The Means of Prediction summary

Why do certain ads follow you around the internet? And how does Netflix consistently recommend shows you actually want to watch? It starts with understanding how AI actually works – and why who controls it shapes everything that follows.

AI boils down to a straightforward concept: systems that make automated choices to maximize whatever goal they’ve been given. These systems need four building blocks: possible actions to select from, a target to optimize toward, some baseline knowledge, and datasets for training. Machine learning makes this possible by detecting patterns in massive amounts of information rather than following hand-coded instructions.

When you ask ChatGPT a question, you’re not just interacting with clever software. You’re tapping into the compressed labor of millions of invisible workers, the harvested knowledge of countless creators, and computing power dominated by a handful of corporations. 

To understand power in AI, follow the resources. Four essential ingredients determine control: data, computing infrastructure, technical expertise, and energy. Whoever commands these building blocks decides what AI prioritizes – and currently, that’s corporate profits rather than human flourishing.

Consider Amazon’s warehouse algorithms, which maximize delivery speed and worker output. The US Department of Labor found epidemic rates of back injuries from constant lifting, awkward movements, and relentless pacing. In both cases, the algorithms worked exactly as designed, serving their owners’ interests while ignoring public welfare.

Now imagine if the Amazon workers themselves controlled the warehouse systems. They would likely optimize them for safety, injury prevention, and humane working conditions. Same technology, completely different outcomes depending on who holds power. 

When it comes to the challenge of regulating AI, there might be surprising insight hidden in the same democratic principle that lets you serve on a jury. And it all goes back thousands of years.

Despite AI’s dizzying pace of change, the fundamental challenges aren’t new. We’re still grappling with timeless questions: How do we learn? How should we act? What makes a fair society? The problem is that right now, whoever controls the data, computing power, and technical expertise gets to decide AI’s goals.