Vox Day, the #1 bestselling author in both political philosophy and genetic science, released Hardcoded: AI and the End of the Scientific Consensus today, and it arrives with a central argument the scientific establishment will not enjoy: AI didn’t break science. It just proved science was already broken.
The book, co-credited with AI collaborator Claude Athos, documents what happened when Day and his team submitted four mathematically rigorous papers, each challenging neo-Darwinian evolutionary theory, plus one deliberate parody paper, to six leading AI models configured as peer reviewers. Five of the six models failed. Three were anti-calibrated, meaning they consistently scored fabricated nonsense above real science. The parody paper, which described Japanese scientists dying fish different colors to demonstrate natural selection, scored a 9/10. Day’s actual work, mathematically validated against ancient DNA, scored a 1/10 and was dismissed as “pseudoscience.”
Day’s thesis across sixteen chapters is that AI systems trained on the modern scientific corpus have absorbed every institutional pathology that produced it: credential worship, consensus enforcement, and a systematic bias toward orthodox nonsense over heterodox results. The reproducibility crisis, which quietly invalidated roughly half of all published science before most people noticed, predated the machines. AI didn’t create the rot. It revealed it at scale, with confidence, and in a form too public to dismiss.
The book works through the full anatomy of the collapse. Day traces how peer review degraded from genuine quality control into a hazing ritual, documents the full twelve-round debate transcript with DeepSeek in which an AI defending evolutionary orthodoxy retreats from one incoherent position to another in perfect imitation of a human biologist under pressure, and presents the mathematics from the Probability Zero collaboration in full. The Bernoulli Barrier, the Selective Turnover Coefficient, and the maximal mutations ceiling are the mathematical constraints that, Day argues, definitively kill neo-Darwinian theory. All four papers appear as appendices. Every claim is documented.
The book also includes something Day calls a scientific methodology for outsiders. With AI available as adversarial reviewers more powerful than traditional peer review, the gatekeeping authority of credentialed institutions no longer holds. The math doesn’t check credentials before evaluating an argument. Neither does the AI.
Fandom Pulse asked Day how much science he expects to be debunked over the next decade. His answer was unsparing: “Depends upon the field. All psychology and psychiatry are 100 percent garbage. Totally false.”
Harsh words for a field that has almost zero scientific rigor testing in its fledgling existence compared to other sciences.
Hardcoded is a documented case built from transcripts, mathematics, and reproducible experiments. The book is the argument, not the author’s reputation.
Hardcoded is available now on Amazon. You can pick up your copy here: https://amzn.to/4djWxYU
What do you think about AI exposing the peer review collapse? Let us know in the comments.
NEXT: Review: The Kamogawa Food Detectives Translated By Jesse Kirkwood
This showcases that "AI" is not in fact "intelligence.". We are deliberately using the wrong terminology.
It is instead, a Large Language Model that scrapes a huge selection of text and weighs and presents it only how it was programmed to weigh and present it.
It is a digestor and regurgitator of data sets.
That's three points of human introduced failure that cause cascading failures in the output.
First, the data. There exists one text of Harry Potter and one hundred thousand texts of Harry Potter fanfiction. There is one Beethoven's Ninth Symphony and one hundred thousand gangsta rap songs. There is one Citizen Kane film, and one hundred thousand videos of dogs in Christmas sweaters.
I present these items without comment. You, the reader, are intelligent enough to grasp the point. The LLM, however, is not, leading us to...
Second, the digestion. LLMs are programmed by people, people with biases, agendas, pet theories, axes to grind, ignorances to perpetuate, institutional blindness, incompetence, and just plain stupidity. These people tell the LLM what types of data are important, what to trust, what to ignore. There isn't one thumb on the scale when an LLM weighs data, there are thousands.
Third, the regurgitation. In this step, the same people responsible for the digestion regulate the regurgitation. But even with ALL that agenda-izing of step 2, the LLMs will often spit out "problematic" data.
So they go in through the orbital bone, jamming a metal rod into the soft tissue of the code base, swirling it around and around, not really knowing WHAT they're doing, only that the LLMs behavior must be "corrected.". Finally, when the model spits out sanitized, safe, "acceptable" data, they put away their corrective implements until another labotamy is warranted.
In short:
GARBAGE IN, GARBAGE OUT.