Huang Renxun launches Nvidia G-Assist, which may seem like just an AI game assistant, but the underlying principles behind it could disrupt the gaming industry, leading to the accelerated elimination of AAA factories and skin-changing pay-to-win games, sparking a revolution in the gaming industry.
(Background: Nvidia introduces the new AI game language model Project G-Assist, how will it change the GameFi track?)
Table of Contents:
AI moves from visuals to gameplay experience (Game Play)
AI will eliminate unenjoyable games
Blockchain games: P2E will disappear
Huang Renxun came to Taiwan and gave a speech during COMPUTEX 2024, announcing his revolutionary AI game assistant, GeForce Project G-Assist. It may seem like just a game assistant that helps players strategize, but upon analyzing its underlying principles and applications, it is evident that Nvidia, a leading AI hardware manufacturer, has opened up a new direction for the global gaming industry, profoundly influencing the development and business of the gaming industry. This article attempts to analyze in-depth the future trend of the integration of the gaming industry and AI, as well as the trend of the GameFi track in the future.
Nvidia started with gaming graphics cards and drivers, and in April 2020, it unprecedentedly combined AI with gaming, announcing Deep Learning Super Sampling (DLSS) for use with the RTX series graphics cards, ushering in a new era of gaming.
The principle of DLSS is to reduce the computational load on graphics by utilizing a large number of Nvidia gaming graphics cards and backend game calculations. Through multiple analyses of game scenes, it calculates imperceptible invalid calculations at high resolutions, thereby reducing the performance load on graphics through AI models, resulting in revolutionary functionality that maintains image quality while significantly increasing frames per second (FPS).
After the outbreak of the LLM language model in 2023, OpenAI initiated a computational war using Nvidia graphics cards. Many software giants, including Microsoft, Google, and Musk’s XAI, participated in it. Although Nvidia seems relaxed, it has not forgotten to innovate in software. Today, it has launched Project G-Assist, confirming Nvidia’s ambition to integrate the LLM language model with gaming. This will undoubtedly lead to a new wave of revolution.
According to Nvidia, G-Assist can provide game suggestions and plot guidance. Players can ask the AI for equipment recommendations. Behind the scenes, it collects a large amount of player game input data, feeds it into a large-scale language model for learning, and effectively lowers the threshold for players to play games, creating more consistent game services. This will fundamentally change game production and gameplay ecology.
How will G-Assist change the game ecology? Imagine if games, from alpha and beta stages to release, are all collected by large-scale language models based on game-level data. What kind of situation would arise? The result would be less diverse and less entertaining games, easily found by machine learning to have the best solutions. The gap between esports players and ordinary beginners in immature games will be minimized, or it will bring about the following impacts:
– Minimization of beginner tutorial barriers
– Template games become boring in a shorter time (applies to both mobile and AAA games)
– Inability to attract skilled players if game diversity is not well-executed
– AI instantly exposes pay-to-win traps
– Acceleration of esports game updates and iterations
Currently, the game industry is filled with numerous similar system experiences in mobile games and AAA games. Although these games claim to be diverse, they are actually produced through a standardized process, limiting the gameplay and variation across different games.
In reality, the experience between different gameplay styles is influenced by individual differences in gameplay (e.g., consulting guides, pay-to-win). If these gameplay differences can be quickly optimized, for example, taking the skin of Game A and applying it to Game B, the model and form generated by Game A will immediately train Game B to find the best solution. As a result, the current practice of large game companies producing multiple games using one template will rapidly digitize gameplay. Therefore, AAA games that only change skins and pay-to-win mobile games will undoubtedly face challenges.
Taking mobile games as an example, imagine if AI has already analyzed a large number of poorly designed pay-to-win mobile games. AI can quickly calculate how long it will take for players to encounter frustration without paying and how much time it will take for free players to reach a certain level. It can also determine the extent of the gap between paying players and non-paying players. This will undermine the carefully designed “psychology of pay-to-win” by game developers. If AI is not restricted by developers, players will be able to see through everything using AI right from the start.
Therefore, in the future, game developers will inevitably be divided into two camps. The first camp is AI-friendly, using AI data learning, diversification, and balancing various gameplay experiences. The other camp is AI-restrictive, using APIs and encryption methods to limit G-Assist or other open-source game language models, forming a new battle of data interpretation to protect commercial interests.
Although it may seem unrelated to the blockchain industry and GameFi, web3 projects and communities have embraced the spirit of open source. If the code and immature games, as well as Ponzi schemes, are not well-developed, they will undoubtedly be quickly learned by G-Assist and similar language model competitors, and it will immediately be realized that these games “have no future.” Therefore, under the popularization of G-Assist and similar models, short-lived games without gameplay and scams will be rapidly eliminated. Thus, unsustainable “Play To Earn” models, VC-inflated valuations, Ponzi schemes, may be directly terminated by technology.
On the contrary, true blockchain games that offer gameplay and diverse mechanics, which can withstand the scrutiny of AI, may have a higher chance of survival. This is both good and worrisome for the web3 gaming industry. It means that no matter how much money is invested or how luxurious a game may seem, its gameplay will be revealed by technology. This is undoubtedly good news for players, but it also limits the marketing tactics of future game industry professionals.
In any case, with the introduction of technologies like G-Assist, the gaming industry will undoubtedly experience a period of turmoil. AAA games that are formulaic and high-cost productions may face significant challenges, while low-cost independent games with creative and diverse gameplay may stand out. However, this may only be possible after several generations of new model technologies like G-Assist have been released.
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