Adaptation: The Missing Layer Between Apps and Foundation Models

The conversation centers on "adaptation" in AI, as an alternative to simply scaling models. Sudip Roy, co-founder and CTO at Adaption Labs, argues that current AI models often fail in the "last 5%" of cases, hindering enterprise adoption. He advocates for gradient-free approaches to lower the unit cost of adaptation, enabling seamless AI improvement. Roy highlights three aspects of adaptability: adapting to changing workload distributions, proportional compute allocation based on task complexity, and continuous learning in secure environments. He positions adaptation as complementary to retrieval-augmented generation (RAG) and agent-based systems, focusing on improving the modeling layer itself. Roy also notes that while open-weight models are currently led by Chinese entities, adaptation techniques can still be applied to proprietary models.