Every tool replaced something worse — and is itself the wrong tool somewhere. For each big idea: what life was like before, honest merits ✅ / demerits ❌, and where to use it 👍 vs not 👎.
Software was rulebooks: expert systems, thousands of hand-written if-thens, brittle grammar engines. Spam filters listed bad words; translators stored phrase tables. Anything fuzzy — images, speech, natural language — stayed embarrassingly bad, because some rules simply cannot be written.
If you wanted a model to know or behave differently, the only lever was full retraining — museum-budget territory. Today there are three ladders, and picking wrong burns months: prompting (free, per-request), RAG (fresh facts on the desk), fine-tuning/LoRA (bake in style).
Company knowledge lived behind keyword search (exact-word-or-nothing), hand-curated FAQ bots that answered 12 questions, and 'ask Priya, she knows where the doc is'. Models answered company questions closed-book — i.e., they made things up (lesson 09).
Automation meant brittle scripts and RPA bots that clicked pixel coordinates and shattered when a button moved. Chatbots could only TALK — every actual action returned to a human. The glue between 'model suggests' and 'something happens' was always a person.
Custom visuals meant stock-photo hunting, commissioning designers for every variant, or GANs — impressive but unstable to train and hard to steer with text. 'A cat astronaut in watercolor' was a commission, not a sentence.
Frontier AI = a handful of closed APIs, take it or leave it. Then LLaMA-family leaks and releases created a genuine second lane: download the weights, run them yourself, tune them freely (LoRA, L07). Now every team faces the build-vs-rent question AWS taught you (EC2 course!) — for brains.
Every AI app hand-wired every tool: one GitHub integration for the IDE, ANOTHER for the chatbot, a third for the agent — N apps × M tools = N×M adapters, none reusable. Capabilities were private wiring, not shareable parts.