In 2022, the Global Generative AI in Gaming Market was valued at USD 922 MN. Between 2023 and 2032, this market is estimated to register the highest CAGR of 23.3%. It is predicted to reach a valuation of USD 7,105 MN by 2032.

Key Takeaway:

  • By technique, the nondeterministic technique segment generated the largest revenue share in 2022.
  • By function, the non-player character (NPCs) dominated the market with a revenue share of 35.2% in 2022.
  • By end-user, the gaming studios segment dominated the market with a revenue share of 51% in 2022.
  • In 2022, Asia Pacific dominated the market with the highest revenue share of 34%.

Market Key Players

Listed below are some of the most prominent generative AI in the gaming industry players.

  • ChatGPT
  • Electronic Arts (EA)
  • NVIDIA Corporation
  • Apex Game Tools
  • Procedural Arts
  • AI Dungeon
  • IBM
  • Kata.ai
  • Pyka
  • Baidu
  • Charisma.ai
  • Latitude.io
  • Other Key Players

Popular Generative AI Tools for Game Development 

  1. Unity ML-Agents: Unity ML-Agents is a development toolkit that allows game developers to create and train AI agents for their games. It provides a variety of features, including a built-in reinforcement learning framework, a variety of pre-trained agents, and tools for debugging and visualizing AI behavior.
  2. DeepMind Lab: DeepMind Lab is a game engine that is specifically designed for training AI agents. It provides a variety of environments, including mazes, obstacle courses, and 3D worlds. DeepMind Lab is also used by researchers to train AI agents for other tasks, such as robotics and self-driving cars.
  3. NVIDIA GANverse3D: NVIDIA GANverse3D is a generative AI tool that can be used to create realistic 3D environments. It uses a GAN (Generative Adversarial Network) to generate images of 3D objects and scenes. GANverse3D can be used to create new levels for games, or to generate realistic environments for training AI agents.
  4. OpenAI MuseNet: OpenAI MuseNet is a generative AI tool that can be used to create music. It uses a neural network to generate melodies, harmonies, and lyrics. MuseNet can be used to create new soundtracks for games, or to generate music for other purposes, such as advertising or education.

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