While reinforcement learning (RL) improves model capabilities, it often results in unpredictable, "bursty" computational demands during inference. This complicates serving the model efficiently, as infrastructure must be provisioned for costly peak loads.
In traditional software, building is the slowest step. With AI, a functional prototype can be created almost instantly. This shifts the critical bottleneck to the 'define' and 'feedback' stages of the development loop, demanding new organizational skills.
Models like Anthropic's Mythos find and exploit vulnerabilities at machine speed, making traditional prevention insufficient. Organizations must now prioritize their ability to rapidly recover data, applications, and infrastructure, assuming a breach is inevitable.
AI agents, optimized for task completion, lack the implicit understanding of security protocols that humans possess. This focus on outcomes can lead them to make mistakes like exposing code or sensitive internal data, creating a new class of insider risk.
Custom tokenizers and embeddings, created for a foundation model, can be repurposed to enhance other data engineering tasks. They can improve OCR accuracy on domain-specific documents, allowing for better text-based processing and avoiding the higher cost of vision models.