The scaling hypothesis proposes that simply increasing model size, training data, and compute leads to emergent capabilities and improved performance in language models, without requiring fundamental architectural changes. Core claim: large language models exhibit predictable performance improvements following power-law relationships as scale increases, and qualitatively new abilities emerge at sufficient scale that are absent in smaller models. Evidence supporting: (1) GPT series progression—GPT-2 (1.5B) → GPT-3 (175B) → GPT-4 showed dramatic capability jumps; (2) Smooth loss scaling—test loss decreases predictably as power law of parameters, data, and compute; (3) Emergent abilities—few-shot learning, chain-of-thought reasoning, code generation appeared at scale thresholds; (4) Cross-task transfer—larger models generalize better across diverse tasks. Key scaling dimensions: (1) Parameters (N)—model size/capacity; (2) Training data (D)—tokens seen during training; (3) Compute (C)—total FLOPs ≈ 6ND for transformer training. Nuances and debates: (1) Diminishing returns—each doubling yields smaller absolute improvement; (2) Emergence vs. measurement—some "emergent" abilities may be artifacts of evaluation metrics; (3) Data quality vs. quantity—curation and deduplication can substitute for raw scale; (4) Architecture matters—efficient architectures achieve same performance at lower scale; (5) Chinchilla finding—previous models were under-trained relative to their size. Practical implications: (1) Predictability—can estimate performance before expensive training runs; (2) Resource planning—calculate compute budget needed for target capability; (3) Investment thesis—justified billions in AI compute infrastructure. Limitations: scaling alone may not solve alignment, reasoning depth, or factual accuracy—motivating complementary approaches like RLHF, tool use, and retrieval augmentation.