The AI Hierarchy — From Broad to Specific

contact@corebaseit.com (Vincent Bevia) — Fri, 20 Mar 2026 00:00:00 +0000

The AI Hierarchy — From Broad to Specific

Part 1 of 4 in the Generative AI Foundations series

Let’s start with the thing that trips up more people than it should: the terminology.

AI, Machine Learning, Deep Learning, Generative AI — these terms get thrown around interchangeably in boardrooms, blog posts, and LinkedIn hot takes. But they’re not the same thing. Each is a subset of the one above it, and understanding the nesting matters. If you’re going to lead AI initiatives, architect AI-powered systems, or even just have an informed opinion, you need to get the hierarchy right.

Artificial Intelligence (AI)

The broadest concept. AI refers to any system designed to mimic human intelligence — perception, reasoning, decision-making, language understanding. It’s the umbrella term that encompasses everything below it. Rule-based expert systems from the 1980s? That’s AI. A modern LLM generating code? Also AI. The term is intentionally wide, and that’s by design — it has to be, because the field has been reinventing itself every decade since Turing.

Machine Learning (ML)

A subset of AI. Rather than being explicitly programmed with rules, ML systems learn from data to perform specific tasks. You give them examples, they find patterns, and they improve with more data. Supervised, unsupervised, reinforcement learning — all fall under this banner.

The key shift here is philosophical as much as technical: instead of telling the machine how to solve a problem, you show it examples of solved problems and let it figure out the rest. That single idea changed everything.

Deep Learning

A subset of ML. Deep learning uses neural networks with multiple layers (hence “deep”) to learn increasingly abstract representations of data. This is what powers image recognition, speech synthesis, and the transformer architectures behind modern language models.

The depth of the network is what gives it the capacity to learn complex, hierarchical features. A shallow network might learn edges in an image; a deep network learns edges, then textures, then shapes, then objects, then scenes. Each layer builds on the one below it — sound familiar?

Generative AI

The most specific layer. Generative AI is the subset of deep learning focused on creating new content — text, images, audio, video, code. This is where LLMs like Gemini, Claude, and GPT live.

The key distinction: traditional ML classifies or predicts; generative AI produces. It doesn’t just recognise a cat in a photo — it can generate a photo of a cat that never existed. That shift from classification to creation is what makes this moment in AI feel fundamentally different from everything that came before.

Natural Language Processing (NLP)

NLP sits alongside this hierarchy as a cross-cutting discipline. It’s the field focused on understanding and generating human language, and it draws from every layer — from rule-based AI (early chatbots) through ML (sentiment analysis) to deep learning and generative AI (modern LLMs). It’s not a layer in the pyramid; it’s a capability that runs through all of them.

Remember: AI → Machine Learning → Deep Learning → Generative AI. Know the hierarchy: broad to specific.

Next in the series: The Generative AI Landscape — A Layered View

Vincent Bevia — corebaseit.com

The Generative AI Landscape — A Layered View

contact@corebaseit.com (Vincent Bevia) — Fri, 20 Mar 2026 00:00:00 +0000

The Generative AI Landscape — A Layered View

Part 2 of 4 in the Generative AI Foundations series

Now that we’ve established the hierarchy — what AI, ML, Deep Learning, and Gen AI actually are — let’s look at how the Gen AI ecosystem is structured as a working system. Because knowing the theory is one thing; understanding the architecture is what lets you build on it.

The Gen AI landscape is a stack. Five layers, each dependent on the one below it, with value flowing upward.

Infrastructure

At its foundation, the Gen AI stack is an infrastructure play. We’re talking about the raw computing muscle — GPUs, TPUs, high-throughput servers — along with the storage and orchestration software needed to train and serve models at scale. Without this layer, nothing else exists.

Google Cloud’s AI-optimised infrastructure includes custom TPUs (Tensor Processing Units), high-performance GPUs, and the Hypercomputer architecture designed specifically for AI workloads.

Business Implication: Organizations don’t need to invest in expensive on-premises hardware. Cloud infrastructure provides scalable, pay-as-you-go access to AI computing power.

Models

Sitting on top of that infrastructure is the model itself: a complex algorithm trained on massive datasets, learning statistical patterns and relationships that allow it to generate text, translate languages, answer questions, and produce content that, at its best, feels indistinguishable from human output. The model is the engine, but an engine alone doesn’t get you anywhere.

This layer includes foundation models (Gemini, Gemma, Imagen, Veo), open-source models, and third-party models available through platforms like Vertex AI Model Garden.

Business Implication: Organizations can choose from pre-built models (reducing time to market) or train custom models. Model Garden provides access to 150+ models, giving flexibility across use cases.

Platform

That’s where the platform layer comes in. Think of it as the middleware — APIs, data management pipelines, deployment tooling — that bridges the gap between a trained model and the software that actually consumes it. It abstracts away the infrastructure complexity and gives developers a clean interface to build on.

Vertex AI is Google Cloud’s unified ML platform for this layer, providing tools for the entire ML workflow: build, train, deploy, and manage.

Business Implication: Platforms abstract away infrastructure complexity, enabling teams to focus on building AI solutions rather than managing servers. Low-code/no-code tools democratise access to AI.

Agents

Next, the agent. This is where things get interesting. An agent is a piece of software that doesn’t just call a model — it reasons over inputs, selects tools, and iterates toward a goal. It’s the autonomous decision-making layer, and it’s the frontier everyone is racing toward right now.

Agents consist of a reasoning loop, tools, and a model. They can be deterministic (predefined paths), generative (LLM-powered natural language), or hybrid (combining both). Examples include customer service agents, code agents, data agents, and security agents.

Business Implication: Agents represent the next evolution of AI applications, capable of autonomous task completion. They can significantly reduce human workload in customer support, data analysis, and software development.

Applications

Finally, at the top of the stack, sits the Gen-AI-powered application — the user-facing layer. This is what end users actually see and interact with. It’s the product surface that translates all the layers beneath it into something useful, intuitive, and accessible.

Examples include the Gemini app, Gemini for Google Workspace, and custom enterprise applications built with Vertex AI.

Business Implication: Applications deliver the tangible business value of AI. They translate the underlying technology into tools that employees, customers, and partners can use directly.

The Missing Piece: Scaffolding

But here’s the thing most people miss: none of these layers work in isolation. What connects them — what makes the whole stack operational — is scaffolding.

Scaffolding is the surrounding code, orchestration logic, and glue infrastructure that wraps around a foundation model to turn a raw API call into a functioning system. We’re talking about prompt templates, memory management, tool routing, output parsing, guardrails, retry logic, error handling — everything that sits between “call the model” and “deliver a reliable result to the user.”

Without scaffolding, you have a model that can generate text. With scaffolding, you have an application that can reason, recover from errors, maintain context across turns, and chain multiple steps together toward a goal. It’s what makes agents actually work in production.

If you’re an engineer, scaffolding is where you’ll spend most of your time. If you’re a leader, it’s the part of the stack you need to budget for — because the model is the easy part. Making it reliable, safe, and operational at scale? That’s scaffolding.

Remember the five layers bottom-to-top: Infrastructure → Models → Platforms → Agents → Applications. Each layer depends on the one below it, and the value flows upward.

Next in the series: Data Quality and Accessibility

Vincent Bevia — corebaseit.com

Generative AI on Corebaseit — POS · EMV · Payments · AI

The AI Hierarchy — From Broad to Specific

The AI Hierarchy — From Broad to Specific

Artificial Intelligence (AI)

Machine Learning (ML)

Deep Learning

Generative AI

Natural Language Processing (NLP)

The Generative AI Landscape — A Layered View

The Generative AI Landscape — A Layered View

Infrastructure

Models

Platform

Agents

Applications

The Missing Piece: Scaffolding