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THE VIDEO SHELF · gambling and game design

Gambling and the desire machine | Pay to Win

A 70-minute video essay by Jimmy McGee on gambling, addiction and game design, tracing horse racing, slot machines and collectible card markets. It is the first in a series on making money from games.

Why HN readers recommended it

Recommended as an engaging, well-researched look at the history of gambling machines and how games borrow their psychology.

This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.

Video & recommendation details

Creator / channel
Jimmy McGee
Topic
gambling and game design
Format
commentary
Level
general
HN recommenders in dataset
3
HN mentions in dataset
3
Recommendation years in dataset
2022–2024

These counts and years describe the included Hacker News records; they are not YouTube views or the video’s publication date.

From the original HN conversations

Excerpts from the recorded comments. Follow each source for the full comment and surrounding discussion.

  • Though the video is fairly long, it's a genuinely interesting exploration on the murky history of video games and gambling. This is part two of a video series, I highly recommend watching both. Part 1: [VIDEO]

    jterrys on Hacker News · 2022-12
    In A real history of video games – Pay to Win
  • I recently saw a good video[1] covering the history and appeal of gambling machines. My takeaway is that the primary reason people play these is not because they think they're going to win money. This especially true of pachinko where "cashing out" is a convoluted …

    AlexandrB on Hacker News · 2023-11
    In Casino-like apps have drained people of millions
  • This is tangential but I watched an interesting video essay about slot machines recently[1], based on the book Addiction by Design by Natasha Dow Schüll, and one of the surprising things Schüll's research turned up in interviews with slot machine gamblers was that for some …

    maxbond on Hacker News · 2024-08
    In A New Theory of Distraction (2015)

Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.