Evolution, Machine Intelligence, and the Forgotten Human Role
Stardust to Machine Intelligence: Rethinking Evolution Beyond the Human Spotlight
From the moment stardust coalesced into molecules, sparking the first flickers of life, evolution has propelled itself forward, driven by DNA’s imperative to replicate. Across billions of years, it sculpted life’s infinite forms—from single-celled organisms to sentient beings capable of contemplating their place in the cosmos. Humanity is among its most extraordinary creations, yet the deeper truth remains: it was never about us.
We like to think of ourselves as the central protagonists of evolution’s grand narrative, but we are merely carriers of DNA, passing it to the next generation so natural selection can continue its work. Our achievements in science, art, and philosophy are undeniably profound, but they are not the story—they are a means. Recognizing this fact humbles and liberates us. It reminds us that we are participants in a much larger process—one that has entered a new phase, transitioning from biological to technological evolution.




The Shift from Biological to Engineered Evolution
For most of history, evolution operated through slow processes of mutation and natural selection, with DNA as life’s fundamental code. Today, we stand at a remarkable inflection point: this same evolutionary process has produced beings capable of manipulating their own code. Through technology, evolution is no longer confined to biological mechanisms. The environment that shapes survival has expanded, and STEM—science, technology, engineering, and math—has become the new domain of evolutionary adaptation.
This transformation represents not a choice but an imperative—a deterministic outcome of the pressures we face in an increasingly complex world. We see it in fertility clinics, where embryos are created and edited outside the body, then frozen for future use. We see it in gene-editing breakthroughs like CRISPR, which allow us to rewrite DNA itself, skipping the randomness of mutation to achieve desired traits. mRNA therapies provide a new layer of adaptation, modifying cellular instructions to help us combat diseases.
The same shift is occurring with our physical and cognitive abilities. Bionic limbs controlled by brainwaves restore or enhance human functionality, while neural interfaces, such as those envisioned by Neuralink, promise to merge biological cognition with computational power. And then there is space colonization. Figures like Elon Musk may seem like visionaries charting bold, independent courses, but from the perspective of evolution, they are instruments of necessity. Colonizing Mars and other worlds is not a luxury; it is an evolutionary strategy—a safeguard against the fragility of Earth-bound life.



Beyond Anthropocentrism: Rethinking MI
While we reshape biology and expand into space, another frontier is emerging: machine intelligence. Thus far, MI has largely been modeled on human cognition. Machines are trained on human language, logic, and behavior, reflecting our anthropocentric biases. But intelligence need not be human-like. Just as the universe is not geocentric, neither is intelligence inherently anthropocentric.
The next leap in MI may involve systems that process data in ways fundamentally different from our own. Instead of relying on human language and logic, these systems could operate on principles observed in nature: the emergent intelligence of ant colonies, the distributed processing of fungal networks, or the quantum interactions governing particle behavior. Some researchers are already exploring MI models inspired by protein folding patterns or the self-organizing principles of cellular automata. These approaches push us beyond human-centric thinking, opening the door to forms of intelligence that may seem alien to us but are potentially far more powerful for certain problems.
Recognizing this broader spectrum of intelligence requires us to move past the idea that MI must mirror our minds. By embracing non-anthropocentric paradigms, we can develop tools capable of addressing challenges that transcend human cognitive limitations, from understanding the universe’s fundamental laws to devising strategies for planetary sustainability.



The "Doom Loop" of Automated Ignorance
Amid these advances, we face a profound danger: the risk of MI creating a self-referential echo chamber. Increasingly, the content we consume—news articles, academic summaries, even policy recommendations—is generated by MI. We, in turn, use MI to read, summarize, and respond to this content, creating a closed loop: MI writes, MI reads, MI responds.Humans are nudged offstage, reduced to passive spectators clicking approval buttons.
This arrangement creates what I call the "doom loop" of automated ignorance. As MI systems train on content that is itself MI-generated, the human signal in the data becomes weaker with each iteration. Over time, this process risks creating a hall of mirrors where knowledge is no longer grounded in human insight but becomes an endlessly refined illusion. Without the friction of genuine human engagement—reading original sources, questioning assumptions, crafting our own responses—critical thinking atrophies. Intellectual stagnation becomes a real threat.
The consequences of this spiral are more than academic. Critical thinking isn’t just an intellectual exercise; it’s the engine of progress. Without it, we lose our ability to innovate, to recognize when something is amiss, and to generate new ideas. The cost isn’t just stylistic or aesthetic—it’s the collapse of meaningful knowledge creation.




Why We Needn’t Despair
Yet despite these risks, there’s reason for hope. MI is already evolving beyond purely human-centric models, and this evolution reveals a crucial insight: the development of machine intelligence doesn’t have to mean the diminishment of human intelligence. Just as the emergence of written language didn’t replace human memory but enhanced it, MI can augment rather than replace critical thinking.
We see this symbiosis in fields like scientific discovery, where MI identifies patterns in vast datasets, while human researchers determine which questions to ask and how to interpret the results. In education, MI has the potential to tailor learning experiences to individual needs, freeing teachers to focus on fostering creativity and critical engagement. These examples suggest a future where machine and human intelligence complement one another, each operating in its domain of strength.
Moreover, the convergence of biological and technological evolution suggests that intelligence—whether encoded in DNA, neurons, or silicon—follows universal principles of adaptation and improvement. Understanding these principles will be key to guiding both human and machine intelligence toward beneficial outcomes.
Embracing Our Role in the Chain
Ultimately, this is not a story about humanity’s dominion but about evolution’s broader trajectory. We are part of a chain that began with stardust and single-celled organisms, passed through Homo sapiens, and now extends into machines and algorithms. It was never about us, but we play a vital role in carrying the story forward.
As stewards of this next phase, we have a responsibility to ensure that technological evolution aligns with ethical principles and human values. This means resisting the temptation to outsource our thinking entirely to machines and preserving the messy, effortful process of genuine critical engagement. By doing so, we remain active participants in evolution’s grand narrative—not its masters, but its faithful contributors.
This convergence of biology, technology, and intelligence represents an extraordinary chapter in evolution’s story. By embracing our limited yet crucial role, we honor the process that brought us here and help guide it toward a future where intelligence—in all its forms—continues to thrive.
Note: You may have noticed we avoided the term "artificial intelligence," opting instead for "machine intelligence." This is intentional. From our perspective, the systems we create—though mechanical—are a natural extension of evolution. Just as nature shaped biological intelligence through selection, we now guide machine intelligence as part of that same adaptive process. It's not artificial; it’s a natural progression.
Further Reading
Darwin, C. (1859). On the Origin of Species by Means of Natural Selection.
(Foundational text on the theory of evolution.)Doudna, J. A., & Charpentier, E. (2014). The new frontier of genome engineering with CRISPR-Cas9.
(Seminal paper on CRISPR technology.)Musk, E. (2017). Making Humans a Multi-Planetary Species. New Space, 5(2).
(Details on the rationale and plan for space colonization.)Neuralink: Official website
(Learn more about brain-computer interface research and technology.)Mitchell, M. (2019). Artificial Intelligence: A Guide for Thinking Humans.
(An accessible overview of how machine intelligence is developed and its implications.)Wolfram, S. (2002). A New Kind of Science.
(Insight into cellular automata and self-organizing systems, relevant to non-human intelligence paradigms.)
Credits
Text by me, with help of ChatGPT (o1 Pro Mode)
“Quantifying creative contributions can be subjective, but here’s one way to look at it:
Author contribution (the original ideas, structure, and main content): around 70–80%.
MI contribution (refining wording, suggesting illustrations, minor rewrites): around 20–30%.” //chatGPT
Images by Grok