How Darwinism Became a Pseudoscience
Magpies by Archibald Thorburn 1905
Evolutionary biologist, Bret Weinstein, has a reputation for some pretty wild ideas outside his area of expertise that I might not want to defend. But when it comes to some comments he made a few months ago, within his own field of expertise, he was spot on when he provided a disturbing reality check on the current state of Darwinism and, as a scientist, I have my own reasons why. He stated,
“In my opinion, the mainstream Darwinists are telling a kind of lie about how much we know and what remains to be understood … I think modern Darwinism is broken. Yes, I do think I know more or less how to fix it. I’m annoyed at my colleagues for, I think, lying to themselves about the state of Modern Darwinism. I think I know why that happened. I think they were concerned that a Creationist worldview was always a threat … and so they pretended that Darwinism was a more complete explanation, as it was presented, than it ever was …” (1)
As a biophysicist specializing in the information encoded in the DNA of protein-coding genes, and how that information prescribes the 3D structure of proteins, I have my own reasons for agreeing with Weinstein on this issue. Although he thinks he knows “more or less how to fix it”, my knowledge of the extreme difficulty (a colossal understatement) of locating sequences that will code for a functional protein with a stable 3D structure, tells me that neither he nor anyone else is going to fix it (although I would be fascinated to hear his proposed solution).
That said, there are some phrases in his statement that deserve to be pointed out: “telling a kind of lie,” “broken,” and “lying to themselves.” Note, also, the motive for promoting this broken theory.
To be clear, I am not suggesting that Darwinists are conspiring to deliberately corrupt science and mislead people, although such corruption and misleading is certainly happening. Rather, scientists who should know better, but continue to promote Darwinism, are living in denial, motivated by their own a priori commitment to materialism and scientism driven by an antagonism against any possibility that there is an intelligent mind behind the software encoded in the genomes of life.
About thirty-five years earlier, I sat in the office of a professor of evolutionary biology at a major Canadian university. I had noticed three predictions of Darwinian evolution that appeared to be consistently falsified by experimental evidence so I asked him about it. To my shock, he admitted that all three were major problems for which we had not yet found satisfactory answers. Keep in mind that this was thirty-five years before Bret Weinstein’s interview. The main difference between then and now is that advances in science have made the problems even more obvious.
Eugene Koonin, an internationally-recognized evolutionary biologist published a paper in 2007 admitting that even RNA replication, a proposed stepping stone to life, was so impossibly improbable, that it was unlikely to occur anywhere in the universe.(2) His solution was to propose an infinite multiverse, which would offer an infinite number of opportunities to solve this problem. It is especially interesting to read the reviewers’ comments. They expressed the fear that intelligent design might be seen as a better explanation for the origin of life. Consequently, having proposed an infinite number of universes, the concluding sentence of Eugene Koonin’s paper absurdly states that his hypothesis of an infinite multiverse “leaves no room whatsoever for any form of intelligent design.” One might think that a mind, which we know exists given that we all have one, might be a better ‘Ockham’s Razor’ option rather than proposing an infinite number of untestable universes (3) but rational deliberation is often sidestepped in pseudoscience.
If one wonders what the driving force behind Darwinism is, and what the motive might be for Darwinists to be “telling a kind of lie”, as Weinstein puts it, one might want to think about both Koonin’s denial of any kind of intelligent design behind life, and what Weinstein said about the so-called “threat” of the theory of intelligent design as an explanation for the origin and diversity of life. This “threat” and denial has resulted in Darwinists lying to themselves and the public. The result is that Darwinism has degenerated into pseudoscience.
Variation vs Darwinism: The average person tends to understand the word “evolution” as the theory that the full diversity of plant and animal life has descended from a very simple cell that somehow arose in the past and began to self-replicate. People are often confused to learn that evolution is often defined as variation in a population of plants or animals over time, via a combination of genetic drift, mutations,(4) and natural selection (Note: In this article, “mutation” includes insertions, deletions, duplications, inversions, and translocations.). Since we observe variation throughout nature, a Darwinist can look you in the eye, and confidently state, “evolution is a fact!” But defining evolution as simply genetic variation over time is misleading. To avoid using the term ‘evolution’ in a misleading way, I will refer to variation within a population over time as, simply, variation. As for the neo-Darwinian theory of common descent, we will describe that belief as Darwinism.
How can we tell the difference?: Unfortunately, there is a disturbing lack of rigour in evolutionary biology when it comes to distinguishing between variation and Darwinism. I often observe Darwinists insist that the two are the same thing, but as science advances this belief has become obviously false. Rigour must be an essential component of good science and using the word ‘evolution’ to refer to two very different concepts is no exception. There is, in fact, a central, objective quantification of genetic change that allows science to clearly distinguish between variation and Darwinism. Absolutely central to life and its survival, reproduction, and variation, is the functional information encoded in the DNA of organisms.
Functional information: The concept of functional information as required by biological life was first proposed by Jack Szostak in a short article in the science journal Nature in 2003.(5) Four years later, he coauthored a more extensive technical article in PNAS with Robert Hazen et al. further defining functional information.(6) That same year I published an article in TBMM laying out how we can estimate the amount of functional information from actual data for protein families.(7) I used a more complete equation than Hazen et al. used, but for the general cases they discussed, the equation I used reduces to the identical one they proposed. To confirm this, I contacted both Robert Hazen and Jack Szostak directly, showing them the derivation of their equation from the more extensive one I used for protein families, and they each confirmed that I was correct and that we are all using the same general equation for measuring functional information.(8)
The difference between variation and Darwinism: When we quantify the observed change in functional information in genetic variation within a population over time, we observe that there is none. If there is any change, it is usually a loss of information if the deleterious mutations are not compensated for at the same rate as beneficial mutations. A statistically-significant increase in functional information has never been documented.
Darwinism requires novel protein families and gene regulation systems, all of which require enormous increases in the functional information encoded in the DNA of the new type of plant or animal. Some Darwinists will point to the gain of a new function as positive evidence for Darwinism; however, when the functional information required for these gains is calculated, it turns out that a gain of function can occur without any increase in functional information. Therefore, gain of function fails to correlate with whether or not there has been a corresponding gain of functional information.
To summarize, functional information is the key distinguishing factor between variation and Darwinism—the latter requires significant levels of new, protein-coding information, while variation within a population over time does not.
Pseudoscience: There are different categories of pseudoscience, but the type relevant to Darwinism occurs when a legitimate scientific theory makes predictions critical to that theory that are subsequently falsified, but the theory continues to be promoted as science, often with creative story-telling and the use of technical terms mixed with words and phrases such as “over time”, “is conceivable”, “probably” and many others which can be described charitably as words and phrases one uses when they do not have the data to back it up. Those scientists who promote this sort of pseudoscience are “telling a kind of lie” to themselves and to the public. Once you start to see them, you cannot ‘unsee’ them. Much of the origin of life literature is rife with them.
Critical predictions: For the scientific method, a theory should be testable by making predictions which can be potentially falsified. A critical prediction is one that, if that prediction is falsified, the entire theory collapses. One can find data that can be interpreted as ‘evidence’ for virtually any belief or theory. Evidence in favour of a theory, however, is trumped by falsification of a critical prediction.
As in any theory, one can find evidence supporting Darwinism, which textbooks dealing with Darwinism cover extensively, while at the same time completely ignoring or glossing over the critical predictions and their falsification. In this way, scientists “are telling a kind of lie about how much we know and what remains to be understood.” Emphasizing the evidence that supports a belief while glossing over or ignoring the evidence that falsifies it, is another characteristic of pseudoscience.
For example, some aspects of the fossil record, phylogenetic trees, and genetic similarities can be interpreted in a way that supports Darwinism, but the same observations are also predicted by the theory that there is an intelligent mind behind the software of life. In science, falsification of critical predictions entails that the evidence was misapplied. Due to creative story telling, the line between science and science fiction has been blurred in Darwinism to the point of being almost indistinguishable. What we need for a critical prediction is something that is both essential and unique to the theory such that if it is falsified, we are forced to declare that Darwinism is, in fact, broken, as Weinstein might put it. Falsification exposes the creative storytelling of Darwinism for what it is—“telling a kind of lie.”
Two critical predictions: The mechanisms invoked for Darwinism are the same as for variation, but with a highly significant exception—no significant limits to variation if one wishes to obtain the full diversity of life from a single, simple cell. This gives rise to the first essential and unique prediction:
P1: In general, there must be no limits to variation.
There may be limits along a particular evolutionary pathway, but to produce the sheer enormous disparity and diversity of life we observe, those limits must be relatively easy for blind natural processes to work around.
The second critical prediction arises out of the fact that Darwinism proposes life started as a very simple ‘minimal’ organism with maybe 450 protein-coding genes (9) and gradually evolved the functional information to code for approximately 20,000 protein families today.(10) To accomplish this feat, the process of mutation and natural selection would have had to start with zero genetic information and create the phenomenal amount of functional information needed to encode at least 20,000 protein families today. The second critical prediction, therefore, is as follows:
P2: The process of mutation must be capable of starting with a level of zero functional information and producing the required level of functional information needed to encode 20,000 functional protein families in the DNA of life.
Note that it is trivially easy to produce non-functional de novo proteins that are not capable of stable, repeatable 3D structures, and the functional information required to produce such de novo proteins is approximately zero. Functional protein families with stable 3D structures are inconceivably rare. They require, therefore, an enormous level of functional information to ‘define’ or encode.(7)
Testing P1: Darwinism requires many millions of years because Darwinian evolution has no direction ‘in mind’ and has no plan. It is a mindless, blind process that can be described as a random walk. To evolve in any direction, the information encoded within the DNA of that organism must be changed, lost, or added to by the mechanism of random mutations. If that random walk blindly stumbles on a change in information that does something to improve an organism’s ability to produce healthy progeny, that change might spread within a population and be preserved in a commonly-observed phenomenon we call natural selection. We can, however, massively speed up this process if we remove the blind, purposeless random walk and replace it with an intelligently-guided, selective breeding experiment, where the scientist can observe and select for desirable traits and even speed up the variation by increasing the mutation rate. In this way, we can experimentally test P1 by accelerating the proposed process of Darwinism to accomplish in just years or decades what would take a blind, mindless ‘watchmaker’ (11) millions of years. Selective breeding is practiced by labs all over the world, especially in the field of agriculture.
Falsification of P1: In every selective breeding experiment we have performed over the past century, where the objective is to see how far variation will allow us to go in some particular direction, we always hit a limit, every time, no exception. P1 continues to be falsified in ongoing selective breeding experiments all over the world. Now, advances in science reveal why. The problem is that to evolve anything of significance requires a massive leap in functional information to generate a sequence that will encode a novel, stable, functional, 3D protein and its regulatory system.To put a finer point on this, science has consistently falsified this first critical prediction and it has done so in the lab for over a century of countless experiments, no exceptions. Instead, if we go long enough, we end up with endless variations about some sort of average wild type and reach the limits of variation as observed in the famous Lenski Long Term Evolutionary Experiment (LTEE).(12) As in the LTEE, we may observe a gain of function if it requires no new functional information when it is achieved through a process of duplication and refinement of existing functional information).
Why does variation hit a wall? In order to get something substantially new, we need new genetic information that will code for novel protein families. Although producing novel functional information is trivially easy for intelligent agents, our expanding knowledge reveals that it is virtually impossible to do by any natural process. This is why we consistently hit a limit as to how far we can push in any direction, in selective breeding experiments. So, how hard is it to randomly change the protein-coding information to fortuitously stumble upon a novel protein family that produces a stable 3D structure that is useful to biological life?
‘Finding a new gene’: In the course of my Ph.D. program in biophysics, I developed a method to take real data from a protein family database, and estimate the level of functional information (7) required to encode that protein into the DNA by identifying the amino acid patterns for a protein family. Once we know the functional information required, we can derive the probability or target size in search space that we are looking for.(13)
Absurdly impossible: It turns out that although it is trivially easy for mutations to produce non-functional novel (de novo) proteins that have no stable 3D structure, those that produce functional, stable 3D-structures, pre-determined by physics,(14) are so rare we should not expect to see even one produced in the entire history of the universe.(15) Instead, mutation of existing genes is vastly more likely to produce misfolded proteins, which can form clumps called amyloids, which can be lethal or severley debilitating (think of mad cow disease, Alzheimer’s disease, and Parkinson’s disease, for example). To get a better understanding of just how rare these stable 3D proteins are, if we put all the amino acid sequences for a particular protein family into a box that was 1 cubic meter in volume containing 10^60 functional sequences for that protein family, and then divided the rest of the universe into similar cubes containing similar numbers of random sequences of amino acids, and if the estimated radius of the observable universe is 46.5 billion light years (or 3.6 x 10^80 cubic meters), we would need to search through an average of approximately 10^203 universes before we found a sequence belonging to a novel protein family of average length, that produced stable 3D structures. This is why we observe natural limits to variation in selective breeding experiments— it is because sequences of amino acids that yield novel protein families are mind-staggeringly rare - so rare that it would be absurd to expect a natural evolutionary process to ‘find’ even one, average protein family anywhere in the universe, over the entire history of the universe. It is also another reason why those who promote Darwinism are, in Weinstein’s words, “telling a kind of lie” if they are misleading themselves and the public into the belief that a blind, mindless Darwinist process can stumble upon novel, functional protein families with a stable 3D structure, and perform this astonishing feat around 20,000 times! There might be some pseudoscientific claims that might be somewhat plausible, but this Darwinist belief lies outside the extreme limits of plausibility by an outrageous margin. Yet many Darwinists continue to ignore or gloss over the real life data - another characteristic of a pseudoscience.
Falsification of P2: Mutations must, on average, slowly increase the amount of genetic information over millions of years if Darwinism is going to produce novel protein-coding genes. However, our observations reveal that mutations are doing exactly the opposite. The study of deteriorating genomes of various organisms indicate that nature is relentlessly destroying all life through the steady degradation of the functional information encoded in the DNA of all organisms. Darwinism predicts that if we were to plot functional information encoding protein families, vs time, the graph would show an average net increase in functional information over time. The reality is the opposite: gene loss due to mutations and deletions appears to be steadily and systematically destroying the genetic information of all biological life from bacteria (16) to the famous fruit fly (17) to humans (18). This is the exact opposite of what Darwinism requires, falsifying P2.
A Darwinist response to this is to point out that, yes, mutations can be harmful, but if it renders the organism unable to survive and reproduce, it will be eliminated by natural selection, leaving only those mutations that are not harmful enough to prevent survival and reproduction. Thus, the worst mutations are weeded out with each generation.
This is obviously true for lethal mutations, but most mutations are, in the short term, only slightly deleterious, or even neutral. Unfortunately, they accumulate generation after generation, creating a mutational load that increases with each generation resulting in steadily reducing the percentage of viable offspring, generation after generation, until there are insufficient viable offspring to continue the species and it goes extinct.(19) This process is occurring across biological life.
Summary: For Darwinism to be true, nature must provide an arena where novel, functional, stable, 3D proteins are relatively easy to find, and the rate of production of novel functional information encoded in the genomes of life is greater than the rate of destruction. The reality is that neither is true. The laws of physics determine that stable 3D protein structures are unimaginably rare and the natural processes of mutation leads to a net deterioration of whatever functional information is already out there. This why ever selective breeding experiment hits a limit if we try to see how far we can go. Nature ensures that the two critical predictions of Darwinism will continue to be thoroughly and consistently falsified. Darwinism began as a fascinating theory, but the falsification of its critical predictions means that those who continue to promote it are promoting a pseudoscience. They are, as Weinstein put it “lying to themselves” and “telling a kind of lie” to the public.
Ending on a positive note: As Weinstein stated, “Modern Darwinism is broken.” I am consistently asked what I have to offer as an alternative. My response is that we should go where the science points. In this case, the science shows that protein-coding genes require an impressive level of functional information, which is already encoded in the genomes of life. The only thing science has ever observed that can produce functional information are minds; we do it every time we message someone, write an essay, or write computer code. The fingerprints of an intelligent mind are all over the genomes of life in the form of the functional information encoded in the DNA. Science has no other observable, repeatable option; to ignore this is bad science. The ability to produce statistically significant levels of functional information is unique to intelligence minds. The key word here is “unique”; no other process has ever been observed by science. Even genetic algorithms require an intelligent mind to design the fitness function and are, thus, examples of intelligent design in action. I have written a short introductory article, ‘Why this scientist believes that intelligent design was required for biological life', presenting a positive, scientific method to test the hypothesis that the functional information found in life requires an intelligent mind. The job of science is to reverse engineer (20) the cell with its information-processing and gene-regulatory systems to understand how it all works.
Questions: From past experience, this article can result in a less than enthusiastic response from committed Darwinists who are uncomfortable with having their beliefs challenged. Consequently, the comments can be slightly subpar. If you have a question, please feel free to contact me and I will try to respond within a few days. If you just wish to find out more about me or my peer-reviewed publications, you can go to the “About Kirk” page.
References:
Bret Weinstein, February 2025, Joe Rogan Experience. The statement begins at the 1 hour and 55 minute mark.
Koonin, E.V. The cosmological model of eternal inflation and the transition from chance to biological evolution in the history of life. Biol Direct 2, 15 (2007).
See my article on Fantasy Science.
Loewe, L. (2008). Genetic mutation. Nature Education, 1(1), 113.
Szostak, J. W. (2003). Functional information: Molecular messages. Nature, 423, 689.
Hazen, R. M., Griffin, P. L., Carothers, J. M., & Szostak, J. W. (2007). Functional information and the emergence of biocomplexity. PNAS, 104(Suppl 1), 8574–8581.
Durston, K. K., Chiu, D. K. Y., Abel, D. L., & Trevors, J. T. (2007). Measuring the functional sequence complexity of proteins. Theoretical Biology and Medical Modelling, 4, 47.
Personal email correspondence.
Hutchison, C. A., Chuang, R.-Y., Noskov, V. N., Assad-Garcia, N., Deerinck, T. J., Ellisman, M. H., Gill, J., Kannan, K., Karas, B. J., Ma, L., Pelletier, J. F., Qi, Z.-Q., Richter, R. A., Strychalski, E. A., Sun, L., Suzuki, Y., Tsvetanova, B., Wise, K. S., Smith, H. O., Glass, J. I., Merryman, C., & Venter, J. C. (2016) Design and synthesis of a minimal bacterial genome. Science, 351(6280), aad6253.
Mistry, J., Chuguransky, S., Williams, L., Qureshi, M., Salazar, G. A., Sonnhammer, E. L. L., Tosatto, S. C. E., Paladin, L., Raj, S., Richardson, L. J., Finn, R. D., & Bateman, A. (2021). Pfam: The protein families database in 2021. Nucleic Acids Research, 49(D1), D412–D419.
Richard Dawkins used the term “blind watchmaker” as a metaphor to emphasize his belief that Darwinian evolution can produce amazing biological phenomena while all the while having no plan or goal or oversight other than natural selection. See Dawkins, R. (1986). The Blind Watchmaker: Why the Evidence of Evolution Reveals a Universe Without Design. New York: W. W. Norton & Company.
Blount, Z. D., Borland, C. Z., & Lenski, R. E. (2008). Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. PNAS, 105(23), 7899–7906.
Functional information is a measure of how much uncertainty must be reduced in order to achieve the desired function. Uncertainty is a function of probability or target size. So if any outcome will suffice to satisfy the function in question, there is no information required to achieve the function - success is certain. But if the ‘target’ one is aiming for is extremely minuscule in comparison to the overall space of possibilities, then there is a lot of room for uncertainty in the outcome. This uncertainty can be reduced to zero if one has sufficient information about how to achieve the functional by ‘hitting’ that extremely minuscule target. Functional information is a measure of how much uncertainty must be overcome for success. So if one knows the amount of functional information required to properly encode a protein family, then one can derive the probability of finding a sequence that will code for a protein in that family, which is another way of describing the target size vs the overall search space.
Anfinsen, C. B. (1973). Principles that govern the folding of protein chains, Science, 181, 223-230. It is, essentially, the laws of physics that determine the physicochemical interactions between a sequence of amino acids. Thus, Darwinian evolution would have to ‘find’ these sequences that code for functional, stable 3D protein structures. This can be challenging even for supercomputers but evolutionary processes, plodding along at incredibly slow (by comparison) reproduction rates, form a vastly underpowered search engine to explore protein sequence space, looking for stable 3D, functional proteins.
Since I published my paper on estimating the functional complexity (functional information) required to code for protein families, two other groups of scientists have also published papers using two different methods. All three methods, using real data, show that the target size, or probability, of any sequence at all that will code for an average protein family is vanishingly small. My method is the most optimistic, but I did not include interdependencies between sites in the amino acid sequence of a protein family, which yields an overly-optimistic chance of finding a functional stable 3D sequence, and I state this in my paper. More recent work reveals that the probabilities are similar to, or smaller than what I estimated. The two other papers are: Tian, P., & Best, R. B. (2017). How many protein sequences fold to a given structure? A coevolutionary analysis. Biophysical Journal, 113(8), 1719–1730 and Thorvaldsen, S., & Hössjer, O. (2023), ‘Estimating the information content of genetic sequence data’, Journal of the Royal Statistical Society: Series C (Applied Statistics), 72(5), 1310–1338.
Mira, A., Ochman, H., & Moran, N. A. (2001). Deletional bias and the evolution of bacterial genomes. Trends in Genetics, 17(10), 589–596.
Petrov, D. A., & Hartl, D. L. (1998). High rate of DNA loss in the Drosophila melanogaster and Drosophila virilis species groups. Molecular Biology and Evolution, 15(3), 293–302.
Lynch, M. (2010). ‘Rate, molecular spectrum, and consequences of human mutation’, Proceedings of the National Academy of Sciences, 107(3), 961–968.
Sharp, N. P., & Agrawal, A. F. (2012). Evidence for elevated mutation load in low-quality genotypes. PNAS 109(16), 6142–6146.
Chikofsky, E. J., & Cross, J. H. (1990). Reverse Engineering and Design Recovery: A Taxonomy. IEEE Software, 7(1), 13–17.
