[While w]e may need to know the particularities of a given set of initial conditions in order to infer the details of later states…we do not regard the resolution of such details as essential or causal components of the explanation itself.
Stephen Jay Gould, 2002, The Structure of Evolutionary Theory, pp. 1332-1333
Tech bro biology – the idea of living things as machines and therefore functionally synonymous with assemblies of silicon chips that underpin AI – needs correctives.
Biologists, who are probably the best placed of the experts to comment on this topic, have not, generally, chimed into this discussion. Scientists, biologists included, often see AI as a device, a tool, an instrument and/or a machine (the differences in terms are significant), perhaps like earlier generations of microelectronics which they used in their research. This is in-keeping also with a historical view that sees AI as a new force of production (as in the industrial revolution); as well as the fact AI emerged from “the hard sciences” of mathematics, chemistry, physics and engineering, not biology, and is, therefore, logically seen as a machine.
However, it is in my view worth reflecting systematically on neglected strands in biological thinking which few, at the moment, are picking up. This reflection has implications for assessing the reliability of knowledge generated both by AI and contemporary biology, some of which are not promising.
The underlying theme of my notes is that the AI industry has created, or discovered, an emergent property of the silicon/silicon dioxide band gap (i.e. microchip) that is alive.
This is at least, as far as I can understand, what parts of the industry ask us to believe (indeed, a web search gives back hits that are explicit about the topic, but they seem to come from sources that are not associated with notably famous institutions).
The actual biological metaphors current in the AI industry are often quite mixed and therefore more difficult to pin down. The idea of intelligent machines is obviously the most commonplace. Example: the 2025 books by Agüera y Arcas [Google], namely, What Is Life? Evolution as Computation; and What Is Intelligence? Lessons from AI About Evolution, Computing, and Minds. (The former is a self-declared “homage” to the 1944 book of similar title by “naive” physicist Erwin Schrödinger that implies a particular intellectual trajectory in 20thC biology that I am seeking to unpack.)
These two books might well adduce a typical “life as machine” vibe without adequately questioning the vibe itself, i.e., to the contrary, that life would be best understood as distinct from a machine. There has been a long history of biologists, historians and philosophers of science debating this vibe or, at the least, seeking to complicate it, such as documented by Riskin in The Restless Clock: a History of the Centuries-Long Argument over What Makes Living Things Tick.
There is tradition of physicists believing they understand biology yet not quite understanding it. Biologists, for their part, might understand physics as a potential tool to understand biology, but not its key, due to physicists’ characteristic susceptibility to teleological explanation.
The title of Fox Keller’s biography of Nobel Laureate Barbara McClintock, A Feeling for the Organism, to my mind captures the essence of that argument, as understood by at least a sizeable minority of biologists (as well as the methodological and other disputes that it implies).* The metaphor of a computer, while perhaps of some value, would therefore be seen as fundamentally misleading when it comes to understanding life. Indeed, it would probably be better to reject metaphors and acknowledge the uniqueness of what we observe.
But there is obviously a political layer. At least, it would be possible to locate Agüera y Arcas on the right of politics, due to the last 30-40 years of academic debate, in which the left of biology resisted teleology, simplistic glosses of determinism (Darwinian and Marxist), “The Selfish Gene” and biotechnology (although, outside of figures such as Richard Lewontin, they did not always do so in explicitly political terms).
There are, however, other biological metaphors in the writing of those employed by Big Tech that are less easy to place. For instance, citing Suleyman [Microsoft] and Bhaskar’s metaphor of “Cambrian explosion” in the 2023 book The Coming Wave: AI, Power, and Our Future. The phrase could refer to the emergence of AI-life-forms which, of course, need not be intelligent as understood by humans. However, the nature of the “eruption of new species” is not defined and is immediately followed by a switch to the more conventional engineering terminology of “applications”.
The coming wave is…an evolutionary burst like the Cambrian explosion, the most intense eruption of new species in the earth’s history, with many thousands of potential new applications.
Mustafa Suleyman and Michael Bhaskar, 2023, The Coming Wave: Technology, Power, and the 21st Century’s Greatest Dilemma, p. 69
The exact thoughts of Big Tech authors often remain obscure. But across the totality of public discussion, there are probably multiple (typically unsaid) strands of thought in play. These might include, notably:
- Perennial, multi-faceted, debates about the definition of living things. My gut impression is of a severe lack of novelty not to mention general amnesia of past contributions, suggesting we are going round in intellectual circles. Eventually, perhaps, we will discover what was already known.
- The application of the term “technology” to include living things (due in part to the rise of biotechnology) thereby blurring the line between the objects of study of biology and the goals of engineering.
- The sense of AI as a material presence, reflected in the debate on data center use of energy and water but also drawing on materialist views of computation dating back to pioneers like Gordon Teal.
- Echoes of niche fields such as astrobiology (concerned with theorizing what extraterrestrials would look like and what chemical traces they would leave) implying that life could take unexpected forms that we can only guess at.
It is all, unfortunately, a topic with an intellectually-expensive entry ticket due to the fact it crosses the physical sciences, engineering, biology, history and philosophy of science, relevant legal scholarship, etc., and not, therefore, a topic I can resolve here. That being said, I am gradually trying to build a bibliography that lets us take a step back from dominant narratives, think clearly, and unpack the various intellectual and political elements.
It would be too much of generalization overall to say the Big Tech view draws on a dogmatic, teleological and Neo-liberal conception of biology, which grew up over the past few decades, but the coincidence with political developments is often quite stark.
It is also worth reflecting on how biologists, mainly on the left (but not exclusively so), had come to terms with that wider trend within their subject, and how the contributions of such biologists, even very prominent ones like Stephen Jay Gould, are airbrushed. Indeed, Google’s Agüera y Arcas explicitly rejects Gould, citing an interview last year (around the 21 min mark). However, he does this only by assertion, so it impossible to place his argument within broader intellectual context. He seems often to be reacting to a straw dog version of biology, not the nuanced academic debates of the past few decades.
Hence, due to the evident difficulty of fairly addressing a complex body of opinion and evidence, there is a risk of producing unreliable knowledge both about AI and biology. This could, however, be remedied relatively easily, were anyone minded to try.
*For evident reasons, favorably reviewed by Stephen Jay Gould in a 1984 issue of The New York Review of Books.
Bibliography
Latest
In this way, historians of science in the 1990s simultaneously reproduced and reformulated a much older notion of mechanical life. Invoking technology shifted the emphasis from the functioning of organisms to their potential for manipulation. This reformulation reflected a moment of rising technophilia, when mid-century anxieties about what Günther Anders called “the obsolescence of the human” were supplanted by a more general optimism about the world that scientists and engineers were building. The resulting histories read almost like a palliative: If flies, cells, workers and rivers were already technological at the century’s beginning, then we had little to worry about as we barreled toward the century’s end.
Kapp Singer, 2026, Organisms as technologies in the history of science (JHI Blog)
Berg, 2026, Design versus discovery in the development of artificial intelligence, in: Artificial Intelligence: a Classical Liberal Approach
Anil Seth, 2026, The mythology of conscious AI, in: Perspectives on Machine Consciousness
SWC Lecture 2024 – Blaise Agüera y Arcas [Google]
Growing an organism is not like booting up a computer…A society that permits biology to become an engineering discipline, that allows that science to slip into the role of changing the living world without trying to understand it, is a danger to itself.
Carl Woese, quoted by Segall and Damer, 2024, The cosmological context of the origin of life: process Philosophy and the hot spring hypothesis, in: Astrophilosophy, Exotheology, and Cosmic Religion: Extraterrestrial Life in a Process Universe, p. 91
Silicon, the second most abundant element in the Earth’s crust, has served as the foundation for modern electronics, including the silicon chips that power computational devices. We proposes that, in the context of artificial general intelligence (AGI), silicon-based life can be seen as a form of life characterized by intelligent agents that are self-aware, autonomous, and capable of adapting to their environment. Unlike traditional forms of life, which are bound by the laws of biology, these silicon-based lifeforms would emerge from circuits and algorithms designed to replicate aspects of human cognition. The life-form here is defined by its cognitive ability and its ability to interact with its environment, rather than any biological processes…To achieve a silicon-based life, an AI model must reach a high level of independence and not reveal privacy.
Lei, et al. [Peking University], 2024, The study of feasibility and challenges on silicon-based lifeforms based on large language model, pp. 18-19
Mustafa Suleyman [Microsoft] and Michael Bhaskar, 2023, The Coming Wave: Technology, Power, and the 21st Century’s Greatest Dilemma. The book espouses a teleological account of history; it draws attention to the material basis of the devices in question, after the tradition of Gordon Teal, but then seems to argue the material has become less important (pp. 67-68).
2010-2020
Nicholson, 2019, Is the cell really a machine?, in: Journal of Theoretical Biology
Elizabeth Hannon and Tim Lewens (eds.), 2018, Why We Disagree About Human Nature
Dominic J. Berry, 2018, Plants are technologies, in: Histories of Technology, the Environment and Modern Britain
Jessica Riskin, 2016, The Restless Clock: a History of the Centuries-Long Argument over What Makes Living Things Tick. Reviewed by Susannah Gibson (H-Sci-Med-Tech): “The stark message…is that modern biology is essentially flawed…many biologists may well balk at her central claim”.
2000-2010
Reus and Cutcliffe, 2010, The Illusory Boundary: Environment and Technology in History, e.g., Vileisis, Are tomatoes natural? and Russell, Can organisms be technology?
Evelyn Fox Keller, 2009, What does synthetic biology have to do with biology? in: BioSocieties
Landecker, 2007, Culturing Life: How Cells Became Technologies
In [Gordon] Teal’s system, creating the material was creating the advanced electronic device…in the junction transistor the material was the device, no more no less.
Christopher Lecuyer and David C. Brock, 2006, The materiality of microelectronics, in: History and Technology, pp. 304 and 307
None of the arguments above proves that anything as exotic as a silicon chemistry-based life using liquid nitrogen or liquid methane as a solvent system actually exists. But some understanding of silicon chemistry suggests that a-polar solvent/silicon chemistry-based life is not as implausible as a biochemist might believe, and that environments where it could exist may be quite common. More generally, there may be many environments where the physical and chemical nature of a liquid allows complex, patterned, self-sustaining chemistry, which we would recognize as life, but which is structurally quite different from the CHON [carbon, hydrogen, oxygen, and nitrogen] life with which we are familiar. Consequently, looking for the equivalent of sugars or nucleic acids, or even just for complex molecules dissolved in water, would not lead to their discovery. However, they could in principle be functionally similar; complex macro-molecules could be built in a programmed way to capture matter and energy in order to replicate the program that directed their own synthesis.
Bains, 2004, Hypothesis paper: many chemistries could be used to build living systems, in: Astrobiology, p. 162
Stephen Jay Gould, 2002, The Structure of Evolutionary Theory. “[W]e cannot validate the actuality of mammalian success by general principles, but only as a…contingency of a historical process with innumerable alternatives that didn’t happen (despite their equal plausibility before the fact)…[therefore] we must face the philosophical question of whether we have surrendered too much in developing a more complex and nuanced view of causality in the history of life…[While w]e may need to know the particularities of a given set of initial conditions in order to infer the details of later states reached by the operation of these [spatio-temporally invariant] laws…we do not regard the resolution of such details as essential or causal components of the explanation itself…[Yet] natural historians have often been too willing to accept an inferior status, based on the principled unpredictability of their largely contingent phenomenology, in order to gain recognition as practitioners of science at all. (For in this Laplacean construction, the frequency of probabilistic inference correlates directly with the weakness of scientific apparatus – for we live, under this fallacy, in a…deterministic world, and the extent of our recourse to probability therefore maps our relative inability to define the true determinism of any particular process.) Wise natural historians…have always rejected this disparagement.” (pp. 1332-1333)
*Pace, 2001, The universal nature of biochemistry, in: Proceedings of the National Academy of Sciences of the USA. “Life is a self-replicating, evolving system expected to be based on organic chemistry… It is predictable that life, wherever we encounter it, will be composed of macro-molecules…Only two of the natural atoms, carbon and silicon, are known to serve as the backbones of molecules sufficiently large to carry biological information. Thought on the chemistry of life generally has focused on carbon as unique…Several of its properties
indicate that water is likely to be the milieu for life anywhere in the universe.”
Lewontin, 2000, The Triple Helix: Gene, Organism, and Environment
20thC
Haraway, 1997, Modest_Witness@Second_Millennium. FemaleMan_Meets_OncoMouse
Lewontin, 1993, The Doctrine of DNA: the Biology of Ideology. “The dream of the human genome” chapter is illustrative.
Stephen Jay Gould, 1988, Kropotkin was no crackpot, in: Natural History
We define a computer “virus” as a program that can “infect” other programs by modifying them to include a possibly evolved copy of itself…Every program that gets infected may also act as a virus and thus the infection grows…A common misconception of a virus relates it to programs that simply propagate through networks…[To the contrary, t]he key property of a virus is its ability to infect other programs, thus reaching the transitive closure of sharing between users…It should be pointed out that a virus need not be used for evil purposes or be a Trojan horse…[T]he biological analogy leads us to the possibility of cure as a means of protection. Cure in biological systems depends on the ability to detect a virus and find a way to overcome it.
Cohen, 1987, Computer viruses: theory and experiments, in: Computers & Security, pp. 23-24
