Can Transistors Think?
The Systome Principle, Non-Determinism, and the Future of Duplication
Sungchul Ji, Ph.D. (with ChatGPT assistance)
Emeritus Professor of Theoretical Cell Biology
Ernest Mario School of Pharmacy,
Rutgers University, Piscataway, NJ
1. A Curious Comment from Geoff Hinton
In a recent lecture, deep learning pioneer Geoffrey Hinton made a striking observation [1]:
“When transistors are run at high power, they behave deterministically; when run at low power, they behave non-deterministically.”
While this might sound like a minor hardware insight, it turns out to have far-reaching philosophical consequences. When viewed through the lens of the Systome Principle [2, 3], it may provide an important key to understanding not only how computing works, but also why true duplication of intelligent systems—biological or artificial—may be fundamentally limited.
2. The Systome Principle [3]: Beyond the System
In 2008, I proposed the concept of the Systome, defined as:
Systome = System + Environment
and the Systome Principle [3]:
“The behavior or function of any system is not determined by the structure of the system alone, but by the combination of the system and its environment.”
In Hinton’s example, the structure of the transistor remains constant—but its behavior changes depending on its power level [1], an environmental variable. When power is high, the transistor operates deterministically. When power is low, it exhibits stochastic, noisy, or even quantum-like behavior.
Thus, what we call “a transistor” is not just a physical component—it is a systome, whose behavior arises from interacting structure and context.
3. Duplication and Determinism: A Generalized Principle
This leads to a more universal formulation about duplication:
Only deterministic systomes can be faithfully duplicated.
Systomes with deterministically chaotic behavior cannot be practically duplicated.
Systomes with intrinsic nondeterminism cannot be duplicated even in principle.
This has major implications. While a high-powered transistor (a deterministic systome) can be duplicated, a low-powered, non-deterministic transistor cannot. The same logic applies to all computing substrates—including the human brain.
4. Implications for AI and Consciousness
If brains operate in low-energy (i.e., at 30 watts as compared to LLM’ running at megawatts [1]), noise-prone regimes—as many neuroscientists believe—then they may be intrinsically non-deterministic systomes. From this follows a bold conclusion:
Conscious brains cannot be exactly duplicated.
This insight challenges the core assumption behind many “mind-uploading,” “whole brain emulation,” and “digital immortality” proposals. If true, artificial systems may simulate cognitive functions but never fully replicate the systomic coupling of conscious systems.
In other words, no transistor can truly “think” like a human unless it becomes part of a systome as rich, dynamic, and self-organizing as the brain’s bio-molecular milieu. But transistors cannot mimic enzymes due to the intrinsic thermal barrier between the two [4].
5. The Black Hole Puzzle: Is Information Ever Lost?
Now let’s turn to a claim that’s generated decades of debate in theoretical physics:
“Information is never lost.”
This is a cornerstone of unitary evolution in quantum mechanics and a central issue in the black hole information paradox. Stephen Hawking once argued that black holes destroy information, but later reversed his view, endorsing ideas from string theory and the holographic principle, which suggest that information is preserved—perhaps encoded in Hawking radiation or on the event horizon itself.
But does this principle hold under the Systome Principle?
6. Veridicality Analysis: Is the Claim Always True?
From a systomic perspective, we cannot evaluate the fate of information by analyzing the black hole (system) alone. The outcome depends on the black hole’s coupling to the entire cosmic environment—which remains unknown. Thus, the statement “information is never lost” is condition-dependent, not absolutely veridical.
7. Toward a Science of Systomes
Geoff Hinton’s transistor comment [1] is more than a quirk of hardware—it is a microcosm of reality. It reveals a fundamental truth:
The properties of any entity—whether a transistor, a neuron, or a galaxy—are not intrinsic to its structure alone, but emerge from its interaction with its environment.
This insight compels a paradigm shift:
Biology becomes the science of living systomes.
Physics becomes the study of environment-sensitive behavior.
Computer Science evolves into a discipline of context-aware, non-duplicable computation.
Consciousness studies shift from the brain as an object to the systomic totality of the brain-in-body-in-world.
8. Key Takeaways
Transistors are not mere systems; they are systomes whose behavior is shaped by their environment.
Only deterministic systomes are duplicable.
The brain, operating in a noisy, low-power regime, may be an inherently non-duplicable systome.
The claim that “information is never lost” is conditionally true, not universally so—unless the entire systome (black hole + cosmos) is taken into account.
The future of science may lie in Systomics: the study of behavior emerging from system–environment couplings.
9. Conclusion: A Category Error in Black Hole Physics
· It is proposed here that the entire black hole information paradox may be a category error—a result of treating B as a system when, under the Systome Principle, information is only meaningfully defined at the level of U = B + R, where U is the Universe, B is the black hole, and R is the rest of the Universe.
· Thus: The black hole information paradox is not a real paradox, but a byproduct of system-based abstraction.
References:
[1] Hinton, G. (2025). Will Digital Intelligence Replace Biological Intelligence?
[2] Ji, S. (2018). System vs. Systome. In: The Cell Language Theory: Connecting Mind and Matter. World Scientific Publishing, New Jersey. Pp. 24-27.
[3] Ji, S. (2025). Why Systems Cannot be Duplicated. https://622622.substack.com/p/why-systems-cannot-be-duplicated
[4] Ji, S. (1991). Molecularization of Machnes and the Thermal Barrier. In: Molecular Theories of Cell Life and Death (S. Ji, ed.), Rutgers University Press, New Brunswick, N.J. .
Pp. 29-35.
[5] Black hole information paradox. https://en.wikipedia.org/wiki/Black_hole_information_paradox
Appendix:
Why the Black Hole Information Paradox May Be a Category Error
The so-called black hole information paradox [5]—whether information is destroyed or preserved when it falls into a black hole—has occupied physicists for decades. But when viewed through the Systome Principle [3], the entire debate may be fundamentally misframed.
1) The Systome Framework
Let us define:
B = the black hole (system)
R = the rest of the Universe (environment)
U = B + R = the systome, i.e., the black hole embedded in its cosmic context
According to the Systome Principle:
2) Information is not an intrinsic property of B. It is a relational property of the full systome U = B + R.
In other words, the idea that a black hole “contains” information is only meaningful if we consider:
The initial conditions of the entire universe prior to matter entering the black hole,
The interactions between B and R,
The observer-relative capacity to encode, decode, and interpret any such information.
Since none of these are defined by B alone, we assert:
A black hole, treated in isolation, does not and cannot possess "information."
3) Why the Paradox May Be Ill-Posed
The classical formulation assumes:
"Information enters B. B either destroys it or releases it later via Hawking radiation."
But according to the Systome Principle, this is a category error:
It treats B as an isolated, informationally meaningful system.
It assumes I(B) (the information content of B) is well-defined.
It ignores the pre-entry systome state that determines what information even means.
Therefore:
The question “Is information preserved by the black hole?” is ill-posed because B cannot be assigned an informational state without R.
4) Reformulated Understanding
5) Final Statement
The black hole information paradox may not be a paradox at all—but a byproduct of system-based reasoning applied to what is intrinsically a systome-based process.
This reframing has broader implications:
“Many conceptual paradoxes in physics (and perhaps even in AI and consciousness
studies) may arise from the failure to distinguish between systems and systomes.”
This is very enjoyable reading. Thank you. It truly prods the mind into other areas of philosophy and psychology.