2.1.1 How Tech Changes What We Know Notes

Technology Changes What Counts as Knowledge

1. You probably think you're using technology to access information that already exists but that's not what's actually happening.
2. Let me give you a concrete example.
3. Your grandmother learned to cook by watching. She tasted failed attempts, developing muscle memory.
4. This created a particular type of knowledge, something embodied, intuitive, and learned through repetition and failure.
5. You on the other hand, learn to cook by watching YouTube videos, reading recipe reviews, following step-by-step instructions optimized by thousands of users.

Quantification Changes What We Think We Know

1. Before fitness trackers existed, walking was just moving your body from one place to another.
2. Now walking is data: steps, pace, heart rate, calories burned, route optimization, sleep quality impact.
3. The activity hasn't changed, but what we think we know about walking has been completely transformed.

Prediction Becomes a New Category of Knowledge

1. Instead of just knowing what is, we now have systems that claim to know what will be.
2. Think about it: Netflix doesn't tell you what you like.
3. It tells you what you will like based on algorithmic predictions.
4. This creates an entirely new category of knowledge: probabilistic preference knowledge.
5. The weird thing is that these predictions start shaping your actual preferences.
6. You begin to see yourself through the lens of algorithmic predictions, which changes how you behave, which changes the data, which changes the predictions.

Authority Gets Inverted

1. This is arguably the most radical change technology has created.
2. Traditional knowledge worked like this:
   1. Experts created knowledge then distributed it to non-experts.
   2. Now it works backwards: non-experts create data, algorithms process it, then redistribute it as expertise.

Example

• Google Maps knows traffic patterns better than city planners.
• Not because Google hired better urban experts, but because millions of drivers using smartphones create real-time data that gets algorithmically processed into traffic knowledge.

Time Compression Changes Knowledge Purpose

1. Technology has collapsed the traditional timeline of knowledge creation.
2. The old model took years: research, analysis, peer review, publication, application.
3. The new model takes milliseconds: data, algorithm, recommendation, action.
4. This speed changes what knowledge is for, because it's no longer about understanding the world so much as responding to it faster than competitors.
5. Knowledge becomes less about comprehension and more about optimization.

Example

• Financial trading algorithms make economic decisions based on pattern recognition in microseconds.
• These decisions move markets before human economists can even process what's happening.
• The knowledge about market trends is being created and acted upon faster than human cognition can follow.

Example

• Social media platforms identify emerging social movements through share analysis before sociologists can study them.
• Twitter/X trending topics reveal cultural patterns in real-time that traditional research would take months to document.

Personal and Shared Knowledge Merge

1. Technology creates hyper-specific knowledge and mass generalization simultaneously.
2. Spotify knows your exact musical preferences at 11:23 AM on Tuesday mornings when you're commuting in light rain.
3. But it uses this ultra-specific personal knowledge to make broad generalizations about millions of users.
4. Your personal knowledge becomes training data for shared knowledge that gets applied to other individuals.
5. The boundary between what you know personally and what algorithms know widely becomes impossible to maintain.

Example

• Ancestry.com analyzes your specific DNA to give you personalized health risks.
• But it only works because millions of people shared their specific DNA to create statistical models.

Algorithmic Curation Becomes Invisible

1. The most interesting aspect of technological knowledge transformation is that you can't see it happening.
2. Search results that feel like discovering information are actually algorithmically curated based on your previous behavior, location, demographic profile, and commercial interests.
3. Your political knowledge gets shaped by algorithms designed to maximize screen time, not inform voters.
4. Most people experience algorithmic knowledge curation as natural discovery but this invisibility makes it impossible to critically evaluate the knowledge you're receiving.