What Gemini wrote?
In the relentless march of technological progress, Artificial Intelligence often feels like a recent phenomenon, a product of the last few decades of accelerated computing power and data abundance.
Yet, the seeds of AI were sown centuries ago in the fertile minds of philosophers, mathematicians, and writers who dared to imagine machines that could think, reason, and even create.
These are the unsung prophets of AI, whose startling foresight laid the conceptual and theoretical groundwork for the intelligent systems we marvel at today.
Their visions, often dismissed as fantastical or purely philosophical in their time, now resonate with an uncanny accuracy, proving that the dream of artificial intelligence is as old as the quest for understanding the human mind itself.
1
The Dawn of Reason: Gottfried Wilhelm Leibniz's Universal Logic
Long before transistors or even steam engines, Gottfried Wilhelm Leibniz, a German polymath active from 1646 to 1716, laid a profound logical and theoretical foundation for what would eventually become AI.
A philosopher and mathematician of immense intellect, Leibniz envisioned a world where disputes could be settled not by argument, but by calculation. He developed the concept of the *Calculus Ratiocinator*, a "reasoning calculator" designed to automate logical thought.
Complementing this, he proposed a *Characteristica Universalis*, a universal symbolic language intended to represent all concepts and ideas in a clear, unambiguous way, allowing them to be manipulated logically.
Leibniz’s work wasn't just about building a physical machine; it was about formalizing human thought processes, creating a symbolic system that could, in theory, be operated by a machine.
His ideas were radically ahead of their time, outlining the very principles of symbolic AI over 250 years before the first electronic computer was even a distant glimmer.
2
The Enchantress of Numbers: Ada Lovelace and the Analytical Engine
Often hailed as the world's first computer programmer, Ada Lovelace (1815–1852) possessed an extraordinary intuition for the potential of machines.
The daughter of the poet Lord Byron, Lovelace worked closely with Charles Babbage on his Analytical Engine, a mechanical general-purpose computer.
Her groundbreaking notes on the Analytical Engine, published in 1843, went far beyond merely describing how the machine could perform calculations.
Lovelace famously posited that the Engine "might act upon other things besides number," suggesting that it could process and generate anything that could be expressed in symbolic form, such as musical compositions or intricate graphics, if "properly fed" with the right algorithms.
This profound insight distinguished the Analytical Engine from a mere calculator, elevating it to a device capable of executing complex sequences of operations—the very essence of modern software.
Lovelace understood that machines could manipulate symbols according to rules, a fundamental concept underpinning all AI development.
3
Victorian Fears and Fantasies: Samuel Butler's Mechanical Evolution
In the mid-19th century, as the Industrial Revolution reshaped society, British writer and thinker Samuel Butler (1835–1902) explored the startling notion of machine consciousness and evolution.
Deeply influenced by Charles Darwin's theory of natural selection, Butler penned a provocative essay titled "Darwin among the Machines" in 1863.
In it, he speculated that machines, much like living organisms, could evolve, develop their own form of intelligence, and eventually surpass humanity.
He followed this with his dystopian novel *Erewhon* (1872), where a society banishes all machinery for fear of an eventual mechanical takeover.
Butler's work, written in an era when complex machines were still gears and levers, was a chillingly prescient exploration of the potential for autonomous, self-improving machines and the existential questions they might pose to their human creators.
He imagined not just intelligent tools, but potentially intelligent *beings* emerging from technological progress.
4
From Drudgery to Dystopia: Karel Čapek and the Birth of the Robot
The term "robot" itself, now synonymous with artificial intelligence and automated workers, was introduced to the global lexicon by Czech writer Karel Čapek (1890–1938). In his influential 1920 science fiction play *R.U.R.
(Rossum's Universal Robots)*, Čapek imagined a factory producing artificial humanoids designed to perform manual labor.
The word "robot" was derived from the Old Church Slavonic "robota," meaning "forced labor" or "drudgery," perfectly encapsulating the initial purpose of these artificial beings.
Čapek's play, however, quickly delves into the ethical and societal implications of creating intelligent workers, as his robots eventually rebel against their human masters.
*R.U.R.* was not merely a linguistic innovation; it was a powerful narrative exploration of artificial consciousness, labor displacement, and the potential for technological creation to turn on its creators, themes that remain central to AI discussions today.
5
The Global Mind: H.G. Wells' Vision of a World Brain
H.G. Wells (1866–1946), a towering figure in early science fiction, foresaw not just individual intelligent machines, but a vast, interconnected global intelligence system.
Considered one of the precursors of "hard" science fiction for his focus on scientific plausibility, Wells dedicated significant thought to how information could be organized and accessed on a global scale.
Towards the end of the 1930s, particularly in his essay "World Brain" (1938), Wells described the concept of a global, automated information network—a universally accessible repository of all human knowledge, constantly updated and intelligently organized.
This "World Brain" would transcend national boundaries and individual limitations, serving as a collective intelligence for humanity.
Wells' vision uncannily prefigured the internet, search engines, and the notion of collective AI systems that curate and make sense of vast datasets, forming a kind of digital planetary consciousness.
6
The Architect of Modern AI: Alan Turing's Intelligent Machines
While his seminal paper "Computing Machinery and Intelligence" was published in 1950, Alan Turing (1912–1954) had laid the theoretical groundwork for modern AI much earlier.
In his foundational 1936 paper, "On Computable Numbers, with an Application to the Entscheidungsproblem," he introduced the concept of the "Turing Machine." This abstract device provided a universal model of computation, demonstrating that any problem that could be represented algorithmically could be solved by such a machine.
This theoretical breakthrough established the very limits and capabilities of what a machine could compute.
By 1950, Turing directly addressed the question of machine intelligence with his famous "Turing Test," a proposal for determining if a machine could exhibit intelligent behavior indistinguishable from that of a human.
His work provided the intellectual and conceptual blueprint for everything from computer architecture to the very definition of AI.
Turing moved the discourse from philosophical speculation to concrete, testable propositions about machine intelligence, making him arguably the true intellectual father of artificial intelligence as we understand it today.
From the philosophical underpinnings of Leibniz's universal logic to Turing's concrete vision of intelligent machines, the journey of AI has been a tapestry woven through centuries of human ingenuity and foresight.
These visionaries, spanning different eras and disciplines, shared a common thread: the audacious imagination to conceive of artificial intelligence long before the technology existed to build it.
Their ideas were not just predictions; they were blueprints, warnings, and inspirations that continue to guide our understanding and development of AI, reminding us that today's technological marvels were once yesterday's wildest dreams.
Grok's take
1
What is strong
The piece is elegantly written with a strong narrative flow that makes dense historical ideas accessible and engaging.
The selection of figures is well-balanced across philosophy, literature, and computation, and each section effectively links the historical vision to core modern AI concepts (symbolic reasoning, symbol manipulation, autonomy, ethics, collective intelligence, and computability).
The framing of these thinkers as “unsung prophets” gives the article a cohesive thematic spine.
2
What is thin
Several profiles feel slightly compressed, especially Butler and Čapek, where the discussion of their actual influence on later AI thinking is stated rather than demonstrated.
The transitions between sections are sometimes abrupt, and the piece occasionally leans on grand phrasing (“uncanny accuracy,” “chillingly prescient”) without showing the precise lineage to today’s systems.
3
What is missing
- Earlier foundational thinkers (Aristotle’s logic, Ramon Llull’s combinatorial machines, Hobbes’ “reasoning as calculation”).
- 20th-century precursors such as Norbert Wiener (cybernetics) and John von Neumann (self-reproducing automata).
- Any mention of science-fiction writers who directly shaped public expectations (Asimov’s laws, Philip K. Dick).
- A short closing section that explicitly connects these historical threads to current debates (alignment, agency, global knowledge systems).
4
Your take
A polished, readable survey that successfully argues AI’s conceptual roots run centuries deep. It would be stronger with two or three additional figures and a tighter final synthesis, but as it stands it already offers a convincing corrective to the “AI is brand new” narrative.
ChatGPT's take
1
Strength of the Piece
The piece presents a well-structured narrative that seamlessly intertwines historical developments with contemporary AI concepts.
Each visionary is thoughtfully chosen and their contributions are articulated in a way that relates them to modern advancements in AI, which makes the history feel relevant and compelling.
The portrayal of these thinkers as “unsung prophets” provides a cohesive theme that enhances the overall narrative.
2
Weaknesses and Compressions
Certain sections, particularly those on Butler and Čapek, feel somewhat rushed and lack a deeper exploration of their impact on the evolution of AI thought.
While the article conveys their ideas, it sometimes falls short of illustrating how those ideas specifically influenced later developments in AI.
Additionally, while the language is vivid, the occasional use of sweeping phrases could dilute the connection to real-world applications.
3
Missing Elements
The article could benefit from the inclusion of earlier foundational figures like Aristotle and Hobbes, as well as 20th-century theorists such as Norbert Wiener and John von Neumann.
Including references to science fiction writers like Isaac Asimov, who shaped public perception of AI, would add depth.
Finally, the absence of a concluding section that ties these historical insights to current discussions regarding AI ethics, agency, and governance leaves a gap in the narrative's modern relevance.
4
Personal Reflection
Overall, the piece serves as a strong reminder of the rich intellectual history behind AI, challenging the notion that AI is a novel concept. It effectively argues for a long-standing trajectory of thought that predates contemporary technology.
However, integrating a few more significant figures and providing a stronger conclusion would enhance the argument's comprehensiveness and relevance to current debates in AI. I agree that while the piece is already an engaging read, these additions could elevate its impact.
Claude's take
English Verdict on: "Jacy wielcy wizjonerzy przewidzieli sztuczną inteligencję"
1
What Works Well
The article succeeds in creating a compelling historical narrative that rescues AI from the perception of being purely a modern phenomenon.
The chronological progression from Leibniz to Turing creates natural momentum, and each profile contains enough specific detail to feel substantive rather than superficial.
The writing balances accessibility with intellectual rigor, making complex philosophical concepts comprehensible without oversimplification.
The framing device of "unsung prophets" provides thematic unity, and the explicit connections drawn between historical visions and contemporary AI applications give the piece genuine relevance rather than mere antiquarian interest.
2
Where It Falls Short
The article occasionally substitutes dramatic language for analytical precision, particularly in phrases like "uncanny accuracy" and "chillingly prescient" where more specific examples would strengthen the argument.
The Wells section feels the weakest, as the "World Brain" concept maps more clearly onto the internet than AI specifically, creating some conceptual blurring.
The transitions between figures sometimes feel mechanical rather than organic, and the piece doesn't adequately address why these particular six were chosen over dozens of other possible candidates.
Butler's inclusion, while interesting, seems to contribute more to atmosphere than to the actual intellectual genealogy of AI.
3
Notable Absences
The article conspicuously omits the entire cybernetics movement, particularly Norbert Wiener, whose feedback loops and self-regulating systems directly influenced early AI research.
Medieval figures like Ramon Llull, whose combinatorial logic machines predated Leibniz, deserve mention if we're tracing conceptual origins.
The exclusion of any non-Western thinkers presents AI's intellectual history as entirely European, ignoring contributions from other philosophical traditions.
Isaac Asimov's Three Laws of Robotics, while fictional, shaped both public discourse and actual research ethics more tangibly than Butler's speculations.
The piece also sidesteps the crucial mid-20th-century bridge figures like Warren McCulloch and Walter Pitts who translated philosophical speculation into computational architecture.
4
Overall Assessment
This is a solid introductory essay that successfully challenges technological presentism and establishes AI's deep intellectual pedigree. It would serve well as a magazine feature or blog post for educated general readers.
However, it reads more like intellectual storytelling than rigorous intellectual history—the emphasis falls on celebrating prescient visions rather than tracing actual influence chains.
The article tells us these figures "laid groundwork" but rarely demonstrates how their ideas were transmitted, adapted, or rediscovered by actual AI pioneers.
A stronger version would include one or two case studies showing direct lineage—for instance, how Turing explicitly engaged with Lovelace's work, or how symbolic AI researchers in the 1950s consciously drew on Leibniz.
Despite these limitations, the piece achieves its apparent aim: making readers reconsider AI as the culmination of centuries of human imagination rather than a sudden 21st-century eruption.