AI for Game Developers: Mundane Utility Talk Archive
Archive and resource hub for my Nerdicon talk on practical AI applications in game development
I gave a talk at Nerdicon about using AI practically in game development - focusing on the mundane but genuinely useful applications rather than the hype. This post serves as an archive and resource hub for that presentation.
Talk Resources
- Interactive Transformer Visualization Demo - See how AI actually processes information
- Presentation Slides - PDF Archive (Google Slides version)
- Lancer Analysis Chat History (Analysis)
- Lancer Analysis Chat History (Compression)
Core Concept: Mundane Utility
The talk focused on AI’s practical applications for indie game developers - the boring but essential tasks that AI handles well today. No promises of AGI or creative breakthroughs, just tools that save time on repetitive work.
Practical AI Use Cases for Game Development
Content Generation & Validation
- Text Generation: Lore variations, dialog snippets, tooltips, item descriptions
- Localization: Quick translation drafts for multiple languages
- Style Validation: Checking if content matches your game’s tone and theme
- Synthetic Focus Groups: Testing content reception at scale
Code & Technical Tasks
- Code Completion: GitHub Copilot for routine programming tasks
- Documentation: Automated API docs and code comments
- Bug Discovery: Generate 50 edge cases for feature testing
- Pseudocode Generation: Turn design specs into implementation outlines
Production Efficiency
- Asset Organization: Batch renaming and labeling of game assets
- Placeholder Assets: Quick generation via Meshy AI (with manual fixes)
- Animation Prototypes: DeepMotion for rough animation passes
Design & Analysis
- Rubber Duck Debugging: Explain your design problems to AI
- Statistical Analysis: Models excel at finding patterns in playtesting data
- Balance Testing: Simulate thousands of combat scenarios (see Lancer example)
- Design Document Processing: Transform natural language specs into state machines
Marketing & Business
- Store Page Copy: Steam descriptions and feature lists
- Devlog Writing: Draft updates and patch notes
- Social Media Content: Platform-specific promotional text
Key Takeaways
- AI is a prediction engine - It excels when working close to its training data
- Context is everything - The more specific context you provide, the better the output
- Manage it like a smart intern - Clear instructions, review output, iterate with feedback
- Use the right tool:
- Hard problems = Reasoning models (Claude Opus, GPT-o3, Gemini 2.5 Pro)
- Simple tasks = Cheaper, faster models
The Future is Already Here
- AI costs are dropping 10x every year
- Current capabilities are the worst they’ll ever be
- New features coming: better agents, test-time compute, fine-tuning
The message: Use the dang thing. Pick a benchmark task you’re good at, test AI against it, and integrate it where it helps.
Sources & Further Reading
Research Papers & Reports
- METR (2025, March 19). Measuring AI Ability to Complete Long Tasks. “The length of tasks that generalist frontier model agents can complete autonomously with 50% reliability has been doubling approximately every 7 months for the last 6 years.”
- Kwa, T. et al. (2025). Measuring AI Ability to Complete Long Tasks. arXiv preprint arXiv:2503.14499.
- Epoch AI (2025, March 12). LLM inference prices have fallen rapidly but unequally across tasks. “Prices declining between 9x per year and 900x per year, with a median of 50x per year.”
- Cottier, B., Snodin, B., Owen, D., & Adamczewski, T. (2025). Trends in AI Supercomputers. Epoch AI. “AI supercomputers double in performance every 9 months.”
- Stanford HAI (2025). The 2025 AI Index Report. “Inference cost for a system performing at the level of GPT-3.5 dropped over 280-fold between November 2022 and October 2024.”
- Andreessen Horowitz (2024). Taming the Tail: Adventures in Improving AI Economics. “For LLM of equivalent performance, the inference cost is decreasing by 10x every year.”
Environmental Impact Studies
- Ren, S. et al. (2023). Making AI Less “Thirsty”: Uncovering and Addressing the Secret Water Footprint of AI Models. UC Riverside. Original research on AI water consumption.
- Altman, Sam (2025, June 10). “The Gentle Singularity”. OpenAI Blog. “The average query uses about 0.34 watt-hours… It also uses about 0.000085 gallons of water; roughly one fifteenth of a teaspoon.”
- Bloomberg (2025, May 8). The AI Boom Is Draining Water From the Areas That Need It Most. “More than 160 new AI data centers have sprung up across the US in the past three years in places with high competition for scarce water resources.”
Books
- Bostrom, Nick (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
- Yudkowsky, Eliezer & Soares, Nate (2025). If Anyone Builds It, Everyone Dies. [Forthcoming]
Online Resources & Blogs
- AI-2027.com - Predictions and analysis of near-term AI development
- LessWrong - Rationality and AI safety community
- Don’t Worry About the Vase - Zvi Mowshowitz’s blog on AI and rationality
- Dwarkesh Patel Podcast - In-depth interviews with AI researchers
- One Useful Thing - Ethan Mollick’s practical AI applications blog
AI Tools for Game Development
- Claude (Anthropic) - Reasoning model for complex tasks ($20/month Pro)
- ChatGPT - OpenAI’s models including o3 reasoning ($20/month Plus)
- Gemini 2.5 Pro - Google’s advanced model (Free tier available)
- GitHub Copilot - Code completion ($10/month, free tier with 2000 completions)
- Meshy AI - 3D asset generation for games
- DeepMotion - AI-powered animation
- Google AI Studio - Large-scale data analysis
- DeepL API - Professional translation (~€1-2 per language for indie games)
- Ludo.ai - Game design assistant and market research
Industry Reports & Analysis
- McKinsey (2023). Unleashing developer productivity with generative AI. “Developers complete coding tasks up to twice as fast with AI assistance.”
- Game Developer Conference (2024). State of the Game Industry Report. Survey of developer AI adoption.
- Creative Bloq (2024). A year on, have indie game devs changed their views on AI?. Industry sentiment analysis.
Technical Benchmarks
- Epoch AI Data Hub - Comprehensive database of AI model capabilities and trends
- Papers with Code - ML benchmarks and implementations
- Artificial Analysis - Independent LLM performance benchmarking
Critical Perspectives
- Aftermath (2024). ‘An Overwhelmingly Negative And Demoralizing Force’: What It’s Like Working For A Company That’s Forcing AI On Its Developers.
- ACM (2024). “I’m a Solo Developer but AI is My New Ill-Informed Co-Worker”: Envisioning and Designing Generative AI to Support Indie Game Development.
For questions or discussions about AI in game development, reach out at: heinke.jonas@googlemail.com
Visit playinsightstudios.com for more resources.