阿里电商 AI 新动向：围绕 Token 重构电商，组织迎来新一轮调整｜独家
文｜彭倩 编辑｜乔芊 杨轩 4月1日新财年伊始，由蒋凡统领的阿里巴巴中国电商事业群正在规划可以结合 AI 实现的增长点。 据36氪了解，淘天集团当下对 AI 的探索将聚焦在 AI to B 方向上，其核心 OKR 是商家侧的 AI 工具留存率以及 AI 带来的 GMV 增长。它的背景是进入2026年， Open Claw 引爆 AI Agent，阿里 CEO 吴泳铭提出阿里组建新的 Alibaba Token Hub（ATH ）事业群，所有关联业务需要围绕 Token 进行商业化。 组织变动也随之发生。据36氪了解，中国电商事业群 AI 业务的负责人张凯夫不再负责，承载 AI 业务的“智能搜推产品”事业部调整为“平台用户及产品”和“智能算法”两个部门，负责多模态的“未来创新事业部”则融入ATH 事业群。 “新的一年由 ATH 统领集团的 AI 战略，别的业务没必要重复造轮子。”一位接近淘天集团的消息人士这样告诉36氪。 去年年初，中国电商事业群成立核心部门搜推智能产品事业部，由张凯夫负责，过去一年他的主要工作是整合搜推算法、用户算法、商家算法和创作算法等多个团队
Overview and Context
文｜彭倩 编辑｜乔芊 杨轩 4月1日新财年伊始，由蒋凡统领的阿里巴巴中国电商事业群正在规划可以结合 AI 实现的增长点。 据36氪了解，淘天集团当下对 AI 的探索将聚焦在 AI to B 方向上，其核心 OKR 是商家侧的 AI 工具留存率以及 AI 带来的 GMV 增长。它的背景是进入2026年， Open Claw 引爆 AI Agent，阿里 CEO 吴泳铭提出阿里组建新的 Alibaba Token Hub（ATH ）事业群，所有关联业务需要围绕 Token 进行商业化。 组织变动也随之发生。据36氪了解，中国电商事业群 AI 业务的负责人张凯夫不再负责，承载 AI 业务的“智能搜推产品”事业部调整为“平台用户及产品”和“智能算法”两个部门，负责多模态的“未来创新事业部”则融入ATH 事业群。 “新的一年由 ATH 统领集团的 AI 战略，别的业务没必要重复造轮子。”一位接近淘天集团的消息人士这样告诉36氪。 去年年初，中国电商事业群成立核心部门搜推智能产品事业部，由张凯夫负责，过去一年他的主要工作是整合搜推算法、用户算法、商家算法和创作算法等多个团队
In the rapidly evolving first quarter of 2026, this development has attracted significant attention across the AI industry. According to reports from 36kr, the announcement immediately sparked intense discussions across social media and industry forums. Multiple industry analysts view this not as an isolated event, but as a microcosm of deeper structural changes in the AI sector.
Since the beginning of 2026, the pace of AI industry development has notably accelerated. OpenAI completed a historic $110 billion funding round in February, Anthropic's valuation surpassed $380 billion, and xAI merged with SpaceX at a combined valuation of $1.25 trillion. Against this macro backdrop, this development is no coincidence—it reflects a critical transition from the "technology breakthrough phase" to the "mass commercialization phase."
Deep Analysis
Technical and Strategic Dimensions
This development reflects several key trends in the current AI landscape. The industry is witnessing a fundamental shift from model capability competition to ecosystem competition—encompassing developer experience, compliance infrastructure, cost efficiency, and vertical industry expertise.
The technical implications are multi-layered. As AI systems become more capable and autonomous, the complexity of deployment, security, and governance increases proportionally. Organizations must balance the desire for cutting-edge capabilities with practical considerations of reliability, security, and regulatory compliance.
Market Dynamics
The market implications extend beyond the directly involved parties. In the highly interconnected AI ecosystem, every major event triggers cascading effects across the value chain:
- **Infrastructure providers** may see shifts in demand patterns, particularly as GPU supply remains constrained
- **Application developers** face an evolving landscape of tools and services, requiring careful evaluation of vendor viability and ecosystem health
- **Enterprise customers** are increasingly sophisticated in their requirements, demanding clear ROI, measurable business value, and reliable SLA commitments
Industry Impact
Competitive Landscape Evolution
The AI industry in 2026 is characterized by intensifying competition across multiple dimensions. Major technology companies are pursuing acquisitions, partnerships, and internal R&D simultaneously, attempting to establish advantages at every point in the AI value chain.
Key competitive dynamics include:
1. **The open-source vs. closed-source tension** continues to reshape pricing and go-to-market strategies
2. **Vertical specialization** is emerging as a sustainable competitive advantage
3. **Security and compliance capabilities** are becoming table-stakes rather than differentiators
4. **Developer ecosystem strength** increasingly determines platform adoption and retention
Global Perspective
This development also has implications for the global AI landscape. The US-China AI competition continues to intensify, with Chinese companies like DeepSeek, Qwen, and Kimi pursuing differentiated strategies—lower costs, faster iteration, and products more closely tailored to local market needs. Meanwhile, Europe is strengthening its regulatory framework, Japan is investing heavily in sovereign AI capabilities, and emerging markets are beginning to develop their own AI ecosystems.
Future Outlook
Near-Term Projections (3-6 Months)
In the near term, we expect to see competitive responses from rival companies, developer community evaluation and adoption feedback, and potential investment market re-evaluation of related sectors.
Long-Term Trends (12-18 Months)
Over a longer horizon, this development may catalyze several trends:
- **Accelerated commoditization of AI capabilities** as model performance gaps narrow
- **Deeper vertical industry AI integration** with domain-specific solutions gaining advantage
- **AI-native workflow redesign** moving beyond augmentation to fundamental process redesign
- **Regional AI ecosystem divergence** based on regulatory environments, talent pools, and industrial foundations
The convergence of these trends will profoundly reshape the technology industry landscape, making continued observation and analysis essential for stakeholders across the ecosystem.