📘 Chapter 1 – The Simple Economics of Artificial Intelligence
🧠 Core Insight
Artificial Intelligence, at its core, is a prediction technology.
The authors strip away the hype and argue: AI does one thing well — it makes predictions cheaper, faster, and more scalable than ever before.
But prediction is only one part of a larger decision-making process. Understanding where prediction fits into this process is crucial to understanding its real economic impact.
🔁 Key Definitions
🔮 Prediction:
Using existing data to generate information you don’t already have — like forecasting sales, labeling images, or guessing customer intent.
🧩 Decision:
Combining prediction with judgment (preferences, values, trade-offs) to choose an action.
💡 Why This Matters
When prediction becomes cheap and abundant, its complementary activities (judgment, data collection, and action-taking) become more valuable.
This shift has echoes in past tech disruptions:
Just as electricity made power cheap and transformed industries, AI is making prediction cheap — and will transform how decisions are made.
🧭 Consultant Takeaways
- Don’t sell “AI.” Sell better predictions in context.
- Every business is already making decisions — AI changes how.
- The economic value of AI is not in the model, but in the decisions and workflows it improves.
- Ask clients: “What predictions are you already making — and how could better, faster ones change your operations?”
✍️ Your Reflections
You noted that the book repeats this concept often — intentionally. Like good teaching, it's hammering in a mindset shift:
“AI isn’t magic — it’s a cheap prediction engine.”
That lens alone can keep you grounded when navigating hype, complexity, or client expectations.