Chapter 2 gets into the mechanics of AI projects, with plenty of concrete examples.

Machine Learning, for instance, requires collecting data, training a model, then testing it in the real world. A Data Scientist collects data, analyzes it, and turns hypotheses into actions. Either way: data is everything.

When starting a project, it’s recommended to pair an AI Expert with a Domain Expert, because:

• AI Expert: Knows what AI can and can’t do technically
• Domain Expert: Knows what actually matters to the business

But the key question throughout the process is: how can AI automate tasks, not jobs?

This hit home for me — I’m currently building a Research AI tool at work. AI can’t replace a Researcher, but it absolutely can help a Researcher finish their daily tasks faster.

So if you find AI can’t complete a Researcher’s entire job and conclude it’s “dumb” — that’s a misplaced expectation. We should expect task-based automation, not job-based automation.

Thanks for reading :D

If you enjoyed this post, feel free to click the coffee button in the lower right to support us and give Lottery a can 🐾

You might also like

AI Dungeon Run | Andrew Ng's AI For Everyone – Chapters 3 & 4

Chapter 3 covers building AI inside companies, and Chapter 4 tackles AI and society. The takeaway: AI is a powerful tool with real limits — treat it accordingly.

Aug 24, 2025

AI Dungeon Run | Andrew Ng's AI For Everyone – Chapter 1: What is AI?

Embarrassingly, during my PhD I had access to Professor Hsieh Shu-kai's incredible AI resources and courses — and I never took advantage of them. Now AI is unavoidable, and I'm finally catching up.

Aug 17, 2025

AI Dungeon Run | Building an MVP: My Insurance Manager

Spent one afternoon building an insurance policy manager with Replit — no technical background needed. Here's what I learned.

Aug 31, 2025