AI's value is overestimated because experts view complex jobs as simple, solvable tasks. The real bottleneck is the unproductive effort required to build a custom training pipeline for every company-specific micro-task. Human workers are valuable precisely because they avoid this “schleppy training loop” by learning on the job, a capability current AI lacks.
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Early AI training involved simple preference tasks. Now, training frontier models requires PhDs and top professionals to perform complex, hours-long tasks like building entire websites or explaining nuanced cancer topics. The demand is for deep, specialized expertise, not just generalist labor.
Even with vast training data, current AI models are far less sample-efficient than humans. This limits their ability to adapt and learn new skills on the fly. They resemble a perpetual new hire who can access information but lacks the deep, instinctual learning that comes from experience and weight updates.
The argument that AI adoption is slow due to normal tech diffusion is flawed. If AI models possessed true human-equivalent capabilities, they would be adopted faster than human employees because they could onboard instantly and eliminate hiring risks. The current lack of widespread economic value is direct evidence that today's AI models are not yet capable enough for broad deployment.
A benchmark testing AI agents against paid freelance jobs found the best performers could only autonomously complete 2.5% of the work. This provides a crucial reality check, showing that while AI excels at discrete tasks, full job automation by general-purpose agents is still far from reality.
Despite marketing hype, current AI agents are not fully autonomous and cannot replace an entire human job. They excel at executing a sequence of defined tasks to achieve a specific goal, like research, but lack the complex reasoning for broader job functions. True job replacement is likely still years away.
If AI were perfect, it would simply replace tasks. Because it is imperfect and requires nuanced interaction, it creates demand for skilled professionals who can prompt, verify, and creatively apply it. This turns AI's limitations into a tool that requires and rewards human proficiency.
The current focus on pre-training AI with specific tool fluencies overlooks the crucial need for on-the-job, context-specific learning. Humans excel because they don't need pre-rehearsal for every task. This gap indicates AGI is further away than some believe, as true intelligence requires self-directed, continuous learning in novel environments.
The argument is that "economic diffusion lag" is an excuse for AI's current limitations. If AI models were truly as capable as human employees, they would integrate into companies instantly—far faster than human hiring. The slow rollout proves they still lack core, necessary skills for broad economic value.
The slow adoption of AI isn't due to a natural 'diffusion lag' but is evidence that models still lack core competencies for broad economic value. If AI were as capable as skilled humans, it would integrate into businesses almost instantly.
A critical weakness of current AI models is their inefficient learning process. They require exponentially more experience—sometimes 100,000 times more data than a human encounters in a lifetime—to acquire their skills. This highlights a key difference from human cognition and a major hurdle for developing more advanced, human-like AI.
