery. This is not just an improvement in profit margins, but an elevation of the business model.
5. Risk Warnings and Trend Summary
Although "algorithmic parity" brings vast imaginative space to the HK-listed AI application industry chain, investment still needs to remain rational. First, the iteration speed of AI technology is extremely fast, and the rapid evolution of open-source models may instantly erase the vertical model barriers that companies have just built, requiring enterprises to possess continuously iterating data flywheels and scenario understanding capabilities. Second, global regulatory policies on AI-generated content are tightening, and data privacy and copyright compliance will become a Sword of Damocles hanging over the heads of AI application companies. Finally, consumer acceptance and replacement cycles for edge-side AI devices are still affected by macroeconomic fluctuations; if the consumption recovery falls short of expectations, the performance realization of the industry chain may face pressure.
In summary, the AI industry in H2 2026 is shedding its frenzied bubble and returning to business fundamentals. Moving from the "brute-force computing" arms race to the universal application of "algorithmic parity" is not only an inevitable law of technological evolution but also a comprehensive reshaping of investment logic. In the HK market, companies that can accurately grasp scenario entry points and achieve low-cost, scaled landing of AI technology will undoubtedly stand out at this industrial inflection point, becoming the core mainline of a new upward cycle. Investors should discard pure technology speculation and examine and lay out this true AI application revolution from a more rigorous commercial monetization perspective.
