ML freshman finds research "solved" by 2025 breakthroughs

The "everything is solved" feeling in ML systems is a symptom of a field currently racing toward a deterministic ceiling of hardware efficiency. The student's intuition is validated by recent state-of-the-art papers like PQCache and Alibaba’s async prefetching that address the KV cache bottlenecks they identified. Systems research often feels closed because it targets well-documented GPU memory hierarchies, making optimal paths look obvious once discovered. Future novelty will likely shift from pure speedups to provable reliability and energy-per-discovery metrics that current benchmarks ignore.