How Neural Networks Work: The Technology Behind ChatGPT and Modern AI

Neural networks are not new — researchers have studied them since the mid-20th century — but they now sit behind most consumer AI products in daily use. OpenAI said at its DevDay event on September 29, 2026 that ChatGPT had reached 1.2 billion weekly users, according to a liveblog of the event by Engadget. That scale of adoption has pushed a basic technical question back to the top of search results: how does a neural network actually work?

The basic building block: neurons, weights, and layers

According to IBM, a neural network is a machine-learning model built from simple units called neurons, arranged in layers, that learn adjustable weights and biases from data in order to map inputs to outputs. Every network has an input layer that holds the raw data, one or more hidden layers that transform it, and an output layer that produces the final result, IBM explains.

Each connection between neurons carries a weight — a number that, according to IBM, functions much like a dial controlling how strongly a given input feature influences the network's decision. A separate value called a bias is added to each neuron so it can activate even when its inputs are weak, shifting the decision threshold. Google's Machine Learning Crash Course adds a third necessary ingredient: activation functions, nonlinear mathematical steps applied after the weighted inputs are summed, which let a network detect patterns that a simple straight-line formula could not capture.

Forward propagation: how a network produces an answer

When a neural network is given new data, that data moves through the network in one direction, from the input layer to the output layer, in what both IBM and Google's course call forward propagation (or the forward pass). At each neuron, the incoming values are multiplied by their weights, summed together with the bias, and passed through the activation function before moving to the next layer. IBM notes that early layers in a network tend to process the importance of individual signals, while later layers combine that information into higher-level patterns that capture broader context.

Backpropagation: how a network learns from its mistakes

A freshly built neural network starts with essentially random weights and produces poor predictions. Training is the process of correcting that. IBM describes backpropagation as the algorithm that propagates the network's prediction error backward through the layers, using calculus's chain rule to calculate how much each individual weight and bias contributed to the overall error. Google's course likewise identifies backpropagation as the core training algorithm, describing its purpose as adjusting weights to reduce, or minimize, the network's prediction loss.

Once each weight's contribution to the error is known, an optimization method called gradient descent nudges every weight slightly in the direction that reduces the error. This forward-pass-then-backward-correction cycle is repeated across large amounts of training data — often millions or billions of examples — until the network's predictions become reliable enough for practical use.

Different network designs for different kinds of data

Not every neural network is built the same way. IBM identifies three broad families in wide use today: convolutional neural networks (CNNs), designed for grid-like data such as images and used heavily in visual recognition; recurrent neural networks (RNNs), which include feedback loops suited to sequential or time-series data; and transformers, the architecture behind most of today's large language models, which rely on an attention mechanism rather than strict step-by-step sequencing.

How this scales up to systems like ChatGPT

Large language models such as the GPT family apply the same core mechanics — weighted neurons, layers, forward propagation, backpropagation — at a much larger scale, using the transformer architecture. OpenAI's own 2020 research paper on GPT-3, published on arXiv, stated the model had exactly 175 billion parameters, describing it as "10x more than any previous non-sparse language model." Parameters is the general term for the full set of a network's weights and biases; more parameters generally allow a model to capture more complex patterns in language, though OpenAI has not published equivalent parameter figures for its newer models.

Within a transformer, a mechanism called self-attention allows the model to weigh how relevant every other word in a passage is to the word currently being processed, rather than reading strictly left to right. That design, first introduced in transformer research and now standard across major language models, is what lets systems like ChatGPT track context across a long conversation.

Why this matters now

The underlying mathematics of neurons, weights, and backpropagation has existed for decades, but the combination of larger datasets, more computing power, and the transformer architecture is what turned neural networks from a specialist research tool into the engine behind consumer products used by well over a billion people a week, per OpenAI's own September 2026 figures reported by Engadget. Understanding the basic forward-pass-and-correction cycle described by IBM and Google does not require advanced mathematics, but it explains why these systems need vast amounts of training data and computing power, and why their outputs are statistical predictions rather than guaranteed facts.