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 - [LLMs may not yet have human-level depth, but they already have vastly superhuman breadth](https://news.ycombinator.com/item?id=42625851).
 - [[Prompt Engineering|Learning to prompt]] is similar to learning to search in a search engine (you have to develop a sense of how and what to search for). A [good example from OpenAI](https://cdn.openai.com/pdf/04d1d1e4-bc75-476a-97cf-49055cd98d31/cdc_prompt.pdf).
 - LLMs have encyclopedic knowledge but suffer from hallucinations, jagged intelligence, and "amnesia" (no persistent memory).
-- AI tools amplify existing expertise. The more skills and experience you have on a topic, the faster and better the results you can get from working with LLMs on that topic.
+- AI tools amplify existing expertise. The more skills and experience you have on a topic, the faster and better the results you can get from working with LLMs on that topic. [AI is not be the solution to a problem](https://ludic.mataroa.blog/blog/ai-mania-is-eviscerating-global-decision-making), but another tool that can be used to solve it.
 - [LLMs are useful when exploiting the asymmetry between coming up with an answer and verifying the answer](https://vitalik.eth.limo/general/2025/02/28/aihumans.html) (similar to how a sudoku is difficult to solve, but it's easy to verify that a solution is correct).
   - [Software 2.0 automates what we can verify](https://x.com/karpathy/status/1990116666194456651). If a task/job is verifiable, then it is optimizable directly or via reinforcement learning, and a neural net can be trained to work extremely well.
 - [LLMs are good at the things that computers are bad at, and bad at the things that computers are good at](https://www.ben-evans.com/benedictevans/2025/2/17/the-deep-research-problem). Also good at things that don't have wrong answers.