Part One

LLMs are projections of our collective intelligence. They marvelously structure and “parameterize” the complexities and vagaries of human expression and cognition. They do not understand. They have no self or intrinsic intention. They reflect the patterns of argument and expression of the documents and media they process. Their agency comes as a projection of our agency upon them. They appear to come to life with our prompts, miraculously summoning and organizing their myriad content to answer our questions and commands. As such, they mirror our collective strengths and weaknesses, our prejudices, our insights, and our pathologies. There is nothing inherently true or untrue about them. They are the “digital spore” of human communication, inquiry, conjecture, speculation, assertion, and expression, but not its subjects or progenitors.

Extreme Rationalism and Artificial Intelligence

Since LLMs appear to reason and converse, solve puzzles, and complex problem sets, we attribute a form of artificial intelligence to them. They are artifacts that are ‘intelligent “because they perform tasks that their creators deem “intelligent”.  There is an unexamined assumption within the AI tech community that Superintelligence is a form of supersized rationality, the capacity to solve complex mathematical and logic problems, facility with formalisms, games, and puzzles, and recall of obscure facts and details. Not surprisingly, these are the skills most prized within the tech, gaming, crypto, and hacker communities. A capacity for scoring high on tests of “rationality”, such as IQ, logic, and math tests, seems to give them an “objective measure” of their relative worth. Given that such skills are often associated with being “on the Spectrum”, many within the AI “nerd culture” experience themselves as exceptional – “gifted” outsiders. Their personality traits can be awkward and trying –literalism, dogmatism, arrogance, lack of empathy, and an obliviousness to social cues and nuance. Yet with the ascendancy of nerds to the heights of franchise-level software developers, influencers, investors, VCs, thought leaders, billionaires, and now political operatives, there is the belief that “bright makes Right”.  And it has imbued some with a messianic calling that they are the arbiters and saviors of humanity and the planet. Rationalism has become a kind of cult secular religion through such movements as Effective Altruism, the Center for Applied Rationality, LessWrong, and Neo-Monarchism, and other intentional communities espousing interplanetary species, transhumanism, the Singularity, immortality, accelerationism, and abundance.

The Rationalists share a common heritage with the eugenics movements of the early 20th century, when IQ tests were first developed and the concept of a singular general intelligence was first promulgated. Combined with Social Darwinism at the time, the concept of "survival of the fittest" was then seen as giving preference in breeding and power to the smartest, who were then considered to be primarily Northern Europeans. This translated into the neo-natalism movements of the Nazis in the 1930s, and more recently with Musk and the Republican Party. Having equated intelligence with very specific, inheritable and measurable skills and seeing relentless competition as a principle of Nature, it is not surprising that the same culture that has given birth to Artificial Intelligence both fears and promotes its success. What if AI agents become smarter than us? Then would they not act like us, and eventually deceive, rule and eliminate us? That is the “doomer” scenario that seems to have wide acceptance: Future of Life Institute, The Center for AI Safety, Pause AI, and many others.

The Science and Myth of Intelligence

Yet is that a scientific fact, or is it perhaps a projection of our own fears and failings? Do we want to optimize that kind of narrow intelligence and values? Is that a fact of Nature, an inevitable feature of increased “intelligence’? Or is it the result of a subgroup of former outliers trying to assert and preserve their control of the technology? There is no scientific basis for equating a singular intelligence with a particular subgroup of neurodiversity. William James, a pioneer in modern psychology, described intelligence as the ability to reach the same goal by different means. Intelligence is inherently biological and is equated with being able to survive under conditions of uncertainty. In biology, intelligence is not the optimization of a single class of cognitive or somatic competencies, but rather the orchestrated selection of a coherent range of competencies to survive and reproduce.  The very traits for which “Rationalist” theories of intelligence optimize for – literalism, lack of empathy, uniform memory recall, minimal self-reflection, and self-optimization- are highly maladaptive in complex situations where the capacity to model, influence, and jointly affect others’ mental models and actions increase the prospects for survival and reproduction. For complex multicellular organisms, the challenge of intelligence is to navigate and organize a bounded multidimensional state space to preserve current and future coherence and prediction.

The Rationalist notion of a hierarchy or a rank ordering of cognitive capacities or “rational choices” as indicative of intelligence is just flat wrong. It fails deductively, in the selection of evolutionary strategies that are maladaptive, and it fails inductively, in accounting for the nested and highly plastic dynamics of not just the brain, but cellular intelligence in general. Effective evolutionary strategies are not necessarily transitive, hence “rational”; I may choose A over B and B over C, and because I chose B, I would not choose A. Feedback loops and the dynamically changing semantic context of choices affect the preference functions and, hence, the efficacy of a strategy. It is also apparent that effective evolutionary strategies entail creating a context – a boundary condition that selects for cognitive competencies, such as those espoused by the “Rationalists”. By arguing that such competencies are fundamental, indeed definitive of intelligence, Rationalists define and control the rules of the game. A good analogy might be changing the rules of basketball so that the only legitimate points are those scored by a dunk. Hence, the game would intrinsically select for very tall players, thereby nullifying the value of the three-point shooter.

The Political Economy of Artificial Intelligence

The current debate over AI ethics and governance, superintelligence, and existential risks has conflated a specific and very limited definition of intelligence with the interests and competences of a neurological subgroup, the Rationalists. It has also conflated the notion of intelligence with a related economic and political doctrine, Libertarianism.  Given the Rationalists’ belief in a hierarchy and meritocracy based on intelligence, it is not surprising that they believe in optimization and unregulated freedom to exercise their form of intelligence. This accounts for their belief in accelerationism and abundance. It is a natural consequence of seeing their limited form of intelligence as natural merit, and therefore, the natural prerequisite for achieving the greatest social and economic good. Boundaries are limits, barriers, and inhibitions of natural freedoms, and once relaxed, would unleash a natural competition of merit. This narrative echoes a Nietzschean view of superintelligence as the superman, who ignores boundaries, and bravely confronts and overcomes convention – “moves fast and breaks things” –to achieve a higher, more “intelligent” order. Such a narrative is contemptuous of caution and consideration, as signs of a feminine weakness and wokeness. It extols the “manliness” of single-mindedness, aggression, and dominance. This is where the current conception of AI as a dominant superintelligence overlaps with a curious and contradictory political and cultural narrative of “decentralization” and “monarchism.” On the one hand, the Rationalist favors an ideology of decentralization to enable the unfettered growth of their technologies, and on the other, they support a superman thesis of dominance by a risk-taking and “intelligent” monarch or elite.

In this, they are firmly in the grip of the ‘manosphere”, and those who hold a winner-takes-all economic thesis. These values and aspirations resonate back to the late 1920s with the sentiments of Andrew Mellon, then Secretary of the Treasury under Herbert Hoover:

“Liquidate labor, liquidate stocks, liquidate the farmers, liquidate real estate. It will purge the rottenness out of the system. High costs of living and high living will come down. People will work harder, live a more moral life. Values will be adjusted, and enterprising people will pick up from less competent people.”

Such draconian measures, which presaged the Great Depression, were popular then and are popular today: favor the concentration of incumbent wealth and assign the risks and costs to those ‘less competent”. The Rationalist and techno-elite also share Andrew Mellon’s contempt for the lower orders:

"Prosperity of the middling and lower orders depends upon the fortunes and light taxes of the rich."

The point here is that the current thesis regarding AI as a superintelligence, and an existential risk that requires significant concessions to the barons of the technology, lacks merit in terms of technology, science, or economics. It is, as it was in the Gilded Age, a pretext to grab and concentrate economic and political power. This same Rationalist, accelerationist, and winner-takes-all mindset infects the entire crypto and meme coin industry as well. The crypto-friendly, regulation-free embrace and promotion of crypto by the current administration has eliminated guardrails and enabled the President and his family to monetize his presidency.

A Science-Based Alternative:  Symbiotic Intelligence (SI)

To challenge the accelerationist and rationalist thesis of AI, is to invite dismissal by the AI barons as an anti-progress, anti-science, and anti-technology Luddite. The opposite is true. The current view of AI is not grounded in science but in a reactionary ideology masked as science and packaged as an inevitably dominant technology. The counter perspective espoused here is grounded in verifiable and robust scientific refutations of a rationalist and reductionist interpretation of intelligence. They are based upon decades of research in evolutionary, synthetic, and computational biology, computational neuroscience, and information physics. Unlike “Artificial” Intelligence, “Symbiotic” Intelligence is not the result of an accident of its origins and its subsequent capture by a Rationalist cult of developers and investors. Rather, the notion of symbiosis as mutual information exchange and recombination into progressively more complex self-synchronizing structures is derived from decades of cross-disciplinary research into the origins of life and mind. Lynn Margulis, a microbiologist, was the first to discover and scientifically demonstrate the prominent role of symbiosis and mutualism in evolution. Her research on “endosymbiosis” is fundamental to understanding the evolution of multicellular organisms. Initially, her findings were vigorously rejected by Neo-Darwinists such as Richard Dawkins. But subsequent research verified Lynn Margulis’ discovery of the importance of endosymbiosis in the broadest evolutionary terms.  Margulis, who was known for her sharp tongue, took to task the Neo-Darwinists for their singular focus on zoological species that occurred 500 million years ago, when life had been on the planet for four billion years. She equated it to “a four-volume tome supposedly on world history but beginning in the year 1800 at Fort Dearborn and the founding of Chicago”. She also bluntly criticized the political agenda in Neo-Darwinism:

“They wallow in their zoological, capitalistic, competitive, cost-benefit interpretation of Darwin – having mistaken him ... Neo-Darwinism, which insists on [the slow accrual of mutations by gene-level natural selection], is in a complete funk”

Symbiotic Intelligence, in contrast to Artificial Intelligence, acts to maintain and discover multi-scale homeostatic coherence and to minimize informational uncertainty within its metabolic limits as synchronized within an ecological niche. The ongoing and fertile transdisciplinary research and modeling of one of the world’s most prolific and influential neuroscientists, Karl Friston (2018, 2021,2023,) and his many colleagues, in conjunction with the groundbreaking discoveries of the transdisciplinary biologist, Michael Levin, (2022, 2023, 2024) form the basis for the notion of Symbiotic Intelligence. Friston developed two key concepts fundamental to understanding and modeling symbiotic intelligence. The first is the notion of the Markov blanket, which he borrowed and slightly modified from the Turing Prize winner statistician, Judea Pearle. (2017) Pearl’s notion of the Markov blanket was originally derived to provide a principled basis for making causal inferences from data. Pearl addressed the proposition: how does one go from simple correlations, that is, a bidirectionality of a relationship, to a single, directed relation? One cannot say A causes B unless one can provide evidence that there are no other dependencies other than those measured. A similar issue of establishing independence arises when one considers what it means to be alive – in effect, to be autonomous. To what extent does an organism, a living thing, act, and survive independently of what is around it? Can one explain its actions, structure, and history by simply understanding all the variables of the context in which it is situated? Or is there something internal to the organism that is uniquely its own that accounts in large measure for its shape, history, and behaviors?

Life and Cognition Are Wrapped in a Markov Blanket

A Markov blanket is defined in formal terms the limits of independence for “living things” by identifying a minimum number of states, observer, internal, external, action, and latent that are represented as nodes in a directed graph. The directionality of the graphic defines the minimum relationships for being an independent “living thing”. It is worthwhile examining this concept in more detail as it is integral to understanding symbiotic intelligence.

Markov blankets are best understood in conjunction with the notion of Active Inference – a form of Bayesian belief generation based on the Free Energy Principle, which entails a living thing’s capacity to make predictions to minimize uncertainty, in this case, “Free Energy”.

Quoting extensively from the article, “The Markov blankets of life: autonomy, active inference and the free energy principle”: Michael Kirchhoff, Thomas Parr, Ensor Palacios, Karl Friston, and Julian Kiverstein, Published:17 January