Ai still sucks

A polished AI answer can still be dangerously wrong. Today, I asked an AI research tool to analyze a major telecom and satellite-spectrum announcement. The response looked professional. It included detailed explanations, confident conclusions and investor implications. But the underlying analysis was misleading and inaccurate. It: Misidentified the significance of the spectrum Failed to recognize its Nextel/Sprint history Incorrectly implied that cell towers were required for direct satellite connections Mischaracterized what AT&T’s CEO had said Added confident interpretation before verifying the primary documents Only after I repeatedly challenged the answers did the tool examine the actual FCC records, transaction filing and AT&T transcript. That is the real risk with AI: wrong answers do not always look wrong. They can be well-written, detailed and completely convincing. In a professional setting, relying on that output could damage someone’s credibility—or cost them their job. The lesson is simple: Do not confuse presentation quality with research quality. For consequential work, AI must: Start with primary sources Preserve exact quotations and context Separate facts from interpretation State clearly what remains unknown Avoid filling evidence gaps with plausible narratives Human expertise is not optional. It is often the only thing standing between an impressive-looking answer and a serious mistake. AI should accelerate judgment—not replace it. #ArtificialIntelligence #AI #Research #DueDiligence #DataAccuracy #Leadership #RiskManagement