October 30, 2025

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Good morning! In today’s issue, we’ll dig into the all of the latest moves and highlight what they mean for you right now. Along the way, you’ll find insights you can put to work immediately

— Ryan Rincon, Founder at The Wealth Wagon Inc.

If you’ve ever scrolled through tech news or LinkedIn posts, you’ve probably seen the terms AI, machine learning, and deep learning thrown around like interchangeable buzzwords. Spoiler alert: they’re not the same thing — but they are part of the same family.

Think of it like this:

AI is the whole family, Machine Learning is one child, and Deep Learning is the overachiever who gets straight A’s in math and computer science.

Let’s unpack what each term really means (without the jargon), how they connect, and why understanding this hierarchy matters — especially if you’re building, investing in, or using AI tools in 2025.

At the top of the pyramid sits Artificial Intelligence, or AI — the broad concept of machines being able to perform tasks that normally require human intelligence.

That includes things like:

AI doesn’t mean “robots taking over the world.” In fact, most AI today is narrow AI — systems designed to do one specific task very well.
For example:

These tools are intelligent, but only within their defined limits.

Fun fact: The term AI was coined way back in 1956 at a Dartmouth conference. Back then, scientists dreamed of machines that could think like humans. Today, we’re kind of there — but with far more math and data than imagination alone.

Machine Learning is the engine that powers AI. It’s a method that allows computers to learn from experience — or, more precisely, from data.

Instead of programming exact rules (“If X, then Y”), machine learning lets algorithms find patterns in data and make predictions based on those patterns.

Here’s an example:

Types of machine learning:

Real-world uses: spam filters, fraud detection, voice assistants, and Netflix’s “Because you watched…” recommendations.

Now, let’s talk about the overachiever: Deep Learning, a specialized branch of machine learning inspired by how the human brain works.

Deep learning uses neural networks — complex layers of algorithms that mimic neurons in the brain — to recognize incredibly detailed patterns in massive data sets.

Unlike traditional machine learning, which might need human help to decide what features to focus on, deep learning models figure that out themselves. That’s why they’re so powerful.

You’ve seen deep learning in action every day:

These models are deep because they have many layers of processing — each one refining the output of the previous layer, just like how our brain’s neurons pass signals to one another.

Here’s the relationship in one simple diagram (no code required):

Artificial Intelligence → The concept of machines acting smart
 ↳ Machine Learning → The technique for learning from data
  ↳ Deep Learning → A powerful subset using neural networks

Or, to put it in plain English:
AI is the goal.
Machine Learning is the method.
Deep Learning is the latest and greatest tool we’ve built to reach that goal.

Understanding this hierarchy isn’t just trivia — it helps you make better business and career decisions.

A 2025 IBM survey found that 42% of companies are now experimenting with AI, but fewer than half have a clear understanding of how it works. That’s the gap you don’t want to be in.

Right now, most AI is task-specific. But the long-term goal — what researchers call Artificial General Intelligence (AGI) — is a system that can reason, learn, and adapt like a human across any domain.

We’re not there yet (and probably won’t be for a while). But every advancement in deep learning, multimodal AI, and reasoning models is bringing us closer.

AI, Machine Learning, and Deep Learning aren’t just tech terms — they’re layers of an ongoing revolution in how humans and machines learn together.

If AI is the art of making machines smart, then ML and DL are the brushes and paint that bring that art to life.

Next time you use ChatGPT, ask your smart assistant a question, or let your phone organize photos by faces, remember — you’re not just using an app. You’re experiencing the results of decades of innovation that started with one simple idea:

“Can we teach a machine to think?”

Spoiler: we’re getting pretty close.

I hope you enjoyed today’s issue of The Wealth Wagon. If you have any questions regarding today’s issue or future issues feel free to reply to this email and we will get back to you as soon as possible. Come back tomorrow for another great post. I hope to see you. 🤙

— Ryan Rincon, CEO and Founder at The Wealth Wagon Inc.

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