It is pointed out that AI is lowering the value of knowledge and universities should reconsider what they teach
The development of generative AI, particularly large-scale language models (LLMs), has made it possible for humans to easily obtain the information they need through dialogue with AI. Patrick Dodd, a professor at the University of Auckland Business School, argues that as AI begins to provide knowledge, the value of universities, which once functioned as a source of knowledge, may be declining.
AI is driving down the price of knowledge – universities have to rethink what they offer
Regarding the nature of universities, Dodd points out, 'For a long time, they operated on a simple principle: knowledge is scarce. University is a place where you pay tuition, attend lectures, complete assignments, and ultimately receive a credential in the form of a bachelor's degree. This process served two purposes: first, to provide access to knowledge that was hard to obtain elsewhere, and second, to demonstrate to employers when you entered the workforce that you had invested the time and effort to acquire that knowledge.'
This model worked because access to specialized knowledge was previously limited and the cost of acquiring it was high, but Dodd argues that AI has dramatically lowered the barrier to accessing knowledge, making this model no longer viable.
An LLM is no longer simply a matter of retrieving facts; it can explain, translate, and summarize them. When supply increases dramatically, basic economic principles lead to a fall in price. The value of knowledge that universities have sold for years is shrinking as a result.
In fact, the labor market is already changing, with the number of entry-level job openings in the UK falling by about a third since ChatGPT launched in 2022. In the US, several states have removed degree requirements for public sector positions, and it is said that the value of a college degree is no longer even being considered.
However, Dodd points out that not all knowledge is depreciating at the same rate. While generative AI can certainly replace existing human labor, this requires new labor and new knowledge. There is also a wealth of knowledge that generative AI cannot replace, such as tacit knowledge, such as overcoming conflict within a team and leading them to success.
'Basic knowledge remains important for operating AI, evaluating its output, and making appropriate decisions. However, wages at supply and demand equilibrium are falling rapidly along the demand curve. What has traditionally been considered 'scarce' is shifting from information itself to things that machines cannot yet imitate: focus, good judgment, strong ethics, creativity, and collaboration. These are the true scarcity capabilities in today's marketplace,' Dodd said.
To instill these values, Dodd believes universities can make the following changes:
1. Lecture review: Since there is almost no value in teaching content that ChatGPT can get high scores for, we will shift the evaluation to judgment and synthesis skills.
2. Learning experiences: Invest resources in mentored projects, real-world simulations, and ethical decision-making studies where AI serves as a tool.
3. Critical Skills: Create micro-credentials for skills such as collaboration, autonomy, and ethical judgment.
4. Collaborate with industry, but emphasize collaboration: Researchers provide the expertise and rigor of education, employers provide real-world use cases, and students focus on validating and refining ideas.
Dodd concluded by saying, 'If universities can gain an advantage in the future, it will be in developing human skills that complement AI. If universities do not adapt, the market (both students and companies) will leave them behind. It is important to shift from being a source of information to a center of judgment, and to teach students how to think with AI rather than against it.'
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