Abstract
The chapter explores the emergence of AI-driven tools within the digital game industry, investigating the shift from experimental applications like NVIDIA’s GameGAN to production-ready tools like Ludo AI and its impact on the contemporary ‘technological imaginary’ of digital gaming. Game-making, particularly the creation of procedural complexity from a few, simple rules, has repeatedly been characterized as a magic trick, which is not fully rationalizable; the work of Jennifer Whitson shows how even game designers often view their software tools as ‘agential,’ ‘so complex as to be fully unknowable,’ and thus, ‘magical.’ The infusion of machine learning into these tools, from pre-production over asset creation to playtesting, is transforming both practitioners’ and players’ assumptions regarding game design, raising questions of authorship and neoliberally informed conceptions of creativity, changing workflows, and equitable working conditions that can be inferred from the already-observable cultural and aesthetic implications of AI in visual design work. The chapter analyzes these hopes, fears, and expectations as part of a broader ‘technological imaginary’ of game-making through a diachronic framing analysis incorporating academic and industry publications as well as ‘software affordances’ of available tools. The material corpus comprises early academic visions of AI in game design and first attempts at standardization but also grassroots experiments using general-purpose tools like GitHub Copilot or ChatGPT to create games and develop game development literacy. The analysis investigates the framing of AI-based tools in game design as a socio-technical process, foregrounding the interplay between human and non-human agents/actants and the potential risk, which only few industry practitioners currently caution against, that AI-driven tools might make game design too ‘frictionless’ and might require more playful ‘tinkering’ with or repurposing of the rapidly evolving AI tools to see beyond their ‘readiness-to-hand’ moving forward.
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Notes
- 1.
For a concise overview see, e.g., https://arstechnica.com/science/2019/12/how-neural-networks-work-and-why-theyve-become-a-big-business/
- 2.
- 3.
The findings below both refer to changes in game design, with a focus on inventing games, and ‘game development,’ with a focus on the technical implementation of both digital and analogue games; both terms will be used depending on the context, but, to combine both, I suggest referring to the technological imaginary of ‘game-making’ as the goal of this chapter.
- 4.
For a general purpose example see, e.g., https://assetstore.unity.com/packages/tools/ai/easy-neural-network-195215
- 5.
- 6.
- 7.
- 8.
For an early application of the diagram to general-purpose AI from 2020 see https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020
- 9.
- 10.
See https://www.futuretools.io/?tags-n5zn=gaming (as of 19 February 2023).
- 11.
See https://www.scenario.gg/ and https://masterpiecestudio.com/blog/3d-generative-ai respectively.
- 12.
- 13.
- 14.
- 15.
In the first half of 2022, more than 6000 titles were published on Steam alone, amounting to “over 34 games a day”; see https://www.gamedeveloper.com/blogs/video-game-insights-report-first-half-of-2022-on-steam
- 16.
- 17.
- 18.
- 19.
- 20.
- 21.
- 22.
See, e.g., the still operational https://machinations.io/
- 23.
See, e.g., https://promptbattle.com/
- 24.
For an interview with a PromptBase artist see, e.g., https://www.theverge.com/2022/9/2/23326868/dalle-midjourney-ai-promptbase-prompt-market-sales-artist-interview
- 25.
For an overview of the eponymous workshop organized by the European Video Games Society in September 2022, see https://digital-strategy.ec.europa.eu/en/library/greening-video-games-industry-winning-solutions-environment
- 26.
- 27.
For an overview of the ecological impact of ML and potential solutions see, e.g., https://www.numenta.com/blog/2022/05/24/ai-is-harming-our-planet/ and https://spectrum.ieee.org/deep-learning-sustainability
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Werning, S. (2024). Generative AI and the Technological Imaginary of Game Design. In: Lesage, F., Terren, M. (eds) Creative Tools and the Softwarization of Cultural Production. Creative Working Lives. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-45693-0_4
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