Deciding the Future - Building New Networks for the AI Era

Author’s Note

Vishal Shukla- CEO, Aviz Networks

When you talk to traditional vendors, the conversation quickly turns into a product catalog. You don’t get a clear picture of the trade‑offs between different approaches, or the historical patterns behind them. You get slides, not a mental model.

That gap is why I decided to write this book.

I happen to be the CEO of Aviz, and yes, Aviz appears in these pages. But this is not meant to be a sales brochure. The true purpose is simpler and, I hope, more useful:

Throughout the book I lean heavily on history: how previous technology waves evolved, how standardization shifted value, how lock‑in played out, and what that means for AI networks. If you understand the pattern, the current noise becomes much easier to navigate.

My hope is that when you finish this book, you won’t just have answers. You’ll have a reusable way to ask better questions.

Key Highlights from the Book

Why AI Is Forcing a Network Rethink

AI workloads are changing traffic patterns, bandwidth assumptions, and infrastructure economics — faster than most organizations realize.

The Two Decisions Every Leader Must Make

Most companies blur the two. This book separates them clearly.

Scale-Up vs. Scale-Out — In Executive Language

Understand the strategic trade-offs behind growth architecture choices — without drowning in protocol jargon.

ROI & TCO in the AI Era

AI changes both revenue opportunity and cost structure.

The book helps you rethink infrastructure decisions through a business lens.

Standardization vs. Lock-In

History shows where value ultimately moves.

Leaders who recognize the pattern early win.

Pragmatic Starting Points

You don’t need a greenfield AI factory to begin.

There are structured ways to modernize without disruption.

Insights from the Leaders Defining the AI-Driven Networking

“Most books about AI talk about models and applications. Networks in the AI Era is the first I’ve seen that explains, in plain language, what the network has to do to make any of that real. It gives executives a way to think about fabrics, observability, and AI‑driven operations without getting lost in product charts.” - Partner, Global Networking Company

“This book captures what we hear from our customers every day: they want freedom to choose hardware and still have a coherent operating model. The frameworks in these chapters make it much easier for teams to align on the path forward for AI data centers.” - Partner, Systems & Manufacturing Company

“When we advise customers on AI infrastructure, we keep coming back to the two questions this book is built around: networks for AI, and AI for networks. Having that structure – plus concrete evaluation worksheets – has already changed the quality of our design conversations.” - Cloud / Integration Partner

Resources

History & Evolution of Networks

AI Data Centers as “AI Factories”

AI Traffic Patterns & Fabrics

Scale-Up Interconnects & GPU Systems

Ultra Ethernet & Open AI Fabrics

Commoditization, Hyperscalers & Linux Analogy

Philosophy of Change

Technology, Operations & AIOps

Streaming Telemetry & Monitoring

Tool Sprawl & Operational Friction

AI Strategy, Roadmaps & Governance

Autonomy & AI System Analogies

AI Factories & GPU-Centric Data Centers

AI Infrastructure Spend (>$100B–$200B)

AI Workloads, East–West Traffic & Fabrics

Bandwidth & 100G–1.6T Evolution

SONiC as Open, Hardware-Agnostic NOS

Mainstream Vendor Alignment Around SONiC

SONiC + AI Networking Startup Wave

LLM-Based AIOps / NetOps

Deep Observability & Software-First Visibility

Overall AI & AI-Infra Spend

AI DC Networking / Ethernet for AI

Deep Observability TAM

AI for Networks / AIOps / NetOps TAM

Agentic AI & IT Budgets