Understanding the generative AI difference for games
So, what sets generative AI apart from its predecessors? On one level, it’s evolutionary. To think back, games have long used the term “AI,” but our definition was a constrained set of rules, decision trees, and behaviors. Players engaged with “bots,” but compared to a human, bots often weren’t as engaging. More recently, game companies have been using true AI and ML frameworks (built on data and analytics) to understand players, manage churn, and increase monetization. Some have even integrated their frameworks to advanced AI algorithms like large language models (LLMs) which brings us to generative AI.
Generative AI goes well beyond an evolutionary step forward, however. It’s a whole new dimension for innovation, and, I posit, the biggest change to the industry of games since the introduction of real-time 3D graphics. Here are two fundamental ways generative AI will transform games:
1. Scalable Development with generative AI: Creating content is one of – if not the largest – expenses that games can incur. According to the UK’s CMA, blockbuster games can have development budgets well over $100 million. Even with these massive investments, game teams can struggle to keep up with player demand for new content, especially as these audiences grow to planet-scale. Generative AI can help accelerate game production across the board – code, art, dialogue, and more, enabling these teams to better serve their players at scale. It can help existing teams create better content at a faster pace, with improved collaboration, ideation, and personalization. What’s more, enterprise-grade generative AI allows developers to leverage this new technology in a way which is respectful of intellectual property, while protecting one’s own proprietary data.
2. Real-Time generative AI: As development teams integrate generative AI into their production process, some have begun integrating it into the game itself. This has the potential of revolutionizing player experiences, with generative AI running in real-time as the game is played. This is generative AI that is responsive and dynamic, distilling huge LLMs to react and interact with a player in real-time. Games themselves will be able to generate content based on the explicit or even implicit actions of players: from instantly generated new items and levels to in-game characters that can have lifelike discussions. Imagine games that can naturally respond to a player’s voice, or generate entirely novel content in response to player behavior. With these examples, one can imagine a new paradigm of player personalization and game interaction which is wholly unlike anything the world has seen before.
