Many people hear “AI” and instantly think of ChatGPT.
But the truth is: AI existed long before ChatGPT, and most AI we used for years wasn’t conversational at all.
- AI: The Big Umbrella
Let’s start from the top.
Artificial Intelligence is the broadest term.
If a system tries to mimic human intelligence — learning, decision-making, perception — it falls under AI.
AI includes many subfields:
• Machine Learning
• Deep Learning
• Computer Vision
• Natural Language Processing
• Robotics
• Expert systems
- ML: The Engine Behind Most “Old AI”
Before ChatGPT, the AI powering our world was mostly Machine Learning.
ML is simply this:
Models learn patterns from data and make predictions — without being explicitly programmed.
For almost 20 years, ML quietly powered:
• Fraud detection
• Credit scoring
• Traffic prediction in maps
• Recommendation engines
• Face recognition
• Spam filters
• Search ranking
And here’s the interesting part:
These models were not “smart” in a conversational way.
They didn’t talk, write, reason, or code.
They were mathematical prediction systems:
• Logistic regression
• Decision trees
• Random forest
• Gradient boosting (XGBoost, LightGBM)
• Early neural networks
This was the AI that shaped the modern internet long before LLMs arrived.
- LLMs: The New Evolution of AI This changed everything.
Suddenly, AI could:
• Understand text
• Generate text
• Reason
• Summarize
• Translate
• Write code
• Assist with workflows
This is where LLMs (Large Language Models) came in.
LLMs like ChatGPT, Claude, Gemini, and Llama aren’t just tools, they’re a new category of AI entirely.
They work on language, not just numbers.
They’re trained on massive datasets.
And they’re capable of general-purpose intelligence that older ML models were nowhere near.
In short:
ML predicted.
LLMs communicated.
The Whole Relationship:
AI is the field.
ML is a major branch inside AI.
LLMs are one specific type of ML model focused on language
Top comments (1)
Nice article - very clear breakdown of the differences between AI, ML, and LLMs.
At WalkingTree Technologies, we see this confusion often with enterprise teams as well. What actually works best isn’t choosing ML or LLMs, but using the combination that fits the use case. For example:
ML models still perform better for structured predictions like credit scoring or fraud flags.
LLM-powered agents add more value for tasks that require language, context, or reasoning - such as document analysis, customer interaction insights, and report generation.
And when you combine LLMs with business constraints or domain data (via RAG or rule layers), you get a more stable and enterprise-ready system than “LLM-only” or “ML-only.”
Curious to know how others see this hybrid approach evolving - will 2025 be more LLM-first or mixed-AI stacks for enterprise workflows?