Generative AI Won’t Revolutionize Game Development Just Yet

Creating a video The game requires hard, repetitive work. How could it not? Developers are in the business of building the world, so it’s easy to see why the gaming industry would be excited about Generative AI. If computers did the boring stuff, a small team could create a map the size of San Andreas. Crunch is a thing of the past; Game release in a finished state. A new age beckons.

There are at least two interconnected problems with this narrative. First, there is the logic of the hype itself – reminiscent of the insane crypto/web3/metaverse gold rush – which, consciously or unconsciously, seems to view the automation of artist jobs as a form of progression.

Second, there is the gap between these statements and reality. Back in November, with DALL-E seemingly everywhere, venture capital firm Andreessen Horowitz published a lengthy analysis on its website announcing a “generative AI revolution in games” that would do everything from shorten development time to Changing the type of titles created. The following month, Andreessen partner Jonathan Lai released a Twitter thread Explanation of a “cyberpunk where much of the world/text was generated, allowing developers to move from asset production to higher-order tasks like storytelling and innovation” and theorizes that AI could enable “good + fast + affordable” game development. Eventually, Lai’s mentions filled up with so many irritated replies that he published one second thread recognizing “there are definitely many challenges that need to be resolved.”

“I’ve seen some, frankly, ridiculous claims about things that are supposedly just around the corner,” says Patrick Mills, acting head of franchise content strategy at CD Projekt Red, the developer of Cyberpunk 2077. “For example, I’ve seen people suggest that AI would be able to expand Night City. I think we’re still a long way from that.”

Even those who advocate for generative AI in video games think that many of the industry’s excited conversations about machine learning are spiraling out of control. It’s “ridiculous,” says Julian Togelius, co-director of the NYU Game Innovation Lab, who has authored dozens of articles on the subject. “Sometimes it feels like the worst crypto bros left the crypto ship when it was sinking, and then they came here and said, ‘Generative AI: start the hype machine.'”

It’s not that generative AI can’t or shouldn’t be used in game development, Togelius explains. It’s that people aren’t realistic about what it could do. Sure, the AI ​​could design some generic weapons or write some dialogue, but compared to text or image generation, the level design is devilish. You can forgive generators that produce a face with wobbly ears or a few lines of gibberish. But a broken game level, no matter how magical it looks, is useless. “That’s nonsense,” he says, “you have to throw it away or repair it manually.”

Basically – and Togelius has had this conversation with several developers – nobody wants level generators that work less than 100 percent of the time. They make games unplayable and destroy entire titles. “That’s why it’s so difficult to just plug in generative AI, which is so difficult to control,” he says. Generative AI Won’t Revolutionize Game Development Just Yet

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