The Second Half of Specialized Chips: From Process Competition to Deep Industry Scenarios

In 2025, as global semiconductor growth is reignited by AI-driven demand, a profound shift is underway: the competitive focus of the industry is moving away from the pursuit of general-purpose, standardized computing power toward custom-defined computing architectures built around vertical industry scenarios.

Wang Jiangping, member of the National Committee of the CPPCC and former Vice Minister of the Ministry of Industry and Information Technology, has explicitly stated that specialized chips targeting specific model architectures will experience explosive growth within the next one to two years. Behind this forecast lies a “silent revolution” in which application scenarios reverse-define hardware architectures.

Trend: Structural Transformation of the Chip Industry

For decades, the semiconductor industry followed a process-driven interpretation of Moore’s Law: improving general-purpose computing performance by shrinking transistors and increasing density. However, with the exponential evolution of complex scenarios such as large AI models, autonomous driving, and intelligent manufacturing, the “averaging” architecture of general-purpose chips (CPUs, GPUs) is increasingly exposing severe efficiency bottlenecks when handling specific tasks.

On one hand, there is a striking gap between performance and energy consumption. Application-specific integrated circuits (ASICs) optimized for neural network computation—such as Google’s TPU—can deliver 15 to 30 times the performance of contemporary general-purpose GPUs on similar tasks, while achieving 30 to 80 times greater energy efficiency. Under the dual pressures of carbon reduction goals and rising computing costs, this efficiency advantage has shifted from “nice to have” to mission-critical.

On the other hand, there is the urgent demand for industrial autonomy. From industrial wireless communication to automotive smart cockpits, control over core chips has become a cornerstone of supply chain security and sustained innovation. China’s ASIC market is expected to reach RMB 58.3 billion by 2025, driven by industries’ growing demand for specialized, trusted, and high-efficiency computing power.

McKinsey, in its Technology Trends Outlook 2025, lists application-specific semiconductors as a key technology shaping the future, noting that competition has shifted from single-chip performance to a comprehensive capability encompassing scenario adaptability, system-level coupling efficiency, and energy control. In other words, those who understand scenarios best will hold the key to defining the next generation of computing power.

Challenge: The Achilles’ Heel of Industry-Specific Chips

Yet the path to the explosion of specialized chips is far from smooth. The core paradox lies in this dilemma: those who understand chips do not understand industries, and those who understand industries cannot define chips.

Chip design is an extraordinarily complex technical system, involving long chains of architecture design, logic design, physical implementation, packaging, and testing. Traditional semiconductor companies excel in silicon physics and circuit design, but often lack deep understanding of industry-specific know-how—such as the dynamic, unstructured, and rule-implicit logic underlying financial risk control, insurance actuarial models, energy dispatching, or chemical process control.

Conversely, industry leaders possess deep insight into business pain points but lack the capability to translate vague business requirements into precise hardware languages—such as instruction set architectures (ISA), memory hierarchies, and data paths. This software–hardware disconnect has led many specialized chips to remain at the level of “customization for customization’s sake,” failing to penetrate the most complex and energy-intensive computational cores of industries, thereby severely limiting their performance gains.

True industry-grade specialized chips require not incremental tuning of general architectures, but architectural rebirth from first principles, built around specific data flows, algorithms, and constraints. This demands designers who possess both deep industry understanding and broad computational insight.

Breaking the Deadlock: FundeAI’s Role and the “Trinity” Approach

It is precisely within this no-man’s land of software–hardware integration that FundeAI, by virtue of its unique ecosystem position, demonstrates the potential to become a chief architect of industry-specific chips. Its strategy is not to fabricate chips itself, but to create an indispensable value hub through a three-in-one pathway: scenario definition – algorithm abstraction – architecture mapping.

1. A “Scenario Computing Panorama” Based on Dynamic Ontology

FundeAI’s core strength lies in the industrial cognition enabled by its Kunlun Dynamic Ontology Platform. Across insurance, finance, and energy sectors, the platform organizes complex business entities—customers, policies, equipment, transactions—and billions of dynamic relationships into machine-understandable and inferable industrial knowledge graphs.

This effectively forms a “scenario computing heatmap”. It precisely identifies which business processes involve massive, rule-stable yet extremely time-consuming computations (such as multi-million-node risk association scans in underwriting), and which real-time decisions are constrained by existing computing power (such as instantaneous portfolio risk calculations in high-frequency trading). This map represents the highest-fidelity blueprint for defining specialized chip computing requirements.

2. Deep Refinement from Software Algorithms to Hardware “Operators”

Based on this panoramic view, FundeAI’s Wuzhen Foundation Model and Zu Chongzhi domain models enable unprecedented “hardware-friendly” reconstruction and refinement of core algorithms.

For example, in financial anti-fraud scenarios, traditional algorithms repeatedly traverse complex graph relationships on general-purpose computing units. Through close collaboration between the dynamic ontology framework and algorithm teams, the most critical and time-consuming components—such as multi-hop associative risk diffusion patterns—can be abstracted and solidified into a series of specialized graph-computing operators. These operators represent the core logic that should be hardware-accelerated on future specialized chips. This ability to penetrate directly from business problems to hardware instructions is key to resolving the software–hardware divide.

3. Architecture Leadership and Ecosystem Aggregation as a “Demand-Side Representative”

Armed with clear scenario requirements and algorithmic operators, FundeAI transforms into a technical translator and co-creator, bridging industries and the semiconductor ecosystem.

On one hand, through strategic partnerships with NVIDIA and domestic computing power providers, FundeAI can deeply participate in defining the features of next-generation computing cards or accelerator modules, driven by its extreme requirements in finance and energy—pushing architectures toward better support for industrial graph computing and real-time stream processing.

On the other hand, at the frontier, FundeAI can initiate industry scenario alliances, opening validated architecture requirements and operator standards to professional chip design firms (such as domestic ASIC design service companies), jointly defining architectures for solutions like Financial Risk Control Processing Units (FPUs) or Energy Dispatch Specialized Chips. This represents the ultimate industry-scale realization of software-defined hardware.

The Future: From Enablement to Co-evolution — A New Ecosystem of Industrial Computing Power

The significance of FundeAI’s approach extends far beyond internal cost reduction and efficiency gains.

It points toward a new paradigm of industrial computing power: a domain-specific computing system driven backward from core scenarios and designed through software–hardware co-optimization.

In the short term, this will significantly strengthen efficiency barriers within the FundeAI ecosystem. In insurance, complex underwriting and claims investigations may shrink from hours to minutes; in energy, real-time grid dispatching and safety warnings will become more precise.

In the long run, FundeAI has the opportunity to productize and platformize this “scenario–algorithm–architecture” definition methodology. Just as ontologies defined a common language for data analysis, FundeAI may define the hardware abstraction layer for industrial intelligent computing, becoming a critical middleware between vertical industries such as finance and energy and the underlying computing infrastructure.

As the chip industry approaches physical limits in process competition and falls into homogenized rivalry, industrial scenarios open up a vast new blue ocean of value.

The rise of specialized chips is not the endpoint—it is the starting point of industrial intelligence. In this transition from “computing power supply” to “value supply,” only by anchoring on scenarios and bridging adaptation gaps can specialized chips truly take root in industrial soil, transforming from technical showcases into engines of productivity.

FundeAI will continue to leverage its three-in-one structural framework to bridge the adaptation gap between scenarios and chips, working alongside industry partners to unlock the limitless potential of computing power in the deep waters of specialized chip innovation.