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Chips, Controversies and Minsky: The Backstage of AI

January 5, 2026 • ☕️ 6 min read • 🏷 computer, software, artificial intelligence

Translated by author into: English


Intellectual Embargo and the Ideological Roots of Hardware

Today, artificial intelligence has surrounded the world, from silicon valleys to the smallest circuit in our pockets. However, this hegemony was born from the ashes of a dark period of stagnation in the 1970s known as the AI Winter. From the perspective of a technology strategist, this collapse in the 1970s was not merely bad luck, but rather the result of a kind of intellectual embargo spearheaded by giants like Marvin Minsky and Seymour Papert. The terms CPU, GPU and TPU, which we take for granted as technical acronyms today, are actually the concrete products of these decades-long battles of ideas, funding cuts and architectural dogmas. To understand modern artificial intelligence, we must look not only at today’s processing powers, but also at why these powers were suppressed for half a century.

AI

The Darth Vader of Artificial Intelligence: Marvin Minsky

Marvin Minsky is often remembered in the history of technology as the man who started the AI winter, or even as a kind of Devil figure. As Robert Hecht-Nielsen puts it, Minsky’s career resembles Darth Vader’s; he started out as one of the greatest pioneers of neural networks and then crossed over to the dark side of the force (Symbolic AI), becoming the most relentless enemy of the community he had created. The irony is that Minsky is the person who built SNARC, the world’s first neurocomputer, in 1951. Regarding this machine made of vacuum tubes, Minsky says:

Because of this crazy random design, we were almost sure it would work no matter how you built it… At that time, even a twenty-tube radio would break down frequently; we never fully debugged our machine, but it didn’t matter. (Bernstein, 1981)

However, Minsky gradually distanced himself from random networks, which he viewed as brute force. His Society of Mind philosophy argued that intelligence should consist of thousands of specialized agents rather than a massive and homogeneous network. At the AI@50 conference in 2006, in response to Terry Sejnowski’s question, “Are you the devil responsible for the neural network winter?”, he initially resisted by giving a tirade on the mathematical limitations of networks and then shouted that historically famous confession: “Yes, I am the devil!”

The XOR Problem: The Great Fallacy Behind a Small Logic Gate

The event that halted neural network research at the end of the 1960s was a simple XOR (Exclusive OR) logic gate. Contrary to popular belief, Minsky and Papert did not claim in their 1969 book Perceptrons that multi-layer networks (MLPs) could not solve this problem; they merely defined the mathematical boundaries of single-layer networks (perceptrons) with precise lines. The real problem was not a technical impossibility, but an epistemological void. The obstacles facing the Connectionist camp in the 1960s were:

  • Lack of Backpropagation: The backpropagation algorithm to train multi-layer networks had not yet been discovered or scaled.
  • Differentiable Functions: At that time, 0-1 type step functions were used; whereas gradient descent required differentiable activation functions.
  • Minsky’s Passion for Mathematical Certainty: Minsky despised experiments conducted with theory-free data that lacked a theoretical foundation and he did not want any architecture that could not be proven mathematically to be funded.

Hardware Wars: Drummers, Soldiers and the Cost of Branching

From a strategic standpoint, hardware is not just a tool; it is an ecosystem that allows ideas to survive. We can analyze the difference between CPU, GPU and TPU using an orchestra metaphor:

  • CPU (Central Processing Unit): Incredibly fast, it is like a solo drummer capable of playing all kinds of complex notes. Its greatest ability is its branching (if/else) capacity. The reason it remains the brain of the computer is its ability to manage complex decision mechanisms.
  • GPU (Graphics Processing Unit): It is like hundreds of drummers in a military march. A single drummer (core) is not as smart as a CPU, but they all make the same beat simultaneously (SIMD - Single Instruction, Multiple Data). Performing branching (if-else) on GPUs is very costly; therefore, they excel in unconditional arithmetic and matrix multiplications (like deep learning).
  • TPU (Tensor Processing Unit): It is a specialized hardware designed by Google exclusively for matrix mathematics (tensors). It has almost no branching capability, but it manages matrix operations even more efficiently than a GPU.

The Scaling Hypothesis: The Triumph of Brute Force

Minsky and Papert argued that neural networks would never be able to solve large-scale problems and that homogeneous architectures could not understand the complex world. Papert’s belief in epistemological pluralism caused him to harbor a philosophical hatred against uniform architectures that melted all knowledge in the same pot. According to them, this was a hegemonic universalism.

History proved these two geniuses wrong on the very point they despised most: Brute Force. The methods Minsky marginalized as theory-free data form the heart of today’s Transformer models. Modern AI won, not through elegant symbolic rules as Minsky envisioned; but through the statistical pressure created by massive data and compute power.

Machine Learning or Deep Learning?

According to Zekros Engineering resources and industrial application data, not every artificial intelligence problem requires a GPU monster. The strategic choice is hidden in the nature of the data:

Feature Machine Learning (ML) Deep Learning (DL)
Data Requirement Less (Tabular data) Massive (Image, Audio, Text)
Feature Extraction Manual (Feature Eng.) Automatic (By Neural Networks)
Processing Power Medium (CPU usually sufficient) High (GPU/TPU required)
Use Case Credit risk, Spam filtering Autonomous vehicles, Chatbots

Strategic Note: When measuring model success, Accuracy alone is not sufficient. In industrial automation where the cost of false alarms is high, Precision is vital, while in security where missing a cyberattack is critical, the Recall metric is of life-saving importance.

Conclusion: Does the Society of Mind Live On?

The history of artificial intelligence is the test of ideas against hardware. The connectionist models that Minsky called unscalable in the 1960s are today simulating the world with billions of parameters. However, another irony is hidden here: Minsky’s Society of Mind idea is perhaps secretly coming to life today within massive homogeneous networks, in the form of neuron groups specialized in different tasks (such as MoE - Mixture of Experts).

In closing, we should ask ourselves this solemn question: Is artificial intelligence really learning the complex symbolic rules of the universe on its own, or are we merely marveling at the statistical reflections provided by a massive calculator? Perhaps Minsky was right; perhaps we are only dealing with applications and the door to true artificial general intelligence is still waiting locked.

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