Ideas Weave Every Narrative with AI.
Most enterprise AI evaluation is broken—metrics like BLEU and LLM-as-a-judge are easily fooled by plausible-sounding garbage. Round-Trip Correctness forces AI to prove it actually understands by reversing its output back into the input. If it can’t reverse, it didn’t understand. This is the metric that exposes the illusion.
Microsoft’s recent products like Teams and Copilot feel frustratingly half-baked, contrasting sharply with their excellent developer tools. The issue isn’t a loss of craftsmanship, but a misaligned incentive system. Internal competition for executive attention rewards speed over polish, turning paying enterprise customers into unpaid beta testers for their AI hype experiments.
The myth that vector databases are a silver bullet is costing enterprises millions. When AI fails on critical compliance queries and precise data retrieval, the bottleneck isn’t the LLM—it’s your retrieval architecture. It’s time to stop treating enterprise search like a semantic guessing game and start building layered, auditable RAG systems.
Microsoft 365 Copilot has a 4.5% adoption rate after three years and a 1% weekly active user rate — yet prices keep climbing. The one feature users actually love, Co-Work, is locked behind a separate token-based paywall. This isn’t a product failure. It’s a pricing strategy that reveals exactly which AI Microsoft thinks is worth keeping from you.