Just So Stories

DARWIN JUST ASSUAGED IMPERIAL GUILT:

A Prehistory of Scientific Racism: The author of “Whiteness” traces the evolution of race as a social and political instrument, from its beginnings in ancient hierarchies through European colonial expansion and into contemporary times (Martin Lund, MIT Press Reader)

By the dawn of the 19th century, race was being turned into biology and classified as something ostensibly “natural.” Supposedly innate differences between whites and “inferior” peoples were increasingly used as a justification for the unequal distribution of rights and resources, even as doctrines of “natural rights” were widely touted. While other thinkers were more influential at the time, ethnologist Arthur de Gobineau’s (1816–1882) posthumous influence would be immense. In his 1853–1855 “Essay on the Inequality of the Human Races,” Gobineau claimed among other things that France’s population consisted of three races — Nordics, Alpines, and Mediterraneans — that corresponded to the country’s class structure. The scientification of race and whiteness continued through uses of naturalist Charles Darwin’s theory of evolution (1859), particularly racialized in so-called social Darwinism, which applied ideas of “natural selection” to humans, and argued that racial and class inequalities were rooted in biological differences rather than social inequities. This worldview was used to oppose social policies meant to help the poor, children, or women, among others, further manufacturing and enshrining differences between not only white and nonwhite people but different classes of white people too. Darwinian assertions were also used to legitimize genocide: the “higher” races were naturally bound to overtake the “lower.”

SYNTHETIC BIOLOGY IS REDUNDANT:

Welcome to post-Darwinism: AI fires silver bullet for creating new life: In a breakthrough experiment, molecular biologists and tech entrepreneurs have teamed up to write the genetic code of a virus that destroys killer bacteria (Ben Spencer, January 24 2026, Times uk)

For the first time in history, scientists had used artificial intelligence to design the genetic code of a brand-new biological organism. In the lab at Stanford University, California, a new virus, codenamed Evo-Φ2147, was attacking a colony of E.coli bacteria in the petri dish.

Clear spots started appearing on the cloudy mass of bacteria, growing across the dish in expanding circles. It showed that slowly but surely, the new virus was killing the E.coli.

The breakthrough marks a turning point in the accelerating field of synthetic biology, allowing scientists to use AI to draw up the blueprints for entire genomes from scratch. It coincides with the development of a sophisticated new DNA construction technology that can turn those blueprints into reality — building long, complex genetic sequences with up to 100,000 times more accuracy than anything seen before.

Only MAGA is Darwinist any more.

THERE’S NO SUCH THING AS SPECIES:

Scientists say most dogs have some wolf DNA – even chihuahuas (AFP, November 24, 2025)

And it is not genetic leftovers from when dogs originally evolved from wolves around 20,000 years ago, but instead suggests that domesticated dogs and wild wolves have interbred within the last few thousand years.

THERE’S NO SUCH THING AS SPECIES:

Mathematical model indicates Neanderthal disappearance can be explained by genetic dilution (Krystal Kasal, 11/11/25 Phys.org)

In a new study, published in Scientific Reports, researchers use a mathematical model to determine if Neanderthal disappearance could be explained only by genetic dilution from mating with humans through periods of recurrent small-scale immigrations.

THERE’S NO SUCH THING AS SPECIES:

‘Grue Jay’ Spotted in a Texas Backyard Is First-of-Its-Kind Hybrid (Russell McLendon, 10/07/25, Science Alert)

An odd bird found in a Texas backyard is the offspring of two distantly related species, separated by 7 million years of evolution, whose ranges only recently began to overlap due to climate change, researchers report in a new study.

WE ARE ALL DESIGNIST:

Is Life a Form of Computation?: Alan Turing and John von Neumann saw it early: the logic of life and the logic of code may be one and the same. (Blaise Agüera y Arcas, MIT Reader)

Although this is seldom fully appreciated, von Neumann was one of the first to establish a deep link between life and computation. Reproduction, like computation, he showed, could be carried out by machines following coded instructions. In his model, based on Alan Turing’s Universal Machine, self-replicating systems read and execute instructions much like DNA does: “if the next instruction is the codon CGA, then add an arginine to the protein under construction.” It’s not a metaphor to call DNA a “program” — that is literally the case.

Of course, there are meaningful differences between biological computing and the kind of digital computing done by a personal computer or your smartphone. DNA is subtle and multilayered, including phenomena like epigenetics and gene proximity effects. Cellular DNA is nowhere near the whole story, either. Our bodies contain (and continually swap) countless bacteria and viruses, each running their own code.

It’s not a metaphor to call DNA a “program” — that is literally the case.

Biological computing is “massively parallel,” decentralized, and noisy. Your cells have somewhere in the neighborhood of 300 quintillion ribosomes, all working at the same time. Each of these exquisitely complex floating protein factories is, in effect, a tiny computer — albeit a stochastic one, meaning not entirely predictable. The movements of hinged components, the capture and release of smaller molecules, and the manipulation of chemical bonds are all individually random, reversible, and inexact, driven this way and that by constant thermal buffeting. Only a statistical asymmetry favors one direction over another, with clever origami moves tending to “lock in” certain steps such that a next step becomes likely to happen.

This differs greatly from the operation of “logic gates” in a computer, basic components that process binary inputs into outputs using fixed rules. They are irreversible and engineered to be 99.99 percent reliable and reproducible.

Biological computing is computing, nonetheless. And its use of randomness is a feature, not a bug. In fact, many classic algorithms in computer science also require randomness (albeit for different reasons), which may explain why Turing insisted that the Ferranti Mark I, an early computer he helped to design in 1951, include a random number instruction. Randomness is thus a small but important conceptual extension to the original Turing Machine, though any computer can simulate it by calculating deterministic but random-looking or “pseudorandom” numbers.

Parallelism, too, is increasingly fundamental to computing today. Modern AI, for instance, depends on both massive parallelism and randomness — as in the parallelized “stochastic gradient descent” (SGD) algorithm, used for training most of today’s neural nets, the “temperature” setting used in chatbots to introduce a degree of randomness into their output, and the parallelism of Graphics Processing Units (GPUs), which power most AI in data centers.

Traditional digital computing, which relies on the centralized, sequential execution of instructions, was a product of technological constraints. The first computers needed to carry out long calculations using as few parts as possible. Originally, those parts were flaky, expensive vacuum tubes, which had a tendency to burn out and needed frequent replacement by hand. The natural design, then, was a minimal “Central Processing Unit” (CPU) operating on sequences of bits ferried back and forth from an external memory. This has come to be known as the “von Neumann architecture.”

Turing and von Neumann were both aware that computing could be done by other means, though. Turing, near the end of his life, explored how biological patterns like leopard spots could arise from simple chemical rules, in a field he called morphogenesis. Turing’s model of morphogenesis was a biologically inspired form of massively parallel, distributed computation. So was his earlier concept of an “unorganized machine,” a randomly connected neural net modeled after an infant’s brain.

These were visions of what computing without a central processor could look like — and what it does look like, in living systems.

THERE’S NO SUCH THING AS SPECIES:

Claims of pure bloodlines? Ancestral homelands? DNA science says no. (Alvin Powell, September 18, 2025, The Harvard Gazette)

Human history is rife with contentions about the purity (and superiority) of the bloodlines of one group over another and claims over ancestral homelands.

More than a decade of work on ancient human DNA has upended it all.

Instead, Harvard geneticist David Reich said on Monday, increasingly sophisticated analysis of genetic material made possible by technological advances shows that virtually everyone came from somewhere else, and everyone’s genetic background shows a mix from different waves of migration that washed over the globe.