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.
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Data from RAMP indicates enterprise AI adoption has stalled at 45%, with 55% of businesses not paying for AI. This suggests that simply making models smarter isn't driving growth. The next adoption wave requires AI to become more practically useful and demonstrate clear business value, rather than just offering incremental intelligence gains.
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.
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.
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.
Despite rapid software advances like deep learning, the deployment of self-driving cars was a 20-year process because it had to integrate with the mature automotive industry's supply chains, infrastructure, and business models. This serves as a reminder that AI's real-world impact is often constrained by the readiness of the sectors it aims to disrupt.
The main barrier to AI's impact is not its technical flaws but the fact that most organizations don't understand what it can actually do. Advanced features like 'deep research' and reasoning models remain unused by over 95% of professionals, leaving immense potential and competitive advantage untapped.
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.
The true threshold for AI becoming a disruptive, "non-normal" technology is when it can perform the new jobs that emerge from increased productivity. This breaks the historical cycle of human job reallocation, representing a fundamental economic shift distinct from past technological waves.
The real inflection point for widespread job displacement will be when businesses decide to hire an AI agent over a human for a full-time role. Current job losses are from human efficiency gains, not agent-based replacement, which is a critical distinction for future workforce planning.
OpenAI's CEO believes a significant gap exists between what current AI models can do and how people actually use them. He calls this "overhang," suggesting most users still query powerful models with simple tasks, leaving immense economic value untapped because human workflows adapt slowly.
