In this article, I would like to introduce “The Turing Trap”, a concept proposed by Stanford professor Erik Brynjolfsson.

The Turing Trap refers to the risk that, by focusing too much on developing Human-Like Artificial Intelligence (HLAI), economic and political power will become increasingly concentrated, leaving ordinary people with fewer means to improve their situations.

1. What Is the Turing Trap?

In 1950, Alan Turing proposed the now-famous Turing Test—an experiment to see whether a machine could imitate human responses so convincingly that people could not tell the difference.

Since then, many researchers, engineers, and entrepreneurs have pursued the creation of intelligence equal to or surpassing that of humans.

However, this pursuit often leads to automation—machines replacing human labor—rather than augmentation, where machines complement and enhance human capabilities. That imbalance is what Brynjolfsson calls the “Turing Trap.”

2. The Dichotomy of Automation vs. Augmentation

The impact of AI depends heavily on how it is used:

Automation: Substitutes for human labor, lowering wages for ordinary workers while concentrating wealth and power among a small elite. Augmentation: Enhances human ability, creates new value, and distributes benefits more broadly.

If AI is used primarily for automation, inequality grows. Think of scenarios where only a few developers of technologies like ChatGPT reap vast rewards, while most others see their roles diminished.

3. Why Automation Is Over-Rewarded

Brynjolfsson identifies three groups—technologists, business leaders, and policymakers—each drawn toward automation because of excessive incentives.

Technologists: It is easier and more straightforward to replicate human tasks (HLAI) than to design superhuman capabilities in entirely new domains. Businesses: Automation looks like “low-hanging fruit”—a quick way to cut costs, scale operations, and satisfy investors. Policymakers: Current tax systems favor capital over labor, encouraging automation instead of human augmentation.

4. Escaping the Trap

The solution is not to slow down technological progress, but to redesign incentives so that AI enhances human potential instead of replacing it.

New Benchmarks: Move away from the Turing Test’s goal of human imitation, toward benchmarks that measure AI’s ability to achieve what humans cannot do alone. Policy Reforms: Prioritize labor income over capital income in taxation, and fund reskilling and training programs to empower workers. Augmentation First: Promote AI as a collaborator, not a substitute—helping doctors with superhuman diagnostic tools, or assisting educators with personalized learning at scale.

5. Conclusion

AI has the potential to enrich our lives, increase productivity, and create unprecedented prosperity. But if development remains stuck in the Turing Trap—focused on imitation and automation—wealth and power will concentrate in the hands of very few.

The way forward is clear:

We must shift AI from being a “human mimic” to becoming a “super collaborator.”

Only then can society as a whole share the benefits of this revolutionary technology.