2021 – 2026

Generative & Agentic AI

"From recognition to creation: AI that reasons and acts."

Chronology of the Generative Era

2021: The Multimodal Spark (DALL-E & CLIP)

OpenAI releases DALL-E, showing that LLMs can understand the relationship between text and visual pixels, creating images from scratch.

2022: The ChatGPT Moment

The release of RLHF-tuned models made AI conversational and useful for the general public, sparking a global race for "Generative" dominance.

2023: Multimodal Dominance (GPT-4 / Gemini)

Models begin to process "Native Multimodality"—they no longer just "read" text; they "see" video and "hear" audio in a single neural stream.

2024 - 2025: The Rise of AI Agents

Shift from Chatbots to **Agents**. AI is given access to web browsers and APIs to perform tasks autonomously, such as booking travel or writing and executing code.

Core Concepts & Methodologies

1. The Transformer Architecture (Attention Mechanism)

The methodology of "Self-Attention": the model looks at every word in a sentence simultaneously to understand context, rather than reading left-to-right.

2. RLHF (Reinforcement Learning from Human Feedback)

The approach of using human "rankers" to tell the AI which answers are helpful and safe, aligning the model's behavior with human values.

3. Chain-of-Thought (CoT) Reasoning

A methodology where the AI is prompted to "think step-by-step," significantly improving its ability to solve math and logic problems.

4. RAG (Retrieval-Augmented Generation)

Instead of relying only on its training memory, the AI "looks up" real-time information from external databases before answering, reducing hallucinations.

The Hardware Shift: The H100 Era (2023 - 2025)

The Level Up: In 2023, the **Nvidia H100 GPU** became the most valuable commodity in the world.

Transformer Engines: Special hardware inside the chip designed only to speed up the "Attention" math.
In-Network Computing: Thousands of GPUs connected via InfiniBand to act as one single, massive super-computer.

Without this hardware scaling, "Agentic" reasoning would be too slow and expensive to function.

The Agentic Approach

The current methodology is Autonomous Planning. Instead of just generating a response, the AI uses a Reasoning Loop:

Plan: Break the user's goal into sub-tasks.
Act: Use a tool (Search, Code, API).
Observe: Look at the result of the action.
Refine: Fix the plan based on what happened.