Token anxiety and the hidden cost of control in the AI era

Automation that lacks clear rules about when AI should stop and when human verification should begin creates a new form of waste. An uncontrolled loop is inefficiency disguised as efficiency. Park Chul-wan The author is a professor of Department of Smart Automotive Engineering and Future Mobility Master's Program at Seojeong University. The atmosphere at Silicon Valley house parties is changing. Developers no longer keep checking social media feeds or stock prices. Instead, many find themselves constantly monitoring the status of AI agents running in the background. Venture capitalist Nikunj Kothari has called this phenomenon "token anxiety." A token is the basic unit of computation used by artificial intelligence. As AI systems work around the clock, humans increasingly worry about what those systems are doing. Tokens also carry a financial cost. The more tokens an AI system consumes, the higher the bill. Yet the deeper issue is not computing expenses themselves but the cost of maintaining human control over increasingly autonomous systems. The trend is closely tied to the rise of "vibe coding." Rather than writing code line by line, developers describe a desired outcome in natural language and let large language models handle the implementation. Users see only the finished result, much like a completed dish placed on a dining table. Hidden from view are countless model calls, debugging cycles and iterations taking place behind the scenes. The less transparent the process becomes, the more invisible costs accumulate. During the prototype stage, this approach can appear revolutionary. Once a product enters real-world operation, however, the tradeoffs become clear. If developers do not fully understand AI-generated code, even minor bugs can be difficult to fix. Users become less like programmers and more like managers. They spend additional time providing context, validating outputs and monitoring errors. When the technical debt and maintenance burden left by AI-generated code are taken into account, the long-term cost of control can easily exceed visible server expenses. The same principle applies to physical AI systems operating in the real world. Because humans cannot continuously monitor and intervene in physical space in real time, systems such as autonomous vehicles must make reliable decisions within milliseconds. Competitive advantage does not come from processing ever-larger amounts of data. It comes from achieving a level of reliability that people can trust while using the minimum amount of computation necessary. For that reason, the next stage of AI competition will not be defined solely by model performance. Systems that achieve greater reliability with fewer tokens are likely to outperform larger systems that consume ever-increasing computational resources. The key measure is not volume of computation but the density of meaningful information. Ultimately, token anxiety reflects fear of losing control rather than concern about computing costs. Automation that lacks clear rules about when AI should stop and when human verification should begin creates a new form of waste. An uncontrolled loop is inefficiency disguised as efficiency. This article was originally written in Korean and translated by a bilingual reporter with the help of generative AI tools. It was then edited by a native English-speaking editor. All AI-assisted translations are reviewed and refined by our newsroom.