What’s in This Week’s Issue…

Good morning. For the last twenty years, technology companies have taught us that every click, photo, search, and swipe makes life more convenient.

What they rarely mention is that those same interactions helped build the data that powers modern AI.

And turned billions of people into an invisible workforce without them ever realizing it.

So this week…

• 🏆 The Big Play: How the quest to build created a surveillance state

• 💪 The Power Move: The hidden pattern that turns ordinary business incentives into systems of power

• 💵 Follow the Money: Is the US facing a more deadly drug crisis with the rise of a new synthetic ‘orphine’? 

-GEN

🏆 The Big Play

The biggest money power story of the week.

How Your Devices Quietly Built a Surveillance State

Pokémon Go, Facebook, Ring doorbells, and AI glasses seem like completely different products serving completely different purposes.

But when you trace them back to the same source, they reveal the same playbook.

And that playbook explains how the race to build AI quietly created one of the most sophisticated data-collection systems in human history:

1. They Needed Humans Before They Could Build AI

The AI industry had a simple problem in the early 2000s. Computers could process information faster than humans, but they still couldn't reliably recognize faces, identify objects in photographs, or understand what they were looking at.

Before machines could learn, somebody had to teach them:

• In 2005, Amazon launched Mechanical Turk, a platform where humans performed tiny tasks that computers couldn't yet do, such as labeling images and identifying objects.

• The platform was named after the famous eighteenth-century chess-playing machine that appeared autonomous but secretly hid a human operator inside.

• Amazon described the model as "artificial artificial intelligence" because humans were doing the work that software appeared to be doing.

• Every completed task became training data that helped machines learn how to perform those same tasks in the future.

Mechanical Turk proved that human intelligence could be converted into machine intelligence.

The problem was that paying millions of people to generate that data was expensive.

So the industry started looking for a way to collect the same labor without making it feel like labor.

2. They Turned Everyday Life Into Training Data

Once companies realized that human behavior was a valuable asset, they stopped collecting data through work and started collecting it through products.

The most successful systems were the ones that made participation feel useful, entertaining, or rewarding:

• Every time someone clicked traffic lights, bicycles, or crosswalks in reCAPTCHA, they were helping train computer-vision systems.

• At its peak, Pokémon Go attracted 143 million players who spent their time photographing landmarks, storefronts, churches, statues, and public spaces while chasing virtual creatures.

• Every visit captured images from specific angles, at specific times, and with precise GPS coordinates, helping Niantic build detailed spatial maps of the physical world.

• Facebook users spent years tagging friends in photos, effectively creating training data for facial-recognition systems that Meta would later use to build more advanced products.

The genius of the model was that the work never felt like work.

At its peak, hundreds of millions of people contributed data while believing they were simply playing a game, sharing photos, or proving they weren't robots.

And once companies had the data, they faced a different challenge: keeping access to it.

3. The Products Changed. The Playbook Stayed The Same.

Collecting data was only half the challenge.

Keeping access to it required something far less technical, getting people to agree to it:

• Privacy policies became increasingly long and difficult to understand, allowing companies to secure broad consent for data collection.

• Meta enables many forms of data sharing by default, while some privacy controls require users to actively search for and disable them.

• Internal Meta documents discussed a proposed feature called NameTag that could identify strangers through smart glasses and surface publicly available information about them in real time.

• Ring demonstrated how AI could scan networks of cameras to locate a lost dog across an entire neighborhood, prompting critics to ask what happens when the target isn't a dog.

A lost dog makes for a heartwarming Super Bowl advertisement. But a system capable of searching thousands of cameras for a specific person raises a very different set of questions.

By the time AI entered the mainstream, the hard part was already finished. The data had already been collected, the models had already been trained, and the infrastructure had already spread into phones, cameras, games, social networks, and smart devices.

The most important thing these companies built wasn't a model. It was a habit.

Because the whole point was never just teaching machines how to understand people. It was teaching people to hand over the information that made those machines possible.

💪 The Power Moves

Playbook for understanding the game of power.

Power Is Usually Built One Incentive At A Time

One of the biggest mistakes people make when studying power is assuming that powerful systems begin with a master plan.

Most of the time, they begin with incentives:

→ Amazon wanted better training data.
→ Meta wanted better products.
→ Niantic wanted more engagement.
→ Ring wanted more customers.

Each decision made sense on its own.

Yet when those incentives pointed in the same direction for long enough, they produced an infrastructure capable of collecting, storing, and analyzing information about billions of people.

The Takeaway:

The most useful question in any money-and-power story is not who benefits after a system exists.

It's what incentives made that system inevitable in the first place.

Because by the time a system becomes visible, its foundations were usually laid years earlier. And the people who understand those foundations are often the ones who spot the next shift in power before everyone else.

💵 Following the Money

Three of the wildest financial and corruption stories from around the world.

#1 - Fentanyl threat grows as DEA detects new synthetic ‘orphine’ compound

#2 - If US oil companies are drilling more, why gas price relief remains elusive

#3 - Why Ukraine’s Zelenskyy is pressing the US to send more anti-ballistic missiles

✨ Poll time!

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