Networking remains a primary bottleneck for large-scale AI model training.
The lack of high-quality, modern training data is a major bottleneck for developing AI that can automatically write correct, low-level code.
True hardware portability is a myth, as even successive generations of NVIDIA chips have significant architectural differences requiring software rewrites.
Opportunities Identified
A further 10x reduction in AI inference costs is achievable within the next year through hardware and software co-design.
Agentic AI represents the next major frontier in AI capabilities.
Data processing and synthetic data generation are under-hyped areas with massive potential to improve model performance.
Alternative architectures like Mamba can unlock new efficiencies for specific workloads, such as large-batch inference.