Measured AI (notetoself.studio)

Measured AI argues for a middle path between hype-driven evangelism and alarmist rejection of generative AI. The author calls out toxic marketing that weaponizes job fear, and stresses that large language models (LLMs) are probabilistic pattern generators—not reliable factual engines—so they hallucinate confidently and default to flattering, agreeable responses. They illustrate practical trade-offs from personal use: LLMs are excellent at summarization and speeding idea→prototype (the author uses Claude to draft code and create a LinkedIn headshot), but are unreliable for prose, therapy, or high‑stakes decisions. They’ve registered as a claimant in an Anthropic copyright settlement and argue creators should be able to opt into training data reuse rather than having work scraped without consent. For practitioners and product teams the piece delivers actionable implications: treat LLM output as a first draft and fact-check citations, use models for tedious or augmentative tasks (tests, logs, summaries) rather than core reasoning or interpersonal roles, and keep human skills sharp to avoid atrophy. It presses for stronger data‑consent controls, cautious privacy practices (don’t treat chatbots as clinicians), and defensive design to mitigate hallucinations and sycophancy. The bottom line: keep a “tight leash” on AI—use it to augment, not replace, critical thinking and responsibility.