Tuesday, October 21, 2025

The Internet Is Changing: How AI Is Reshaping Traffic Flows, Routing Economics, and Network Architecture (2022–2025)

 


Abstract



The structure of the internet is undergoing a profound transformation driven by the adoption of artificial intelligence (AI)–mediated information retrieval. Traditional web navigation—search, click, and browse—is rapidly being replaced by direct AI-generated responses. Between 2022 and 2025, global web referral patterns have shifted markedly, resulting in measurable declines in organic traffic to content providers such as Wikipedia, and changing the fundamental economics of internet routing, traffic distribution, and network infrastructure design. This paper examines current data and research to characterize this transition, evaluates its impact on routing and interconnection models, and outlines the technical and institutional responses by router OEMs, cloud orchestrators, and internet governance bodies.





1. The Decline of the Search–Click–Browse Model



Recent web analytics confirm the erosion of the traditional search-driven browsing paradigm. According to DataReportal (2025), monthly Wikipedia visits declined by 18% between March 2022 and March 2025—approximately 200 million fewer visitors—with Semrush reporting a reduction in daily visits from 263 million to 226 million. In April 2025 alone, SimilarWeb measured a further 8.5% fall in organic search referrals.

These losses mirror a structural change in search behavior. Google’s AI Overviews, launched broadly in 2024, now appear in 13–14% of all queries (Semrush 2025). When an AI summary is shown, Ahrefs data reveal that the top organic result’s click-through rate (CTR) drops by roughly 33%, and the overall CTR for the first link has fallen from 28% to 19% (Search Engine Journal 2025). Bain & Company estimates that “zero-click” AI results have reduced organic traffic for many publishers by 15–25%.

The shift is not limited to search engines. Conversational assistants (ChatGPT, Claude, Perplexity.ai) increasingly intercept user intent and return synthesized answers without generating page impressions, disintermediating the web’s hyperlink economy.





2. Implications for Internet Routing and Traffic Patterns



From a network topology perspective, this evolution alters both traffic concentration and routing economics.


(a) Centralization around AI inferencing and aggregation hubs.

As users increasingly query AI systems hosted by hyperscalers (Google, OpenAI, Anthropic, Meta, Microsoft), outbound traffic from access ISPs consolidates toward a few large AI inference clusters. These clusters behave as super-destinations in Border Gateway Protocol (BGP) routing tables, concentrating global request flows through a small set of Autonomous Systems (ASNs). This consolidation strengthens the bargaining power of hyperscalers in peering arrangements and reduces the relative transit volume toward legacy web content providers.


(b) Enterprise workflow transformation: From 3-tier to MCP–LLM–API architecture.

Enterprise content discovery is shifting from browser-based applications to Model Context Protocol (MCP)–driven agents that query LLMs, invoke APIs, and retrieve content semantically rather than via HTTP browsing. This workflow transition redistributes traffic: latency-sensitive inference requests traverse edge nodes, while training and vector synchronization move large datasets toward centralized data centers. The resulting pattern inverts the legacy client-server hierarchy, producing edge-heavy east-west inference traffic and core-intensive north-south training flows.





3. Infrastructure and Industry Response



Router and switch OEMs.

Vendors such as Cisco, Juniper, and Arista have accelerated investment in AI-optimized routing fabrics. Cisco’s Silicon One and Juniper’s Express 5 chips integrate 800 G and 1.6 T interfaces with enhanced telemetry for AI workload visibility. Arista’s 7800R3 series and EOS CloudVision AI provide real-time congestion analytics aligned with distributed inference fabrics. The emphasis is on deterministic latency, adaptive flow scheduling, and energy efficiency to support continuous LLM inference at the edge.


Cloud network orchestrators.

Cloud providers are redesigning network overlays to serve AI traffic classes. AWS introduced AI-optimized Local Zones and Elastic Fabric Adapter v2, while Google Cloud’s Cross-Cloud Interconnect (CCI) enables direct high-bandwidth paths to AI endpoints. Microsoft Azure’s AI Backbone merges InfiniBand, Ethernet, and optical routing to link edge inference nodes with central training clusters, effectively unifying routing domains across GPU fabrics.


Standards and governance institutions.

The IETF has initiated drafts in the Adaptive Routing for AI Workloads (ARAW) and LLM Service Discovery (LLMSD) working groups to standardize telemetry and resource-advertisement protocols for AI services. ARIN and APNIC have issued policy consultations on IPv6 address allocation for GPU clusters and data-center edge nodes, acknowledging the spatial expansion of AI edge presence. Both RIRs emphasize traceability and energy-reporting compliance. These developments signal a convergence between internet governance and AI infrastructure regulation.





4. Outlook



The redirection of user behavior—from browsing to prompting—marks the first large-scale semantic re-routing of the internet. Peering and transit revenues will increasingly depend on AI query aggregation rather than web hosting, while content distribution shifts from HTML to API-delivered knowledge graphs. The emerging network will blend data-centric routing, MCP-based context propagation, and telemetry-driven orchestration, redefining both economic and technical foundations of the global internet.

Over the next three years, network operators and policymakers must adapt BGP peering economics, inter-domain QoS, and energy-aware routing standards to accommodate AI’s bidirectional traffic asymmetry—lightweight queries at the edge, heavy model updates at the core.





References



  • DataReportal (2025). Digital 2025 Global Overview Report.
  • Semrush (2025). Global Search Insights: Impact of AI Overviews on CTR.
  • SimilarWeb (2025). Traffic to Wikipedia, Q1 2025.
  • Ahrefs (2025). Zero-Click Search Analysis.
  • Bain & Company (2025). Digital Monetization in the Age of AI Search.
  • Search Engine Journal (2025). CTR Decline under AI Overviews.
  • Cisco, Juniper, Arista (2024–2025). Product Technical Briefs.
  • IETF ARAW & LLMSD Drafts (2025). Adaptive Routing for AI Workloads.
  • ARIN & APNIC (2025). Policy Consultation Papers on AI Data Center Addressing.


Sunday, May 11, 2025

The Matter Protocol - A New Perspective in Smart Home and Office Automation


: The Matter Protocol – Driving Efficiency in Smart Home and Office Automation 


---


### **Abstract**  

The Matter protocol, developed by the Connectivity Standards Alliance (CSA), is revolutionizing smart home and office ecosystems by unifying connectivity standards across devices, brands, and ecosystems. This white paper explores how Matter streamlines interoperability, reduces complexity, and enhances energy efficiency, security, and scalability in residential and commercial environments.  


---


### **1. Introduction to the Matter Protocol**  

Matter (formerly Project CHIP) is an open-source, IP-based connectivity standard designed to unify smart devices under a single protocol. Key features include:  

- **Cross-platform compatibility**: Works with Apple HomeKit, Google Home, Amazon Alexa, and others.  

- **Unified connectivity**: Supports Wi-Fi, Thread (low-power mesh), and Ethernet.  

- **Simplified setup**: QR code/NFC-based provisioning.  

- **Built-in security**: End-to-end encryption and secure device authentication.  


As of Matter 1.3 (2024), the protocol supports 20+ device types, including thermostats, lights, locks, sensors, and energy management systems.  


---


### **2. Efficiencies in Smart Home Management**  


#### **a) Interoperability & Reduced Fragmentation**  

- **Problem**: Pre-Matter, users juggled multiple hubs and apps (e.g., Zigbee vs. Z-Wave).  

- **Matter Solution**:  

  - Single protocol for all devices, eliminating siloed ecosystems.  

  - Example: A Matter-certified Philips light bulb works seamlessly with Apple Home and Google Nest Hub.  


#### **b) Energy Efficiency**  

- **Smart Energy Management**:  

  - Matter-enabled devices (e.g., thermostats, plugs) integrate with renewable energy systems and grid signals.  

  - Example: Lights and HVAC systems auto-adopt energy-saving modes during peak pricing.  

- **Low-Power Devices**: Thread protocol reduces energy consumption by up to 75% compared to Wi-Fi.  


#### **c) Enhanced Security**  

- **Zero Trust Architecture**: Mandatory encryption and authentication prevent unauthorized access.  

- **Unified Updates**: Over-the-air (OTA) firmware updates for all devices via a single interface.  


#### **d) Simplified User Experience**  

- QR code setup reduces installation time by 50%.  

- Voice assistants (e.g., Siri, Alexa) control Matter devices without custom skills.  


---


### **3. Efficiencies in Office Management**  


#### **a) Scalable Building Automation**  

- **Centralized Control**: Matter integrates with building management systems (BMS) to monitor HVAC, lighting, and security.  

- **Example**: Office lights and blinds adjust based on occupancy sensors and daylight levels.  


#### **b) Cost Savings**  

- **Predictive Maintenance**: Matter sensors detect equipment anomalies (e.g., HVAC faults) early.  

- **Energy Optimization**: Smart plugs and meters reduce office energy waste by 20–30%.  


#### **c) Hybrid Work Support**  

- **Hot Desk Management**: Matter-enabled occupancy sensors guide employees to available desks via apps.  

- **Air Quality Monitoring**: CO₂ and VOC sensors trigger ventilation systems for healthier workspaces.  


---


### **4. Case Studies**  


#### **a) Smart Home (Residential)**  

- **Scenario**: A Matter-integrated home uses solar panels, a smart thermostat, and EV charger.  

- **Efficiency Gains**:  

  - Solar energy prioritizes EV charging during off-peak hours.  

  - Lights auto-dim when rooms are unoccupied.  


#### **b) Smart Office (Commercial)**  

- **Scenario**: A Matter-powered office with 100+ devices across floors.  

- **Efficiency Gains**:  

  - Unified dashboard manages lighting, air quality, and access control.  

  - Energy costs drop 25% via automated peak-load shedding.  


---


### **5. Challenges & Considerations**  

- **Legacy System Integration**: Retrofitting non-Matter devices may require bridges.  

- **Adoption Pace**: Not all brands fully support Matter 1.3 yet.  

- **Network Reliability**: Thread border routers must be strategically placed.  


---


### **6. Future Outlook**  

- **Matter 2.0 (2025 Preview)**: Expanded support for robotics, water management, and industrial IoT.  

- **AI Integration**: Machine learning for predictive automation (e.g., “Anticipate my morning routine”).  

- **Global Sustainability**: Matter’s role in achieving net-zero buildings via EU’s Energy Performance of Buildings Directive (EPBD).  


---


### **7. Conclusion**  

The Matter protocol is a cornerstone of the modern smart ecosystem, bridging gaps between devices, users, and sustainability goals. For homes, it simplifies life; for offices, it unlocks scalable, cost-effective automation. As Matter adoption grows, its impact on energy efficiency, security, and user experience will redefine IoT’s future.  


---


**Recommendations for Adoption**:  

1. Prioritize Matter-certified devices for new installations.  

2. Use Thread border routers to maximize low-power efficiency.  


Monday, May 5, 2025

Future of Internet Routing and GenAI influence


MCP or Model context protocol, has hit the headlines in the GenAI world. Essentially this is a framework providing a set of guildelines to manage and optimise the context, in a given context window, in a large language model


CSP’s, who own the CPE/Router of the enterprise broadband service, and has visibility in terms of routing and network insights/demands of the enterprise, at the ingress/egress point. Being at this vantage point, can CSP’s provide agency for the enterprise LLM, in an MCP scenario ? 


Essentially, CSP’s augmenting their service to be the MCP client, for multiple MCL server, services southbound and northbound to the enterprise network ? This will be transformative, and as enterprise IT evolves to Data/AI, will be congruent to stay resilient and relevant for the CSP. 

#GenAI

#InternetRouting 

#ModelcontextProtocol