Understanding that nesting is the fastest way to stop guessing which term applies to which system — and to make better calls about models, cost, and risk.

This post walks through the four layers, the discriminative-vs-generative split that distinguishes across them, and the three technical enablers behind the current Gen AI wave.

1. The Russian doll hierarchy

Open the biggest doll and you find a smaller one inside. Same idea here.

Read it outside → in: AI holds ML, ML holds DL, DL holds Gen AI.

Note the order — ML comes before DL. Deep learning is a kind of machine learning, not a separate thing sitting next to it. Here’s a simple and clear breakdown of AI vs ML vs DL vs Gen AI with easy definitions and examples:

🤖 Artificial Intelligence (AI)

• Definition: AI is the broad concept of machines or software that can perform tasks that normally require human intelligence, such as problem‑solving, decision‑making, or understanding language.

• Example: Siri or Alexa answering your questions and setting reminders.

📊 Machine Learning (ML)

• Definition: ML is a subset of AI where machines learn patterns from data and improve their performance over time without being explicitly programmed.

• Example: Netflix recommending movies based on your watch history.

🧠 Deep Learning (DL)

• Definition: DL is a specialized branch of ML that uses artificial neural networks (inspired by the human brain) to process large amounts of complex data.

• Example: Facial recognition systems that unlock your phone by detecting your face.

🌟 Generative AI (Gen AI)

• Definition: Gen AI is a type of AI that can create new content (text, images, music, code, etc.) based on the data it has been trained on.

• Example: ChatGPT writing an essay for you, or DALL·E generating an image from a text prompt.

✨ Quick way to remember:

• AI → The big umbrella (smart machines).

• ML → Machines learning from data.

• DL → Advanced ML using neural networks.

• Gen AI → AI that creates new things.

The subset relation can be seen as:

Gen AI  ⊂  Deep Learning  ⊂  Machine Learning  ⊂  Artificial Intelligence

A simple way to keep it in mind:

Think of AI as the umbrella, ML as the toolkit, DL as the power tool, and Gen AI as the creative brush.

However, AI, ML, DL, and Gen AI are overlapping approaches, often illustrated as nested categories, though not strictly, because there are some practical scenarios where one subset contains algorithms or functions that do not perfectly fit the context of its superset.

2. Discriminative vs generative

There’s a different set of distinctions between types of AI models.

• Discriminative models learn P(y | x) — given an input, predict the label. In simple terms, they draw a boundary between classes.

  • Example: If you show the model an image of an apple, it predicts the label “apple” instead of “orange” or “banana.” The reason is that it has been fed with tons of images with classes like apple, orange, and banana, so by analyzing that data, it classifies the input.

• Generative models learn P(x) — the shape of the data itself. That lets them create new samples.

  • Example: If trained on apple images, a generative model can create a new, realistic apple image that didn’t exist before.

3. Why Gen AI happened when it did

It wasn’t a single breakthrough. Three things arrived together — and that’s why Generative AI suddenly took a sharp turn.

Three Pillars that powered the Gen AI Wave :

Compute:

Training hardware got dramatically cheaper per unit of compute — roughly four orders of magnitude since 2010. Three reasons:

• Each NVIDIA generation raised throughput: Kepler → Ampere → Hopper → Blackwell.

• Tensor Cores made low-precision math (BF16, FP8) fast in hardware.

• Custom chips like TPU and Trainium brought similar economics.

Data

We finally had enough text and code to feed giant models.

• Common Crawl, GitHub, Wikipedia, arXiv, and licensed archives together hold tens of trillions of tokens.

• The hard part has moved. It's no longer how much data; it's how clean: removing duplicates, stripping personal data, catching benchmark contamination, and generating good synthetic data.

Architecture

The right design arrived at the right time.

The Transformer (2017) was the missing piece. It gave three things at once:

• It scales predictably — add data and compute, get better results (Kaplan 2020, Chinchilla 2022).

• It trains in parallel, unlike RNNs, which run step by step.

• Self-attention connects distant tokens directly.

4. A short timeline

5. Why the distinction matters at work

When people say “AI”, it can mean anything from a simple ML algorithm to a massive generative model. But in practice, the differences matter:

• Picking an approach. Churn prediction on tabular data is a discriminative ML problem. Reaching for an LLM adds cost and latency for no benefit.

• Sizing infrastructure. ML classifiers can run fast on CPUs. Gen AI models often need GPUs and large memory footprints.

• Planning for failure. Discriminative models are overconfident. Generative models hallucinate. Different problems, different fixes.

• Talking to stakeholders. Saying “we use AI” is vague. Saying “we use a gradient‑boosted tree on 15 features” is concrete and builds trust with stakeholders.

6. Further reading

• Vaswani et al., Attention Is All You Need (2017) — the Transformer paper.

• Kaplan et al., Scaling Laws for Neural Language Models (2020).

• Hoffmann et al., Training Compute-Optimal Large Language Models (Chinchilla, 2022).

• Bommasani et al., On the Opportunities and Risks of Foundation Models (2021) — where "foundation model" came from.

• Rich Sutton, The Bitter Lesson (2019) — short, and worth reading twice.

Key takeaways

• The four terms nest, outside → in: AI ⊃ ML ⊃ DL ⊃ Gen AI.

• Discriminative models predict labels. Generative models create new data.

• Compute, data, and the Transformer arrived together. Remove any one and the wave is delayed by years.

• Using the right word leads to the right technical decision.