Summary of Key Points
Over the past two years, most AI games have remained at the stage of generating a “one-sentence demo” – capable of quickly producing small-scale works to showcase technology, but unable to retain players or generate revenue. This phase is coming to an end: AI game companies are beginning to move in a more practical direction. They are focusing on enabling AI to understand the dynamic systems within games (rather than simply assembling pre-made elements), transforming internal tools into products that can help developers, using AI to reduce costs and sustain their operations, and exploring new formats such as building, management, and interactive short-story elements. The article highlights six representative projects that are exploring the next phase of AI gaming from different perspectives – not competing in who can generate the fastest results, but rather finding truly viable AI-based game mechanics.
Detailed Explanation
1. From “Generating Content” to “Understanding the World”: AI Games Are Finally Starting to “Understand” Games
Previously, AI game demos were like hastily constructed buildings made of blocks – visible but uninhabitable. Now, some companies (such as Seele) are trying to teach AI how to “build real houses,” meaning understanding the space, rules, and character relationships within a game, and even allowing the world itself to evolve. For example, Seele’s PEGA world model incorporates physical laws, character states, and player behavior. If a player cuts down a tree in the game, AI might cause nearby NPCs to raise prices due to a shortage of wood or prompt other players to compete for the remaining trees – this world is alive and not fixed by pre-written scripts. This is the key to retaining players: a world that responds to your actions and continues to change.
2. AI as a Tool: Helping Small Teams Overcome Development Challenges
Many independent developers struggle with AI-generated images that are of poor quality, lack consistency in style, or cannot be directly used in game engines (requiring extensive manual modification). The studio Meowgito has turned the tools they used in their own development into a product called Meowa, which generates usable game assets such as pixelated characters, animations, maps, and UI elements. For instance, if you’re making a pixel game, the characters generated by Meowa have stable animation frames and consistent styles, ready to be directly integrated into the engine. This solves the problem that generating attractive images is useful only if they can be used in games, allowing small teams to create playable products without spending a lot on art resources.
3. Prioritizing Commercialization: Using AI to Help Small Teams Survive
The more practical question is whether AI can generate revenue. Soda Game is a typical example of this approach; instead of creating demos, they integrate AI into the actual development process (using it for coding, music production, and multi-language translation). Currently, AI accounts for 10%-20% of the development in their two games, with plans to increase this to over 50% in the future. The benefit is that small teams can faster produce playable products (like “Interstellar Raider” on Steam), reducing costs and shortening development cycles. After all, without a viable business model, even the most impressive AI technology is useless.
4. AI Games Going Beyond Chat: Driving Entire Game Systems
Previously, AI games were mostly text-based adventures involving conversations with AI NPCs. Now, new formats are emerging:
- Dynamic World RPGs: The economic system in “Faraway Planet: Builder” is driven by AI – commodity prices fluctuate based on NPC interactions and factional changes. For example, if you sell a weapon to one faction, the price may rise for the other faction, affecting the entire galaxy’s outcome.
- Interactive Short-Story Elements: The company DreamFilmAI can convert short-story scripts into interactive games. You provide a brief outline, and the system generates multiple story paths, along with technical packages for integrating into WeChat or TikTok mini-games. Creators don’t need to know how to code; they can turn viewers from passive spectators into active players who can also earn revenue through ads.
5. AI Games Leaving the “Toy Stage”: Exploring More Practical Applications
These projects all share a common goal: moving away from showcasing technical prowess and focusing on real industry needs. Some companies are providing essential infrastructure (like Seele’s world model), others are helping developers reduce costs (like Meowgito’s tools), and still others are testing commercial viability (like Soda Game). This indicates that AI games have moved beyond the stage of being mere demonstrations of technology to becoming practical solutions. The next successful companies may not emphasize “AI games” as a label; instead, they will integrate AI into gameplay elements that players or developers cannot do without.
Final Thoughts
There is no definitive answer for how AI games should develop, but current efforts are more grounded and practical than those of two years ago. AI games are no longer just about generating games quickly; they aim to truly integrate into the game-making process and enhance the player experience. Perhaps soon, AI will no longer be a secondary feature but an integral part of the gaming experience itself – whether it’s a world that evolves on its own or tools that help developers create games more efficiently. That would be the true value of AI in gaming.