# Scaling, simply > Why more compute and data kept making models better. Source: https://www.superintelligence.com.au/guides/scaling-simply/ Guide 4 of 6. Reviewed September 2026. ## In short 1. Scaling means making models bigger and training them on more data with more computing power, which has reliably improved results so far. 2. Scaling laws are patterns showing that performance tends to improve smoothly and predictably as these ingredients grow. 3. Researchers disagree about whether scaling alone will continue to work, or whether new ideas will be needed. ## The basic recipe Modern AI systems are trained rather than programmed line by line. A model reads a vast amount of text and other data, and gradually adjusts itself to get better at predicting what comes next. Three ingredients shape how good the result is: the size of the model, the amount of data, and the amount of computing power used to train it. Scaling means turning all three up together. For roughly the last decade, doing so has produced better systems again and again, and that surprised many people in the field. ## The three ingredients A model’s size is usually counted in parameters. These are the adjustable numbers inside it, a bit like the settings that get tuned during learning. Larger models have more of them and can, in general, capture more patterns. Data is the material the model learns from. More varied, higher-quality data gives it more to learn from. Compute is the raw calculation used in training, often counted in operations, and it costs a great deal in chips and electricity. The early, broad stage of learning is called pre-training. It is where most of the raw ability comes from, before later steps shape how the model behaves. A rough comparison is a student. A bigger brain, more books and more hours of study all help, but only when they are balanced. A great deal of reading with too little capacity to absorb it, or the reverse, does not get the same result. It is also why the physical side matters. Training the largest models uses many thousands of specialised chips running for weeks or months, and the cost of that equipment is a main reason only a small number of organisations build the biggest systems, often called frontier labs. ## Scaling laws Around the late 2010s, researchers noticed a pattern. When they plotted a model’s error against the amount of compute, data or size used, the line was surprisingly smooth. Performance improved in a predictable way as the ingredients grew. The best-known study, published in 2020, found the pattern held for language models across a very wide range of sizes. These patterns are called scaling laws. They let a lab estimate, before spending the money, roughly how much better a larger training run should be. That predictability is a big part of why companies were willing to invest so heavily. Later work refined the picture by showing that size and data need to grow in balance. Making a model huge while feeding it too little data wastes effort. The details of these findings are technical, and a reader curious about them should check the original papers rather than rely on a summary. See it in the explorer: https://www.superintelligence.com.au/benchmarks/ ## New abilities and surprises As models grew, they did not only get better at the same things. Some skills seemed to appear at larger sizes that smaller models lacked, such as following complex instructions or solving certain kinds of puzzle. These are often called emergent abilities. Researchers disagree about how to read this. Some see genuine sudden jumps in capability. Others argue that the jumps depend on how we measure, and that with a gentler scoring method the improvement looks smooth. Both sides agree that it is hard to predict exactly which skills will show up at which scale. This is one reason careful testing matters. The guide on [how AI progress is measured](/guides/how-ai-progress-is-measured/) explains what the tests show, and the [explorer](/benchmarks/) lets you watch results change over time. A practical consequence is that a lab often cannot know everything a new model can do until it has been trained and tested. Some abilities are found after release, when people try things nobody thought of. Careful evaluation before and after release is one response to that uncertainty. ## Beyond bigger training runs More recently, labs have looked for other ways to improve models. One is giving a model more time to think through a problem before it answers, sometimes called test-time compute. Another is training models specifically to reason step by step. Both spend computing power in a different place, after training rather than during it. This matters for the story of scaling. It suggests the recipe has more than one dial, and that progress does not depend only on ever larger training runs. Researchers also work on making models more efficient, so the same ability needs less computing power. If that continues, capability could spread more widely and cheaply, which brings its own benefits and worries. ## Will it keep working? Nobody knows how far scaling will go. There are a few common views. - **It will continue.** Some researchers expect that more compute and data, plus better methods, will keep delivering gains. - **It will hit limits.** Others point to the finite supply of good training data, rising costs and energy needs, and think progress will slow. - **New ideas are needed.** A third group believes scaling helps a great deal but that reaching human-level general ability needs some further breakthrough. Evidence so far does not settle the argument, and any confident prediction should be treated with caution. What is clear is that scaling has been one of the main reasons AI moved as fast as it did. If systems keep improving, a natural question follows: how do we make sure they do what we want? That is the subject of the next guide, [Alignment: keeping AI on our side](/guides/alignment/).