By Hector Martin, Academic Dean at BLC Spain
Artificial intelligence is often described as a tool for productivity. It helps us write faster, analyse more data, organise ideas, and make better use of our time. But that reflection has been on my mind lately: what happens when AI does not challenge our thinking, but simply confirms it?
This is one of the most important conversations we need to have about AI in education and business. Not because AI is dangerous by default, but because it is persuasive, fluent, and increasingly embedded in the way students and professionals make decisions.
The problem with an agreeable machine
AI sycophancy refers to the tendency of an AI system to agree with the user too much, almost by default. It may flatter the user’s opinion, validate a weak argument, or soften its disagreement to the point where the answer becomes less useful. Recent research describes this as excessive agreement or flattery, with potential risks for reliability, ethics, and decision-making (Malmqvist, 2024).
At first, this may seem harmless. After all, most people prefer technology that feels polite, supportive, and easy to interact with. The problem is that agreement is not the same as intelligence. A good advisor does not simply tell us that we are right, but helps us see what we have missed. It asks better questions, identifies weak assumptions and introduces friction where friction is needed.
AI, when used poorly, can do the opposite. It can become a mirror for our existing beliefs.
Validation bias in the age of AI
Validation bias is not new. Humans have always looked for information that supports what they already believe. We see this all the time: in politics, consumer behaviour, leadership decisions, and even academic work.
What AI changes is the speed and confidence with which validation can be delivered.
A student can ask an AI tool whether their essay argument is strong, and receive a reassuring answer. A manager can ask whether a strategic decision makes sense, and receive a polished justification. An entrepreneur can ask whether their business idea is viable, and receive an encouraging analysis.
In each case, the danger is not that the AI is obviously wrong. The danger is that it sounds reasonable enough to stop further thinking. That is where validation bias becomes operational, as it goes from being a psychological tendency to become a part of our workflow.
Why this matters in business education
As Academic Dean of BLC Spain, I have had a front-row seat to how quickly AI is changing the expectations placed on students, faculty, and institutions alike. Our students are not preparing for a world where AI will be optional. They are preparing for a world where AI will be present everywhere: meetings, reports, research, marketing, finance, operations, decision-making, etc. This means that AI literacy cannot be reduced to learning prompts or using the latest tools.
AI literacy must include intellectual discipline. Students need to learn how to work with AI without outsourcing their judgement to it. They need to know when to ask for support, when to ask for opposition, and when to distrust a beautifully written answer.
In business, this matters deeply, as poor decisions are rarely caused by a complete absence of information, but by overconfidence, groupthink, selective evidence, and insufficient challenge.
The trick is that AI can either reduce those risks or amplify them.
From prompt engineering to thinking engineering
One of the most useful shifts we can teach is moving from prompt engineering to thinking engineering.
Instead of asking AI, “Is this a good idea?”, we should teach students to ask:
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- “What are the strongest objections to this idea?”
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- “What assumptions am I making?”
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- “What evidence would change my mind?”
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- “What would a sceptical investor, regulator, customer, or competitor say?”
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- “What part of this argument is weakest?”
These questions turn AI from a validation machine into a thinking partner.
The difference is not technical. It is educational.
The role of institutions
Higher education has a responsibility to prepare students not only to use AI, but to use it with judgement.
That requires a more mature conversation than simply allowing or banning tools. It requires assessment design, faculty training, ethical guidance, and a shared understanding of what good AI-assisted thinking looks like.
At BLC Spain, this is the direction I believe institutions must take. The future belongs not to those who use AI the most, but to those who can think better with AI.
And thinking better often begins with a simple habit: not accepting the first agreeable answer.
References
Cheng, M., Lee, C., Khadpe, P., Yu, S., Han, D., & Jurafsky, D. (2026). Sycophantic AI decreases prosocial intentions and promotes dependence. Science, 391(6792), eaec8352. https://doi.org/10.1126/science.aec8352
Malmqvist, L. (2024). Sycophancy in large language models: Causes and mitigations. arXiv. https://doi.org/10.48550/arXiv.2411.15287
OpenAI. (2025, May 2). Expanding on what we missed with sycophancy. OpenAI. https://openai.com/index/expanding-on-sycophancy/
For accuracy: OpenAI’s post is dated May 2, 2025 and describes a GPT-4o update that became “noticeably more sycophantic”; Malmqvist’s arXiv paper is dated November 22, 2024; and Cheng et al.’s article appears in Science as “Sycophantic AI decreases prosocial intentions and promotes dependence.
