What does 'training' an AI mean?
When we talk about “training” an AI, it’s easy to picture a digital student sitting in a classroom, absorbing facts from a textbook. But the reality is both more mechanical and more fascinating than that. At its heart, training an AI is about teaching a massive mathematical model to recognize patterns in information so it can predict what should come next.
Whether it’s the latest ChatGPT model from OpenAI, Anthropic’s Claude, or Google’s Gemini, every frontier model goes through a rigorous, multi-stage process before it ever sees a user prompt. Understanding this process helps explain why these models are so capable, but also why they sometimes struggle with simple facts.
The pre-training phase: Building the foundation
The first and most intensive stage is called “pre-training.” Think of this as the AI’s early childhood, where it’s exposed to a truly staggering amount of data—basically a significant portion of the public internet, books, and specialized datasets.
During this phase, the goal isn’t for the AI to learn specific facts, but to learn the structure of human knowledge. It plays a never-ending game of “guess the next word” (or, more accurately, “guess the next token”). By doing this billions of times across trillions of words, the model develops an internal map of how concepts relate to each other.
To handle this massive workload, companies use thousands of specialized chips. Currently, NVIDIA’s H100 and the newer GH200 are the gold standard for this. These chips are so powerful and important that they’ve become a flashpoint in international trade, with the US maintaining strict export controls to manage who can build these frontier systems.
Fine-tuning and alignment: Teaching the rules
Once pre-training is finished, you have a “base model.” It knows how to talk, but it doesn’t know how to be a helpful assistant. It might finish your sentence, but it won’t necessarily answer your question or follow safety guidelines.
This is where “fine-tuning” and “alignment” come in. Developers use smaller, higher-quality datasets to teach the model how to follow instructions. A major part of this is Reinforcement Learning from Human Feedback (RLHF), where humans rank different AI responses to help it “learn” what a good, safe, and helpful answer looks like.
This stage is what turns a raw pattern-recognition engine into the polite, helpful assistants we use every day. It’s also where companies like Anthropic have focused on “Constitutional AI,” giving the model a specific set of principles to guide its behavior.
The new frontier: Reasoning and test-time compute
As we head into 2026, the definition of “training” is evolving. In the past, all of an AI’s “intelligence” was baked in during the training phase. If a model was small, it was usually less capable.
However, the latest generation of “reasoning” models has introduced a concept called test-time compute (or inference-time compute). Instead of just spitting out the first thing that comes to “mind,” these models are trained to stop and think before they respond.
They generate “hidden” thoughts—reasoning steps that you don’t see—to verify their own logic before giving you a final answer. This means a model can actually become “smarter” by spending more time (and computational power) thinking about a specific problem, rather than just relying on what it learned during its initial training. This shift is why you might notice some models taking a few extra seconds to respond to complex coding or math questions; they aren’t lagging, they’re reasoning.
Why training never really “ends”
You might have heard about “knowledge cutoffs”—the date after which an AI doesn’t know what happened in the world. This exists because training a frontier model is incredibly expensive and time-consuming. You can’t just “add a fact” to the model’s brain easily; you usually have to wait for the next major training run or use “agents” that can browse the web for current information.
However, we are seeing more “continuous” training approaches and smaller, more efficient models that can be updated more frequently. While the big frontier runs still take months and cost hundreds of millions of dollars, the way we “train” AI is becoming more dynamic, moving from a one-time event to a constant process of refinement.