Compression is one of the core patterns of this era of LLMs

AI tooling is revolutionizing various domains through a pattern of compression, which involves reducing the time, effort, and specialized skills required to access and utilize knowledge. This concept of compression isn't about technically making files smaller but rather about collapsing extensive processes into more accessible forms. For instance, AI systems such as Retrieval-Augmented Generation (RAG) allow legal professionals to access relevant case law instantly, eliminating the need for lengthy manual research. Similarly, AI can compress entire workflows, enabling a single person to execute projects that previously required multiple specialists, thereby reducing the production of intermediary artifacts like design documents and tickets. This compression extends to skills, as AI tools bridge the gap between understanding a problem and executing a solution, allowing individuals to operate across domains they aren't specialized in, although this raises questions about the future training of specialists. The compounding effect of knowledge, time, and skill compression accelerates the creation and sharing of knowledge, but it also introduces risks of losing serendipitous discoveries, missing fundamental design flaws, or overlooking nuances that experts would catch. Ultimately, while compression offers significant advantages, it requires careful judgment to determine which processes can be safely accelerated and which require more deliberate attention to avoid accumulating hidden costs.