Why Smarter Models Aren’t Enough

Applying LLMs to complex legal workflows requires moving beyond general-purpose frontier models toward specialized, agentic systems capable of exhaustive document analysis. Legal data rooms often exceed current context windows, necessitating advanced compaction strategies like neural KV-cache compression to maintain information density. While frontier models excel at general tasks, high-value legal processes—such as due diligence for mergers—benefit from domain-specific training and synthetic data generation, which allow firms to encode expertise without compromising sensitive client information. Future development will likely prioritize agentic loops that manage their own context and tool use, balancing the high cost of frontier intelligence with the efficiency of specialized, on-premise models. This evolution reflects a broader shift in knowledge work, where success depends on integrating specialized models into regulated environments rather than relying solely on generalist intelligence.