The fear of 'superhuman' AI is based on a flawed premise. Our definition of measurable intelligence—tallying numbers, memorizing lists—was created for the industrial workforce. AI is simply automating these now-outdated tasks, suggesting we need to recalibrate our measurement of human intelligence itself.
Despite public focus on benchmarks, the market for AI evaluation is profoundly underdeveloped, lacking mature tools, methods, model access, and legal protections. For most non-tech companies, standard benchmarks are irrelevant, forcing reliance on subjective, context-specific, 'vibes-based' assessments.
Many companies successfully govern AI with small, cross-functional review boards. However, this trusted manual process becomes a bottleneck when moving from a few internal AI projects to hundreds, especially when dealing with third-party tools and generative AI.
Unlike frontier model companies, traditional enterprises in sectors like retail or finance are more receptive to governance and cautious AI rollouts. Since AI is a tool and not their core identity, they can objectively assess its risks without challenging their fundamental business model.
The 'augmentation trap' shows that while AI can boost immediate productivity, it leads to cognitive offloading. This causes existing employees' skills to atrophy and prevents new employees from ever developing crucial discernment, creating a less capable workforce in the long run.
The 'call and response' nature of large language models (LLMs) is not truly revolutionary for workflows. The significant shift comes from agentic AI, which can connect to various systems and execute multi-step tasks. This moves AI from a content generator to a powerful workflow automation tool.
The true risk of AI isn't just automating entry-level tasks, but preventing new workers from developing 'discernment'—the domain-specific expertise to distinguish good output from bad. Without performing foundational tasks, junior employees may never acquire the judgment of a seasoned professional.