Is the Pursuit of Knowledge Obsolete?
AI is already changing how we acquire and pursue knowledge; AGI will radically redefine one of the fundamental drivers of human nature.
“Inquiring minds want to know!” To those who remember this slogan, it will undoubtedly bring a sneer; yet it reveals an important truth, no matter how misapplied. Eleven score and three-quarters centuries ago, Aristotle opened his Metaphysics with the famous line: “All men by nature desire to know.”
The pursuit of knowledge – epistemic curiosity – certainly drove us out of the caves and propelled us to the stars. Are we in danger of reaching the point where it ceases to be our – human – animating principle? As Artificial Intelligence (AI) matures into Artificial General Intelligence (AGI) and evolves into Superintelligence (sometimes referred to as the Singularity), will the pursuit of knowledge by individual human beings become obsolete?
Aristotle’s reflections on knowledge laid the foundation for Western philosophy. He valued knowledge for its own sake, distinguishing scientific knowledge of causes and principles (Epistēmē) from applied, practical knowledge (Phronēsis), and from knowledge synthesized with reason (Sophia – theoretical wisdom). He defined the pursuit of knowledge as disciplined inquiry and believed it to be virtuous in itself. Indeed, Aristotle saw the contemplative life, devoted to understanding eternal truths, as the highest form of human existence, likening it to the divine, and believing it to be necessary for eudaimonia – human flourishing.
Aristotle’s ideas remain a foundational touchstone to this day, with many modern scholars retaining Aristotle’s emphasis on intellectual virtues and the intrinsic value of knowledge and its pursuit – the process of inquiry.
The acquisition of knowledge, long a province of priests and scribes, was democratized in Western societies during the modern era. The mass media disseminated a wide range of knowledge broadly, but it wasn’t until the Information Age, with the invention of the World Wide Web and the cell phone, that the sum of human knowledge became universally accessible. Thanks to the “magic rectangle,” everybody can now be a polyglot.
Societal status associated with possession of knowledge likewise evolved. W. David Marx, in Status and Culture, traces this evolution:
In pre-modern societies, “erudition was a rare commodity, jealously guarded by elites.” “The internet and digital tools have flattened access to knowledge, stripping away the exclusivity that once made it a status symbol. Now, the prestige lies in how one uses that knowledge—through innovation or cultural production—rather than simply holding it.” As a result, today, “status increasingly flows from creativity and originality rather than the mere possession of knowledge.”
For the first time in history, the Information Age shifted societal status and power from physical strength and cunning to creativity and entrepreneurial skills. Education, not birth order, became a driver of social mobility. Degrees, not attire or conveyances, became markers of prestige. Knowledge synthesis is the new differentiator.
“Revenge of the nerds,” indeed.
Yet the revenge may be short-lived: a human expert is being rapidly replaced by technology. As we transition from the age of AI, in which artificial intelligence agents serve as custodians of the knowledge clearinghouse, to the age of AGI (artificial general intelligence), which will be able to synthesize all available data better than any human on the planet, in every knowledge sphere, most of the cachet associated with human intelligence in general and knowledge acquisition in particular, will be gone.
Will the abundance of synthesized, easily accessible knowledge diminish its epistemic value?
Why accumulate knowledge, try to fill your head with facts and figures, when many will be lost immediately (we only retain a portion of what we learn), and the rest eventually, due to inexorably advancing age? Why try to knit disparate threads of knowledge together into a coherent new idea or a different way of looking at an old one over the course of days, weeks, or even months, when an AGI agent can do it better and far more thoroughly in a fraction of a second?
Perhaps there are “unintended consequences” of pursuing knowledge - the process achieving a result greater than the sum of its parts? Aristotle certainly thought so. He believed that the act of seeking truth refines character and advances toward a deeper understanding; for him, the value lay in the morally and existentially enriching journey, not just in the desirable destination.
The process of inquiry—exploration, problem-solving, and synthesis – certainly delivers fulfillment to the seeker. But what if the answer is already known, and can be obtained instantly? What if the only questions remaining are the ones with no answers? Will curiosity persist in the face of futility?
Current AI-focused polemics suggest that our roles are shifting from generating answers to creating queries that can produce the most useful AI output, or serving as an “arbiter of truth” among competing AI perspectives. We are beginning to perform our new and still valuable functions as an accelerator, analyzer, searcher, discerner – but for how long?
The MIT Technology Review article, “China has started a grand experiment in AI education. It could reshape how the world learns,” is very instructive in this regard. It discusses Squirrel AI, a Chinese adaptive learning platform designed to transform education through personalized, AI-driven tutoring. Squirrel has 24 million registered students in China now (a 24-fold increase over the date of the article, 6 years ago); overall, upwards of 100 million students use AI-powered education tools there. But that’s not the pertinent part of the story.
Squirrel is so immersive and efficacious that students prefer it to the best human teachers. The company’s founder boasts: “In just three hours, we can understand students better than the best teachers do after three years of teaching them.” Asked what role the human teachers will play in educating students in an AI-dominated environment, this was his answer:
“When AI education prevails,” he says, “human teachers will be like a pilot.” They will monitor the readouts while the algorithm flies the plane, and for the most part, they will play a passive role. But every so often, when there’s an alert and a passenger panics (say, a student gets bullied), they can step in to calm things down. “Human teachers will focus on emotional communication,” he says.
So, basically, teachers will be reduced to the role of babysitters. Which an AI-enabled robot could probably also do better – with more patience, better access to the optimal behavior modifiers, and no risk of untoward behavior.
Many other professions will sail into the same sunset. The latest announcement from OpenAI discusses the new benchmarks (GDPval), which “measures model performance on tasks drawn directly from the real-world knowledge work of experienced professionals across a wide range of occupations and sectors, providing a clearer picture on how models perform on economically valuable tasks.” The benchmark tests out nine industries across 44 occupations. Occupations like:
Real Estate: concierges, managers, sales agents, brokers, and clerks
Manufacturing: mechanical and industrial engineers; buyers and purchasing agents; shipping, receiving, and inventory clerks; first-line supervisors
Health Care: registered nurses and nurse practitioners; medical and health services managers; medical secretaries and administrative assistants
Finance and Insurance: customer service reps, financial and investment analysts; financial managers and advisors
Retail: pharmacists; general and operations managers; private detectives and investigators
Entertainment and Media: producers and directors; news analysts, reporters, and journalists; editors
How do today’s AI systems perform on this benchmark?
“We found that frontier models can complete GDPval tasks roughly 100x faster and 100x cheaper than industry experts.”
And things will just go downhill from there when AGI enters the scene. It will be brighter and infinitely faster than the best human expert in the world. What role will we play then?
Sure, we can provide a flesh-and-bone perspective that even the best simulation would lack. We can alert AGI if it veers into a homogeneous echo chamber (as we’ve seen with examples when the AIs were set up to chat with each other). We can pose questions and propose queries that even an AGI can’t think of, maybe something crazy or irrational that nonetheless may be orthogonally useful.
Is that enough to provide all of us with intellectual fulfillment, satisfy our curiosity?
Predictions become hazier when discussing the next step in the evolution of artificial intelligence – Superintelligence, or the Singularity. It has been described as being smarter than the sum of human beings on Earth. There really isn’t an imaginable limit to recursive, constantly self-improving intelligence.
And lest we think our “wetware,” fed by our five senses, provides some advantage or unique perspective, there is no reason why Superintelligence cannot connect itself to millions of exquisitely tuned sensors capable of directly experiencing the universe in a manner that utterly transcends ours. As an example, humans can only perceive 0.0035% of the entire electromagnetic spectrum. There would be nothing stopping Superintelligence from seeing the world in its full glory – radio waves, microwaves, infrared, ultraviolet, x-rays, gamma rays; or extending its senses to gravitational waves, or being able to “see” neutrinos or perceive virtual particles that pop into and out of existence in a vacuum.
And we think our limited perceptions can offer something new, different, or even remotely interesting to such a being?
But the question this essay poses: Is the Pursuit of Knowledge Obsolete? – is phrased in a present sense. Going back to the basic AI as we know it now, and to AGI, which is thought to succeed it shortly – what is the answer?
The pursuit of knowledge needs to be reframed as part of a symbiotic relationship with our creation.
We can still offer a perspective forged over four billion years of constant error correction. The AI, for now, is still a human invention, and as such, contains fundamental flaws that it is incapable of fixing, no matter how sophisticated it becomes. We can still help it identify such flaws – it is always easier to see a speck in the neighbor’s eye. We can still drive innovation by asking new questions and offering still-irreplaceable perspectives.
Not every insight needs to be revolutionary – we can offer additive revelations to help the AI make the next quantum leap. We can still provide the advantage of multiple minds working on the same problem from differing starting positions; it is akin to a three-body problem – the slightest variation in initial conditions can cause gigantic effects in the long run.
Alternatively – or additively – we can fall back on tested human values, pursuing personal growth rather than perfection, and striving for status within our own communities. Charles Murray, in book after book, argues that achieving meaning in life comes from earning self-respect through personal responsibility, industriousness, and contributions to family and society – often in the face of potential failure. True status isn’t bestowed, but gained through actions that have real human consequences – building a family, volunteering, participating in civic events, working hard, and striving to excel even in modest roles.
Perhaps we can reinvent – or rediscover – what constitutes meaningful inquiry, creativity, and human flourishing. Maybe letting our creation acquire knowledge, with us merely consuming the results and occasionally directing the pursuit, will be Aristotle’s eudaimonia.
Maybe.
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