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.

Hard to Produce

Producing an AI game requires integrating AI into the conventional game development process, making the process challenging.

• 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.

• 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

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.

• 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.

• Market Recession: Difficulties in commercialization in the game industry led by macroeconomic downturn and market saturation.