Claims that artificial intelligence (AI) is on the verge of surpassing human intelligence at the moment are commonplace. Accordingly some commentatorsRapid advances in large language models signal an impending tipping point – often framed as “Superintelligence“ – that may fundamentally reshape society.
However, comparing AI and individual intelligence misses something essential about human intelligence. Our intelligence doesn’t operate primarily at the extent of isolated individuals. It is social, embodied and collective. When taken seriously, the claim that AI will surpass human intelligence becomes far less convincing.
These claims are based on a special comparison: AI systems are measured by the person cognitive performance of humans. Can a machine write an essay, pass an exam, diagnose an illness, or compose music in addition to a human? With these tight benchmarks, AI appears impressive.
But this framework reflects the constraints of traditional intelligence tests themselves: cultural bias and a reward for familiarity and practice. The rise of AI should subsequently stimulate greater reflection on what we mean by intelligence, pushing us to maneuver beyond narrow cognitive measures and even popular extensions resembling emotional intelligence, towards broader, more contextual definitions.
Intelligence just isn’t individual brilliance
Human cognitive achievements are sometimes attributed to exceptional individuals, but that is misleading. Research in cognitive science and anthropology shows that even our most advanced ideas emerge from collective processes: shared language, cultural transmission, collaboration, and cumulative learning across generations.
No scientist, engineer or artist works alone. Scientific discoveries rely on shared methodologies, peer reviews, and institutions. Language itself – arguably humanity's strongest cognitive technology – is a collective achievement, refined and transformed over millennia through social interaction.
Studies on “collective intelligence” show consistently that groups can outperform even their most capable members when there’s diversity of perspectives, communication and coordination. This collective ability just isn’t an optional addition to human intelligence; it’s its foundation.
In contrast, AI systems don’t cooperate, negotiate meaning, form social bonds, or engage in shared moral deliberation. They process information in isolation and reply to prompts without awareness, intent, or responsibility.
Embodiment and social understanding are necessary
Human intelligence can be embodied. Our pondering is formed by physical experience, emotions and social interaction. Developmental psychology shows that learning begins in infancy through touch, movement, imitation, and shared attention with others. These embodied experiences establish abstract pondering later in life.
The AI lacks this grounding. Language models learn statistical patterns from texts, not meanings from lived experience. They don't understand concepts the way in which humans do; They approach linguistic answers based on correlations in data.
This limitation becomes clear in social and ethical contexts. Humans control norms, values and emotional signals through interaction and shared cultural understandings into which we’re socialized. Machines don't.
A narrow section of humanity
Proponents of AI often make progress show on the huge amounts of knowledge used to coach modern systems. Still, these data represent a remarkably small portion of humanity.
Around 80% of online content is produced in only ten languages. Although greater than 7,000 languages are spoken worldwide, only a number of hundred are consistently represented on the Internet – and much fewer in high-quality, machine-readable form.
This is essential because language carries culture, values and ways of pondering. Training AI based on a largely homogenized data set means embedding the perspectives, assumptions and biases of a comparatively small portion of the worldwide population.
In contrast, human intelligence is defined by diversity. Eight billion people living in diverse environments and social systems contribute to a typical but plural cognitive landscape.
The AI has no access to this wealth and can’t generate it independently. The data it trains on comes from a highly biased sample that represents only a percentage of the world's knowledge.
The limits of scaling
Another issue rarely addressed in claims of “superhuman” AI is data scarcity. Large models improve by ingesting more high-quality data, but this can be a limited resource. researchers have already warned that models are reaching the boundaries of accessible human-generated text suitable for training.
One proposed solution is to coach AI using data generated by other AI systems. However, there’s a risk of making a feedback loop wherein errors, prejudices and simplifications are reinforced slightly than corrected. Instead of learning from the world, models learn from distorted reflections of themselves.
This just isn’t a path to deeper understanding. It's more like an echo chamber.
Useful tools, not superior minds
None of that is to disclaim that AI systems are powerful tools. They can increase efficiency, support research, support decision-making and expand access to information. When used rigorously and supervised, they’ll bring societal advantages.
But usefulness just isn’t the identical as intelligence within the human sense. AI stays narrow, derivative and depending on human input, evaluation and corrections. It doesn’t form intentions, doesn’t take part in collective deliberation, and doesn’t contribute to the cultural processes that make human intelligence what it’s.
The rapid progress of AI has caused excitement – and in some cases excessive expectations. The danger just isn’t that machines will outperform us tomorrow, but that inflated narratives distract from the true issues: bias, governance, impacts on the world of labor, and the responsible integration of those tools into society.
A category error
Comparing AI and human intelligence as in the event that they were competing under the identical conditions is ultimately a category error. People usually are not isolated information processors. We are social creatures whose intelligence comes from collaboration, diversity and shared meaning.
Until machines can take part in this collective, embodied and ethical dimension of cognition – and there isn’t any evidence of this – the concept that AI will surpass human intelligence stays more hype than insight.
