He invented modern graphics as a practical problem by himself as the sole researcher. Given the tools at the time that may have been a harder problem.
We don't have the laws of AGI like we had the laws of optics (Asimov notwithstanding.) Tons of research effort was poured into the wrong avenues in vision (hand-tuned HoG, transforms, optical flow analysis) and ML (support vector machines, computational learning theory) until a chain of breakthroughs hit on the right mathematical approach for vision and supervised learning more generally.
We have some mathematical approaches to try with AGI (e.g. policy optimization/max-Q in reinforcement learning), but they equations are plagued with fundamental issues (e.g. reward sparcity, easily-gamed artificial objectives.)
Carmack optimized some very difficult equations when he worked on graphics, but in AGI we still don't have the right equations to optimize.