Understanding the AI Industry Chain: Chips, Models, and Algorithms

Understanding the AI Industry Chain

Don’t be confused by terms like “large models,” “AI algorithms,” and “deep learning frameworks.” This article aims to clarify the AI industry chain and the relationships among these concepts in simple terms.

I. The AI Industry Chain as a Three-Story Building

If we compare the AI industry to a building, it has three floors:

1️⃣ First Floor: Foundation (Base and Materials)

AI Chips: GPU (NVIDIA), TPU (Google), Ascend (Huawei) — equivalent to the “brain cells” of AI.
Computing Facilities: Servers, data centers, cloud platforms — providing the “power” for AI.
Data Services: Collection, annotation, and cleaning — the “food” for AI.

Without this layer, the upper floors are just castles in the air.

2️⃣ Second Floor: Technology Layer (Construction Team and Blueprint)

AI Algorithms: Mathematical logic and methods (e.g., Transformer).
Machine Learning Frameworks: PyTorch, TensorFlow, PaddlePaddle — like a toolkit for construction.
Large Models: GPT-4, Llama, Wenxin Yiyan — the “skyscrapers” built using blueprints and tools.

This layer is the “core engine” of the industry chain.

3️⃣ Third Floor: Application Layer (The Occupants)

Industry Applications: Security, finance, healthcare, autonomous driving.
Consumer Products: Smart assistants, AI art, AI smartphones.
Solutions: Digital humans, enterprise knowledge bases.

99% of AI that ordinary people interact with is on this layer.

II. Three Easily Confused Terms: Algorithm, Framework, Large Model

Many people confuse “deep learning frameworks” with “large models,” even thinking that “large models are just a new type of algorithm.”

This is a big mistake.

Using a metaphor:

AI Algorithm = Recipe (tells you how much salt to add, how long to cook).
Machine Learning Framework = Kitchen + Pan + Stove (enables you to actually cook the dish).
Large Model = A “Buddha Jumps Over the Wall” made with 200 pounds of ingredients, cooked for 10 hours.

1. AI Algorithm — Design Blueprint

An algorithm is a mathematical step to solve a problem. For example, the Transformer algorithm proposed in 2017 defines how to compute the “attention mechanism.” However, it is just a paper and a bunch of formulas, not something that can run directly on a computer.

2. Machine Learning Framework — Engineering Toolkit

A framework packages the algorithm into a ready-made code library. With just three lines of code in PyTorch, you can call the attention module of the Transformer without manually writing backpropagation or GPU acceleration code.

Without a framework, training a large model would require writing hundreds of thousands of lines of low-level code.

3. Large Model — The Final Super Product

A large model is a specific model file created by implementing a super-sized version of an algorithm using a framework, fed with massive data and trained on thousands of GPUs. For instance, GPT-4 is essentially the Transformer algorithm + PyTorch framework + internet text + tens of thousands of graphics cards.

Key Point: The “large” in large models refers to scale, not a new algorithm. Its core algorithm is still a minor modification of the Transformer.

III. How Do They Collaborate? Taking ChatGPT as an Example

Algorithm Layer: Researchers propose the Transformer algorithm (2017).
Framework Layer: OpenAI implements this algorithm using the PyTorch framework and adds distributed training logic.
Large Model Layer: Running code on the PyTorch framework, feeding 45TB of text data, using thousands of GPUs to train GPT-3/4.
Application Layer: You open the ChatGPT webpage and chat with it.

Algorithms provide theory, frameworks provide production lines, and large models are the finished products.

IV. Three Common Misconceptions

Misconception	Truth
“Large models are a brand new AI algorithm”	Large models are still based on the Transformer algorithm, just with more parameters and larger data.
“Frameworks are only for training and have little to do with large models”	Without the automatic parallelization and memory optimization of frameworks, large models cannot be trained.
“More complex algorithms are better”	Good algorithms are often simple and elegant; the core idea of the Transformer fits on a single page.

V. Conclusion: What Will the Next Few Years Look Like?

Foundation Layer: Domestic AI chips will accelerate their catch-up, and cloud computing will become as essential as water and electricity.
Technology Layer: Large models will shift from “who is bigger” to “who is cheaper and faster,” leading to an explosion of vertical industry large models.
Application Layer: AI will no longer be a “toy” but will truly enter factories, hospitals, and courts.

The simplest principle remains unchanged:

Algorithms are the soul, frameworks are the means, large models are the products, and applications are the value.