Back in my first decade in NLP, we didn't have the luxury of that choice. In the distant days of BERT (yes, it's been seven years), models had to be fine-tuned—and evaluated—before they could transfer to a new task. That process often took weeks per iteration, even though the models were tiny compared to today's LLMs. For fast-moving applications, especially pre–PMF, such slow feedback loops are a deal-breaker. That was a bitter lesson from my last startup, where I trained models from scratch for open information extraction and semantic search. Then came GPT-3 and Flan-T5, and my in-house models became irrelevant overnight. Ironically, those same models marked the beginning of in-context learning—and a whole new path forward.