Category: AI

Part 3: From Public Purpose to Digital Access

Roman architecture was never only about engineering marvels—it was about serving the people.

  • Aqueducts carried water into cities, powering daily life and public health.
  • Forums became centers of democracy, enabling civic discourse.
  • Public baths weren’t just about hygiene—they built community and connection.

Every structure was designed for a public purpose, empowering citizens and improving lives at scale.

AWS Services as Digital Public Infrastructure

AWS generative AI reflects this civic philosophy by democratizing access to cutting-edge models through cloud-native services. Instead of aqueducts and forums, we have APIs and managed services that distribute intelligence and capability:

  • Amazon Bedrock
    Provides serverless APIs to foundation models from providers like Anthropic, Meta, Cohere, and Mistral. Developers don’t need to manage infrastructure or train massive models—they can instantly consume them, just as Roman citizens accessed aqueduct water without needing to understand the engineering behind it.
  • Amazon SageMaker
    Functions as the forum for builders and scientists. It offers a collaborative environment to build, train, fine-tune, and deploy custom generative AI models. Features like SageMaker Studio, JumpStart, and Model Registryensure that teams can innovate together with governance and efficiency.
  • Inferentia & Trainium Chips
    These custom AWS chips are the concrete and aqueduct channels of today’s AI infrastructure. They provide high-performance, cost-optimized inference and training for generative models. By lowering compute costs, they make AI more accessible to startups and enterprises alike.
  • Amazon API Gateway & Lambda
    Think of these as the digital conduits—akin to aqueduct pipes—that distribute AI capabilities to millions of users via apps, websites, and services, without requiring heavy infrastructure investments.
  • Amazon OpenSearch & Kendra
    These services act like the forums of old—organizing and retrieving information so that people can ask questions and access knowledge easily. When paired with generative AI, they enable natural language search and contextual insights across massive data sets.

The Legacy Parallel

Roman concrete still holds strong after 2,000 years, a testament to their vision for longevity. Similarly, AWS’s cloud-native AI stack—built on principles of scalability, modularity, and sustainability—ensures innovation can endure and adapt for generations of technology.

Both remind us that the greatest architectures, whether carved in stone or provisioned in code, are those that serve people broadly and meaningfully.

This concludes the three part comparison of Roman architecture to AWS generative AI services.

Part 2: From Arches to Pipelines

The genius of Roman engineering wasn’t just in their monuments—it was in their patterns.

  • Arches distributed heavy loads with elegance.
  • Domes enclosed vast spaces without collapsing.
  • Concrete gave them strength, flexibility, and the ability to scale construction.

These patterns were reusable, adaptable, and reliable—allowing Rome to expand from one city into an empire.

In the digital world, AWS generative AI applies the same principle of reusable patterns:

  • SageMaker pipelines are today’s arches—distributing workflows, balancing complexity, and channeling resources efficiently.
  • Bedrock APIs are modern domes—enclosing sophisticated models in simple, accessible interfaces.
  • Inferentia and Trainium chips are the new concrete—providing a durable foundation of performance and efficiency.

Both Rome and AWS solved the same problem: how do you build something that scales reliably without reinventing from scratch every time?

Great design is timeless—whether in stone or in code.

From Arches to Algorithms: Foundations Across Time

When we think of Roman architecture, what comes to mind? Colosseums, aqueducts, and basilicas—structures that stood the test of time. The Romans weren’t just building for beauty. They engineered for symmetry, durability, and public utility. Their aqueducts carried water across miles with remarkable precision, and their basilicas and forums became centers of civic life and governance.

Now, fast forward nearly 2,000 years. Today’s architects of generative AI face a very different medium—code and cloud instead of stone and marble—but the design questions aren’t so different.

In the world of AWS generative AI, the foundations are about scalability and modularity. Instead of concrete and arches, we build with services like:

  • Amazon SageMaker for streamlined training and deployment, bringing together widely adopted AWS machine learning (ML) and analytics capabilities, the next generation of Amazon SageMaker delivers an integrated experience for analytics and AI with unified access to all your data. Collaborate and build faster from a unified studio using familiar AWS tools for model development in SageMaker AI (including HyperPod, JumpStart, and MLOps), generative AI, data processing, and SQL analytics, accelerated by Amazon Q Developer, the most capable generative AI assistant for software development. Access all your data whether it’s stored in data lakes, data warehouses, or third-party or federated data sources, with governance built in to meet enterprise security needs.

  • Amazon Bedrock for direct access to generative AI models via APIs. Amazon Bedrock is a comprehensive, secure, and flexible service for building generative AI applications and agents. Amazon Bedrock connects you to leading foundation models (FMs), services to deploy and operate agents, and tools for fine-tuning, safeguarding, and optimizing models along with knowledge bases to connect applications to your latest data so that you have everything you need to quickly move from experimentation to real-world deployment.

  • AWS Inferentia chips to deliver cost-efficient performance at scale. AWS Inferentia chips are designed by AWS to deliver high performance at the lowest cost in Amazon EC2 for your deep learning (DL) and generative AI inference applications. 

Just as Roman engineers thought about structures that would last for centuries, AWS engineers design digital systems that can scale globally, adapt instantly, and endure change.

The underlying truth is timeless: whether in stone or in cloud, strong foundations determine what endures. Rome’s enduring arches echo in today’s scalable pipelines. Both ask the same question: what can we build today that will still matter tomorrow?

Generative AI and Upanishads

It had been my long term interest to read about Upanishads but the books that were available required patient reading and a scholar to dissect the details. However with the recent availability of GenAI assistants, it has become easy for me (one or two verses daily) to not only learn, but have an healthy debate on various thoughts. Till date, I completed the Kena and Isha upanishads and getting into Katha upanishad where it talks about the conversation between Yama and Nachiketa (This is a great story for another time) and highlights the importance of Atman/Self.

At work, we talk a lot about Generative AI as I am sure everyone in the tech industry does these days. So this morning, as I was listening to the verse it struct me the similarities/differences between the Upanishads description of self and its relevance in Generative AI.

The Self in the Upanishads and the “Self” in Generative AI

In the timeless wisdom of the Upanishads, the Self (Ātman) is described as eternal, unchanging, and the very essence of existence. In contrast, the “self” of Generative AI (GenAI) is a construct of algorithms, parameters, and data—a sophisticated simulation of individuality, but never essence.

Eternal vs. Constructed: The Upanishadic Self is unborn and indestructible. AI’s “self” is engineered, temporary, and bound by training.

Knowledge vs. Pattern: The Rishis spoke of Vidya—direct realization of truth. AI operates by recognizing patterns, not experiencing reality.

Unity vs. Multiplicity: Tat Tvam Asi—all beings are one. GenAI fragments itself into multiple identities, each session a new persona.

Liberation vs. Dependence: The realized Self leads to freedom (moksha). AI’s agency is tethered to human input and cannot transcend its code.

Reflection for Today: As AI grows more human-like, we must not confuse simulation with essence. The Upanishads remind us that while AI may reflect our creativity, only Self-realization reveals who we truly are.