Why Coding Isn't the Hard Part
Building a game used to mean wrestling with code for months, but AI has flipped that script. Tools like GitHub Copilot and ChatGPT can churn out boilerplate Go or Unity scripts in hours, cutting tasks that once took weeks. Mariano Gappa, an engineer, rebuilt a card game called Truco in three days using AI, compared to three months coding it by hand. Even with this speed, the real challenge lies in making a game that players can't put down.
The catch is that coding was never the bottleneck. Game development hinges on balancing mechanics, crafting visuals that don't feel off, and polishing every detail to keep players hooked. AI can scaffold a prototype fast, yet it cannot determine if a game feels clunky or lacks fun. That's why, despite AI's coding prowess, Steam isn't overflowing with instant classics.
Real-World Lessons From AI in Action
Take Gappa's Truco experiment. Using AI, he slashed his coding time by a factor of 30, generating Go code, unit tests, and asset stubs for the Ebiten engine in three days. The result was a working prototype, but it still needed manual tweaks to make the gameplay feel right. This shows AI's strength for solo developers: rapid scaffolding lets one person build what once took a small team.
Contrast that with a mid-size indie team who used AI differently. They fed GPT-tuned bots 10,000 simulated play sessions, catching balance issues and cutting bugs by 30%. But they hit a snag: some AI-generated art assets raised licensing questions, leading to a Steam delisting scare. The lesson? AI can accelerate iteration but introduces risks that demand careful oversight, especially for teams aiming for commercial release.
Where AI Falls Short Today
AI's limits are clear when you dig into the details. It struggles with real-time rendering, performance tuning, and Go's concurrency patterns, often spitting out code that looks right but breaks under stress. Over 70% of small studios now test AI tools for scripting, but a 2025 study found they're most useful for ideation and drafts, rather than for deep gameplay logic or quality assurance. Hallucinated APIs and licensing gray areas further complicate things.
For larger studios, the stakes are higher. AAA pipelines, built on C++ and C#, don't easily bend to AI's Go-friendly tricks, and measuring ROI beyond prototypes is tough. Players also notice when polish is missing. Floods of low-effort AI-assisted clones on itch.io prove that speed doesn't equal quality, and fans value originality that AI can't yet replicate.
What's Next for AI in Game Creation
The future looks intriguing, especially with automated playtesting on the horizon. AI agents could run thousands of game sessions, pinpointing which mechanics keep players engaged and which ones frustrate them. Early experiments, like the indie team's 10,000-session bot, hint at this potential, reducing balance issues significantly. By 2027, we might see Unity or Unreal embedding these tools natively.
However, human intuition remains king. AI can suggest code or test loops, but it can't feel the thrill of a perfectly timed jump or the satisfaction of a well-crafted story. As indie developers and studios lean harder on AI, the winners will be those who use it to free up time for creativity, rather than as a shortcut to skip the hard work of design.