Artificial intelligence (AI) began as a quest to simulate the human brain.
Is it now in the process of changing the role of the human brain in everyday life?
The Industrial Revolution reduced the need for physical labor. As someone who studies the application of AI in international business, I can’t help but think that this is driving the cognitive revolution. It uncovers the need for specific cognitive processes to shape how students, workers and artists write, design and decide.
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Graphic designers use AI to quickly create a slate of potential logos for their clients. Marketers test how AI-generated customer profiles respond to advertising campaigns. Software engineers deploy AI coding assistants. Students will equip AI to draft AI at record times, and teachers will use similar tools to provide feedback.
The economic and cultural meanings are profound.
What about writers who no longer struggle with perfect phrases, or designers who no longer sketch dozens of variations before finding the right phrase? Just as GPS use reduces navigation skills, will they become increasingly dependent on these cognitive prostheses? And how can human creativity and critical thinking be preserved in an age of algorithmic abundance?
Echo of the Industrial Revolution
We were here before.
The Industrial Revolution replaced the craftsmanship of artisans with mechanized production, allowing goods to be replicated and manufactured on a large scale.
Shoes, cars and crops can be produced efficiently and uniformly. However, the product was also more bland, predictable and stripped of personality. Craftsmanship retreated to the margins as a form of luxury or resistance.
Related: Openai’s “smartest” AI model was explicitly told to shut down – and it refused
Today, there are similar risks to thinking automation. Generic AI tempts users to combine speed with quality, productivity with originality.
The danger is not that AI will make us fail, but that people accept the mediocrity of its output as the norm. When everything is fast, frictionless and “good enough”, there is a risk of losing the depth, nuance and intellectual wealth that defines an extraordinary human work.
The rise of the mediocrity of algorithms
Despite the name, AI doesn’t really think about it.
Tools like ChatGpt, Claude, and Gemini often process a lot of human-generated content that is scraped off the Internet without context or permission. Their output is a statistical prediction of what words or pixels may be followed, based on the pattern of processed data.
They are essentially mirrors that reflect the collective creative output back to the user, repositioned and recombined, but fundamentally derivative.
And this is exactly why they work so well in many ways.
Think of the myriad emails people write, things slide deck strategy consultants prepare, and social media-filled ads. Much of this content follows predictable patterns and established formulas. It was there in some way before.
Generic AI is great at creating competent sounding content (lists, summaries, press releases, advertising, etc.). It thrives in a good enough context when the demand for originality is low and is “good enough.”
When AI is creativity and when it’s suffocating
But even in a world of standard content, AI can be incredibly useful.
In one set of experiments, researchers appointed people to complete various creative tasks. They found that people who used generative AI, on average, were more creative and performative participants who were either using web search or generating ideas that were not using AIDS at all. In other words, AI can actually improve creative performance in your baseline.
However, further analysis revealed important trade-offs. The reliance on AI systems for brainstorming has significantly reduced the diversity of ideas created. This is a key element of a creative breakthrough. Rather than exploring unconventional possibilities at the edge, systems tend to converge towards a predictable middle.
I was not surprised by these findings. My student and I have found that the output of generative AI systems is most closely aligned with the values and worldviews of wealthy English-speaking countries. This inherent bias very naturally constrains the variety of ideas these systems can generate.
Even more troublesome, the short interaction with AI systems can subtly reshape the way people tackle problems and imagine solutions.
One experimental set was tasked with giving medical diagnosis to participants with the help of AI. However, researchers designed the experiments so that AI gives some participants flawed suggestions. Even after those participants stopped using AI tools, they were still more likely to unconsciously adopt those biases and make mistakes in their decisions.
What starts as a handy shortcut risks a self-enhancing loop of reduced originality. Not because these tools produce objectively poor content, but because they quietly narrow the bandwidth of human creativity itself.
Navigating the Cognitive Revolution
True creativity, innovation, and research are more than just stochastic recombination of past data. They require conceptual leaps, interdisciplinary thinking, and real-world experience. These are qualities that AI cannot replicate. You can’t invent the future. All I can do is remix the past.
What AI generates can meet short-term needs. A simple summary, plausible design, and a readable script. But that rarely changes. And true originality is drowning in the ocean of algorithmic identity.
Therefore, the challenge is not just technology. It’s cultural.
In this flood of synthetic content, how can we preserve the irreplaceable value of human creativity?
Historical similarities with industrialization provide both caution and hope. Mechanization drove many workers, but also new forms of labor, education and prosperity. Similarly, AI systems can automate several cognitive tasks, but they can also open up new intellectual frontiers by simulating intellectual capabilities. In doing so, they may take on creative responsibility, such as inventing new processes or developing standards for evaluating their own output.
This conversion is only in the early stages. Each generation of the new generation of AI models produces output that once seemed to be in the SF range. The responsibility lies with experts, educators and policymakers to deliberately shape this cognitive revolution.
Does it lead to intellectual prosperity or dependence? To the renaissance of human creativity or its progressive obsolescence?
For now, the answer is in the air.
This edited article will be republished from the conversation under a Creative Commons license. Please read the original article.
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