Neural Network
A neural network is a machine learning model built from layers of simple computing units, called neurons, connected by adjustable weights. Loosely inspired by how brain cells pass signals to one another, it learns by example: you feed it labeled data, it makes a prediction, and an algorithm nudges the weights to shrink the error. Repeat that millions of times and the network learns to recognize patterns, classify things, or predict the next word in a sentence. Every large language model you talk to, including the ones that decide whether your brand gets mentioned, is a neural network trained this way.
Why it matters for AI visibility
Every assistant that might mention your business, ChatGPT, Claude, Gemini, Perplexity, is a neural network that learned its picture of the world from training data and, increasingly, from live retrieval. ChatGPT alone reported 900 million weekly active users in February 2026, and Google's Gemini app passed 1 billion monthly users in August 2026, so what these networks have learned about your brand now reaches an audience most sites never see through search alone. You're not competing for a ranking position in the way you would on Google. You're competing for whether the network has absorbed enough consistent, credible information about you to surface your name as a plausible answer. That is a different game, and understanding the mechanics underneath it changes how you play it.
How a neural network works
Picture rows of simple switches. Each switch, a neuron, takes numbers in, multiplies them by a weight, and passes a result to the next row. At the start the weights are random, so the network's guesses are worthless. During training it sees enormous numbers of examples with a known correct answer; when it gets one wrong, an algorithm adjusts the weights slightly to reduce that error, and this repeats until the network generalizes to inputs it has never seen. In a language model, the task being trained is simpler than it sounds: predict the most likely next word given everything before it. Do that well enough, at large enough scale, and fluent, useful-seeming text falls out of pure statistics rather than any stored understanding.
Common misconceptions
The biggest misconception is that a neural network stores facts like a lookup table and retrieves them on demand. It doesn't. It has learned statistical patterns and reconstructs an answer each time, which is exactly why it can produce fluent, confident, and wrong statements, known as hallucinations. Columbia Journalism Review's Tow Center tested this directly: across 1,600 queries asking AI search tools to identify a news article's source, headline, date, and URL, more than 60% of responses were wrong across all eight tools tested, and ChatGPT alone misidentified 134 of 200 articles. A second misconception is that adding more schema markup or an llms.txt file makes a network more likely to know or cite you. Google has stated plainly that no special markup or AI-specific files are required for AI Overviews or AI Mode, and John Mueller confirmed in 2025 that no Google Search system reads llms.txt at all; an Ahrefs analysis of roughly 137,000 sites that published one found about 97% got zero measurable referral traffic from it. What actually shapes what a network has learned about you is the volume and consistency of real information about your brand across the open web.
Relation to AI recommendations and GEO
Generative Engine Optimization, or GEO, is the discipline of showing up in the outputs of neural networks the way SEO once meant showing up in search results. The term comes from a specific 2024 paper, "GEO: Generative Engine Optimization," presented at ACM SIGKDD by researchers from Princeton, IIT Delhi, and independent contributors formerly of Georgia Tech and the Allen Institute for AI. Its central finding still holds: optimizing for what a network cites is a different exercise from optimizing for what Google ranks. Ahrefs has found that only 6 to 8% of URLs cited by ChatGPT overlap with Google's top 10 for the same query, and about 80% of ChatGPT's cited URLs don't rank in Google's top 100 at all. What does correlate with citation is being talked about: Ahrefs' analysis of roughly 75,000 brands found web-mention frequency correlates with AI citation rate at about 0.664, roughly three times stronger than backlinks at 0.218. In other words, earning genuine third-party mentions matters more here than link-building does.
Example
Think of spam filtering in an email inbox. A neural network is shown thousands of emails, each already labeled spam or not spam. It doesn't learn a rigid rule like "word X means spam." It learns subtler patterns: certain word combinations, sender behavior, suspicious links, all weighted together. When a new email arrives, the network estimates the probability it's spam and acts accordingly. A small logistics company like a regional freight broker in Rotterdam could use exactly this kind of model to triage its support inbox. Language models run on the same underlying principle at a vastly larger scale: instead of scoring spam, they predict the next fitting word, over and over, until a full answer forms, an answer in which your brand can appear or be left out entirely.
Common questions
Is a neural network the same thing as artificial intelligence?
No. Artificial intelligence is the broad umbrella term for any system that behaves in ways we'd call intelligent. A neural network is one specific technique for building such a system, and it happens to be the technique behind nearly every modern AI assistant. It's a major building block, not a synonym for the whole field.
Can I directly influence what a neural network has learned about my brand?
Not directly, you can't retrain someone else's model yourself. But you influence its training and retrieval inputs: consistent facts about your business repeated across independent, credible sources on the web. Since brand mentions correlate with AI citation far more strongly than backlinks do, getting talked about accurately in more places does more for you than any technical markup.