AI companies can deliver a software product in a month because they’re not building everything from scratch — they’re leveraging shortcuts in the form of prebuilt tools, frameworks, and infrastructure. Here’s how they pull it off: 1. Reusing existing AI models Instead of training a model from scratch (which can take months or years), they fine-tune or adapt existing large language models (like GPT, Claude, LLaMA). This drastically cuts R&D time — they just tweak the model for their niche use case. 2. Off-the-shelf building blocks Frameworks like LangChain, Hugging Face, PyTorch, TensorFlow and APIs from OpenAI, Anthropic, Google, etc., mean they can focus on integration, not invention. Prebuilt UI kits, cloud infrastructure, and hosting services make deployment much faster. 3. Cloud-first development Services like AWS, Azure, and GCP allow instant scaling without setting up servers. Managed databases (like Firebase, Supabase) and vector stores (...