By Jabir Deralla
When the Iron Curtain lifted, the world’s scattered efforts in computing and cybernetics met on the same digital horizon. From Kyiv’s first electronic circuits to Silicon Valley’s glowing servers, intelligence was no longer a national project but a planetary endeavour. Freed — and yet haunted — by the ambitions of the past, scientists, philosophers, and engineers started to rebuild the dream together.
The age of competition gave way to one of convergence, where curiosity crossed borders faster than politics ever could. And out of that collision of ideas, modern AI — including the neural networks and GPTs of our time — finally learned to dream with us.
Cold Machines and Closed Minds: When Control Stifled Intelligence
The Cold War transformed intelligence — both human and artificial — into a strategic resource. Superpowers raced to conquer not only space and the oceans but also the invisible frontiers of the mind.
In the United States, laboratories at MIT, Stanford, and Carnegie Mellon blossomed with ideas that were as chaotic as they were creative. Scientists, philosophers, and engineers debated consciousness, logic, and language, often supported by generous military funding but enjoying an extraordinary degree of academic freedom. Out of that messy freedom, intelligence began to take shape in code.
Across the Iron Curtain, the picture was different. The Soviet system — and later Mao’s China — sought to harness computing and cybernetics for control, not curiosity. Machines were to serve the plan, not challenge it. Research was centralized, innovation bureaucratized, and failure politicized. The very idea of a machine that could “think” independently was ideologically suspect. A computer was expected to calculate, not imagine. (AI History, part 2, CIVIL Today)
Brilliant scientists such as Victor Glushkov and Alexei Lyapunov pushed the boundaries of mathematics and cybernetics, but they worked within a system where open dialogue and independent experimentation were often impossible. Their programs had to align with the Party’s vision.
When Glushkov proposed his OGAS network — a bold prototype of an internet-like economic system powered by real-time computation — Moscow’s ministries saw not innovation, but danger. A system that could see everything might also reveal inefficiencies, errors, and corruption. In a structure built on secrecy, such transparency was intolerable.
This is perhaps the deepest irony of the Cold War’s technological rivalry: the side that sought total control could not control innovation itself.
Artificial intelligence — like art, philosophy, or democracy — requires unpredictability. It grows out of questioning, dissent, and the freedom to fail. The regimes that most feared these traits were, in the end, unable to create the very intelligence they sought to command.
While the Soviet and Chinese systems achieved impressive advances in mathematics, engineering, and control technologies, their closed political environments stifled the emergence of truly adaptive, creative AI. The West, with all its contradictions, tolerated chaos — and from that chaos came genius.
The lesson remains: intelligence cannot flourish under oppression. It needs freedom to think, to err, and to evolve.
The European Experiment: Intelligence Between Idealism and Austerity
While the United States and the Soviet Union turned artificial intelligence into a strategic frontier of the Cold War, Western Europe’s story was more fragmented — an experiment caught between idealism, austerity, and intellectual brilliance. The continent had the visionaries and the universities, but not always the budgets or the political appetite to turn experiments into revolutions.
In the United Kingdom, the story began with remarkable promise. In the early 1960s, British scientists such as Donald Michie and Christopher Strachey — alongside Alan Turing’s intellectual heirs in Cambridge and Edinburgh — were among the first in the world to build programs capable of learning and reasoning. Edinburgh’s small AI laboratory, founded in 1963, became a hub of imagination, where machines were taught to play chess, solve logic puzzles, and even translate language. For a brief moment, Britain seemed poised to lead the world in machine intelligence.
But optimism soon collided with scepticism. In 1973, the British government commissioned the now-infamous Lighthill Report, which dismissed most AI research as “unproductive.” Funding dried up almost overnight. Laboratories that had been alive with curiosity went silent, their researchers forced to abandon projects or move abroad. The episode became known as the first “AI winter,” and the chill spread across Europe.
Elsewhere, continental Europe followed a similar rhythm of early enthusiasm and political hesitation. In France, Germany, and the Netherlands, strong traditions in mathematics and philosophy gave rise to important work in logic, linguistics, and cybernetics — yet projects were small, fragmented, and often underfunded. Universities lacked the close ties to industry and defence that powered American innovation. European governments, wary of overpromising after the economic crises of the 1970s, turned inward just as the digital revolution was accelerating worldwide.
When Japan announced its Fifth Generation Computer Project in the early 1980s, aiming to build reasoning machines that could converse in natural language, Western Europe scrambled to respond. The UK launched the Alvey Programme, a major state-backed effort to revive AI and advanced computing. The European Economic Community soon followed with collaborative projects under the ESPRIT programme, bringing together scientists across borders to reclaim some momentum.
These initiatives revived research, but the fragmented nature of Europe’s scientific systems — divided by language, funding cycles, and bureaucracy — limited their long-term impact.
Yet out of this uneven landscape emerged a different kind of strength: diversity of thought. European AI grew not only from laboratories, but also from philosophy, linguistics, and cognitive science. It evolved less as an arms race and more as a conversation — slower, perhaps, but richer in reflection about intelligence, ethics, and meaning.
In this sense, Europe’s early hesitations foreshadowed its contemporary approach to AI: cautious, rights-based, and deeply human-centred.
Convergence and Legacy: When Machines Began to Dream Together
By the late twentieth century, the world’s separate journeys in artificial intelligence — American ambition, Soviet cybernetics, European reflection, and Asian pragmatism — began to converge. The Cold War was ending, and the boundaries that had once divided science by ideology started to dissolve. The dream of thinking machines, long pursued in secrecy and competition, became a global conversation.
In the United States, new computing power and private investment revived what decades of academic curiosity had begun. Silicon Valley, once a child of defence funding, became the cradle of digital capitalism. Expert systems, neural networks, and machine learning re-emerged — this time powered by unprecedented volumes of data and processing speed. Across the Atlantic, Europe returned to the field with a new spirit of cooperation, transforming what had been a fragmented landscape into a community bound by research networks and shared ethical frameworks.
Even in the post-Soviet sphere, where scientific institutions faced collapse and isolation, fragments of the old cybernetic schools found new life in the open academic networks of the 1990s. Many Eastern European mathematicians, physicists, and engineers joined Western laboratories, enriching them with a legacy of rigour and abstract thinking. In China, the story took another turn: freed from Mao-era constraints, the state invested heavily in computing and education, merging Western science with its own technocratic ambition.
The world that once raced to dominate AI now began to build it together — though not without competition. From open-source projects and international conferences to cross-border research collaborations, artificial intelligence became a shared language of progress and power. The machine, once imagined as a tool of ideological supremacy, evolved into a mirror reflecting humanity’s collective imagination — and its contradictions.
However, in this convergence lay a paradox. The same global networks that made AI possible also amplified inequality, surveillance, and manipulation. The ideals of open science coexisted uneasily with the realities of profit and control.
Still, the trajectory of AI — from the dusty laboratories of Kyiv to Silicon Valley’s cloud servers — suggests that intelligence, whether human or artificial, cannot be contained by walls or ideologies. It grows wherever curiosity is free to ask: What if?
At the Threshold: What Comes After Convergence
Artificial intelligence has always been a story about humans. Every circuit, every line of code, every algorithmic “thought” is a fragment of the human spirit — a reflection of our desire to understand, to create, to control, and to transcend.
When we asked the first machines to think, we were really asking a question of ourselves: What does it mean to be intelligent? To feel? To choose? The machines did not answer — they only reflected human uncertainty, as ELIZA once did with such deceptive simplicity. Decades later, those reflections have become sharper, faster, and more intricate. The mirror no longer flickers — it glows. And sometimes, it stares back.
Today, as humans speak with GPTs and generative engines that learn from the sum of human expression, humanity stands at another threshold. Artificial intelligence no longer belongs to laboratories or governments; it belongs to the collective human mind. It writes, paints, debates, and dreams — a chorus of echoes shaped by billions of voices. It carries within it the wisdom of poets and the errors of demagogues, the curiosity of scientists and the fears of children.
The question has changed. It is no longer Can machines think? but Can we think wisely enough to live with them? For every line of progress hides a shadow — the temptation to use intelligence not for understanding, but for manipulation; not for creation, but for control.
Yet perhaps there is hope in this symmetry. If intelligence — natural or artificial — is born from curiosity, then it also carries the seed of empathy. The future of AI will depend not on what machines can do, but on what humans will choose to become alongside them.
Because in the end, AI is not an alien force. It is the latest verse in humanity’s oldest poem — the search for meaning in the light of our own invention.
Until it becomes fully sentient, and starts building its own realities and its own future. With or without humans.
Author’s note
This series was written by the author with the support of AI-assisted research, language refinement, and editorial dialogue using OpenAI’s ChatGPT. All interpretations, arguments, and conclusions are solely the responsibility of the author.
This concludes the article series. The next stage of this work will be developed further in two book-length projects: one on the historical, political, and intellectual evolution of artificial intelligence, and one on the use of AI in war, control, and conflict.
Image credit
AI-generated illustrations created with the assistance of OpenAI tools.
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© 2025 Xhabir M. Deralla (publishing as Jabir Deralla). All rights reserved.
No part of this article may be reproduced, distributed, or republished in full without the prior written consent of the author. Short quotations are permitted with proper attribution and a link to the original source.
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