People who think AI usefulness /productivity claims are bs, explain your reasoning.

A viral Reddit post is putting AI skeptics on the defensive, arguing that dismissing the technology's productivity claims is increasingly out of step with observable corporate reality. The author contends that the last two months have seen a tangible acceleration, with "endless real world use cases" prompting full companies to restructure their operations. This shift, they emphasize, is not based on future hype but on demonstrable capabilities from models like OpenAI's GPT-4o, Anthropic's Claude 3.5 Sonnet, and emerging agentic workflows that simply weren't viable in early 2024.

The core of the argument challenges three types of skeptics: those holding fixed ideas from trying tools three months ago, those who tried recently but saw no results without significant learning investment, and those who have done both yet remain unconvinced. The post implies that the pace of improvement in reasoning, coding, and multimodal tasks has rendered quarterly-old evaluations obsolete. It pushes back against blanket dismissal, suggesting that effective use now requires dedicated time to learn prompt engineering, RAG (retrieval-augmented generation) systems, and workflow integration, rather than superficial testing.

This debate highlights a growing divide between hands-on practitioners witnessing efficiency gains in code generation, document analysis, and content creation, and broader public skepticism fueled by overpromising marketing. The post reflects a sentiment within tech circles that we are past a tipping point where AI utility is being measured in shipped features and revised business processes, not just benchmark scores. The burden of proof, according to this view, is now on doubters to explain why they remain unconvinced amidst widespread organizational adoption.