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Technology / SaaS
March 18, 2026
Technology / SaaS

Hybrid Network Architecture Economics: Balancing Fiber, Microwave, and FWA for 5G Readiness in High-Geography, Low-ARPU Markets like Indonesia

Examines Indonesia's hybrid telecom model, focusing on fiber and microwave trade-offs, the challenges of scaling 4G/5G, and the evolving importance of AI-driven planning in infrastructure deployment.

65 Mins
Former Head
Indonesia
Public
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Companies Discussed
Centratama (CENT), Ceragon (CRNT), Ericsson (ERIC), IBM (IBM), Indosat (ISAT), Mitratel (MTEL), Moratelindo (MORA), Nokia (NOK), Nvidia (NVDA), ProtoLindo (TOWR), Smartfren (FREN), Surge (WIFI), Telkom Indonesia (TLKM), Tower Bersama Infrastructure (TBIG), XL Axiata (EXCL)
Executive Summary
Topics Covered
Methodology
Free Preview — Executive Summary

This transcript outlines Indonesia’s telecom strategy, balancing fiber, microwave, and wireless architectures to optimize deployment speed and long-term capacity. Fiber is ideal for urban hubs but constrained by regulatory issues, while microwave is pivotal for rural and remote regions, despite its limitations. Hybrid networks are emerging to meet varied demand. Operators face significant CAPEX constraints, and AI is improving site selection and traffic forecasting. As demand grows, backhaul remains the key bottleneck in scaling 4G/5G, with infrastructure sharing models like TowerCos providing economic solutions.

Topics Covered
  • Cost vs coverage vs capacity trade-offs in network design
  • Fiber vs microwave: strengths and weaknesses in deployments
  • Hybrid network architecture design in emerging markets
  • Deployment speed vs scalability decision-making
  • ROI modeling across fiber, microwave, and wireless technologies
  • Role of AI in network planning, traffic forecasting, and prioritization
  • Impact of spectrum, geography, and regulations on architecture choices
  • Backhaul as the key bottleneck in scaling 4G/5G
  • Economics of rural, semi-urban deployment and infrastructure sharing
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Q: Can you walk us through the current GPU allocation framework at your organisation? How are you deciding between internal AI workloads and enterprise customer commitments? A: Sure. So the fundamental tension right now is that our internal AI teams — the ones building our own foundation models and inference services — are consuming GPUs at a rate that nobody anticipated even 18 months ago. We're talking about 3-4x the original projections. And that creates a real squeeze on what's available for enterprise customers. The allocation committee meets weekly now, which tells you everything. It used to be quarterly. We have a scoring matrix that weighs revenue potential, strategic importance, and internal capability gaps. But honestly, internal teams almost always win because the economics of our own AI services are so compelling compared to renting compute to enterprises...

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Expert Profile
Former Head at Protelindo
Duration
65 Mins
Call Date
April 15, 2026
Geography
Indonesia
Transcript Tier
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