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How AI Changes the M edia Business

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Summary

In this article, Evan Armstrong discusses how AI is disrupting the media business by reducing the cost of content creation and increasing the effectiveness of recommendation systems. He argues that AI will lead to more addictive services and the personalization of content, which will shift the competition for attention to the network effects of social cohesion. AI will also power personalized dreams, where media companies are relegated to being databases and the top .001% of publishers win the remaining spoils. In this future, journalists will focus on cultivating sources and punching data points into the AI and publications will be competing on social dimensions.

Q&As

How does AI disrupt the media business?
AI disrupts the media business by reducing the cost of content creation and improving customer acquisition through AI-powered recommendation systems.

What is the potential for AI to create personalised dreams?
The potential for AI to create personalized dreams is that it could generate video games and movies that are perfectly tuned to the individual, eliciting near ecstasy each time they are turned on.

What are the implications of AI-driven creation and aggregation for media companies?
The implications of AI-driven creation and aggregation for media companies are that they will be forced to compete on social dimensions, such as group experiences, discussion, and community forums, in order to win attention.

How will AI affect the power law of the internet?
AI will affect the power law of the internet by amplifying the dynamic, so that the top .001% of publishers will win the remaining spoils, while personalized AI bots and content sweep up the rest.

What is the role of social and community networks in the AI-driven media business?
The role of social and community networks in the AI-driven media business is that they will be key to winning the future, as they are the only channels that are not subject to the power of AI-driven recommendation systems.

AI Comments

👍 I really enjoyed this article and the insight it provides into how AI is changing the media business. It is well written and provides a comprehensive overview of the potential impacts.

👎 This article presents a potentially bleak future for media companies, and while it provides some thought-provoking ideas, it fails to offer any solutions or alternatives.

AI Discussion

Me: It's about how AI is changing the media business. It talks about how AI is disrupting how content is created, distributed, and consumed. It also talks about how AI is making content more personalized and how this could lead to a more centralized media system with fewer independent publishers. It's a pretty interesting read.

Friend: Interesting. It definitely makes me think about how much power AI will have in the future. How will this affect the media industry?

Me: Well, it suggests that AI will have a huge impact on the media industry. AI will make content more personalized, which could lead to fewer independent publishers and more centralized media. It could also make content more addictive, meaning that companies will have to focus on creating experiences that drive network effects. So, it could lead to a lot of disruption in the industry.

Action items

Research AI-driven content creation and aggregation technologies to understand how they could disrupt the media industry.
Explore the potential of AI-powered recommendation systems to increase user engagement and addiction.
Consider ways to leverage network effects and social cohesion to create content that stands out from the competition.

Technical terms

AI (Artificial Intelligence): AI is a branch of computer science that focuses on creating intelligent machines that can think and act like humans.
Deep Learning: Deep learning is a subset of machine learning that uses algorithms to learn from data in order to make predictions.
GPT (Generative Pre-trained Transformer): GPT is a type of deep learning algorithm that is used to generate text.
Midjourney: Midjourney is an AI-powered image generator.
Recommender System: A recommender system is an AI-powered system that uses algorithms to recommend content to users based on their preferences.
Network Effects: Network effects refer to the phenomenon in which the value of a product or service increases as more people use it.
Network Cohesion: Network cohesion is the degree to which members of a network are connected to each other.
Power Law: The power law is a mathematical equation that describes the relationship between two variables.
A/B Testing: A/B testing is a method of comparing two versions of a product or service to determine which one performs better.
TL;DR: TL;DR stands for "too long; didn't read" and is used to indicate that a text is too long to read in its entirety.