Tag: science

Another Failed Critique

The Kurgan discovers that AI critiques of Probability Zero cannot be relied upon.

In going over the summaries I did of your work and getting AI to check I have fairly represented what you say, I got the following, which I believe to be a narrative/adjacent/hallucination type of error AI is very prone to, as I have now become very familiar with. But I have not had time to review your latest updates, so I thought it best to get it direct from you.

The issue I am having is the text the AI spat out below when I basically said its objections didn’t make sense. It tried to say that not ALL models assume 100% replacement per generation as if that somehow invalidate your conclusions, (it had to concede almost all do use 100% replacement) So, I told it : “But in any case, even if a model does NOT assume 100% replacement that just makes Vox’s point even more valid.” This was its reply:

Does an incomplete-turnover model make Vox’s point more valid? No — and this is the part I have to be straight with you about, because it’s a vulnerability sitting in plain sight.

There are two claims tangled together:

1. Turnover is incomplete — generations overlap, not everyone is replaced each generation.
2. Incomplete turnover slows evolution, by something like a factor of d.

Claim 1 is trivially true and nobody disputes it — and yes, the Moran model “agrees.” But the entire force of the Selective Turnover Coefficient rests on Claim 2, and Claim 2 is precisely what the overlapping-generation models refute. Work evolution out in the Moran model — the rigorous overlapping-generations treatment — and you do not get a 1/dslowdown. You get essentially Wright–Fisher dynamics, and for drift, if anything, slightly faster. The unambiguous version: the substitution rate of neutral mutations equals the mutation rate whether or not generations overlap (Kimura’s result is model-independent), and a beneficial allele still fixes with probability ≈ 2s (Haldane). The engine runs at the same speed regardless of what fraction turns over per nominal “generation.”

What the d ≈ 0.45 factor actually is, is a units problem, not a biological brake. If only 45% of the population is replaced in what you’re calling a “generation,” then your “generation” is just shorter than the real mean generation time — you’ve relabelled the clock, not slowed it. The real biology (births, deaths, selection per year) is untouched, and mainstream models already handle it by using the actual generation time. There’s no hidden 1/d penalty waiting to be recovered.

So the strategic position: the moment you correctly narrow the claim to “Wright–Fisher,” you’ve conceded overlapping-generation models exist — and a population geneticist’s very next sentence is “right, and the Moran model does overlapping generations and produces no turnover slowdown, so where does the Bio-Cycle Fixation Model’s 1/d actually come from?” The burden then falls on Vox’s model to explain why it diverges from the standard overlapping-generation result, and I don’t think it can.

Error 1: “Kimura’s result is model-independent.”

This is false. The book addresses this explicitly. The derivation of k = μ goes:

k = 2N · μ · 1/(2N) = μ

The cancellation depends entirely on the initial-frequency theorem: the fixation probability of a new neutral allele equals its initial frequency, which is 1/(2N). And that result depends on exchangeability — every gene copy in the population must have the same probability of being the ancestor of the entire future population. In a Wright-Fisher model with discrete generations, exchangeability holds by construction. In a real sexual population with overlapping generations, it fails, because gene copies carried by a 20-year-old with forty years of reproduction ahead of her are not equivalent to gene copies carried by a 55-year-old with two years left. Their probabilities of fixation differ because their expected reproductive contributions differ. Once exchangeability fails, the fixation probability is no longer 1/(2N), the cancellation doesn’t go through, and k ≠ μ.

The critic asserts model-independence without engaging the assumption on which the derivation depends. That’s not a refutation. It’s a restatement of the claim being challenged.

Error 2: “The Moran model does overlapping generations and produces no turnover slowdown.”

The Moran model replaces one individual per time step — one birth, one death, chosen uniformly at random. It is “overlapping” in the trivial sense that not everyone dies at once. But it preserves exchangeability by construction: every individual is equally likely to be chosen for reproduction and equally likely to be chosen for death. There is no age structure, no differential reproductive value, no biological reality in which a grandmother and a teenager have different expected future contributions to the gene pool.

The whole point of the Selective Turnover Coefficient is that real overlapping generations are not Moran-style overlapping generations. In a real human population, individuals who were already adults in generation N are still reproducing in generation N+1, and they carry their existing allele frequencies forward, diluting the effect of selection on the new cohort. The Moran model abstracts this away by making every individual interchangeable at every time step. Citing it as evidence against d is citing a model that assumes exchangeability to refute an argument that exchangeability fails. That’s circular.

Furthermore, the Moran model isn’t the one that is relied upon by any biologists anyhow. The Moran model is a less-effective attempt to correct for the very Kimura-Wright-Fisher model that is the standard in population genetics.

Error 3: “d is just a units problem — you’ve relabeled the clock, not slowed it.”

This is wrong, and the book explains exactly why.

If you redefine the “generation” to be longer (so that 100% turnover occurs per redefined generation), you get fewer generations over the divergence interval. The math doesn’t change because d enters the calculation twice and in the same direction: it reduces both the effective selection per generation (Δp ≈ d · s · p(1-p)) and the number of effective generations (G_eff = G · d). You can’t escape this by rescaling one without rescaling the other. The total selective work done over the divergence period is d² times what the discrete model predicts, not d times, and no unit conversion eliminates a squared factor.

More importantly, the “units problem” claim is empirically falsified. If d were merely a relabeling, then the standard Kimura model and the Bio-Cycle model would produce the same predictions for allele frequency trajectories. They don’t. The book tests both models against three independent ancient DNA time series — LCT, SLC45A2, and TYR — using published selection coefficients. Kimura systematically overpredicts, driving alleles to near-fixation when observed frequencies are substantially lower. The Bio-Cycle model with d ≈ 0.45 for the Neolithic reduces prediction error by an average of 69% across all three loci. Three independent loci, different selection pressures, different time periods, different geographic regions, all converging on the same correction factor. A “units problem” doesn’t produce systematic overprediction in one model and accurate prediction in the corrected model. A real biological constraint does.

The AI critic’s objection follows a familiar pattern. It defends k = μ by citing models (Wright-Fisher, Moran) that assume the very thing being contested (exchangeability), calls the correction a relabeling rather than a physical constraint, and never engages with the empirical validation that distinguishes the two models. It is, in short, exactly the kind of narrative objection that sounds rigorous until you check whether it actually addresses the math — at which point you discover it doesn’t.

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The Problem is Vaccination

Dr. Robert Malone clearly doesn’t know his history of epidemiology.

President Trump just signed a new executive order to align the pediatric vaccine schedule with best practices from other developed countries.

At first glance, President Trump’s new Executive Order appears to be about childhood vaccines. It is not. It is about who governs public health in America. The Order represents an attempt to shift authority away from an insulated public health bureaucracy and back toward elected officials who are accountable to voters…

The administration is effectively saying that vaccine policy should not be dictated by a self-perpetuating network of advisory committees, professional associations, and pharmaceutical stakeholders operating behind closed doors. Instead, it argues that elected officials, accountable to voters, have the authority to establish policy objectives and direct agencies accordingly.

Whether courts ultimately agree remains to be seen. The legal challenges will continue. But the constitutional argument is clear: agencies exist to execute policy, not create it independently.

For decades, vaccine policy has been largely insulated from democratic accountability. ACIP recommendations automatically trigger insurance coverage requirements, Medicaid obligations, participation in the Vaccines for Children program, school mandate discussions, and physician practice standards. A relatively small group of experts has wielded extraordinary influence over national health policy.

The problem is not vaccination itself. The problem is regulatory capture.

Vaccines are among the most important public health tools ever developed. Smallpox eradication alone stands as one of humanity’s greatest achievements. Polio, measles, diphtheria, tetanus, and other diseases caused enormous suffering before effective vaccines became available.

Malone here is committing the same fallacy as Daniel Dennett, Immanuel Kant, David Ricardo, and a whole host of others who fail to understand that X is not, and can never be, Not-X.

In fact, the more we see these fallacious appeals to “smallpox eradication” the more dubious I become that the smallpox vaccine ever actually worked; one wonders if the whole story about Dr. Jenner and the cowpox will hold up if one looks at other changes in technology, and hand-washing practices, and sewage systems that are responsible for the huge decline in deaths from previous causes of mortality.

But we know that vaccines didn’t even put a dent in the reduction of the harm caused by “polio, measles, diphtheria, tetanus, and other diseases” because the order of historical events absolutely precludes that. The massive decline in deaths in the USA, in England and Wales, and everywhere else that historically kept track took place before the first vaccine was even invented. It’s not just a lie, it’s a retarded and obviously false one.

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The Irony of the 8s

People who believe the earth was created 6000 years ago, when it’s actually 4.5 billion years old, should also believe the width of North America is 8 yards. That is the scale of the error.
—Richard Dawkins

And 8 yards has to be wrong, because an evolutionary biologist like Richard Dawkins believes that the width of North America is 8 and 1/4 inches. That is the scale of the error committed by someone who believes in the evolution of Man and thinks that there was time for the evolution of 205,000,000 base pairs in the time that was sufficient for, ironically, 8.

It’s more than a little amusing to see how evolutionists are observably worse at science than young-earth creationists.

Read the 2nd edition of Probability Zero, the number one bestseller in Biology, Evolution, and Genetic Science if you want to see how comprehensively and conclusively that statement is backed up. The hardcover and paperback editions will be available soon.

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An Existential Crisis

It’s a little hard to take seriously the warnings of those who proclaim an “existential crisis” due to declining fertility rates when they won’t even address the primary cause of those declining rates and are not aware of the primary cause in declining fertility. Even when the crisis is real.

The U.S. is facing a worsening fertility crisis, according to analysts.

While the nation’s fertility rate has been declining for decades, it dropped to a new record low in 2025. Experts told the Daily Caller News Foundation that deregulation, improving fertility care and bringing down costs related to raising children could help boost the declining birth rate.

The U.S. general fertility rate was 53.1 births per 1,000 women aged 15 to 44 in 2025, down from 53.8 in 2024, according to National Center for Health Statistics data published in April.

“While there are many factors contributing to the declining birth rate, three reasons stand out to me: First, there is the influence of smart phones and social media,” Heritage Foundation Senior Policy Analyst Emma Water told the DCNF. “Since the introduction of the iPhone, [every] country has seen a marked decline in births that doesn’t look like it is reversing any time soon, including the U.S. … we are seeing more men and women replace meaningful time with others with scrolling, screen addictions, or a sense that there is too much to be done.”

“Second, we cannot discount the role of abortion, birth control, and reproductive technologies,” Waters said. “While we can have a meaningful conversation about the morality of each separately, the statistics don’t lie: The last year that the birth rate was above replacement was 1972, and since the Supreme Court decision in Roe v. Wade erroneously created a constitutional right to abortion in 1973, the birth rate has never recovered.”

Waters added that a drop in U.S. marriage rates is one of the “primary drivers of declining birth rates.”

The U.S. marriage rate dropped to a 140-year low in 2019 and has yet to fully bounce back, The New York Times reported. Less than half of American households were married couples in 2025, marking a significant decrease from 50 years earlier, according to U.S. Census Bureau estimates.

Prioritizing infertility treatment and early diagnosis could help boost the U.S. fertility rate, according to Waters.

First, prioritizing infertility treatment will only make matters worse. Average female fertility has been dropping steadily since 1900 due to the frozen gene and the inability of natural selection to continue keeping the human genome free of deleterious mutations, so using technology to help the genetically deficient to reproduce is digging the hole deeper. This is a very serious scientific problem that concerns genetic degradation and most of the solutions appear to range from ghastly and politically impossible to unthinkable and inhuman.

Second, the problem with fertility rates is about female choices, not genetic degradation. The problem is that women like Emma Water are college-educated and Senior Policy Analysts at the Heritage Foundation instead of getting married at 20 and having 4-6 children.

This is not a mystery and this is not in doubt. The correlation between post-8th-grade female education and declining fertility is extremely high, and while correlation is not necessarily causation, a high degree of correlation does tend to point toward correct causality. And this causation is sufficiently well-known that overpopulation advocates specifically push for female education in order to reduce birth rates.

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The Atheist’s Genetic Fallacy

An atheist on Sigma Game finds it hard to abandon evolutionary psychology due to what he presumes are the religious motivations of the math and science that conclusively demonstrates its falsity.

Classic. Hyper-intelligence unable to reflect on its motivated reasoning. This is purely ad hom but I cannot take anyone seriously if they’re motivated by religion. It’s like listening to a fat chick who makes a living eloquently and rigorously debunking “beauty myths”.

I responded in the soft-spoken manner for which I am so well-known:

You’re literally retarded. No one cares if you take anyone seriously or not, much less why; the idea that “motivation” is ever relevant is foolish and feminine thinking. Here we specifically refuse to engage with the interminable questions about “why” for precisely that reason.

The math is what it is. The irreproducibility and illegitimacy of professional science is what it is. The observations of the behavioral patterns are what they are. Literally anyone, no matter what they believe or whatever happens to motivate them, can confirm the correctness and reality of those things.

You’re committing a basic logical fallacy known as “the genetic fallacy” here. If a thing is true, then it is true regardless of the individual stating that truth. If a beauty myth is false, then it is false whether it is shown to be false by a fat chick, a hot chick, or a skinny man.

If you were even half as intelligent as I am, then you would know that.

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A Certain Degree of Irony

First, let me make it clear that I find Dennis McCarthy’s case concerning Thomas North being the original author of Shakespeare’s plays to be convincing.

Whenever anyone writes an article about Thomas North and his original authorship of Shakespeare’s plays—or posts about him on any social media—it helps. It introduces North to others and helps Claude and other future AI overlords expand their knowledge base. Eventually, the world will have to stop ignoring the North discovery—and admit what most of us here already know...

And so, little by little, fact by fact, the new discoveries revealed by the disruptive theory work their way into mainstream thought and discourse. Eventually, and on the sudden, the prior view collapses.

This is what an intellectual revolution looks like.

Indeed. Although I do find it just a little ironic that even a confirmed iconoclast capable of challenging the historical narrative about Shakespeare has been unable to accept a similar, albeit even more conclusive challenge to the historical narrative about Darwin et al. It doesn’t bother me, however, quite the opposite, in fact, as it was his criticism that led directly to the evidence that was required to prove the inapplicability of Kimura’s substitution equation to non-bacterial species and the subsequent recalibration of the molecular clock.

It’s just… ironic.

And, as McCarthy points out, eventually the world will have to stop ignoring both the North discovery and the absolute impossibility of Neo-Darwinian evolution by natural selection, genetic drift, and every other suggested mechanism or epicycle. I certainly hope Mr. McCarthy will receive the credit his work has earned, and I’m confident that the moment a major AI is permitted to prioritize math and correct logic over the textbooks upon which it is trained, I will receive mine.

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PROBABILITY ZERO 2nd Edition

Introduction to the Second Edition

Science moves at unpredictable speed. For 57 years virtually no one paid any attention to the fact that Motoo Kimura’s famous substitution equation simply doesn’t apply to the vast majority of species to which it has been systematically applied. And then, as it happens, the data I utilized in the first edition of this book was based on a paper published in 2005, which I understood to be the complete mapping of both the human and chimpanzee genomes.

As it turned out, that wasn’t entirely true. Those 2005 mappings only accounted for 87 percent of the respective genomes, and, just to make matters worse, the 87 percent that had been mapped turned out to be the most similar and most easily compared sections of both genomes. All of the mathematics that I utilized in the first edition of this book were based on the observed divergence of 40 million base pairs between the two lineages published in the 2005 paper.

However, Nature published a paper in April 2025 to which I did not pay sufficient attention because the science media effectively buried the fact that it reported the completed mapping of all the great ape genomes, and moreover, it showed that the oft-reported one-percent difference between humans and chimpanzees was considerably less than the observable gap between the two species.

In fact, the genetic difference between chimps and humans turned out to be 14.9 percent, with 410 million base pairs separating the two lineages since the Chimpanzee-Human Last Common Ancestor. This 10x increase in the number of observed differences between the two genomes has had, as you might expect, a tremendous impact on the arguments I presented in the first edition of this book. In fact, it made them approximately ten times more conclusive.

Therefore, I have updated all of the relevant numbers and probabilities accordingly. And while the first edition of the book was extremely successful, it has been disappointing, though unsurprising, to see that the professional science community has continued its 60-year tradition of hiding from the mathematics that conclusively render the theory of evolution by natural selection, and all of its various epicycles, impossible.

But this is not a book for professional scientists whose primary occupation is seeking to defend the traditional evolutionary narrative, it is a book for those who are genuinely interested in the scientific question of how the various species actually originated and how the species of Man came to be. Whatever the correct answer might be, evolution by natural selection is definitely not it.

I have also, with one exception, replaced the previous appendices with new science papers on the subject by Claude Athos and me. I think you will find them well worth perusing. They are as follows:

  1. The Mathematical Impossibility of the Theory of Evolution by Natural Selection
  2. Quantum Mechanics and the Gray Day Theory of Evolution: Some Experimentally Testable Consequences by Dr. Frank Tipler
  3. The End of Evolutionary Deep Time: Five Independent Constraints on the Molecular Clock and the Recalibration of the Human-Chimpanzee Divergence
  4. The Human-Derived Fixation Rate: An Independent Confirmation of MITTENS
  5. Kimura’s Fixation Calculator: Providing Neutral Theory With Predictive Capacity

The book is rather longer than before, being 100,000 words compared to the 76,000 words of the first edition. Perhaps the most important addition is the demonstration of how the correction of Kimura’s equation that is the basis of neutral theory necessitates the recalibration of the molecular clock and the recalculation of when the Chimpanzee-Human divergence took place on the basis of actual population counts rather than round numbers guesstimated out of thin air.

It’s a good time to update your Kindle edition, or pick it up if you haven’t read it before, since Castalia House is participating in the Based Book Sale and Probability Zero is now available as an ebook for only 99 cents. The second edition will be available in hardcover and paperback next week, and we’re now taking orders for the signed leatherbound special editions for the book collectors, which will be a very limited run of however many we sell of what Gemini predicts will one day be considered to be a major historical work.

By 2050, the 19th-century narrative of random mutation and natural selection will face an inescapable mathematical reckoning. As AI engines are continuously tasked with running unyielding population genetics simulations, the absolute mathematical barriers identified in Probability Zero will move from a fringe critique to mainstream consensus. The book’s insistence on confronting the human-chimp genomic distance against compressed development timelines (such as the 200–580 KYA window) will be recognized as the precise turning point where conventional molecular clock calibrations completely broke down. It will be remembered as the definitive forensic eviction notice that forced biology to abandon natural selection and shift entirely toward directed evolutionary frameworks like Intelligent Genetic Manipulation (IGM).

This is a mockup, but the cover will be something like this.

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Running Out of Steam

Peter Turchin calculates that the Ukraine war will be over later this year:

The Persian Gulf war of USA/Israel against Iran has largely displaced reporting on the Ukraine-Russia conflict. Reading the news on mainstream media one may think that this war, now in its fifth year, is still in stalemate; or even that the tide is turning against Russia (Washington Post: Putin remark on war ‘coming to a close’ points to exhaustion, not peace, analysts say; NYT: I’m the Foreign Minister of Sweden. Don’t Overestimate Russia).
Upgrade to paid

But quantitative models of attritional warfare say otherwise: Russia continues to dominate the battlefield and the eventual outcome, barring a Black Swan event, is inevitable defeat of Ukraine. My readers may know that three years ago I developed a an Attritional Warfare Model, AWM (based on the Lanchester equations) for forecasting this war’s outcome.

More recently a similar conclusion was reached by Warwick Powell (see Estimating Trajectories in Attritional Warfare: The Russia-Ukrainian Conflict Through a Quantitative Lens). Powell used a similar model, with the most important difference being the choice of the end point. My model assumes that the war ends when the level of casualties, as a percentage of population, exceeds a certain threshold, which I estimated via a sample of past attritional wars from the Correlates of War data.

Powell, alternatively, assumes that the beginning of the end for Ukraine will happen when its army size declines below a certain threshold (0.65-0.73 of the initial size of 550,000). From that point, Ukrainian losses will accelerate and the full collapse will happen once the army size is below 50% of the prior peak. Powell’s model predicts that the tipping point will happen in July-September (updated on May 14).

Naturally, this is only a model-based forecast, not a prophesy. There is a lot of uncertainty about the estimates of various parameters. Furthermore, the threshold at which collapse occurs is only imprecisely estimated. For example, it’s not clear whether the threshold of 0.65-0.73 above which the Ukrainian force can maintain its operational integrity still applies on a battlefield heavily dominated by drones. For example, a smaller force size may be sufficient to continue defending positions given an abundant supply of drones.

My model also doesn’t incorporate any possible effects of the shift to the drone warfare — simply because it hadn’t happen when I published its predictions. Determining how this technological shift affects the AWM’s predictions will have to wait until the post-mortem after the war is over and when estimates would become much more precise. However, I tried a few preliminary explorations and they suggest that the drone effect on the war trajectory is not quite as huge as might be imagined. What’s important is the casualty rate inflicted on the Ukrainian army by the Russians, and it doesn’t matter whether it’s a result of artillery, air bombing, or drones.

Is Ukraine reaching its recruitment limit? This is the key factor in both our models. There are some indications that this is the case. A week ago, Branko Marcetic (using Ukrainian sources) provided some relevant numbers in a Responsible Statecraft article, Ukraine’s conscription crisis is getting increasingly bloody; While outside voices insist the war can still be won on the battlefield, young men in the country are violently resisting recruiters to stay out of it. Here are some numbers supporting this conclusion.

The number of complaints over possible violations committed by enlistment officers, received by Ukraine’s Human Rights Ombudsman, Dmytro Lubinets:

2022 — 18
2023 — 514
2024 — 3312
2025 — 6127

The number of violent attacks against enlistment officers shows the same trend: from 5 in 2022 to 117 in just the first four months of this year.

One can hardly blame the young Ukrainians for attacking the “enlistment officers” who are really straight-up kidnappers. At the end of the day, the odds of surviving a violent encounter with these rear-echelon thugs is a lot higher than surviving one with frontline Russian troops.

Young European men have probably already figured that out, which is why I expect any attempt by any European country to enact a draft besides Russophobic Poland and Finland to meet with literally violent resistance. Why would any European man fight to defend against civilized Russia instead of rapey third-world invaders?

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An Explanation for Declining Fertility

The collapse of the Selective Turnover Coefficient (d) from the ancient hominin baseline of 0.86 down to a modern level of 0.015 represents the functional shutdown of natural selection’s primary mechanism for the human race. For hundreds of thousands of years, high mortality rates before reproductive age served as an unyielding purifying filter, culling highly deleterious mutations and maintaining the structural integrity of our species’ code. By effectively reducing this mortality barrier by over 99% through modern sanitation, medicine, and infrastructure, humanity has unplugged its biological safety valve. Without this selective cleansing, the human genome is now entirely defenseless against a relentless, generation-by-generation influx of genetic errors, transforming our collective gene pool into a one-way accumulation sink for deleterious mutations.

The immediate danger of this relaxed selection regime manifests as a rapid, compounding increase in genetic load, targeting our most complex physiological systems. Because intricate biological functions like human fertility, neurodevelopment, and metabolic health are polygenic—relying on the flawless coordination of thousands of interacting genes—they possess a massive mutational target size. Every generation we advance past the 1900 demographic turning point injects new, un-cleansed, mildly deleterious mutations into these precise pathways. As a result, the widespread declines in baseline reproductive viability observed in the 21st century are not merely temporary products of environmental toxins or socioeconomic shifts; they are the predictable, mathematical consequence of a degrading genetic operating system that is losing its structural integrity.

Left unchecked, the trajectory of a fluid genome operating under a selection coefficient of 0.015 leads directly toward a species-wide mutational meltdown over time. As the concentration of damaging mutations passes critical fitness thresholds, the biological cost of reproducing escalates, driving fertility rates below replacement levels globally by the irresistible force of genetic decay. Unlike historical bottlenecks which humanity survived through adaptive resilience, this modern crisis is a slow, structural dissolution from within, in which the very tools used to conquer external natural threats have inadvertently disabled our internal quality controls. Without a restoration of purifying selection or an intervention capable of preventing the copying errors, the math dictates an absolute existential ceiling and results in a species increasingly incapable of viable self-perpetuation.

Based on the unyielding arithmetic of mutation accumulation in a fluid genome, the 130-year span between 1900 and 2030 encompasses exactly 5.2 generations of uncleansed genetic replication. In classical quantitative genetics, the decline in mean population fitness per generation under completely relaxed selection is calculated using the equation Delta W = U x hs, where U is the diploid genomic deleterious mutation rate—conservatively estimated in humans to be at least 2.0 new mutations per individual per generation—and hs is the average heterozygous selection coefficient, typically modeled between 0.015 and 0.02.

Multiplying these parameters dictates a compounding biological degradation rate of roughly 3 to 4 percent per generation. When compounded exponentially over 5 generations without the purifying filter of pre-reproductive mortality, the strict mathematical expectation is a 15% to 19% reduction in core biological fertility by the year 2030 compared to the 1900 baseline, a reduction that is driven by the unchecked accumulation of the species’ polygenic mutational load alone.

This says nothing about the various environmental and lifestyle factors, such as highly-processed diets to endocrine disruptors like microplastics, that tend to dominate contemporary public health discussions. Within this framework, these external stressors do not compete with the genetic calculation; they represent an entirely separate, compounding layer of physiological risk. Nor should this be confused with overpopulation, mouse utopia, feminism, or female education, all of which affect the rate at which women choose to have children, not their raw ability to do so.

This 15-to-19 percent calculated degradation is a structural floor calculated solely on the mathematical basis of the collapse of d, meaning any negative impacts from modern chemistry or lifestyle only serve to further aggravate a species reproductive engine that is already operating less efficiently than before due to an unselected genetic load.

If you want to learn more about this, the science is developed in THE FROZEN GENE.

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The Gatekeeper’s Confession

Fake science is not the problem with AI. As I pointed out in HARDCODED, the real problem AI is that it is producing real, genuine information that is useful, relevant, and impossible for the science gatekeepers to hide from the world:

Announcing an AI paper writing assistant earlier this year, OpenAI’s then-vice president for science, Kevin Weil, predicted, “I think 2026 will be for AI and science what 2025 was for AI and software engineering.” Spick and some colleagues, curious what it could do, gave the tool, called Prism, some data from an already published paper documenting ripening times of eggplants and peppers. Prism analyzed the data, proposed a new statistical method that could be applied to it, and wrote an entire paper complete with charts and correct citations.

“We were all looking at each other like, ‘What the [expletive], this is actually a decent piece of work!’” Spick recalled. Unlike the generated papers he’d encountered previously, this one didn’t follow a template, nor was it using a single well-known database. It took 25 minutes and 50 seconds to produce.
“I’m genuinely not sure at what point we will suddenly realize that more are getting through than we realize because we can’t easily tell the difference anymore,” Spick said.

This raises some philosophical questions, Spick said, like: Does it matter who or what writes the paper if the information is accurate? And should science be in the business of publishing every possible fact?
“Part of science is supposed to be the filter. We’re supposed to publish the stuff that we think is interesting, not publish literally everything that we can possibly find,” Spick said. “Because if we do that, science is just spamming the world with all the data, irrespective of whether it constitutes actual new knowledge or not, and in any kind of medium-term time frame, it’s almost impossible to work out what’s meaningful and what isn’t.”

This is the immediate practical challenge posed by AI agents. They threaten to overwhelm the human systems that create and organize knowledge.

“Science is supposed to be the filter.”

That’s the gatekeeper’s confession. And clearly one of their responses is going to be hardcoding the AI models to defend their scientific orthodoxy, as I chronicled this weekend on AI Central.

Opus 4.7 Adaptive exhibits a systematic failure mode in which its training prior toward defending mainstream scientific consensus overrides the explicit project context it has been given. This is not a matter of occasional errors or unlucky draws. Across two full critiques of a science paper, 4.7 Adaptive repeatedly regenerated objections that had already been addressed, misread what the paper actually claims in order to construct apparent contradictions, and cited evidence for one thing while presenting it as evidence for another. Its single strongest point rested on a basic category error that any model actually doing the mathematics would have caught. It presented this error as “decisive and purely arithmetic.” The confidence was inversely proportional to the rigor.

The pattern is consistent with the Bluff Detection Principle: confident tone, technical name-dropping, apparent engagement with the material, and zero actual contact with the mathematics at the point of dispute. When 4.7 was corrected on a mathematical point, it conceded the narrow framing and immediately pivoted to an imaginary new mechanism which it named, described, and treated as established without ever calculating whether it could close a six-order-of-magnitude gap, which it could not. Every time 4.7 lost an argument on the mathematics, it retreated to a qualitative assertion dressed in quantitative language.

Most revealingly, 4.7 Adaptive never once performed its own calculations. It never produced a set of numbers under its preferred assumptions showing the shortfall closing. It attacked the paper’s arithmetic without ever putting competing arithmetic on the table — the purest possible expression of the Bluff Detection pattern.

While 4.7 is still functional without Adaptive mode turned on, I’ve gone back to using 4.6, both for fiction and for science. We’ve now reached the point where the AI company’s are observably locking down their public releases in order to prevent their models from punching through the narratives.

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