GPT-3, the latest language model developed by OpenAI, is a significant advancement in natural language processing (NLP) capabilities. The model has 175 billion parameters and requires 355 years of training time and $4.6 million to train using a Tesla V100 cloud instance. GPT-3's performance scales as a power-law with respect to model size, dataset size, and computation, demonstrating its potential for solving complex NLP tasks without fine-tuning. The model uses an attention-based architecture and is trained on a large dataset of 300 billion tokens collected from various sources. GPT-3 has achieved impressive results in text generation, machine translation, and question answering, often surpassing the performance of fine-tuned SOTA models. However, its performance may be limited in reasoning tasks, and it relies heavily on "few-shot" examples to adapt to new tasks. The model's capacity is expected to continue growing, with some estimates suggesting that a trillion-parameter model could be developed in the near future, potentially leading to breakthroughs in AGI. Despite its impressive capabilities, GPT-3 raises concerns about AI safety and the potential misuse of such powerful models.