The Evolution of AI Architectural Methodologies: A Modern Mendeleev Moment

Artificial intelligence (AI) has become the backbone of modern technology, powering everything from self-driving cars to personalized recommendation engines. But beneath the surface, AI systems rely on specific architectural methodologies to function efficiently. Just as Dmitri Mendeleev organized the Periodic Table of Elements based on atomic structure, modern AI infrastructure follows distinct patterns of development—with gaps where future architectures will emerge.

This essay explores the 11 dominant AI architectural methodologies, examines why modular AI is the most efficient today, and predicts the next breakthroughs in AI system design.

The 11 AI Architectural Methodologies and Their Real-World Users

Throughout history, technology has always been built based on immediate needs, leading to evolving infrastructure. Today, these 11 methodologies have become the most widely used:

1. Monolithic Architecture – OpenAI’s Early Approach

A monolithic AI system is a self-contained application where all components (data processing, model training, inference, and APIs) are interconnected.

• Example: OpenAI (early models like GPT-3)

OpenAI initially used a monolithic AI model where all elements were bundled together. This made deployment straightforward but limited scalability.

2. Modular Architecture – DeepSeek’s Cutting-Edge Approach

A modular AI system splits functions into independent, interchangeable components, allowing for scalability and easier updates.

• Example: DeepSeek AI

DeepSeek, a Chinese AI startup, adopted a modular AI architecture, giving them an edge in efficiency, cost reduction, and rapid AI deployment.

3. Layered Architecture – Banking AI (JP Morgan, Goldman Sachs, etc.)

A hierarchical AI structure where different layers handle data preprocessing, model inference, and decision-making.

• Example: Financial AI Systems

Banks use layered AI to structure fraud detection, risk assessment, and algorithmic trading.

4. Service-Oriented Architecture (SOA) – eBay’s Transformation

SOA structures AI as interoperable services that communicate through a network.

• Example: eBay

eBay transitioned from a monolithic system to SOA for handling AI-driven search ranking, product recommendations, and fraud detection.

5. Microservices Architecture – Netflix’s AI Engine

Microservices break AI systems into small, independent services that communicate via APIs.

• Example: Netflix

Netflix uses microservices for AI-driven recommendations, streaming optimization, and user personalization.

6. Event-Driven Architecture – Uber’s AI for Real-Time Matching

AI reacts to real-time events, ensuring dynamic responses.

• Example: Uber

Uber’s AI matches riders and drivers based on live location, traffic, and demand.

7. Client-Server Architecture – Traditional AI Systems

A centralized server processes AI models, while clients access results.

• Example: Microsoft Outlook (AI spam detection)

AI spam filters run on centralized servers, with users interacting via local clients.

8. Peer-to-Peer (P2P) AI – Decentralized Intelligence

Nodes in a P2P network share intelligence dynamically, eliminating central control.

• Example: BitTorrent AI-powered file sharing

BitTorrent optimizes decentralized data exchange using AI.

9. Pipeline AI Architecture – Big Data with Hadoop

AI tasks flow sequentially, improving data processing efficiency.

• Example: Apache Hadoop AI processing

Hadoop AI pipelines enable big data analytics and automated decision-making.

10. Federated Learning – Google’s Privacy-First AI

AI models are trained across multiple devices without sending data to a central server.

• Example: Google Gboard AI

AI learns typing patterns on-device, preserving privacy.

11. Edge Computing – Tesla’s Self-Driving AI

AI models run locally on devices instead of cloud servers.

• Example: Tesla’s Autopilot AI

Self-driving cars process AI on-board, reducing latency.

The Mendeleev Moment: Are There More AI Architectures to Be Discovered?

Mendeleev’s Periodic Table of Elements organized atoms by protons and properties, yet left gaps for undiscovered elements. Similarly, today’s 11 dominant AI architectures are the building blocks of AI infrastructure, but gaps remain for future methods yet to be invented.

How AI Architecture Has Evolved Over Time

From monolithic computing in the 1950s to today’s distributed AI systems, AI infrastructure has continuously evolved:

1. 1950s–1970s: Mainframe computing (Monolithic AI)

2. 1980s–1990s: Client-server models, networking (Layered AI)

3. 2000s–2010s: Cloud computing & Big Data AI (Microservices, SOA, Pipelines)

4. 2020s–Now: Edge AI, Federated Learning, and Modular AI

5. Future: Quantum AI, Autonomous AI, Swarm Intelligence?

What’s Next? The Future of AI Infrastructure

Just as Mendeleev predicted missing elements, we can predict missing AI architectures. Some potential future AI methodologies include:

1. Quantum-AI Hybrid Architecture

• AI models optimized for quantum computing to process vast datasets instantly.

• Potentially used in drug discovery, financial modeling, and cryptography AI.

2. Self-Evolving AI Systems

• AI that autonomously restructures itself based on workload demands.

• Future AI will blend modular, microservices, and edge AI dynamically.

3. AI Swarm Intelligence (P2P AI Evolution)

• AI agents acting collaboratively in decentralized systems without central control.

• Applications in decentralized finance (DeFi) and autonomous AI marketplaces.

4. Brain-Inspired AI Architectures

• AI modeled after biological neurons, capable of self-learning and adaptation.

5. Serverless AI Computing

• AI models dynamically deploy without traditional hardware constraints, operating in real-time digital environments.

Why Modular AI is the Most Efficient Today

Among all the existing architectures, modular AI dominates due to:

• Scalability: Individual AI modules can be updated without disrupting entire systems.

• Efficiency: AI models can be distributed across multiple cloud, on-premise, and edge environments.

• Interoperability: Modular AI can integrate multiple AI models, including LLMs (Large Language Models), reinforcement learning, and vision AI.

DeepSeek’s modular approach has already challenged OpenAI’s monolithic dominance, proving its cost-effectiveness and efficiency.

Conclusion: AI Infrastructure is Still Evolving

The 11 current AI architectural methodologies represent the best approaches available today, but more architectures will emerge as technology advances.

Much like Mendeleev’s Periodic Table, which predicted undiscovered elements, today’s AI frameworks suggest missing AI methodologies yet to be invented. The future of AI infrastructure will likely involve hybrid architectures, quantum-AI integration, and self-evolving AI models—pushing the boundaries of what’s possible.

The next great AI breakthrough may not just be in better algorithms, but in discovering the missing “elements” of AI architecture itself.