We didn't just move through time; we moved through Phases of Intelligence. Each phase wasn't defined by a bigger model, but by a shift in how we integrated that intelligence into our systems 🧱
In this present tech world, we don't measure time in years anymore; we measure it in Model Generations. This timeline isn't just about the dates; it’s about the "Great Moves" that forced the rest of the industry to adapt or die.
Phase 1: The Disruption & The Open Source Gambit (Late 2022 – 2023)
Theme: The end of "AI as a Research Project" and the start of "AI as a Product." 🛠️
Nov 2022: The OpenAI "UX" Gambit. ChatGPT launches. The world focuses on the AI, but the real move was the interface. By making RLHF accessible via a simple chat box, OpenAI turned a complex model into a global utility overnight 🌍
Early 2023: The LangChain Boom. This was the era of the "Abstraction Layer". LangChain became the go-to tool for developers trying to wrap their heads around LLM plumbing. It helped us move from simple prompts to complex chains, though we eventually learned that too many abstractions could lead to the "Spaghetti Code" of 2024🍝
March 2023: The GPT-4 Shockwave. The bar was set. GPT-4 didn't just code; it reasoned. It forced every developer to ask: "Is my job just writing boilerplate, or is it designing systems?" 🧠
July 2023: Meta’s Llama 2 "Strategic Nuke" Mark Zuckerberg made the most disruptive move of the year: releasing a frontier-class model with open weights. This effectively killed the "Intelligence Moat" and birthed the massive ecosystem of local-first AI we use today ☢️
Phase 2: The Context Wars & The RAG Reality (2024)
Theme: Realizing that "Model Knowledge" isn't enough — we need "Data Context" 🗄️
Feb 2024: Gemini 1.5 & The "Memory" Explosion Google dropped a 1-million-token context window. It forced architects to decide: Index it in a Vector DB, or just feed it to the model? 📏
Mid 2024: The RAG (Retrieval-Augmented Generation) Gold Rush We realized that LLMs are only as good as the data you give them. RAG became the industry standard for enterprise AI. We stopped building chatbots and started building "Knowledge Engines" 💎
Late 2024: The MCP (Model Context Protocol) Move Anthropic’s release of MCP was a masterstroke in standardization. It solved the "Connector Nightmare", allowing models to swap tools and data sources like LEGO bricks. It was the "USB-C moment" for AI infrastructure 🔌⚡
Late 2024: Apple Intelligence & The "On-Device" Pivot Apple entered the fray, not with a chatbot, but with System-Wide Integration. They forced the industry to care about Small Language Models (SLMs) and NPU performance on the edge 📱
Phase 3: The Agentic Crisis & The Reliability Shift (2025)
Theme: Moving from "Chatting with AI" to "Agents doing the work" 🤖
Early 2025: The "Agentic Overhang" We saw a surge of autonomous agents that promised to run businesses. Most failed. We learned the hard way that an AI without a State Machine is just a fast way to blow through an API budget 💸
Mid 2025: The Inference Economy As GPU costs peaked, the "Great Move" was Mixture-of-Experts (MoE) and Quantization. Companies like Mistral and DeepSeek proved that you could get 100B-parameter intelligence for a fraction of the compute 📉
Late 2025: The Great Consolidation Architects (like us) began the "Microservice Rollback". We realized that for AI agents to be fast and coherent, they needed to live in Modular Monoliths with unified context. Complexity was finally identified as the enemy of Agentic Reasoning 🏛️
Phase 4: Today (February, 2026) – The "Collaborative Partner" Era
Theme: AI is no longer an "Other"—it is a part of the OS and the Business. 🤝
- Jan 2026: The Apple-Google Deal In a massive consolidation of power, Apple chose Gemini to power the next generation of Siri. The "Frontier" became a utility
+1 Also The UCP (Universal Commerce Protocol). A brand new open-source standard was launched to let AI agents "talk" to e-commerce platforms natively. We moved from "Searching for products" to "Agents negotiating and buying".
Current State: Today, we are seeing the rise of ChatGPT Health and Google’s Business Agent. AI is moving from "Generalist" to "Specialized Digital Worker" 👷♂️
The Evolution of the "Great Moves"
2023: The Reasoning Spark. OpenAI’s release of GPT-4 proved that LLMs could pass the Bar Exam. We realized intelligence was no longer a human monopoly.
2024: The Context & Multimodality War. Google’s Gemini 1.5 Pro (1M+ context) and OpenAI’s Sora changed the game. AI stopped just "reading" and started "seeing" and "remembering" entire codebases at once.
2025: The "Inference Economy" & Distillation. We learned that bigger isn't always better. The best move became Distillation—using a "Teacher" model to train a tiny 8B specialist that runs on a phone but codes like a senior.
Today’s Benchmarks: The Feb 2026 "Expert Tier" 🏛️🧠
A Note from the Editor: This list is an opinionated perspective from my own hands-on experience in the trenches. Technical choices are personal, and you may have found different models that sing for your specific use case — I totally agree to that! Let’s compare notes in the comments ❤️
As of today, February 3, 2026, these are the "Domain Kings" currently ruling the stack. If you’re building a production system, these are the specialists you’re hiring:
The Architecture Shift: "Composer" Models 🏗️🎨
The biggest move of 2025/2026 has been the rise of Composer Models. These aren't just LLMs; they are "Orchestrators".
When you give a task to a Composer model, it doesn't just start typing. It breaks the task into a State Machine, assigns sub-tasks to smaller specialist models, and verifies the output at every step. It’s the "Project Manager" of the AI world.
As I’ve discussed in my post on State Machines, we are no longer "prompting"; we are orchestrating.
The Verdict: Reliability is the New Intelligence ⚖️
In 2023, we wanted the "Smartest" AI. In 2026, we want the "Most Reliable" AI. We’ve realized that intelligence without determinism is just a high-tech hallucination.
We are finally building software again. Only this time, the "functions" we’re calling are silicon-based experts.
The Next Great Move: Now that we have Domain Experts, what is missing? Is it Emotional Intelligence, Long-term Strategic Memory, or something else?
Top comments (2)
Very neatly described 👏 !
Really excited how AI turns out to be in future 😅
Thanks that it resonates!