AI at Scale: The Physics and Playbooks of Trillion-Parameter Systems examines the infrastructure, economics, engineering, and physical constraints shaping the next generation of artificial intelligence
BENGALURU, INDIA — As artificial intelligence pushes toward increasingly large models, autonomous systems, and more capable AI applications, the central challenge is no longer limited to algorithms and model architectures. In AI at Scale: The Physics and Playbooks of Trillion-Parameter Systems, Bengaluru-based GenAI Architect and enterprise AI leader Manav Shrivastava examines what happens when artificial intelligence reaches a scale where infrastructure, economics, energy, and physical constraints become as important as model design itself.
Now available on Amazon, AI at Scale is positioned as a practical guide for technology leaders and practitioners building, operating, or evaluating large-scale AI systems. The book explores the engineering realities that emerge when AI moves from experimentation into production and infrastructure reaches increasingly complex levels of scale.
Rather than focusing solely on model architectures and software frameworks, the book examines the fundamental constraints that influence whether large AI systems can be built, operated, optimized, and sustained in the real world. Its central thesis is that AI scaling is increasingly governed by physical and operational limits—including compute density, thermal behavior, memory capacity, high-speed networking, data movement, energy requirements, infrastructure economics, and cost.
At the center of the book is a practical approach to understanding AI at industrial scale. It explores the playbooks, engineering principles, and operational considerations involved in building and running large-scale AI systems, covering areas ranging from AI architecture and infrastructure utilization to inference optimization, governance, sustainability, and the broader concept of the AI factory.
The release comes at a time when enterprises are moving from AI experimentation toward production deployment, where reliability, latency, cost control, security, and governance can be as important as model capability. In this environment, the role of the AI architect is expanding beyond models and applications into distributed systems, GPU infrastructure, inference optimization, data engineering, observability, and operational resilience.
The Engineering Reality Behind Large-Scale AI
One of the key themes explored in AI at Scale is the growing intersection between artificial intelligence and the physical infrastructure required to support it.
As AI systems become larger and more computationally demanding, organizations must consider not only the intelligence of a model but also the infrastructure required to train and serve it. Memory bandwidth, networking, thermal management, power availability, cooling, data pipelines, and infrastructure economics increasingly influence the practical limits of AI deployment.
The book examines these challenges through a physics-first perspective, presenting AI scaling as a multidisciplinary engineering problem rather than simply a software or machine learning challenge.
This perspective is particularly relevant for organizations making significant investments in AI infrastructure. Technology leaders must increasingly evaluate not only which model to use, but also how to design the underlying systems, manage costs, optimize performance, and build infrastructure capable of supporting sustained AI workloads.
From AI Experimentation to Industrial-Scale Deployment
The rapid adoption of generative AI has moved many organizations beyond early experimentation. Enterprises are now evaluating how to integrate AI into core business processes, products, customer experiences, and operational systems.
That transition introduces a new set of questions.
How should organizations architect AI systems for scale? How can inference costs be controlled? What happens when thousands of GPUs need to operate as a coordinated system? How should enterprises approach AI governance and operational reliability? And what physical constraints ultimately determine how far AI infrastructure can scale?
AI at Scale explores these questions through the lens of large-scale AI engineering, offering readers a framework for understanding the relationship between model capability and the infrastructure required to deliver that capability reliably.
The book is intended for a broad audience that includes AI and cloud architects, infrastructure engineers, ML engineers, technology leaders, founders, operators, and business decision-makers involved in AI strategy and adoption.
Beyond Larger Models
The broader argument of AI at Scale is that the future of artificial intelligence will not be determined solely by who builds the largest or most capable model.
It will also depend on who can build the infrastructure required to operate AI efficiently, reliably, securely, and economically.
As AI systems become more sophisticated and move into enterprise workflows, autonomous agents, and eventually physical-world applications, infrastructure engineering is expected to become an increasingly important competitive advantage.
The book therefore looks beyond the immediate AI model race and examines a broader transformation: the industrialization of artificial intelligence.
In this emerging era, AI infrastructure becomes a strategic capability. The organizations that understand the relationship between compute, memory, networking, data, energy, cooling, cost, and governance may be better positioned to turn AI investments into sustainable business value.
About the Author
Manav Shrivastava is a GenAI Architect, enterprise AI leader, author, and startup innovation strategist based in Bengaluru, India. He specializes in building scalable AI systems, with deep expertise in large language models, AI infrastructure, inference optimization, and enterprise AI adoption. His work sits at the intersection of advanced AI engineering, cloud-scale systems, and practical business impact.
As the author of AI at Scale: The Physics and Playbooks of Trillion-Parameter Systems, Manav brings a physics-first perspective to the future of AI infrastructure, exploring the real-world constraints that shape trillion-parameter systems—from compute, memory, and networking to energy, cooling, economics, and governance.
He is known for translating complex technical ideas into clear strategies for architects, operators, builders, and business leaders navigating the rapidly evolving AI landscape.
In addition to his technical leadership, Manav is a public speaker, technical writer, and community builder who shares insights on artificial intelligence, SaaS scaling, voice AI, and the industrialization of AI. His work focuses on bridging deep engineering expertise with product thinking, executive strategy, and innovation leadership.
Book Details
Title: AI at Scale: The Physics and Playbooks of Trillion-Parameter Systems
Author: Manav Shrivastava
Category: Artificial Intelligence / AI Infrastructure / Enterprise Technology
Availability: Amazon India
Book: Amazon India
Author Profile: LinkedIn
About the Book
AI at Scale: The Physics and Playbooks of Trillion-Parameter Systems explores the engineering realities behind the rapid expansion of artificial intelligence. The book examines the physical, technical, operational, economic, and governance challenges that emerge as AI systems grow in size and complexity.
It is intended for technology professionals and decision-makers seeking to understand what it takes to design, build, operate, and scale AI systems in the real world.
The central question explored by the book is simple:
As AI models continue to grow, what are the physical and engineering limits of scaling intelligence—and how can organizations build systems that operate successfully within those limits?
