# AI Game Developer Challenges

Producing an AI game requires expertise and resources distinct from conventional game production, and for most game developers, developing an AI game is challenging.&#x20;

## Hard to Produce

Producing an AI game requires integrating AI into the conventional game development process, making the process challenging.&#x20;

* **Not For Gaming**: Most game developers are limited in their familiarity with AI capabilities beyond GPT-like general AI tools, while general-purpose AI models underperform gaming-specific tasks.
* **Integration Difficulties**: Bridging the gap between AI's prompt-based interactions and game engines is intricate, and the current game backend is incompatible with AI prompt-based input.&#x20;
* **High Cost and Uncertain Result:** AI model development is expensive due to AI talent scarcity, data costs, budgeting, and performance uncertainties due to AI's "black box" nature.

## Hard to Operate&#x20;

Launching an AI game necessitates an inferencing backend to support continuous player interactions, presenting two key challenges:

* **Technical:** Deploying and maintaining AI models and scaling compute resources require specialized AI and cloud infrastructure expertise to ensure reliable latency and uptime.
* **Resource:** Securing cost-effective GPU services is a significant obstacle, particularly for operations outside major regions like the US and EU.

## Hard to Monetize

* **High Cost:** Significant expenses for 1) AI talent, 2) data acquisition/annotation, and 3) model fine-tuning/inferencing make budgeting difficult.&#x20;
* **Unclear Market:** Limited AI games are available due to 1) the aforementioned R\&D limitations and 2) the lack of market validation for AI games, making established developers hesitant to invest in major IPs amid costly risks.&#x20;
* **Market Recession**: Difficulties in commercialization in the game industry led by macroeconomic downturn and market saturation.
