[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-scaling-laws::en":3,"gloss-cluster-scaling-laws::en":26,"gloss-next-scaling-laws::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"scaling-laws","core-ai","Scaling Laws","Scaling laws are the empirical finding that a model's performance improves in a smooth, predictable way as you increase three things together: parameters, training data, and compute. Plotted on log axes, loss falls in a near-straight line, which lets labs forecast how good a bigger model will be before training it, and decide how to spend a fixed compute budget. The influential \"Chinchilla\" result showed many early models were oversized and undertrained: for a given compute budget you often want a smaller model trained on far more data. SaaS builders will never fit these curves themselves, but scaling laws explain the industry you're buying into — why capabilities keep climbing on schedule, why \"just make it bigger\" worked for years, and why the frontier is now shifting toward data quality and test-time compute as raw scaling hits practical limits. The takeaway for planning: assume the model tier you use will get cheaper and more capable on a predictable cadence, and design so you can swap it in without a rewrite.","Scaling laws are the finding that loss falls predictably as parameters, data, and compute grow together — so labs can forecast a model before training it.",null,[11,14,17,20,23],{"slug":12,"name":13},"foundation-model","Foundation Model",{"slug":15,"name":16},"gpu","GPU (Graphics Processing Unit)",{"slug":18,"name":19},"model-weights","Model Weights",{"slug":21,"name":22},"parameter","Parameter",{"slug":24,"name":25},"test-time-compute","Test-Time Compute",[27,31,35,39,42,45,48,51,54,57,60,63],{"slug":28,"category":5,"name":29,"updated_at":30},"agentic","Agentic AI","2026-08-24T02:46:36+00:00",{"slug":32,"category":5,"name":33,"updated_at":34},"alignment-tax","Alignment Tax","2026-08-24T02:46:37+00:00",{"slug":36,"category":5,"name":37,"updated_at":38},"artificial-intelligence","Artificial Intelligence (AI)","2026-08-24T02:46:38+00:00",{"slug":40,"category":5,"name":41,"updated_at":30},"attention","Attention",{"slug":43,"category":5,"name":44,"updated_at":38},"beam-search","Beam Search",{"slug":46,"category":5,"name":47,"updated_at":34},"benchmark-contamination","Benchmark Contamination",{"slug":49,"category":5,"name":50,"updated_at":34},"catastrophic-forgetting","Catastrophic Forgetting",{"slug":52,"category":5,"name":53,"updated_at":38},"computer-vision","Computer Vision",{"slug":55,"category":5,"name":56,"updated_at":34},"constitutional-ai","Constitutional AI",{"slug":58,"category":5,"name":59,"updated_at":30},"context-window","Context Window",{"slug":61,"category":5,"name":62,"updated_at":38},"deep-learning","Deep Learning",{"slug":64,"category":5,"name":65,"updated_at":30},"diffusion-model","Diffusion Model"]