Somewhere right now, a leadership team is approving budget for a machine learning team, and nobody in the room can name the decision the models will make. The demos were dazzling, a competitor has just announced an “AI-first strategy”, and the board has started asking what ours is. The plan almost writes itself: hire a head of data science, recruit a few scientists, choose a platform. It feels less like a decision and more like a deadline. This post is the pause button, written for the people in that room: the honest signals that you need machine learning, the equally honest signals that you don’t yet, and the pitfalls waiting for the teams that skip the question.

Intro

Table of Contents

Disclaimer: There has been a large amount of work performed by people when it comes to understanding of LLMs and the like. As a result this post isn’t made with the intentions to re-work the efforts of many but rather guide it’s exploration, focusing on some of the key, personally biased, interesting aspects. Also, at the end of the day ChatGPT is a proprietary model, meaning unless you work at OpenAI you don’t acutally know the specifics beyond the information that has been publically released or empirically evaluated. Always ensure you read multiple sources when trying to verify your own thoughts.

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