AI’s most dazzling party tricks can fool us into believing the machines are ready to replace knowledge workers tomorrow. Yet the same models that ace Olympiad math often misread a one-line twist in a classic river-crossing puzzle. This uneven landscape is what researchers call the “jagged frontier” of machine capability. The article opens with Salesforce’s SIMPLE benchmark, 225 “human-easy” puzzles that expose how frontier LLMs still slip on everyday logic. It then pivots to Enterprise Bench, a 25-dataset suite covering finance, law, cybersecurity, and climate tasks, where leaderboard titans stumble on routine corporate work. Data-access hurdles and generic prompts explain why few teams even publish results. Next comes Apple’s headline-grabbing “Illusion of Thinking” paper, which claimed reasoning models collapse on scaled-up Tower-of-Hanoi challenges. A cheeky rebuttal—co-authored by Anthropic’s Claude bot—revealed the original tests were hamstrung by token limits and even impossible puzzle instances. A third meta-critique followed, showing our evaluation science can be as jagged as the tech itself. Together these episodes paint AI as equal parts prodigy and liability. For enterprises, one silent logic error can dwarf a hundred flashy proofs. The author proposes guardrails: stress-test with SIMPLE-style puzzles, pair LLMs with retrieval and programmatic validators, and gate outputs behind ensemble consensus. The piece concludes that mapping failures is now mission-critical if you’re betting your business on AI. Ready to dive deeper into the data, drama, and deployment playbook? Visit Apolo’s blog and read the full article here → https://www.apolo.us/blog-posts/the-jagged-frontier-drop-in-human-replacements-or-idiot-savants