On February 4, 2026, several important signals emerged from overseas AI-native infrastructure space. Enterprise AI deployment is accelerating deeper into relational databases and recommendation systems, edge computing and AI integration have taken a key step through standardization protocols, hyperscale cloud vendors continue to increase AI computing investments, and the open source ecosystem is making breakthroughs in both high-performance models and local agent frameworks.
🧭 Key Highlights
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🌐 EdgeLake advances to LF Edge Stage 2, integrating Model Context Protocol for real-time edge data access
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💻 OpenAI launches desktop Codex app, GPT-5.2 lands on Snowflake Cortex AI
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🏢 Oracle plans to invest $45-50 billion in 2026 to expand OCI computing capacity
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📱 Meta to embed LLMs in recommendation systems, 2026 AI capital expenditure projected at $11.5-13.5 billion
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🇰🇷 Korean consortium exports complete AI tech stack to Saudi Aramco Digital
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🧬 StepFun releases Step 3.5 Flash open-source MoE model, 196B total parameters, 11B activated
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⭐ OpenClaw GitHub stars reach 145k, local agent framework rapidly gaining adoption
Data Path and Edge Computing
🌐 EdgeLake Advances to LF Edge Stage 2, Accelerating AI-Edge Data Integration
According to the Linux Foundation official announcement, the EdgeLake project has advanced to LF Edge Stage 2 maturity and integrated Model Context Protocol (MCP), enabling agents and large language models to query distributed edge data directly through natural language and SQL.
The introduction of MCP protocol allows AI workloads to directly access distributed edge data sources without first centralizing data in cloud data centers, reducing latency and bandwidth consumption. This “edge data in-place” availability pattern complements traditional centralized vector retrieval in RAG architectures, enabling local decision-making in factories, retail stores, and base stations without cloud round-trips.
Enterprise AI Deployment
💻 OpenAI’s Dual Push: Desktop App and Data Warehouse Integration
According to Fortune, OpenAI has launched a desktop Codex application featuring the GPT-5.2-Codex model; meanwhile, GPT-5.2 has landed on Snowflake Cortex AI platform, enabling users to build multimodal agents on their own data through SQL.
The desktop application shows AI expanding from browsers and APIs to local native applications, reducing usage friction for developers. Snowflake Cortex AI integration demonstrates an “AI goes to data” deployment model—enterprises can run inference directly within data warehouses without exporting proprietary data, particularly important for privacy-sensitive industries like finance and healthcare. When relational databases become runtime environments for AI inference, AI infrastructure boundaries descend to the enterprise data layer.
📱 Meta Embeds LLMs in Recommendation Systems, AI Deeply Integrates into Social Infrastructure
According to Medianama, Meta plans to embed large language models in Facebook, Instagram, and Threads recommendation systems while testing intelligent shopping assistants and business assistants. The 2026 AI capital expenditure budget is projected at $11.5-13.5 billion, primarily for AI training and ranking systems.
Embedding LLMs within recommendation systems means AI capabilities are descending from “application layer” to “system layer.” When recommendation systems possess semantic understanding and contextual reasoning capabilities, platforms can more precisely match user interests with content, especially in long-tail interest and cold-start scenarios. However, deploying LLMs in real-time recommendation systems poses compute scheduling challenges—the cost per refresh is far higher than traditional models, potentially requiring caching, distillation, or hybrid architectures to balance effectiveness and cost.
Computing and Cloud Infrastructure
🏢 Oracle’s $45 Billion Bet on OCI, Hyperscale Clouds Race for AI Computing
According to The Register, Oracle plans to raise $45-50 billion in 2026 to expand OCI (Oracle Cloud Infrastructure) computing capacity to meet AI demands from OpenAI, xAI, Meta, NVIDIA, and other clients.
This investment plan confirms continued tight supply-demand expectations for AI computing in 2026. Oracle has gained significant share in AI training markets through OCI, partly due to its ability to quickly provide large-scale GPU clusters and optimize network interconnects. When a single vendor plans to invest nearly $50 billion in capacity expansion within a year, the entire industry’s GPU, data center, and network equipment supply chains will be affected. Rapid expansion of computing supply may bring price pressure in 2026-2027, but more abundant computing also enables larger-scale model training and more complex system architectures.
National and Industrial AI
🇰🇷 Korea Exports Complete AI Tech Stack to Saudi Arabia, National AI Infrastructure Goes Global
According to Chosun English, a Korean consortium has partnered with Saudi Aramco Digital to export a complete AI technology stack, including AI chips, industry LLMs, and cloud infrastructure, serving energy and manufacturing scenarios.
This collaboration demonstrates cross-border export of “national AI full-stack” models. Unlike single product exports, this transaction covers the complete chain from hardware (AI chips) to software (industry LLMs) to platforms (cloud infrastructure), meaning Saudi Arabia can rapidly establish local AI capabilities based on Korea’s technology stack. This model is attractive for countries seeking rapid AI deployment, especially in energy and manufacturing industries dependent on specialized models. The Korea-Saudi cooperation may become a typical case of “technology exporter + scenario provider” models.
Open Source Ecosystem
🧬 StepFun’s Step 3.5 Flash Open Source, High-Speed MoE Model Reduces Inference Costs
According to Reddit community discussion, StepFun released Step 3.5 Flash open-source large language model, adopting sparse MoE (Mixture of Experts) architecture with 196B total parameters and approximately 11B activated parameters, supporting 256K context length, with inference speed reaching 100-300 tokens per second (hardware-dependent).
The design of 196B total parameters with 11B activated means the model only calls approximately 5.6% of parameters per inference, significantly reducing memory and computational requirements compared to equivalent-scale dense models. 256K context length enables processing long documents, codebases, and complex conversations, particularly important for enterprise RAG systems and coding assistant scenarios. As the open source community continues exploring MoE architecture, we may see more “large model capability, small model cost” solutions emerge.
⭐ OpenClaw Hits 145k Stars, Local Agent Framework Rapidly Gains Adoption
According to Medium summary, the OpenClaw project has gained approximately 145,000 stars on GitHub and is listed as a key technical dependency in OpenAI’s Codex desktop application, marking rapid adoption of local agent development frameworks.
OpenClaw’s adoption reflects developer demand for “local-first” AI deployment models. Unlike agents dependent on cloud APIs, OpenClaw allows running models and toolchains on local devices, which is particularly important for data privacy-sensitive scenarios, low-latency requirement environments, and offline workflows. When OpenAI’s official application lists it as a dependency, it indicates local agent frameworks have moved from community experiments to production readiness. The maturity of local agent frameworks may drive AI workflows from “cloud API calls” toward “local-cloud hybrid” architectures.
🔍 Infra Insights
Today’s news points to two structural trends in AI infrastructure: enterprise deployment descent and open source ecosystem maturation.
Enterprise AI is moving from “external service calls” to “internal system integration”—whether LLM inference in relational databases or semantic models in recommendation systems, AI capabilities are embedding into enterprise core infrastructure. Meanwhile, the open source ecosystem is making breakthroughs in both models and frameworks: MoE architecture reduces inference costs for large-scale models, while local agent frameworks provide developers with deployment options independent of cloud vendors. When open source solutions can match or approach closed-source product performance, AI infrastructure barriers to entry will gradually lower.
Parallel development of edge computing, enterprise deployment, and open source ecosystems indicates AI-native infrastructure is evolving from single-cloud models to distributed, multi-layer complex systems.