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AI and the future of gaming for game devs

Inworld Team
August 24, 2023
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Artificial intelligence has been a major part of video game development since the industry’s inception. The first examples of AI in gaming date back to 1951 with the mathematical strategy game Nim, where players had to compete against an in-game AI. Today, AI doesn’t just power in-game opponents; it’s used to populate entire digital worlds filled with engaging non-playable characters, as showcased in titles such as Red Dead Redemption 2 and Grand Theft Auto V. 

With the advancements in generative AI, many believe that these new forms of artificial intelligence in game development will revolutionize video game development. Based on recent news, that revolution seems to have already begun. For example, Roblox announced they’re building a platform that will, “allow every user to be a creator,” using generative AI gaming tools. Unity just launched a new AI marketplace featuring generative AI and behavioral AI solutions alongside several new gaming AI tools that help developers enhance their gameplay.  Meanwhile, gaming companies like NetEase and Niantic are incorporating AI NPCs.

These recent examples of generative AI in gaming are only the beginning of a new wave of innovation. Generative AI in games will help developers build more extensive and immersive worlds by automating much of the legwork, enabling them to focus on designing creative new mechanics and features. 

Below, we dive deeper into how AI in game development is being leveraged right now with some examples of how generative AI in games will be used that hint at the future of AI in gaming.

What type of AI is used in gaming?

AI in gaming has typically leveraged one of two forms: of artificial intelligence deterministic or nondeterministic. Today, we also have a third player on the field with the use of generative AI in games becoming more prominent. 

Deterministic AI 

Deterministic AI behavior follows a specified, predictable algorithm. For example, a character might be programmed to move toward a specific location, such as how the Goombas in The Super Mario Bros. games walk along a defined route.

Nondeterministic AI

Nondeterministic AI behavior is the opposite of deterministic AI and follows varying degrees of uncertainty, depending on the sophistication of the deployed AI. 

One of the most famous applications and a great example of AI in gaming is in Lionhead Studio’s strategy game, Black and White, which features a creature that develops based on the player’s interactions. For example, the creature can be taught that it’s not okay to eat innocent villagers by punishing it after it does so.

Generative AI

Generative AI is a type of machine learning where computers can generate original new content in response to prompts from the user, most commonly text (as in ChatGPT) or images. 

The possibilities this gaming AI unlocks for game developers are almost limitless. For example, Asobo Studio used generative AI to help build its enormous 197 million square mile recreation of Earth for its Microsoft Flight Simulator. 

At Inworld, we worked with the creator of The Matrix Awakens to launch Origins, a playable short game where players must investigate an explosion in the fictional city of Metropolis by questioning completely unscripted NPCs powered by Inworld AI. Both of these examples show the exciting potential of leveraging AI in game development.

How is AI being used in gaming?

Pathfinding algorithms were one of the first applications of AI in gaming and, as the name suggests, were used to determine the trajectory of in-game characters. Today, AI for game developers has several additional applications which game developers can use to create better, more expansive gaming experiences. Here are some of the most popular AI for gaming applications: 

  • Decision trees - These gaming AI use a form of machine learning to create a branching storyline where players’ decisions influence the game's future. They’re common in most modern video games but are especially prominent in those with a heavy narrative focus, such as Life is Strange or The Dark Pictures Anthology.
  • Enemy AI - Simple advancements in enemy AI can transform a video game. Released in 2006, FEAR is still highly regarded for its sophisticated Goal Oriented Action Planning (GOAP) gaming AI system, which allows enemies to react dynamically during a shootout. For example, when a soldier realizes it's in danger, it will retreat but if it can’t identify a safe path to do so, it will hunker down and fire on the player.
  • Procedural generation - Some games use gaming AI for procedural generation to create open-world environments, levels, and other assets. For example, No Man’s Sky uses this technology to generate planets and build an entire galaxy for players to explore.

How AI in games will be used in the future

With generative AI in games becoming increasingly sophisticated, we expect game developers will find all-new ways to leverage AI in game development. Here are some predictions about how AI game development might evolve in the future and how game developers will use AI tools to circumvent some of the current disadvantages of AI in games. 


Games such as No Man’s Sky already use AI for procedural generation, creating environments based on the rules a level designer inputs to guide the AI. Generative AI in games can be utilized similarly but can deliver far more impressive results, including worlds built in a specific style or time period. 

For example, one generative AI tool, Promethean AI, was used to recreate the world in Stranger Things in just 15 minutes. As one of the biggest advantages of AI in games is its ability to streamline tasks that typically take a lot of time and resources, generative AI can help indie game developers build more diverse and expansive game environments providing more gameplay value to gamers and helping them compete against bigger titles.

It might also help AAA developers create prototypes or concept art of game environments speeding up the environment production process and allowing all games to create larger worlds -- thus giving players more gameplay options. 

NPCs (Non-playable characters) 

One of the most exciting aspects of generative AI in games is its ability to help create more believable and interactive characters. At Inworld, we built a Character Engine to craft characters that have distinct personalities and demonstrate genuine contextual awareness, meaning they can dynamically react to the world around them while staying in the game world. Better yet, they are seamlessly integrated into real-time applications with built-in performance optimizations.

As games like NetEase's Cygnus Enterprises show, having NPCs that can respond and react to in-game actions can be transformative. Imagine this technology's impact on some of the biggest triple-AAA open-world games, such as a future Red Dead Redemption? A study we conducted in collaboration with Bryter market research found that 99% of gamers think AI NPCs like Inworld’s will enhance a core aspect of gameplay.


Image generation is one of the most common uses for generative AI in gaming. Tools such as Midjourney, Stable Diffusions and Dall-E 2 can be used to create high-quality 2D image from text, and these techniques have already made their way into some of the biggest video game studios. However, the images often still need editing to fix common AI mistakes like too many fingers and unnatural body positions.  

As tools continue to develop, many suspect that generative AI won’t be used for just 2D assets and concept art, but also to help create and animate 3D models. Or to create a first draft of a model that a designer can refine, saving design time. 


Some of the most successful video games of all time are not just recognized for their outstanding gameplay, but also for their iconic soundtracks, something that’s particularly notable in series such as The Legend of Zelda and Final Fantasy

Creating a soundtrack that captures the game's look and feel without becoming repetitive (and let’s face it, annoying) during its lengthy runtime is challenging, especially for smaller and indie developers. The dynamic nature of music also needs programming through audio tools such as Wwise, which can take up a lot of time and resources. 

It’s for this reason AI for game developers is steadily expanding into music with tools such as Soundful, Musico, Harmonai, and Aiva already available. 


In time, the advancements being made to gaming AI will benefit in-game mechanics. For example, most games today have several difficulty options, usually easy, normal, or hard. Using AI for gaming, game developers could build an adaptive difficulty that alters the game's rules based on the player's performance, creating a more personalized and satisfying experience and enhancing player agency and sense of flow. 

While experiences such as these do already exist in games such as the original Resident Evil 4, they’re few and far between due to the difficulties of programming them. Advancements in AI game development are already streamlining this process significantly. 

AI NPCs run by Character Engines like Inworld’s can also transform game mechanics. For example, Inworld has recently introduced an updated goals and actions feature, a player profile feature, and a relationship feature. 

Goals and actions give NPCs defined goals that they then work to execute dynamically therefore moving the narrative forward or changing game play. Meanwhile, player profiles allow NPCs to respond contextually to a player. For example, if a character is from one faction, an NPC from another faction will respond to them rudely but if they choose to play as another faction when they play next, the NPC would respond to them in a more friendly way. 

Finally, our relationship feature allows players to progress in their relationships with each NPC based on every interaction with them. If they’re rude to an NPC, they could end up making themselves an enemy. If they’re nice to them? They could end up becoming their best friend and getting more support from that NPC. Dynamic features like these could have a significant impact on game mechanics. 


Many people believe AI-assisted coding will make a huge impact on game development. However, while it is possible to generate code using AI, this still requires extensive testing and verification from the developers to ensure everything works as intended, so there are only limited gains to productivity compared to other areas. 

Until further advancements are made, adoption will likely be limited, which means coding is one of the current disadvantages of AI game development due to the additional time it needs to check code.

Examples of AI in games

We’ve touched on many of the most impressive applications of AI in games development, but there are still many more fascinating examples that are worth checking out for yourself. These titles use a variety of AI for gaming applications to power gameplay mechanics and are must-plays for anyone looking to learn more about how generative AI in gaming is currently being leveraged.  We also have a whole article exploring AI games if you're looking for more examples.

  • NetEase’s Cygnus Enterprises uses Inworld’s Character Engine to power the game’s sidekick PEA. Not only can you chat voice-to-voice with PEA, but she’s also contextually aware and can warn you of upcoming dangers in the game world. Learn more about how NetEase implemented Inworld in their game.
  • Alien: Isolation uses an advanced AI to power its digital take on Ridley Scott’s iconic sci-fi predator, the Xenomorph. The creature behaves differently every time the player encounters it, and it learns from past mistakes, ensuring each meeting is as terrifying as the last.
  • Middle Earth: Shadow of Mordor (and its sequel, Shadow of War) is widely acclaimed thanks to its Nemesis System, which allows the Uruks players face-off against to adapt and grow alongside them. The Uruks can even remember past encounters with the player and taunt them.
  • Left 4 Dead constantly feels new thanks to an in-game AI which manages each level, referred to as ‘The Director.’ This decides when to send a horde of zombies at the players, where to spawn challenging encounters, and when to give the players time to recover.

Advantages of AI in gaming

When implemented correctly, generative AI in games can have a transformative effect on game development -- which suggests a bright future for AI in gaming. Below are some of the advantages of AI in gaming:

  • AI can be used to create highly-intelligent NPCs helping to create the illusion of a genuinely real in-game world, boosting immersion.
  • Certain games, such as Microsoft Flight Simulator and the upcoming Starfield, are only possible because they use generative AI to achieve their enormous environmental scale.
  • Generative AI in gaming can release NPCs from their scripted shackles so they can have almost human-like interactions with players.
  • Generative AI in games development can speed up development process and reduce production costs by automating simple or repetitive tasks.

Disadvantages of AI in gaming

Countless AI game development tools can benefit players, but there are some drawbacks. These are the main disadvantages of AI in gaming that developers need to be aware of:

  • Using generative AI reduces production time, but it also means that developers have less control over certain aspects of their game. For example, the ‘palaces’ players explore in Persona 5 were the first in the series not built using procedural generation. As they were handcrafted, critics felt they had more personality than previous installments.
  • Generative AI is still a very new technology, meaning there are only a select number of game developers with the expertise to use it. If you plan to use AI in game development, you may need to invest in additional training or expand your team.
  • Leveraging generative AI in gaming can allow you to build entire virtual worlds in minutes, but that doesn’t mean they will be perfect. Developers should invest extra time into testing to ensure everything works as they intended -- or they risk increasing the number of potential bugs.

The promise of generative AI in game development

Generative AI in games has seemingly endless potential. It can help build more expansive worlds filled with more lifelike characters while potentially reducing production costs and speeding up development by automating menial tasks, enabling teams to focus on building more imaginative game mechanics and other features.

There’s no need to imagine what generative AI in modern video games might look like. One modder went to the trouble of implementing our character AI directly into The Elder Scrolls: Skyrim, allowing them to converse with their companions and other characters about almost anything. 

AI for game developers

Gaming is the most expansive entertainment industry worldwide and is estimated to be worth more than £321 billion by 2026. As the industry grows increasingly competitive, video games must find new ways of delivering innovative experiences to capture players' attention. By adopting generative AI for game development, studios will be able to build immersive worlds and exciting new mechanics to help them stand out on the market. 

Our research found that almost everyone who plays video games feels that AI will enhance gameplay, with 81% even going so far as to state they would be willing to pay more for a game that uses these features to provide a more immersive experience through NPCs. 

Want to learn more about how generative AI can be used to create AI NPCs? Head to our studio. 

Click here for examples of studios implementing the Inworld Character Engine and other AI in gaming: 

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