Let's say in drug discovery, if we licensed our technology, you have a breakthrough. The drug takes off, and we get a licensed portion of all its sales,
Context engineering has emerged as one of the most critical skills in working with large language models (LLMs). While much attention has been paid to prompt engineering, the art and science of managing context-i.e., the information the model has access to when generating responses-often determines the difference between mediocre and exceptional AI applications. After years of building with LLMs, we've learned that context isn't just about stuffing as much information as possible into a prompt.
AI companies are spending untold billions of dollars building out data centers to support behemoth AI models - and a return on their investments is still nowhere in sight. Valuations have soared well past the trillion-dollar mark, with AI chipmaker Nvidia becoming the world's most valuable company with a market cap that recently topped $4.5 trillion. But that's all, despite AI company revenues barely making a dent on balance sheets.
