Transformers have become the foundation of modern AI, reshaping how products are built and how businesses operate. In this talk we will explain why transformers replaced earlier models, what makes them highly scalable and how they expanded from language systems to multimodal models that reason across text, images, audio and more.​ We’ll introduce core concepts like attention, embeddings and tokenization, examining how these models learn and generalize. We’ll trace the evolution from GPT‑style language models to vision transformers and multimodal systems that enable capabilities such as in‑context learning.​ We’ll explore practical considerations, including latency, memory, cost, KV caching and quantization. And we’ll highlight trends like long‑context models, on‑device AI and mixture‑of‑experts. Attendees will gain a practical understanding of how transformers work and how to apply them in product decisions.