AI is changing mobile games in two distinct ways: it helps teams build and maintain games, and it is beginning to appear in player-facing features such as generated dialogue, coaching and in-game tips. The clearest established example in the available evidence is production assistance at Candy Crush Saga; other examples include Google demonstrations and a platform feature rollout. They show what may be possible, not that AI-generated levels or characters are already standard across mobile games.
How is AI changing mobile games?
For now, much of AI’s role is behind the scenes. Developers can use it to assist with repetitive or time-consuming work, while teams remain responsible for deciding what belongs in a game and how it is checked. Player-facing uses—such as a character responding to a question or a game offering tailored tips—are developing separately.
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A 2025 Google Cloud and The Harris Poll survey found that 90% of surveyed game developers were already using AI in their work. The survey covered 615 developers in the United States, South Korea, Norway, Finland and Sweden, with fieldwork in late June and early July 2025. It was not a survey of mobile studios alone, so the figure should not be read as the share of mobile games or mobile developers using AI.
AI in development workflows
The same survey describes AI use in playtesting and balancing, localization and translation, and code generation and scripting. Specifically, 47% of respondents said AI was speeding up playtesting and balancing mechanics, 45% said it was assisting localization and translation, and 44% said it was improving code-generation and scripting support. These are developers’ survey responses across games, not mobile-specific adoption rates or independent measurements of productivity.
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AI assisting with game content
The Associated Press reported in May 2025 that King uses AI to help create levels for Candy Crush Saga and revisit older ones. The game had more than 18,700 levels at the time of that report. That example is best understood as production assistance, not proof that a system autonomously designs and publishes entire levels. Todd Green, general manager of the Candy Crush franchise, described the aim as using AI on existing problems to help teams work faster or more accurately.
Will AI make mobile game characters more realistic?
It may make some characters more responsive, but responsiveness is not the same as realism. A Google Developers Blog article from May 9, 2025, describes a sample called Gemma Journey, where players can question non-player characters (NPCs) whose dialogue is generated in real time. The sample uses Gemma with a designed character persona and a game prompt to shape responses.
This illustrates how a character might answer questions beyond a fixed set of scripted lines. It does not establish that the character has human-like understanding, that the feature is widely deployed in commercial mobile games, or that unscripted dialogue will always fit the game. The example is a vendor-described sample rather than independent evidence of adoption at scale.
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Are mobile games using AI to create levels?
There is a documented example of AI helping a team work on levels: King’s use of AI in Candy Crush Saga, as reported by the Associated Press in May 2025. The report describes assistance with creating new levels and revisiting existing ones; it does not establish that AI independently creates, tests and releases levels without human involvement.
That distinction matters. “AI helps create levels” can refer to a tool that assists a designer, suggests changes or speeds up a step in a content pipeline. It does not necessarily mean a game generates a complete level during play, or that every level is machine-made. The available examples support the former, narrower claim—not a general claim that mobile games now routinely generate their own levels.
What can players see today?
Google’s examples illustrate several different kinds of player-facing AI, but they do not show that one approach has become universal.
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Coaching during play
In its May 2025 developer article, Google described a mobile baseball coaching demo hosted on Google Cloud and powered by Gemini 2.0 Flash. The demo shows a route to real-time coaching; it is not, by itself, evidence that similar coaching is common in released mobile games.
Tips through a platform overlay
Google’s Android Developers Blog described Play Games Sidekick as an in-game overlay for rewards, achievements and quest progress, with AI-generated Game Tips. Google said on March 11, 2026, that Sidekick had debuted in over 90 games. That is a dated rollout figure from Google, not a current count guaranteed to remain unchanged or proof that every listed game uses the same AI feature.
How do on-device and cloud AI differ?
Where a model runs affects how a feature is built, but the available examples do not establish that one architecture is always better for a mobile game. Google’s developer material describes an on-device Unity plugin built on Gemma.cpp and a Gemma 3 model family ranging from 1B to 27B parameters. It also describes the cloud-hosted baseball coaching demo and Google Cloud services, including Vertex AI and Google Kubernetes Engine, for game and live-service use.
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| Approach | What the examples show | What is not established |
|---|---|---|
| On-device inference | Google describes a Unity plugin built on Gemma.cpp for running models on a device. | No comparable production measurements are provided for latency, battery use, privacy, cost, model quality or moderation burden. |
| Cloud-hosted inference | Google describes a mobile coaching demo hosted on Google Cloud and identifies cloud services for game and live-service uses. | No comparable production measurements are provided for latency, battery use, privacy, cost, model quality or moderation burden. |
These are architectural examples, not a benchmark. A developer weighing the options would need to assess the needs of the feature, its data flows, performance on target devices, operating costs and safeguards; the cited examples do not settle those trade-offs for every game.
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Player data and ownership
In the 2025 Google Cloud and The Harris Poll survey, 63% of respondents expressed concern about data ownership and 35% about player data privacy. Those results describe developer concerns, not a finding that a particular game mishandled data or a legal conclusion about any studio. They do underscore why teams need to consider what information a feature receives, where it is processed and how it is handled.
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Google Play’s AI-generated content policy places responsibility on developers to prevent prohibited output from generative AI apps. Its examples include offensive content, content that may exploit or abuse children, and content that can deceive users or enable dishonest behavior. A game that generates dialogue, images or other content therefore needs suitable controls and a review of the current platform policy before release. The details depend on the feature and the policies that apply to it.
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What is the realistic outlook for AI in mobile gaming?
The most grounded expectation is more AI assistance in development, alongside selective player-facing features. The Candy Crush Saga example shows a studio applying AI to existing production work; Google’s demos show possible approaches to dialogue and coaching; and Sidekick shows a platform feature offering AI-generated tips in a dated rollout. These examples make the direction tangible, but they do not establish how widely mobile studios will adopt such features or how players will respond.
For players, the practical change may be characters that can answer more varied questions, tips that respond to what is happening in a game, or content teams that can produce and revise material more efficiently. How convincing, useful or trustworthy those features are will depend on implementation and oversight—not simply on whether a game includes AI.
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