Researchers believe that one of the most serious challenges arising from the convergence of humans and machines is not the rebellion of technologies against their creators, but rather the deepening of the social divide.
A new analytical study published in the journal Science China Information Sciences revealed that the most serious challenges that may arise from the increasing convergence between humans and artificial intelligence are not a "machine rebellion," but rather the widening gap between the speed of technological development and human biological capabilities, and the resulting social and ethical repercussions.
The researchers explained that the human brain develops according to a biological rhythm that spans thousands of years, while artificial intelligence systems undergo radical updates in just months, and sometimes weeks, describing this difference in the pace of development as one of the most prominent challenges that humanity may face in the coming years.
The researchers pointed out that the human brain reached its current form about 300,000 years ago, and its basic structure and mechanisms for transmitting nerve signals have remained largely stable since then.
The brain consumes only about 20 watts of power, while nerve signals travel at speeds between one and 100 meters per second, with neurons firing at a rate of only 100 to 200 times per second. Furthermore, the skull and metabolic constraints naturally limit processing speed and working memory capacity.
Although the brain has the ability to adapt through what is known as neuroplasticity, these changes occur gradually through the reshaping of neural connections, which makes keeping up with technological acceleration extremely difficult.
In contrast, artificial intelligence systems are changing at an accelerating pace, as modern models rely on the coordination of tens of thousands of processors, and can be redesigned and deployed globally within a few weeks.
But this speed comes at a high price, as training large models requires enormous energy consumption and advanced computing capabilities within data centers.
Researchers have termed this discrepancy "evolutionary mismatch ," where technology accelerates at a pace that human biology and social institutions cannot keep up with. They have proposed the concept of "natural selection overriding ," which involves using technology to enhance human capabilities at a rate faster than traditional biological evolutionary mechanisms allow.
The researchers pointed out that artificial intelligence had long been confined to the digital world, limited to processing texts, images and software, but that developments in the fields of robotics and bioelectronics had begun to remove the barriers between the digital and physical worlds.
They described this stage with the term "embodiment threshold ," that is, when artificial intelligence becomes part of a direct interaction loop with the human body or its nervous system.
This transition begins with wearable devices that monitor vital signs, and extends to brain-computer interfaces capable of directly reading neural activity.
The study confirmed that flexible bioelectronics represents the essential link between artificial intelligence and the human body, which prompted researchers to develop flexible, self-healing materials and advanced tissue devices.
Some of the most notable examples of these technologies are smart fabrics that measure glucose and cortisol levels, an artificial larynx capable of picking up muscle signals, and electronic systems implanted through blood vessels to reach the motor cortex in the brain.
However, these innovations still face challenges related to large-scale production, biocompatibility, and the provision of suitable energy sources.
The study warned that human-machine convergence will not automatically lead to a better future, as access to these technologies will remain linked to economic capabilities and supply chains.
The researchers called this scenario "bio-technological stratification," warning that the monopolization of technologies that enhance cognitive and physical abilities by a limited group could transform inequality from an economic gap to a gap in human capabilities themselves.
They also warned of the risks of violating the privacy of neural data if brain signals become collected and traded as consumer data.
The study proposed a phased regulatory framework based on the level of technological development, rather than fixed timetables, and comprising three main stages
Imposing transparency standards, including disclosure of computing capabilities and training data sources in the model development phase.
After reaching the “threshold of embodiment”, attention is focused on the safety of the interaction between humans and machines, while ensuring the user’s right to stop the system whenever he wants.
Phase three: Strengthening international governance through cross-border oversight mechanisms, similar to the model of the European Organization for Nuclear Research (CERN) and the International Atomic Energy Agency, with the aim of reducing the monopoly of a limited number of companies over basic technologies.
The researchers stressed that the main value of the study lies not in predicting the future of artificial intelligence, but in bringing humans back to the center of the discussion, emphasizing that the goal should not be merely to maximize the capabilities of machines, but to employ technology to enhance human capabilities, while establishing regulatory frameworks that ensure that humans remain the decision-makers in shaping their future.
