LLM Failures Demand Specialized Evaluation

Leading foundation models struggle on high-stakes topics like geopolitics, mental health, finance, and hiring, where nuance trumps simple facts. Gemini cites Chinese Communist Party sites for unrelated stories, most models show left-leaning bias, and they often omit context, ignore perspectives, or straw-man arguments. These issues arise because model makers prioritize coding and math over information accuracy, leading to unreliable outputs as the primary info funnel—echoing Meta's engagement-optimized failures that killed fact-checking and left users misinformed.

To fix this, demand real benchmarks over checkbox audits. New York City's hiring bias law exposed over half of AI audits missing violations because they ignore edge cases; domain experts must test murky scenarios that generalists overlook.

Build Expert Consensus with AI Judges

Forum AI, founded 17 months ago post-ChatGPT launch, solves this by partnering with world-class experts—Niall Ferguson, Fareed Zakaria, Tony Blinken, Kevin McCarthy, Anne Neuberger—to design benchmarks for complex topics. They train AI judges to evaluate models at scale, hitting 90% agreement with human experts. This scales human judgment without losing nuance, providing actionable fixes that dramatically improve model performance on the same topics.

Apply this for production AI: integrate expert-vetted evals into pipelines for hiring tools or financial advisors to catch biases early, avoiding liability from bad decisions.

Enterprise Liability Forces Truth Over Engagement

Consumer chatbots still deliver 'slop' despite hype, eroding trust—Silicon Valley touts world-changing tech while users get wrong answers. But enterprise users in credit, lending, insurance, and hiring prioritize accuracy to dodge lawsuits, creating demand for rigorous evals like Forum AI's. This pressure can shift AI from engagement traps to truth-optimization, benefiting society. Forum AI bets on this market despite immature compliance, having raised $3M from Lerer Hippeau to scale.