AI Must Not Replace Intellectual Effort

In the age of large language models (LLMs) and generative AI, we are witnessing an unprecedented transformation in how knowledge is produced, disseminated and consumed.

- VIRGINIA DIGNUM

These tools can summarize dense texts, write code, draft legal contracts, or respond to philosophical questions in seconds.

LLMs, we are told, make us more efficient, simplify complex work, automate mundane tasks and allow us to focus on what matters. But as we marvel at their capabilities, a pressing concern emerges: Are these models genuinely boosting efficiency, or are they subtly eroding our capacity for independent thought, judgment and critical reflection?

Efficiency is not a neutral term. It reflects values, what we choose to prioritize, what we define as valuable, and what we are willing to sacrifice. The current narrative around generative AI treats efficiency as synonymous with progress. It suggests that the faster something is done, the better. But faster is not always better. And not everything that can be automated should be.

The popular belief is that LLMs "free up" cognitive bandwidth. That is, they allow humans to delegate repetitive thinking to machines and reserve their energy for more reflective tasks. But the opposite is often true. As more intellectual labor—writing, summarizing and decision-making, for example—is handed over to AI, the less we will engage with it ourselves. Instead of reserving our thoughtfulness for higher tasks, we will increasingly lose the opportunities, and perhaps even the ability, to think critically.

An apt example is the increasing synthetic content online. Not only are images and text being fabricated by machines, but so too often are the public reactions to them. Content no longer spreads because it presents the truth or is relevant, but because of its emotional pull. Fake images spark fake outrage in comments, which then fuel real engagement from users who cannot distinguish between what is human and what is AI generated.