B2B AI Localization and Future Trends
This chapter focuses on two core issues: the current state and opportunities of domestic substitution for China's B2B AI, and the evolution trends of B2B AI over the next 5 years.
1. Current State of Enterprise AI Domestic Substitution
1.1 The Three-Layer Structure of Domestic Substitution
China's enterprise AI domestic substitution is divided into three layers:
| Layer | Content | Current Status | Representative |
|---|---|---|---|
| Foundational models | Domestic large models | Have caught up with the top tier | Ernie, Tongyi, DeepSeek, Doubao, Zhipu |
| Platform/Cloud | Domestic AI cloud platforms | Intense competition | Baidu Qianfan, Alibaba Bailian, Tencent Cloud |
| Industry applications | Domestic industry solutions | Differentiated competition | Vertical vendors in finance/government/healthcare |
1.2 Localization Rates Across Segments
| Segment | Localization Rate | Description |
|---|---|---|
| General-purpose large models | ★★★★☆ | Already close to overseas (DeepSeek, etc.), narrowing the reasoning capability gap |
| AI chips (training) | ★★☆☆☆ | NVIDIA still dominates; domestic (Ascend, Cambricon) accelerating catch-up |
| AI cloud platforms | ★★★★☆ | Domestic cloud vendors dominate (overseas clouds cannot enter) |
| Industry applications | ★★★☆☆ | Domestically led in government and enterprise markets, but deep applications are still being refined |
| Development frameworks/tools | ★★★☆☆ | Primarily PyTorch open source; domestic frameworks (MindSpore) as supplements |
1.3 Drivers of Domestic Substitution
- Policy push: "AI+" action, Xinchuang (information technology application innovation), East Data West Computing
- Data security: Finance, government, and healthcare require data to remain within their domains, mandating domestic private deployments
- Supply chain security: Sanctions on chips and computing power are forcing the development of a domestic computing ecosystem
- Cost considerations: Price wars among domestic models (DeepSeek and others drastically cutting prices) improve cost-effectiveness
2. Opportunities and Challenges of Domestic Substitution
2.1 Opportunities
- Huge government and enterprise market: Finance, government, telecom operators, and energy are major customers, with policy-driven procurement favoring domestic products.
- Data compliance dividend: Data-residency requirements create a strong demand for domestic private deployment.
- Cost advantage: The inference cost of domestic models is dropping rapidly, lowering the barrier for enterprise adoption.
- Deep scenario cultivation: China's rich industry scenarios (manufacturing, retail, payments) provide fertile ground for AI implementation.
2.2 Challenges
- Underlying computing power gap: High-end GPUs are subject to sanctions, and domestic training chips still have a performance gap.
- Immature ecosystem: The CUDA ecosystem is monopolistic, and the domestic chip software stack needs to be improved.
- Weak willingness to pay: Compared with overseas markets, domestic SMBs have a lower willingness to pay for software.
- Homogeneous competition: Price wars at the model layer, and vertical scenarios are prone to herd behavior.
- Profitability challenges: Project-based gross margins are low, SaaS monetization is difficult, and most vendors experience "revenue growth without profit growth."
3. Eight Major Trends in B-end AI Over the Next 5 Years
Trend 1: Comprehensive Implementation of Agent-based Solutions
From "AI Assistants" to "AI Employees": Multi-Agent collaboration completes complex business workflows. Enterprises will see the emergence of "digital employee" roles, and AI Agents will become standard productivity tools.
Trend 2: Deepening of Verticalization / Industry-Specific Large Models
Beyond general-purpose models, industry-specialized models (finance, healthcare, legal, manufacturing) continue to deepen, building moats through industry data and know-how.
Trend 3: RAG Becomes the Standard, with "Enterprise Knowledge" at the Core
Enterprise proprietary knowledge + RAG retrieval-augmented generation becomes the standard; enterprise knowledge bases (documents, data, experience) serve as the core carrier of AI value.
Trend 4: Edge-Cloud Synergy + Privacy-Preserving Data Computing
AI operates collaboratively across cloud and edge; privacy computing and federated learning make data "usable but invisible," meeting compliance requirements.
Trend 5: Pay-for-Performance Becomes the New Paradigm
From "selling software" to "selling outcomes": revenue sharing based on labor hours saved and conversion rate improvements. AI vendors and customers share aligned interests, reshaping trust and pricing.
Trend 6: AI-Native Organizations and Process Restructuring
More than just "tool stacking," enterprises restructure their organizations and processes around AI (human-machine collaborative teams, AI workflow orchestration), with AI deeply integrated into governance.
Trend 7: From Efficiency Gains to Decision-Making
AI advances from "helping people work" (efficiency) to "helping people decide" (prediction, optimization, autonomous decision-making); Decision Intelligence becomes a high-value capability.
Trend 8: Compliance and Trustworthy AI Become the Baseline
AI governance (explainability, auditability, hallucination prevention), data compliance, and security evaluation become entry requirements for B-end procurement; trustworthy AI determines whether vendors can access highly regulated industries such as finance and healthcare.
4. Future Landscape Assessment of China's B-end AI
4.1 Landscape Layering
First Tier (Platform Layer): Cloud vendors (Baidu/Alibaba/Tencent/Huawei) + Leading model companies (Zhipu/DeepSeek)
Second Tier (Application Layer): Vertical Agent/SaaS companies (customer service/legal/code/healthcare, etc.)
Third Tier (Ecosystem Layer): Integrators, channels, industry ISVs (project delivery)4.2 Key Judgments
- Model Layer: Winner-takes-all, price war accelerating, eventually 2-3 platform-level companies will remain
- Application Layer: Vertical scenarios flourish, "small but refined" players have opportunities, but must go deep into industries
- Project Layer: Project-based model has low gross margins, but the government/enterprise market is the foundation of domestic AI
- Profit Inflection Point: Expected in 2025-2026, some vertical SaaS companies will achieve scaled profitability first
5. Recommendations for Industry Participants
For Entrepreneurs
- Don't build general-purpose models (red ocean, heavy cash burn, can't compete with tech giants)
- Focus on vertical scenarios: seek out industries with "high willingness to pay + clear ROI + data moats" (legal, customer service, coding, finance support)
- Prioritize RAG and private knowledge base scenarios (this is the opportunity for SMBs)
For Large Enterprises
- First pursue "risk control + cost reduction" (clearest ROI), then "efficiency gains + revenue growth"
- Data governance comes first: 80% of AI effectiveness depends on data quality
- Build AI organizational capabilities: develop business and IT teams that understand AI
For Investors
- Focus on companies with healthy "scenario + data moat + unit economics"
- Beware of "revenue growth without profit growth" (high model costs, long sales cycles)
- Watch renewal rates and LTV/CAC, not just revenue growth rate
Chapter Summary: Driven by policy, data compliance, and cost advantages, China's B-end AI is accelerating domestic substitution, with government and enterprise markets as the bedrock. Over the next 5 years, Agentification, Verticalization, Pay-per-outcome, and Trustworthy AI will be the four main trends. The true winners will be those companies that "create quantifiable value in core business flows + build data/scenario moats."