One of the big issues in epistemology is the problem of infinite regress. “I believe the sun will rise.” “How do you know that?” “Because it always has.” “How do you know that?” “Because my memory and human records confirm it has.” “How do you know that?” “Because I’ve examined those memories and records.” “How do you know that?” And so on. It looks like this could go on forever. It seems like any answer you give can be doubted. We can always keep asking “How do you know that?” And that isn’t the only line of regress. “I believe the sun will rise.” “How do you know that?” “Because it always has.” “How do you know something that’s always happened will continue to happen?” And so on.
The difference between those two lines of questioning is that the first is about the facts, while the second is about inferences, the question of which rules are valid when interpreting those facts. Every rule is doubtable, because exceptions might always be possible; and every fact is doubtable, because we could always be mistaken. Someone could always have made an error, or lied, or our memories could be inaccurate or false, and so on. Thus, the problem of regress is just this: Where is it reasonable to stop doubting, to stop asking questions? When should we just shut up and believe?
Warrant and Proper Function
Some of you may already recognize much of this article. The original is still on my old blog from twenty years ago. But I am rewriting it here, and substantially updating and improving it. Both because it needs a rewrite and because I am gradually salvaging the articles on my old blog that are most worth preserving, in anticipation of a possible deprecation of Blogger. It’s a Google property and though there is no particular reason to think they’ll abandon it anytime soon, my past experience with old platforms makes me wary. So I’m starting early and going slow. I’ve already rewritten my articles on defining the supernatural and the mathematical universe and host them here (as Defining Naturalism: The Definitive Account and All Godless Universes Are Mathematical), and several more were already rewritten into chapters in Hitler Homer Bible Christ. I have a list of my favorites I might work through (but not all of it).
Today I’m redoing an article I refer to a lot, because it often comes up. I won’t belabor its backstory, but just catch you up with some cliffsnotes. Alvin Plantinga wrote a book Warrant & Proper Function (among others) which tried to argue that Christians don’t need evidence to warrant believing something, while atheists can never have warranted belief. The latter crystalized into his Evolutionary Argument against Naturalism that was widely panned by philosophers as pseudoscientific and irrational. I’ve already refuted that (in Why Plantinga’s Tiger Is Pseudoscience and The Argument from Reason). It trades on the scientifically illiterate mistake of confusing sensory systems with learning and problem solving algorithms and what their evolved functions (and thus limitations and biases) are expected to be, and of confusing evolved with human-made learning and problem solving algorithms.
But that leaves the other point: is it rational to believe things without evidence? The answer is obviously no (see On Andrew Moon’s Defense of Circular Arguments and Which Is ‘Rational’: Theism or Atheism?). But if you reject that, you still have to provide a theory of rational warrant. I did that in Sense and Goodness without God (which is being rewritten and updated and will be published as A Better World without God next year) with my own theory of warrant and properly basic beliefs. This was then challenged by claiming it doesn’t escape the problem of infinite regress because reasoning depends on trusting our memory at every step of any line of reasoning, and there is no non-circular way to do that.
So what is Plantinga’s solution to this? To just assume Christian Theism is true, and that we are fully justified in assuming this without needing any evidence Christian Theism is true. “I don’t need a reason to believe it.” This is irrational. But Christians are not rational. Nevertheless, regress must end somewhere. So where is the rational end game? It is tautological that there must always be some fundamental “givens” that end an infinite regress of reasons to believe something, since that is literally what it means for regress to end: something is just believed for no reason—or the reason to believe it is itself. The mistake made here is to assume a false equivalence between these two options. But to believe something for no reason is to rest all your conclusions on something you have no reason to believe, entailing no reason to believe those conclusions. And you can’t just gainsay your way out of that.
There is clearly only one sound solution to epistemological regress: the end game is always something self-evident. That is, all beliefs rest ultimately on a bunch of things you believe because they are sufficient evidence for themselves and thus no further evidence is required, and thus no further regress. This is called Cartesian Knowledge: raw, uninterpreted, present experiences, which alone have a zero probability of not existing when they exist for an observer because “they exist for an observer” is what a raw, uninterpreted, present experience is. They are thus self-evident. I don’t need any extra other reason to believe I am having the experiences I am having right now. I can doubt past experiences. I can doubt whether my present experiences correspond to anything outside the theatre of my mind. But I can’t doubt the theatre is there and has the contents it does right now.
All other knowledge is built on top of that foundation (see Epistemology without Insurmountable Regress or Fallacious Circularity and Hypothesis: Only Those Who Don’t Really Understand Bayesianism Are Against It). As Keith Parsons pointed out to Plantinga in God and the Burden of Proof, if we get to just assume Christian Theism is true, then we could just as easily assume Great Pumpkinism is true (or Pastafarianism or Simulation Theory or Scientology). The reason we can’t just start with assumptions like those is because they are dangerously and irresponsibly arbitrary. Declaring Cartesian Demons as a basis for a belief is just as foolish as declaring them as a basis for doubt: of all logically possible things, without a good reason, the epistemic likelihood of you picking the correct thing is functionally zero (the complexity of assumptions in allowing there even to be such a demon is so great, yet with no evidence for any of them being true, that their compound logical probability approaches zero: see why We Are Probably Not in a Simulation and The Principle of Indifference).
And adopting a belief whose probability of being correct is effectively zero entails unacceptable risks of bad outcomes (see What’s the Harm? Why Religious Belief Is Always Bad and Christianity Is a Conspiracy Theory and Dear Christian: You Might Be Worshiping the Antichrist and Justin Brierley and the Folly of Christianity and Debunking John Davidson’s “Pagan” America and so on). You need to behave more responsibly than that.
Cartesian Knowledge
The foundation of rational belief can never be arbitrary guesses. Guesses can be hypotheses you test against the evidence, but never as presumptions by which you interpret the evidence. The foundation must be where all reasoning stops: the undeniables of direct present experience. Basic beliefs are things you believe without further justification and on top of which you build and justify all other beliefs. Properly basic beliefs are basic beliefs that you are justified in believing without further justification. Everything else is improper—and thus simply unjustified, and therefore irrational. Properly basic theism is irrational, because no such thing exists. Theism is always a derivative, never actually a basic belief, and even when treated as basic, can never be properly basic.
The fundamentals of experience go beyond just the five senses (there are actually dozens of senses), but include thoughts and feelings, everything you experience in any given moment when you are experiencing it. So, for example, an “interpretation” of an experience is itself a basic undeniable experience—because whether the interpretation is correct can be doubted, but not that you are experiencing it. And that you “experienced” it could be doubted, as your memory could be fake; but that you are experiencing a memory of it now cannot be doubted. And so on. Raw, uninterpreted experience is undeniable because it is logically impossible for it to be false, because for it to be false would mean that it does not exist, so when it self-evidently does exist, it would be a logical contradiction to say that it simultaneously does and does not exist.
Even illusionism accepts this point, whereby experiences are merely the belief that you are experiencing and no more real than that: the data still exist, and cannot not exist at the same time they exist (see What Does It Mean to Call Consciousness an Illusion?). Even an illusion exists—as an illusion. You can say a mirage is an illusion, meaning there is no water at the end of the road, but that you are seeing a reflective surface that looks like water is still happening. You cannot at the same time be experiencing that and not at all experiencing it—experiencing it is reductively what its existence is. Ultimately all logical laws derive from the singular law of identity, that a thing is what it is and not something else, or else it isn’t that thing in the first place (and therefore contradictions cannot exist, nor excluded middles), and that all derives from the undeniables of experience.
Hence all logic simply describes a singular law of physics: that distinctions exist (see The Ontology of Logic). “Experiencing P” and “Not experiencing P” is a distinction. And the existence of P as an experience is the distinction here being made. Therefore no other extra thing has to be the case for it to be true that P exists. Which means it can never be false that P exists when P exists. And hence our Cartesian knowledge is always known to a true with absolute 100% certainty. Everything else is a probability, and as such, needs external information to warrant believing it has that probability and not some other. This does not mean Cartesian knowledge requires no justification to believe it. Experiencing it is sufficient justification to believe it. The only difference is that nothing else is needed to justify that belief but the belief itself.
Properly Basic Belief
That means Cartesian knowledge is the only properly basic belief. To say something is “properly basic” is to declare that it’s something we get to assume without needing a reason to believe it—other than itself. We need another reason, at least some reason, to believe anything else. In fact anything that could be false requires a reason to believe it other than itself. Therefore only things that cannot be false can be properly basic. And that means, quite simply, Cartesian knowledge. It doesn’t even include logically necessary facts, because we can always be mistaken about those. As I explain in Proving History, even if a thousand mathematicians confirm a formal proof is correct, there is still some nonzero probability that they all made a mistake. And if we vet it ourselves, there is some nonzero probability that we will make a mistake. We can always be mistaken. Therefore we always need a reason to believe we are not mistaken. Except for undeniable present experience: because about that we can never be mistaken. Everything else is doubtable.
And so it is an inescapable fact that if there is any possibility a belief could be false, then we need some reason to believe it isn’t false. It needn’t be a weighty or elaborate or air-tight reason. Any genuine reason will do. But if we need even a tiny little reason to believe something before we are warranted in believing it then that belief cannot be called properly basic. And that’s that. Theism, therefore, can never be a properly basic belief.
For example, the fact that our thoughts and “interpretations” exist at the moment we experience them is undeniable, regardless of whether they are true or correct, and therefore our belief in the existence of those thoughts and interpretations is properly basic. Likewise, it can also be undeniable that there exists at this moment an experience of our “interpretations” cohering well—or not cohering well—with everything we are experiencing at the same moment. Obviously, just because we are experiencing an interpretation of the facts that is cohering well with everything else going on doesn’t mean it is cohering well. We could be in error about that. Nor does such coherence mean our interpretation is true. Since there may be countless explanations of the same facts that are equally coherent. But the fact that we are experiencing that coherence is undeniable. Since it cannot be false that we are experiencing it right here and now, it is properly basic. We get to believe we are having that experience without needing any reason to believe that, other than the one reason entirely contained within itself: the fact that it is there, and thus cannot be false for as long as it remains there. But whether it is correct is another matter.
The same holds for memories, which are also properly basic. Whether they are true memories or false, or accurate or inaccurate, or flawed or precise, none of that is properly basic. But the conclusion that they are true or accurate or precise can be (and ought to be) an inference from properly basic experiences, which include thoughts and thus “interpretations,” as well as other memories, “experiences of coherence,” and “experiences of an idea making sense,” and so on (as having an intuition can be undeniable regardless of whether that intuition is true). These basic beliefs can even include undeniable desires, like an experience of having the desire to follow a certain rule in your thinking.
There is literally nothing else left. When you add up all the reasons (all the reasons) you have right now to believe any x, you will always (always) end up with a finite collection of experiences (whether a combination of perceptions, emotions, thoughts, memories, etc.), which are in turn entirely and without remainder reducible to a finite (not infinite) collection of properly basic, in fact literally undeniable, experiences (again, whether these be perceptions, emotions, thoughts, memories, etc.).
Therefore, every rational epistemology avoids infinite regress. Because there is always a point where the justifying evidence simply stops: at the basement of Cartesian knowledge. You couldn’t continue the train of evidence from there even if you wanted to. Therefore, there is no such thing as infinite regress in epistemology (see Epistemology without Insurmountable Regress or Fallacious Circularity). Which means the only difference between my epistemology and Plantinga’s is that he stops with an arbitrarily selected set of deniable assertions, whereas I argue we must keep going until we’ve gotten to the bottom, which is always a finite set of undeniable experiences. This is all it can ever be, because in the end, there isn’t anything else left (see Defending Naturalism as a Worldview).
Memory and the Subconscious
Hence I do not believe we must presume memory is true in order to reason. If that were the case, we could never identify a false or inaccurate memory. Instead, we hypothesize the reliability of our memory, and constantly test that hypothesis against current experience, which includes current experiences of those and other memories, and experiences of coherence among all our presently occurring experiences and memories, and so on.
Of course this goes on unconsciously or nonpropositionally most of the time, but the process is the same then as when it is fully cognitive (there is plenty of evidence to believe that, and no reason to believe otherwise). So just because a process of reasoning is unconscious does not mean we believe its conclusions for no reason. We likewise take for granted that a science textbook tells us the truth about what scientists have observed, but we don’t take this for granted for no reason. We have accumulated a great deal of evidence regarding the reliability not only of science textbooks, but of the processes and events that go into producing their contents. Just because we don’t reason through all this evidence every time we pick up a science book doesn’t mean our trust in that book is properly basic. It is not even basic.
There is a difference between working assumptions (things we don’t believe yet but are testing out), and empirical assumptions (things we assume only because we have some evidence or reason to believe them), and basic assumptions (things we assume but with no evidence or reason to believe them) and properly basic assumptions (things we assume but with no evidence or reason to believe them except themselves—the fact that they literally cannot be false is the evidence, the reason, we believe them).
Hence when we intuit that someone is lying to us, that they are lying is not a properly basic belief. If we can’t work out specific reasons why that intuition is valid, we won’t be justified in trusting it at all (see Correcting 5 Mistakes Atheists Make About Epistemology). But if we can work out such reasons, then we’re not looking at a properly basic belief. Because if there are reasons to believe it, it cannot even be a basic belief—much less properly basic. And if it could possibly be false, it cannot be properly basic anyway. So even when our intuition has become reliable enough that we trust it even without examination, we are still doing that for a reason, such as the fact that we have a large number of experiences of memories of our intuition’s success in relevantly similar cases. That these experiences exist is properly basic, but not what we induce from them, like that our intuition in such cases is reliable.
Thus, we don’t have to work out all the steps of reasoning or all the evidence we actually are working from. But we still need all that evidence and reasoning in order to be warranted in believing something. A belief can only be properly basic if we don’t have all that evidence and reasoning stashed away in our subconscious, and yet still are warranted in maintaining that belief. And only Cartesian knowledge meets that bill. In the end, we operate on the assumption of the reliability of memory in reasoning not as a given but as a justified hypothesis, based on long experience—in other words, a ton of evidence. We have good reason to assume memory is sufficiently reliable for us to successfully reason. The experience of those memories is properly basic; but the content of those memories is not. And it is that content that we use and rely on in reasoning.
Why We Believe in Logic
Our trust in logic is not a basic belief. We have accumulated extensive evidence (and hence reasons) to trust it (see The Argument from Reason and The Ontology of Logic).
Deductive reason appears to involve simultaneous perception of the major and minor premise, while induction is simply deduction with the major premise being some general inductive principle (like that “how things have gone is likely how they’ll go”). Our experience of the overlapping content of those premises, which simply is the conclusion, is a properly basic belief. So we do not require memory to arrive at a conclusion from two premises; we can do that in our immediate apprehension. We only require a memory to have previously stored, and thus recall at the present, the premises we are using in our reasoning at any given time, and thus to keep track of more than two premises and the results of their overlap. Likewise we need memory to keep testing our apprehensions (our conclusions) to ensure we have sufficient reason to believe them. But the reasoning itself, which we continually test with all these memory and sensory experiences, is properly basic.
Obviously, most remembered premises are the conclusions of previous acts of reasoning. But we still don’t require memory to reason, only to recall the results of past acts of reasoning, or to juggle multiple or complex premises. That this all works (and thus our premises and conclusions can be trusted) is not basic: it’s evidence-based. Because our working memory has a very small limit in terms of RAM: only about three bytes, maybe five, in terms of a lexical index—and though these bytes of information can index to far more complex models in our cognition, the limit on their number at any one time remains. So we do need to trust memory to engage in complex reasoning. We just don’t trust it because it is properly basic. We trust it for reasons. “But those reasons circularly include other memories you have to trust” is true, but the end game is the experience of all this, which includes an experience of its coherence: that it is working now is evidence that it has and will.
I go into how indexes and models work, and their importance to understanding all conscious thought, in What About Propositions? But note here that this is often the reason people are gullible or bad reasoners: they cannot keep track of more than two premises, and thus more than one step of reasoning, at any given time. That they fail where we succeed remains continual present evidence that our reliance on memory is working. The takeaway here is that we do not need to pretend even that the belief “our working memory is reliable” is properly basic. Only our experience of it is. That its content is reliable is something we need a lot more reason to believe. And indeed we know our memory’s reliability is not total, and therefore the possibility of its being unreliable is not even theoretical: we have to continually guard and manage against its repeated actuality. Which is why our belief in it cannot be properly basic, or even basic at all.
All Epistemologies Are Fundamentally Normative
This does get us to a realization, though: all epistemologies are fundamentally built on axioms that are, in fact, imperative propositions. In other words, every epistemology is constructed on top of a set of “I ought to believe x when y” propositions, and therefore, if it is true that any epistemology ought to be adopted by everyone, then epistemology as such is a subset of morality—and it would therefore be immoral to knowingly violate the axioms of a true epistemology. There is an ethics of belief.
All epistemological arguments end with a set of imperative propositions, not with a lone set of non-normative facts. For example:
- “I ought not to believe x when I have no reason to believe x“
- “I ought to believe x when x coheres with all other current experiences (including experiences of memories, etc.), with less fudging than any alternative I know”
- “I ought to believe the relative probability of x is y when y is (or is as close as I can know to) the actual frequency of x relative to ¬x in my experience”
And so on. Each of these general principles is irreducible in the sense that they cannot be made the conclusion of any non-question-begging set of premises. In a sense they are true by definition, insofar as I might choose to define such terms as “true,” “plausible,” “probable,” “credible,” etc., in exactly these ways. But in choosing to define these terms in such a way I am making a normative judgment, which rests on some belief regarding what I ought to do, which in turn rests ultimately on what I want. Do I want any of my desires fulfilled or thwarted? Do I want any of my plans to succeed or fail? Do I want any of my expectations to come true or be dashed? The answer to these questions entails a particular course of action, as much in epistemology as in any other matter.
To say that these principles are irreducible doesn’t mean I can’t defend them. It’s just that any such defense will ultimately still rest on some normative-making premise, such as “things will go better for me if I follow these principles,” which is itself, ultimately, a conclusion based on those same principles. Hence we always end up in some circular argument, a fact even Plantinga admits of his own and in fact every conceivable epistemology—even God’s. Yes, God as well. For even He can never be certain he is not the victim of a Cartesian Demon, or that there isn’t some flaw in his knowledge or memory or reasoning that prevents his detecting it. Because even he can only reason his way out of this by some circle or other (see, again, Andrew Moon’s Defense of Circular Arguments).
But this circularity ends infinite regress, which is why no epistemology really suffers from the problem of regress. The only difference between epistemologies is not whether they avoid regress, but whether they work. Which requires us to decide what counts as “working.” To choose “how I feel” instead of “whether it reliably gets me to what is the case” has the catastrophic outcome of failing to “reliably get me to what is the case” and thus failing to “reliably get me to results I really want.” This is a “you can’t serve both God and Mammon” moment (I discuss why in What’s the Harm? Why Religious Belief Is Always Bad). You have to choose whether “reliably gets me to results I really want” is the best metric for your epistemology, or something else. There is no third option. And when you work it out rationally, you’ll find that anything less than “reliably gets me to results I really want” will not be what you really want. So a rational person is always stuck with that metric (see The Objective Value Cascade). Everyone else is behaving irrationally.
How so?
The Foundation of Rational Belief
Well, remember how I started this whole essay by pointing out that there are always two lines of questioning, one about the facts and one about the principles we use to interpret those facts?
Okay.
The first line of questioning ends with a full dead stop at some finite set of undeniable experiences. For once you end up with an answer that cannot be false, you can no longer ask, “How do you know it’s true?” How do I know? Because it can’t be false. It would be nonsensical to then ask, “How do you know that?” For if I said it were false, then I would be saying “this experience exists right now and this experience does not exist right now,” which is a meaningless sentence, because it asserts what it also denies, and therefore it describes nothing. Thus, obviously, I cannot fail to know I am having such-and-such an experience right now. To express doubt about this would then be to question not the facts, but the rules I choose to follow when interpreting those facts—in particular, how I choose to define “true” and “false.”
Hence we end up with the second line of questioning, about our inductive and deductive principles. But all such lines of questioning end with a circular argument: our principles are true because our principles are true. There is therefore nowhere else to go. This is like Hawking’s notion of nutshell cosmology, where there is no first moment of time because as you approach it you eventually just curve back around and end up where you started. In such a case the timeline is not infinite, and yet also has no starting point. Because it’s circular. So, too, in epistemology: once your only justification for adopting a principle is the principle itself, you can no longer ask, “How do you know that principle is true?” How do I know? Because I observe it to be true. How do I know I observe it to be true? That I am observing it to be true is an undeniable experience. Hence we are back to the first line of questioning, about what I am experiencing, where all questioning ends.
Thus, there is no regress. But the underlying normative nature of this end game must not be overlooked. In effect, my entire epistemology rests on a conjunction of just three premises, which I will greatly oversimplify for the point here:
- A: “Following certain principles will probably make things go better for me than not following them will”
- B: “If I want things to go better for me, I ought to follow the principles that will probably make things go better for me than not following them will”
- C: “I want things to go better for me.”
Properly interpreted, C is an undeniable experience of desire and thus properly basic. Of course, what I mean by “I” and what it means for “things to go better for me” is a whole other matter, and not the subject at present, but whatever we settle on, we can certainly assign some meaning to “I” and “things going better for me” that would make C a properly basic belief. Because I don’t need to work out whether my beliefs are true to accept that the principle is true.
For example, I can be wrong or confused about what “I” means and what “better for me” means, but I cannot be confused about the fact that on some construction of those two terms it is always true that “I want things to go better for me.” That realization is properly basic. Because it can’t actually be false. Even if I incidentally, irrationally, want things to go worse for me, I am then simply redefining what is better for me. There is no way around the fact that whatever I really want is what I really want and thus is best for me. What can be false is what “what I really want” happens to be. I can be wrong about that. But there is some truth to find there, and whatever it is, it will tautologically be what will most satisfy me to pursue. So the principle is properly basic.
Meanwhile, in support of A and B, I have the evidence of cause and effect, which consists of a collection of undeniable experiences (including experiences of memories) of these causal hypotheses being fulfilled, combined with the undeniable experience of my lacking any memory or perception of these causal hypotheses being invalidated. In other words, that following certain principles more often than not makes things go better for me—better than not following them does—is not a basic belief, because I justify it by appeal to a ton of evidence making it probably true. But at the bottom of all that evidence is nothing but a ton of properly basic beliefs: all of it raw, uninterpreted experiences—of the now and of the past (in my memories: see Bayesian Analysis of the Barkasi-Sant’Anna Defense of Naive Memory Realism). Both A and B thus reduce to a future subjunctive: “I predict things will go a certain way.” Which means simply “they will” go that way (which is then either true or false) and thus that you “ought” to thus act a certain way means “you will” act that way when you are sufficiently informed and behaving rationally (as I lay out in my chapter on moral reasoning in The End of Christianity).
Thus, the buck stops with the evidence, and the evidence is a finite collection of undeniable experiences. Though these undeniable experiences are also compatible with a contrary hypothesis (¬A or ¬B), the experiences I am having do not entail that contrary hypothesis unless we introduce an intervening premise, such as “a Cartesian Demon has fooled with my memory and is deceiving my senses.” Which we only have reason to believe is always improbable. Thus, as long as we have no reason to believe any such premise, then we have no reason to believe any hypothesis contrary to A and B. And as long as we have some reason to believe A and B instead, then we have reason to believe A and B. In other words, among the total collection of our undeniable experiences at any given moment, all we have is evidence supporting A and B and none supporting ¬A or ¬B, which is to say, none supporting any premise that would have to be true in order for ¬A or ¬B to be true.
So our bottom basement of circularity arrives here, at the point when we decide on the most fundamental principle underlying all of the above, which I will call principle K:
- K: “I ought to believe x when I have (a) evidence supporting x and (b) no evidence supporting what would have to be true for me to have (a) and yet for x to be false.”
Here, too, I am oversimplifying. A complete principle, for instance, would include the relationship between degrees of evidence and degrees of belief. And I am hiding within term (a) all cases when x being undeniable is evidence supporting x. And I am hiding within term (b) the distinct case of having direct evidence against x (being just another way of having “no evidence supporting” that). And I am setting aside complex cases when (a) obtains but not (b), which need more tangled principles to resolve. And so on. But we can expand K to include all these things. It’s just easier to keep track of the point I want to make if we focus on this simple version of it instead. Such is K.
The contrary inductive principle ¬K would then be:
- ¬K: “I ought to doubt x when I have (a) evidence supporting x and (b) no evidence supporting anything else that would have to be true for me to have (a) and yet for x to be false.”
We are thus faced with an ultimate choice: K or ¬K? Which principle do I follow? I can try them both out right now, and immediately see that following K leads to correct predictions and the satisfaction of my desires and the fulfillment of my plans, while following ¬K does much poorly in all three respects. I can repeat this test endlessly. It still remains that a Cartesian Demon could be meddling with my mind so that I keep falsely experiencing and remembering the good performance of K and the poor performance of ¬K, when all the while, unbeknownst to me, ¬K has been performing better than K (or equally as well). But as long as any Cartesian Demon keeps doing this, what’s the difference between that, and K actually performing well and ¬K performing poorly?
Until you allow that there could be some potential difference between the world created for us by a Cartesian Demon and a real world, there is no difference between them that matters. The world created by the Cartesian Demon is a real world: our desires are actually fulfilled, our plans are actually realized, our predictions actually come true, and continue to do so, always and forever (until we’re dead). Only if you allow that it might not be “always and forever” does it become meaningful to talk about a difference, because then, and only then, is there a difference that matters (hence theists want God to be this Cartesian Demon: see The Argument to the Ontological Whatsit). But then we have the possibility of predicting different outcomes for each hypothesis, and so the two hypotheses become testable, which means a Cartesian Demon hypothesis is only ever confirmed when its unique predictions come true. But as long as it remains unconfirmed, we continue to lack any reason to believe that hypothesis—even if (unbeknownst to us) it turns out to be true.
And Thus Regress Ends
This is the epistemological end game. We always end with a conundrum Plantinga faces every bit as much as you or I. So pointing out the inevitable circularity of rejecting all Cartesian Demon hypotheses (by relying on our evidence-based trust in reasoning on a foundation of the undeniables of direct experience) is no objection to my epistemology, since it is equally an objection against all epistemologies. It forces us all against the rocks of the same dilemma: we simply have to choose how to behave. Will it be in accordance with K, or ¬K? We constantly observe, in every waking moment that we bother to test, that the undeniable facts of both our desires and our immediate and present experiences are only satisfied by following K. Therefore it makes no sense to follow ¬K, even if it happened to be the case (unbeknownst to us) that ¬K is true. So that’s where all epistemologies end: “I ought to follow K.”
This is not a properly basic belief, because we admit K could be false, and yet we have a reason to follow K rather than ¬K, namely the undeniable fact that we desire things, combined with the undeniable fact that in any moment we put to the test, we will usually experience the fulfillment of our desires only when following K, but not when we follow ¬K. These two facts do not combine into a deductive proof that K is true, but they do provide a reason to follow K—in fact a quite good one—and as long as we have no reason not to follow K, having a reason to follow K is a sufficient reason to do so—and even more so when having a good reason to. And yet ultimately all of those reasons, which are all that justify my embracing the fundamental principle underlying the whole of my epistemology, end at undeniable facts, which are therefore properly basic.
Regress ends.
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Note to My Readers: Since this is a difficult and convoluted subject I may have made errors in its precise wording in places. Accordingly I will continually revise this article‘s wording whenever I or anyone find problems to correct. Ask about any you find or are unsure about in comments, and I’ll look into it and see if any revision is needed.





Strengths
Engagement with Core Philosophical Issues
The essay tackles the classic problem of infinite regress in epistemology—a foundational topic in philosophy. This is handled with clear and familiar “how do you know that?” dialogues, making the abstract issue relatable and easy to grasp.
Critical Analysis of Plantinga
The discussion of Alvin Plantinga’s ideas, especially around “properly basic beliefs,” is knowledgeable and thoughtful. The essay rightly notes the criticism Plantinga’s “Evolutionary Argument against Naturalism” has received, and it attempts to offer a secular alternative to properly basic beliefs.
Development of an Alternative
The author provides a robust account centered around “Cartesian Knowledge” (the undeniability of present experience), explaining why, in their view, only such experiences can serve as a terminus for epistemological regress. This is well-integrated, both referencing historical sources (Descartes) and contemporary debates.
Nuanced Treatment of Memory and Subconscious Reasoning
Rather than simplistic trust in memory, the essay describes the process as empirical and makes a distinction between properly basic beliefs, basic beliefs, and other inferential beliefs. This adds nuance and depth.
Clarity about Normativity
It insightfully argues that all epistemological systems are ultimately normative—they are built on “oughts,” not just “is” statements. This is a sophisticated point, often overlooked in introductory discussions.
Weaknesses
Length and Redundancy
The essay is long, sometimes to the point of redundancy. Several points (e.g., the undeniability of present experience, or the status of memory) are repeated in varying forms. Shortening and more concise articulation would improve readability and impact.
Heavy Reliance on Self-Citation
While the essay references a wide range of the author’s previous work, and this is not necessarily negative, it can give the impression of insularity. Engaging more directly with a broader range of philosophers—especially critics—would strengthen the argument’s credibility.
Overly Dismissive Tone Toward Opposing Views
There are several moments where opposition (notably Plantinga and “Christian theism”) is dismissed as “irrational,” “arbitrary,” or “pseudoscientific” without exploring the strongest counterarguments in detail. Philosophical writing is more persuasive and charitable when it engages the strongest form of the opposition (the principle of charity).
Insufficient Attention to Objections
One significant philosophical challenge is the status of “self-evidence” and the “indubitability” of present experience. There’s a rich literature in philosophy (especially from phenomenology, cognitive science, and contemporary analytic philosophy) that raises problems for the idea that present experience is fully undeniable (e.g., dreams, hallucinations, radical skepticism beyond the “Cartesian theater”). The essay briefly acknowledges these issues but tends to treat them as secondary.
Circularity and Normativity Not Fully Addressed
While the essay argues that epistemic “oughts” are ultimately circular or reducible to desires, it does not fully address classical objections to epistemic relativism or subjectivism. The risk is that rationality itself becomes merely a function of contingent (even arbitrary) desires or norms. Engaging more with critics who see this as problematic would strengthen the analysis.
Clarity and Structure
At times, the essay’s structure meanders, with long digressions on related topics (memory, cognitive science, the author’s blogging history). Stronger section breaks or summaries would help keep the reader focused.
Suggestions for Improvement
Condense and Focus: Trim repeated points and maintain a tighter narrative structure. Each section should have a clear purpose and direct link to the thesis.
Engage Charitably: Address opponents’ arguments in their strongest form, and avoid pejorative language.
Incorporate More External Voices: While self-citation is valuable, referencing standard philosophical works and objections would help situate the argument in broader discourse.
Clarify the Status of “Properly Basic” Beliefs: The essay could benefit from a more detailed discussion of challenges faced by the idea that “undeniable present experience” is immune to all forms of error.
Section Headings: The addition of explicit section headings and/or bullet-pointed summaries at the end of sections would aid readability and emphasis.
Overall Assessment
The essay offers a well-informed, passionate, and thorough account of the problem of epistemic regress—and the proposed solution, rooted in Cartesian knowledge and a naturalistic epistemology, is both interesting and defensible. However, its impact would be greater with more concision, a broader and more charitable engagement with critics, and more explicit structuring. Philosophically, the piece stakes out a bold position, but further engagement with skeptical and rival viewpoints would elevate the analysis and persuasion.
That looks AI generated.
There aren’t any specific suggestions (nothing actionable for improving the piece) and no evidence is presented for any point made (e.g. that pejorative evaluations are unjustified).
I would much rather have those two things.
Wow, AI sucks.
So Richard is a blogger. Apparently AI can’t detect the genre of the fucking thing it is criticizing. Of course he’s going to refer to himself more than other folks: you’re at his site, you may not be aware of his work, and he’s talking more about his own opinion than he would in a peer-reviewed article.
(Moreover, while I do always appreciate when Richard surveys opinion that agrees with him and links elsewhere, I actually appreciate writers who don’t bury their own opinion and perspective under a false need to find consensus. It doesn’t solve bias to pretend that you’re just saying what everyone who’s thought about it is clearly thinking. And, of course, when you have a perspective, self-citation helps other people know what you’re saying . When you cite other people too much, it leads down obnoxious rabbit holes where people go and read and then ask you what you think about a formulation that you didn’t cite because you don’t agree with it as much and you’re now not actually discussing the idea).
It’s worth pointing that out: my blog is a hypertext (it is one large cross-referenced book that builds points on other, previously-established points). So the AI saying that is like saying that of a book (“you refer to previous chapters too much”).
It’s also worth noting that the point of my work is to get right what the field hasn’t been, or has been neglecting. It is not a reference book, where I am just summarizing what other people think. A lot of arguments here are going to be original. Because that’s the point of this.
Notably, if AI were not pseudo, it could do what it pretends to, and then would actually be useful.
For example, if it thought something wasn’t original but had been well-explored in other articles or books (and not just “explored,” but I mean well enough to be worth the bother of noting), it could list (accurately, without error, misdirection, or hallucination) the bibliography for my point, and thus correctly and usefully direct readers to other good demonstrations of the same points. But as pseudo-AI cannot comprehend what it is doing, it has no actual knowledge of what would even count as “other good demonstrations of the same points.” If you asked it to, it would give you a list of things vaguely statistically related but that probably won’t even discuss the same point, much less particularly well enough. And it might even make some up (as recent examples of doing this in US government and legal briefing have famously proved).
I really can’t believe anyone still trusts AI with anything substantial like this. I’ve seen it “praise” my work in Bayesian reasoning and get literally every single piece of Bayesian reasoning wrong. I’ve watched it try to explain to me that vulgar manuscripts of Homer contain the text of the Vulgate Bible. I’ve dealt with it critiquing an argument in which it misses half the key points it should have assessed, and half the points it did make were either wrong or vacuous. AI has some very limited uses, but it simply can’t do this sort of thing. It can’t do science. It can’t do math. It can’t philosophize. And it is not a critical thinker.
It cannot replace your mind.
So people really need to stop letting it.
And all that is why I am heretofore banning AI content in my blog comments.
It’s garbage. It’s overprone to disinformation. And it’s a waste of everyone’s time.
Thank God.
It is nightmarish that ordinary people are using AI as if it can find truth in garbage. When Carroll destroyed Weinstein on Piers, Weinstein said “BUT WHAT IF AI FINDS OUT THAT MY INCOMPLETE PAPER I SAID IS AN APRIL FOOL’S JOKE IS ACTUALLY SUPER SMART SEAN?” He literally made that argument.
But the companies are worse. They are acting as if this tech can, now, replace injury attorneys. Or come up with ideas for you for your novel.
AI could certainly “come up with ideas for you for your novel.” That just won’t be any more impressive than you just doing it yourself. It will even cost you more (if you are paying for your AI). So it’s actually a reduction in efficiency.
But that AI can’t replace lawyers has been spectacularly proved of late.
To offer a better example: AENEAS is a good use of AI. Because it just speeds up something we already know how to do, and no one is claiming it can replace anyone, because you need the same experts as ever to vet and correct its output in order for that output to be of any use.
Another example (of mixed value) is automated audio (having text read in a way that sounds exactly like a specific person is reading it), which we already had (voice imitation is not new), but AI can do it faster and cheaper. That leads to new ways to con or deceive people, but it’s really still just “conning or deceiving people,” which has always been a thing. Now we just need to on guard against this new tech, just like we always need to be on guard against new tricks (whether its hacking tools or phishing).
This is how AI will wash out generally. It will just automate fairly monotonous processes, which humans will still have to vet and correct, overall saving time and little more, and mostly for obscure or trivial functions, and the same “utility and crime” as every other tool gets adapted to, and society has to adapt to react, but that’s not new. That describes every year of human civilization for at least the last two hundred years.
A precedent exists already in the CGI community, where automation of animation already existed before AI. And all AI does is come in as a fancier version of what they were already doing. It’s just one more update in a continuous development curve, which if you didn’t “call” it AI, no one would even notice it was any different from any other points on that curve before it. It would just be another automation tool. Because, really, that is literally all it is.
Yeah, the concern for the short term is companies and institutions using AI as an excuse to undercut working conditions, as well as the reality that what we are going to ultimately industrialize through AI (as Dan Olsen of Folding Ideas pointed out) is spam and slop. That’s already what it’s settled out at: YouTube is now full of AI slop, including straight up disinfo. (The religious apologist low-tier channels are becoming unwatchable as they’re increasingly infected by AI video and even AI scripts). This stuff is inherently garbage and so it actually commands very little value individually, but the industrial scale still warps the incentives and does harm discoverability for the people trying to create anything of merit. (Just like how Steam became a slop platform and so good indie games are tainted by association, and the Switch has started to similarly enshittify).
Underneath all the above is the notion that belief is a valid way to organize your life. We might define belief as acting on an assumption that some proposition is true. Professing a belief (e.g., in life after death) but acting otherwise (e.g. clinging to life without benefit to others) would be lying. But it is not exactly satisfactory, because we might be undecided between facts A calling for action M and B for N, but can only do M or N. Having flipped a coin and done M, we can only hope A was right, but cannot be said to believe it.
Ultimately we are denied certainty and must retreat to probabilities, and probabilities about probabilities. Being ill equipped by nature at managing those, we fall back on approximation and squat on propositions that seem likely or anyway comfortable, and reconsider only when prompted by revelations incompatible with it. The experience of doing M and thereby never learning what N might have brought seems like the essence of the human condition.
Just a helpful note:
The ontology of belief is relevant to your point. I discuss that in my article on the Argument from Reason (see also my discussion of belief as probability in The Gettier Problem). The tl;dr is: “belief” just means confidence in a correspondence between model and reality. All rational decision-making (and even most nonrational decision-making, as this starts way down the line in the animal kingdom, with at least the first reptiles, and is standard across mammals) is fundamentally dependent on building degrees of confidence in model-reality fit. And the metric for reliable and unreliable belief-making processes (which includes techniques) is how often or well a built confidence correlates with actual model-reality fit.
The result is that beliefs are really just beliefs in a probability, and reliable belief-making is what gets that (epistemic) probability closer to the (actual) probability things will go a certain way. Everything else is error. Often fatal.
A Pragmatist’s Reply: Dropping the Quest for a Foundation
The author’s essay is a masterclass in a certain kind of philosophy: the search for a secure, non-arbitrary foundation upon which to build the edifice of knowledge. It is a noble, Cartesian quest to find the “undeniable” ground, the one point that cannot be doubted, and from there, to build a rational system (Principle K) that will “reliably get me to what is the case.”
From the perspective of a pragmatist like Richard Rorty, however, this entire project, as elegant as it is, is fundamentally misguided. It is a search for what Rorty called a “skyhook”—a point of contact with Reality (with a capital R) that is outside our contingent, historical, language-bound situation. The author thinks he has found it in “Cartesian knowledge,” but he has only found the first step in a particular language game he happens to prefer. The solution isn’t to find a better foundation; it’s to abandon the quest for foundations altogether.
Antifoundationalism: The Myth of “Raw” Experience
The author’s entire system hinges on the idea of “raw, uninterpreted, present experiences.” This is the bedrock, the “properly basic” belief that needs no further justification. A Rortian pragmatist would argue that no such thing exists.
The moment you say, “I am having an experience of red,” you are not reporting a raw datum. You are deploying the concept ‘red,’ a word whose meaning is determined by its place in a public language and a web of social practices. Experience does not come to us pre-packaged and uninterpreted. It is always already “cooked” by the linguistic and conceptual scheme we inhabit. There is no unmediated access to “the given.”
Therefore, the author’s proposed foundation is not an absolute, undeniable starting point for all rationality. It is simply the point where his vocabulary, the vocabulary of Enlightenment empiricism, happens to begin. To claim it is the only rational starting point is to mistake the starting line of one’s own preferred race for the only one on the field.
Redefining Pragmatism: From Correspondence to Coping
The author uses pragmatism as a tool to justify his realism. He claims we should follow Principle K because it “reliably gets me to results I really want,” which he equates with getting to “what is the case.”
This is a profound misunderstanding of pragmatism. For Rorty, pragmatism is what you get after you give up on the idea of truth as “correspondence to reality.” The goal of our beliefs and vocabularies isn’t to create a perfect “mirror of nature.” The goal is to help us cope.
Truth is not a metaphysical property of beliefs that match the world; it is, in Rorty’s famous and often-misunderstood phrase, “what our peers will let us get away with saying.” It’s the compliment we pay to beliefs that have proven useful for a particular community’s projects.
The author’s Principle K is a wonderful description of the rules of the scientific language game. This game has proven astonishingly useful for the project of predicting and controlling our environment. But to say it is the only game of rationality is to be a kind of tool-monomaniac, insisting that a hammer is the only proper tool for all jobs.
Scientific Antirealism and Epistemic Relativism
This leads directly to scientific antirealism. The author believes science discovers the truth about the world. The Rortian pragmatist sees science as a highly successful set of tools and a vocabulary that allows us to achieve specific goals. Terms like “electron” or “gravity” are not necessarily names for things that “are really out there”; they are useful nodes in a descriptive network that allows us to build technologies and make predictions.
This doesn’t make science “false.” It makes it useful for a particular set of purposes. This is the heart of epistemic relativism (or what Rorty preferred to call “ethnocentrism”). There is no neutral, God’s-eye-view from which to judge which vocabulary (science, religion, art) is “truer.” We are all embedded within our community’s “final vocabulary”—the set of words we use to justify our actions and beliefs, for which we can offer no further justification.
The author is simply asserting the superiority of his community’s final vocabulary (secular, scientific, liberal) and calling it “Rationality.” A Rortian would call it what it is: a preference for a particular, and admittedly very useful, way of life.
The Pragmatic Justification for Mysticism
This is where mysticism-based theism finds its opening. The author dismisses theism by lumping it with “Great Pumpkinism,” calling it “dangerously and irresponsibly arbitrary.” From a Rortian perspective, this is simply the author judging another vocabulary by the standards of his own.
A community of religious mystics is playing a different language game with different goals. As William James’s radical empiricism shows, the data for this community includes powerful, transformative experiences of the numinous, of unity, of a divine presence. These are their “undeniable experiences.” Their goal is not primarily to predict and control the material world, but to cope with finitude, to find meaning in suffering, to build moral community, and to feel at home in the universe.
For this project, the vocabulary of theism is not arbitrary at all. It is a time-tested, pragmatically successful tool. The belief “works” by providing hope, resilience, and a framework for a meaningful life. In the only sense of “truth” a pragmatist recognizes—its utility for helping a community flourish and achieve its goals—mysticism-based theism meets the pragmatic theory of truth.
Conclusion: The End of Epistemology, The Beginning of Conversation
The author’s attempt to end the regress fails because his chosen foundation is no less arbitrary than any other when viewed from the outside. He simply stops at the presuppositions of his own culture.
The real way to end the regress is to stop asking the question in the first place. We should abandon epistemology, if epistemology is defined as the search for a universal theory of justification. Instead, we should see the clash between the author and the theist not as a battle between Reason and Unreason, but as a conversation between two different communities with two different vocabularies, each useful for different purposes.
The choice is not between K and ¬K, as the author frames it. The choice is about what kind of person you want to be and what kind of community you want to build. Both the scientist and the mystic have developed powerful vocabularies for coping with the world. The pragmatist does not ask which one is “true,” but rather, “What is it good for?” And for the project of living a fully human life, the answer may be that we need both.
What a bunch of nonsensical gobbledygook.
Definitely AI generated tripe.
I think I’m going to have to ban AI generated comments from now on.
Please don’t post any more garbage from AI here.
Dr. Carrier,
Please consider posting an article on AI, how it is taking jobs, will take more jobs, how that will negatively impact capitalism as we currently know it, how recent graduates will simply be out of luck, how it might lead to increased mental distress, how it might alter what is called the middle class, etc…
Regarding AI, I see something dark coming. An advent. There will be the world we knew before AI, and the world after.
None of that is actually known to be true.
So I don’t have anything to say about it yet. Lots of other people have. But the truth of the matter is, we are not actually seeing this happen. Despite enthusiasm for AI, on the inside, corporations are growing frustrated with it and finding that it can’t actually replace as many people as they’d hoped. AI is so unreliable it’s like hiring a sub-minimum-wage high school dropout to do your clerical work. There is a reason corporations are already not hiring sub-minimum-wage high school dropouts to do their clerical work.
Whether this will change remains to be seen. I am skeptical. But I can’t “prove” anything either way, so there is no article to write. We just have to watch and see what happens.
I expect, though, that the same thing will happen as happened every time before: with mechanized manufacturing (which the Luddites predicted would replace everyone; it didn’t, it actually created more jobs than it took, and substantially multiplied productivity far in excess of what any population of its own could achieve), robots (the manufacturing industry still exists and can’t even fill the human labor positions it has; even recently, attempts to replace cashiers in the fast food industry with robots has conspicuously failed), and then computers (mainstream since the 1980s; still haven’t replaced people).
I do not expect any significant effect of AI on jobs, capitalism, mental health, or the middle class. The effects will just be shuffling, not radical change (same as with machines, robots, and computers). The threats to jobs, capitalism, mental health, or the middle class remain the same as has been for hundreds of years: capitalism itself, and its inevitable hoarding of production to sustain a plutocratic oligarchy.
But others have already covered that better than I could. I don’t have anything to add to that that I haven’t already done (see my “politics” and “economics” category tags).
So I think there’s some nuance here. So the Luddites’ specific concern was that technology would deskill workers (https://www.google.com/books/edition/The_Playful_Entrepreneur/sadvDwAAQBAJ?hl=en&gbpv=1&bsq=luddite). And they were right. Taylorism, McDonaldization and other mechanisms expressly were intended and had the effect of breaking up skilled workers and artisans and making workers interchangeable and easier to fire. The criticism wasn’t of technology; it was of technology deployed to aid management over workers . The same technology could have been designed to make a small team of skilled workers produce much more efficiently. The systems always choose to deploy mechanisms that increase managerial power, and in fact they will do so even when it’s actually inefficient.
And when it comes to AI, the concern is that what will happen is that the slop machine will be used as a justification to attack wages and skilled workers (yes, in a sense irrationally, but the systems don’t care because the incentives line up). And, again, that is already happening .
And the very systemic robustness that does allow us to eventually adapt in the long term is all being repeatedly tested by Silicon Valley and fascism. We’re getting hit by technologies like social media that have immense long-term consequences incredibly rapidly. Again, the problem is not technology or even rapid development, it’s rapid development by especially cynical capitalists.
The Luddites were a jobs-and-wages movement, 100%. The idea that they were only “asking questions” about losing skills is simply not supported by the evidence. That’s like “lost cause” apologetics for the Confederacy.
It is also true that replacing a craft with a machine threatens to extinguish the craft, but whether that’s actually true (hand weavers still exist) or even a bad thing (do we really need that skill preserved as a prefession?) is a case-by-case question. And as with most panic narratives, the whole question is a non sequitur. Because if it mattered merely to preserve a skill, then that is an argument for funding small physical antiquarian societies (like we actually do), not for smashing machines or banning entire technologies.
The management over workers narrative is also false, insofar as that had been the case since at least the Roman Empire (when slave-wage mass production began). And family-based cottage industries already had the same poisonous tendencies (of creating social and wealth hierarchies, sweat-shop conditions, management over workers, and gatekeeping access to the industry—there was just as much shady shit in “cottage trade” than industrial; the only thing that really changed was who got to control it all and “rake” the surplus).
And the factories did “make a small team of skilled workers produce much more efficiently.” This is economically measurable and is one of the most pronounced effects of the industrial revolution across all industry metrics between 1650 and 1850. Worker productivity increased manyfold, production output manyfold more. The result was real wages substantially increased because the cost of manufactured goods substantially shrank, the inevitable effect of increasing productivity.
One area where this has been documented by someone who lived through it all is in machining: Fred Colvin, whose memoir goes up to the end of the 1940s but who personally lived to see robotic automobile manufacturing, after having started as a hand machinist in 1880 (Sixty Years with Men and Machines). The story doesn’t fit the Luddite fantasy (and do recall, the Luddite’s own story is a myth: there was no Ned Ludd and they never did any of the things they claimed for the 18th century, that was all mythology).
As for AI, I have yet to see it having any effect on wages. The attempt to use it to replace fast-food cashiers has gone nowhere, and what has played out instead is a standard labor war that is slowly increasing cashier wages and jobs. Yes, capitalists always fight and oppose this and thus slow it down, but that isn’t new. That’s been the case for centuries. And their particular success over labor the last thirty years is due to political capture, not new technologies.
Likewise the automation of cashiering in Walmart has not eliminated jobs: it simply moved them (to warehousing, stocking, delivery, etc.). Relative to store-count and revenue, Walmart employee-count has not meaningfully changed in ten years. And wages are increasing. So even successfully automating cashiering has no effect.
Ironically, so far, AI is killing its creators while increasing jobs and wages everywhere else: study, study, etc. (even studies claiming otherwise actually support this conclusion when you check their actual results against their inaccurately alarmist abstracts).
And actually, AI isn’t killing its creators either. Layoffs in tech are a result of general economic factors (future uncertainty is causing restructuring and downsizing), not AI.
So we just aren’t seeing the apocalyptic things AI-phobes are talking about. Until we get some real indicators supporting their narrative, we have no business buying into it. Because there is no evident reason to see it any differently than every other previous paradigm like it (machines, robots, computers).
Yes, the fact that consolidation through machines can have the effect of increasing stratification isn’t new, which doesn’t make that concern invalid. Their social systems also promoted hierarchy and the needs of management, just differently.
Factory systems did not routinely make small teams of highly skilled workers. The huge industrial armies and the McDonaldization of work is just reality. And real wages have stagnated recently, so it’s not a uniform procedure either. The real benefits for real wages were in the 1950s to 1970s, precisely when industrial systems finally were balanced enough by regulations and a market that was much more equitable evened out the problems of previous generations.
You can look at the coverage that Adam Conover did during and after the strike. AI was expressly used as a threat against screenwriters. I don’t think it’s actually a meaningful threat to blue collar workers yet, but middle-level professional workers are definitely at risk, again not because the systems work but because management can bluff that they think it’ll work well enough. Jobs have also already been cut: https://www.youtube.com/watch?v=YtMNeskPICM . (Now, would the companies have found some other excuse for those firings and layoffs without AI? Possibly).
My whole point is that you can’t separate the political capture from the new tech. In a system where political capture is high, then the tech will then be designed to further that capture and control. Uber fucking over taxis and their drivers. Indeed, the entire venture capital-funded gig economy. Again, that’s not inherent to the idea of “Let’s use tech to make taxis and buses more efficient”, it’s inherent to Silicon Valley developing specific tech modes. This I think is where the anti-tech people get it wrong: Tech can be developed and implemented in lots of ways, and we shouldn’t be surprised that corporations routinely find ways of maximizing the socially destructive potential of a technology… but that’s just an indication that corporations suck, not tech.
It definitely is quite funny that some of the worst disruptions have been to the very people making and pushing AI, but even that isn’t something to be too happy about. I’m not excited that a lot of devs may end up losing work. (Account executives a little less so).
And I don’t read the study you call alarmist as being as sanguine as you do. For example, they say that the expansion is slightly helping low-skilled workers, but that’s assuming that the AI business expansion is sustainable, and while it’s almost certainly not going to be an NFT-sized collapse, there absolutely could be a quite serious bubble due to it being over-hyped and pushed too hard… and then that would hurt those low-skilled workers.
I agree that what we’re seeing won’t be apocalyptic, probably. Uber wasn’t apocalyptic. AirBNB wasn’t apocalyptic. But that isn’t the same as positive, and definitely isn’t the same as it being positive without regulation . Unfortunately, our political system has become unable to deal with even far more serious problems than AI right at the time that we need legislators and executive leaders to be dealing with the intellectual property, libel and slander, and other major implications with a good regulatory framework.
AI will have no significant direct effect on jobs.
Indeed it’s the change in political-economic regime between 1950-1970 and 2000-2025 that is causing the decline of capitalist society. It is eating itself, just like Marxist societies do. Both worldviews have a vision that sounds nice, but always fails in practice, as control of resources simply gets hoarded by a narrow elite who inevitably mismanage it all and the remaining population becomes increasingly impoverished and stressed until the system collapses altogether.
But AI will have no effect on this story. It is just another tool that will shuffle the board but change no rules or averaged outcomes, and will be used for good and ill as any tool, but in twenty years no one will have any of idea of AI being any more radical or revolutionary than, again, robotics in manufacturing in the 1960s or computers in every industry in the 1980s or smartphones in every hand in the 2000s.
These don’t affect fundamental measures. They do not significantly affect the unemployment rate, the minimum wage, real wages, cost of living, or average household wealth, because those things are all driven by corporate and state decisions. In principle robotic factories and computers and smartphones and AI could improve all those things, but only if corporations and states decide to leverage them that way. And they never do. So I don’t expect they ever will.
And insofar as anyone tries to blame those things for worsening those metrics, it will be a red herring, because it won’t have been those things that did it—remove them causally from the timeline and there would be no change in outcome. Because they’ll just use whatever tools exist anyway. So it will always simply be the decisions of capitalists and their state cronies that make things worse, not the technologies that just by chance happen to exist in any given decade.
So AI panic is a problem because it is overblown and misdirected, distracting people from the real problem—which isn’t AI.
-:-
Look more closely. Look at what they measured (it isn’t wages or jobs; their chosen metric is irrelevant to those things) and its effect size (almost zero).
The reason its voiced panic is inapt is that they are making claims that don’t exist in the report. The report doesn’t even measure anything they are panicking about, and what it does measure is so insignificant in scale as to be laughable that it was even published.
This is what I mean.
No they haven’t. Media is unreliable. So are abstracts. Read the actual report, its actual methods and data, and hence whether its conclusion is even merited by its own data.
It’s not: look at their lead graph—almost all unexpected job cuts are in a single now-past spike when Trump announced tariffs, then cuts returned to the same baseline rate as the last three whole years, and then ticked up only slightly precisely as the August 1 tariff deadline approached—which AI cannot explain, but tariffs 100% entirely explains.
Also look at the effect size: 10k is so few as to be below margin of error for any causal analysis—it’s thus useless data.
Also look at the defective method: the study asked companies to “say” whether they cut jobs due to AI—and self-report of self-interested and dishonest capitalist bosses is never a reliable source of this kind of information—but never asked them to say whether they added any jobs due to AI, so the report was biased at its very methodological instrument to fake net loss numbers for AI, which invalidates all its results (you cannot use this report to argue net losses for any reason, much less AI, because it didn’t measure net).
Both flaws become apparent in the actual report’s Table 4 where inexplicably there were only 75 supposedly-AI-related cuts for the entire year (Jan, Feb, Mar, Apr, May, and June), and 10,300 lost in July. Explain to me how all those jobs were lost just last month alone. AI cannot be the reason. But the final August 1 implementation of Trump’s threatened tariffs does. Which signals a false-reporting effect: it looks like corporations are hiding their real reasons for sudden July layoffs behind something already unpopular (and which the Trump admin won’t scold them for blaming), thus circularly using the panic narrative to bolster that narrative in turn, to their advantage. Supporting that is the fact that these same corporations credited only half as many cuts to tariffs, a wildly implausible result given all other economic reporting on what is causing job loss, which is orders of magnitude more than that.
And so once we catch all these bullshit flags, we should be inspired to ask—who the fuck is “Challenger, Gray & Christmas,” the for-profit corporation writing this report? Oh, right, a company selling jobs placement services. Hm. I wonder why a company like that might want to inflate fears of AI job capture? Why would we trust them? Is the media really this dumb? Yes. Yes they are.
This is what I am talking about.
Leaping to any panic claim anyone makes simply because it exists, rather than checking whether it’s bullshit or not first. There simply is no evidence AI has led to any net loss in jobs, just as self check-out did not. And we need to pay attention to this. We need to wait for real evidence. Not pretend it already exists when it doesn’t.
I wrote a whole article on the importance of not falling victim to these kinds of bad-study-driven false narratives: Three Models of Critical Thinking: Remote Work, Generational Wealth, and Election Polling. We need to remember we are supposed to be critical thinkers.
None of this means AI won’t do anything to be worried about. Rather, the panic narrative so far is not supported by any actual evidence. So we need to not be selling it, any more than we should be selling the converse AI messiah narrative the corporations selling it are, which is just as factless.
[And remember, “labor share” metrics tell us nothing useful about the effect of AI on jobs or wages. See my comment below.]
Agreed on the problem being the systems. I think that the criticism people are offering is precisely, “AI can’t do the things you guys are saying it is, so you’re going to use it as an excuse to just not do things“. Like with screenplays. They won’t actually use an AI screenplay for the foreseeable future. But what they will do, unless blocked (and I think the writer’s strike did get some assurances in this regard), is say “Here’s an AI script, we’re going for it, now you rewrite it for scale”. For the cost of nothing, you get a script concept that your boardroom literally likes (because they wrote the prompt) and then you save a ton on the screenplay. And I am already seeing clearly AI-generated ads, which is denying work to people who are trying to get writing and acting work in ads. Ads don’t need to be any good on YouTube, they can just be slop. Just like how Uber was an excuse to destroy and worsen taxis using an app. (And why self-driving is really taking a long time: Why bother with all this R&D outside of key markets when you can just have poor people drive?)
And social problems don’t need to rise to the level of measurably impacting aggregate macroeconomics to be a serious issue. If they materially harm the wages of specific sectors while giving nothing of value back to society, that’s an issue. I agree that the apocalyptic concerns are silly (our economy is already so oriented at people doing tedious crap work that is already functionally so cheap that trying to replace it with AI is nonsense and they can’t even meaningfully do it any more than they have – e.g. customer phone lines are already as automated as they can be and the whole point of the phone banks is for when you need to talk to someone who either doesn’t understand automated prompts or has a problem that is outside of your first tier common issues), but the problems can be, again, at the scale of things like Uber and AirBNB… which are serious.
Their measure is labor share, which is compensation paid to workers. They’re detecting that that measure has gone down to high and medium-skilled workers and is offset by increases to low-skilled workers. That’s serious, especially if what is happening (and their data can’t support this but does suggest it as a serious possibility) is that high-skilled work is being transmuted by the systems into low-skilled work. That deskilling is very bad for people.
So I don’t think Minniti et al. have mentioned good enough controls, but at the same time they have a pretty clear measure of labor share and skill ratios, and they’ve demonstrated those have shifted. More importantly, it’s not terribly plausible that, say, tariffs would have the effect of shifting labor down-skill while also expanding the base. In particular, the fact that the effect tracks the doubling of AI investment means that no other variable makes a ton of sense: no other variable is going to be changing by multiples. I think they make a good argument that the only real culprit for what they’re surveying is AI. The effect they are surveying is small, precisely because their method does seem to be extracting technology effects as opposed to others.
(And, on the flipside, their positive, that the low-skill increase may be due to AI, is also only one hypothesis).
And for the Challenger Gray data: The fact that an effect hit in May, spreading outside of sectors which would be affected by DOGE and during a period where tariffs were of oscillating credibility thanks to TACO (and also would have hit different sectors), I think is indicative. I don’t think that’s smoke, not at least some fire. They’re obviously having to make inferences based off of a huge number of variables, but I think that plus the Minniti data is enough to reasonably conclude that some jobs are under threat because of deskilling.
https://futurism.com/ai-impossible-find-job makes a similar analysis from the jobs data for this year that I think is pretty plausible: companies are using AI as a threat (and also using it to justify layoffs they already would have done) and are also worried about money they’ve sunk in which is combining with tariff fears.
And while Challenger Gray obviously has a vested interest, their analysis includes when there have been positive spots or flat trends. They’re not just cherry-picking.
The effects have been around for too short of a period and have been paired with too much political instability to be sure, but again, the fact that it came up in the writer’s strike and is coming up in labor discussions tells me that it’s a reasonable concern. Of what magnitude? Hard to tell yet. I agree that doom and gloom (nor tech-optimism) is not reasonable to justify based on the data we have, but there are quite real concerns.
As for “The capitalists would do it anyways”: While that is broadly true, it is important to bear in mind that capitalists are not completely omnipotent in terms of using deceptive approaches. AI plausibly looks like something that could do what they’re saying, if you squint. In contrast, NFTs were so obviously a bubble even ahead of time that their impact on the economy really was marginal. (Crypto partially escaped containment but still mostly was just the obsession of drug dealers and gambling addicts). So I’d argue that LLMs are pretty perfect for these effects, and without LLMs existing (or, much more importantly, LLMs being deployed differently), other systems may recognize the problem much more effectively and not buy-in. And insofar as companies damage themselves or other companies (which will have knock-on effects – layoffs for example) from the hype, I think that can be blamed entirely on the AI.
Again, I really doubt we’re going to see even something like a 90s Internet bubble for AI bursting. Venture capitalists may lose their shirts in some of these applications, but they’re playing with money they can afford to lose. So the current scale of the problem is, I think, on the level of the gig economy: a worsening of already serious problems.
A concept Andor nailed: actually, prison labor is cheaper than droids.
Incorrect.
Labor share means the amount of an investment that goes to labor.
For example, a steel factory has a low labor share because most of its overhead is buying steel and coke.
Their study simply says that companies that invest in AI divert capital to hardware, software, etc., which is simply just always the case (every year capital is redirected to new things, like new cash registers, new market development, building new stores, etc.); doesn’t have anything to do with taking money away from labor (where the capital is directed from, or whether new capital is raised for it, is not being measured here); and their effect size (the amount of capital they claim is diverted) is so small as to be literally LOL.
I should also add:
That diverted investment (like every other) is paying someone. Every dollar diverted to investing in AI hardware, software, etc. is paying for jobs somewhere else (the people building the hardware, software, etc., and selling it, delivering it, maintaining it, and managing all those people, and all the support effects, e.g. for every dollar of this, some is going to pay for the guy who fills the coke machine in the AI development office, the janitors, the security guards, etc.).
Even the companies that are growing overhead share for AI acquisitions are also likely hiring or paying existing employees to use it. Which effect is also not being measured by “labor share.”
So “labor share” is a completely useless metric for our purposes here. It tells us nothing useful about the effect of AI on jobs or wages.
No, labor share is quite clearly (and is in Minnitti et al. ) a national metric. To quote Wikipedia: “In economics, the wage share or labor share is the part of national income, or the income of a particular economic sector, allocated to wages (labor). It is related to the capital or profit share, the part of income going to capital,[1] which is also known as the K–Y ratio.[2] The labor share is a key indicator for the distribution of income”. Minnitti clearly discuss national inequality metrics, so that is clearly the context they’re using it in. In their Appendix, they state clearly and repeatedly, “The dependent variable is the labor share, defined as the ratio of employees’ compensation to regional gross value added (at current prices)”. The regional gross value is not a firm-based calculation, it is the economic value of a given region. Regional gross value is how you calculate regional GDP: “It is the aggregate of gross value added (GVA) of all resident producer units in the region, and analogous to national gross domestic product”.
They’re not discussing the internal share of tasks in a firm. And, on the scale of a national economy, a small effect can affect hundreds of thousands of lives. As their conclusion states, “Our findings indicate that AI innovation is associated with a significant decline in the labor share, potentially accounting for up to one third of a percentage point of the overall decrease observed since the early 2000s. This highlights the notable impact of AI in exacerbating income inequality in terms of functional income distribution, particularly in regions more engaged in developing AI-related technologies, underscoring AI’s role in driving regional disparities in labor income distribution”. They’re referring to national macro statistics and their share is since the early 2000s so this effect when taking into account how relatively recent the AI we’re discussing is could be quite serious. (Also, I think that Minnitti et al. are using “AI” to mean computerized automation writ large, which means it’s not very useful for our purposes here, but they don’t have a good operational definition section).
So obviously it’s true that companies may lie about why they fired some workers to exaggerate the effect, but they also may lie when they did in fact fire someone for AI when they didn’t, and they mean fire folks to cover up for losses due to AI. So while that method is not ideal (it’s always hard to get at the internal strategy of companies), I don’t see a reason why it inherently biases one direction rather than the other. And if they add jobs due to AI, great, but that doesn’t help the folks who were laid off. If the result of the system is large-scale localized disruptions (for, again, systems that are of limited value), and then they also expand some enterprises for jobs that are likely to collapse if they realize these systems suck, that’s pretty bad.
And July was actually a really big month for AI. China announced they were going deep into it, AWS and Google had a number of new services, etc. Also, companies could very well have had an internal time (which lines up six months into the year) to determine if they were making redundancies, or that could have been when reportage happened. And I don’t find the idea that they’d blame AI, something that the capital sector is deeply interested in pitching as a total good, over tariffs, something most business have indicated is an issue (even against Trump’s retaliation), all that compelling either. I agree their method can’t sort that out.
As https://fortune.com/2025/08/08/ai-layoffs-jobs-market-shrinks-entry-level/ points out (though it definitely is frustrating that it is only the Challenger study on this topic), “Layoffs are surging in the U.S., with companies announcing more than 806,000 job cuts so far in 2025, the highest figure for that period since 2020, according to Challenger, Gray, & Christmas. The tech sector has been hit the hardest, with over 89,000 layoffs in the industry alone. The firm found that more than 27,000 tech job losses since 2023 have been directly attributed to AI-driven redundancy, as companies streamline operations and restructure departments. At the same time, companies are becoming more selective about who and where they hire. Entry-level roles are feeling the worst of this impact as the technology is increasingly good at automating junior-level work. Many firms are seeing easy cost-cutting opportunities at the entry level. “A lot of entry-level work when you’re fresh out of college is knowledge-intensive jobs where you’re collecting data, transcribing data, and putting together basic visualizations, and learning the organization from the ground up,” Tristan L. Botelho, associate professor of organizational behavior at Yale School of Management, told Fortune. “AI can do that quite well, and I’ve heard many managers say things like: ‘We can reduce our entry-level headcount.’ … The biggest disruption is likely among these low-level employees, particularly where work is predictable, tech-savvy, or more general'”. So that’s corroboration from experts, including McKinsey saying they are actively using AI and using it to make employees redundant. And the fact that the annual job cuts are not only so large but consolidated in tech (which, even given component costs going up from tariffs, is internationally diversified enough and service-centered enough to not be really as affected by tariffs as much more capital-intensive companies are, and also got some exemptions: https://www.ainvest.com/news/political-cost-tariffs-impact-tech-sector-valuations-2508/) I think is indicative of a potential trend. (Of course, tech companies are famous for bullshit layoffs, so that could easily be them just preparing for a lean time and using AI as an excuse, but it could also be them getting sucked into the AI hype, and the number of people in this Fortune article testifying to the latter makes me dubious that it’s all purely dishonest).
That diverted investment could be paying someone if it doesn’t end up being locked into various financial instruments due to our crap monetary velocity. But if it does, it could be going to massive consulting firms, tech firms with huge internal inequality, etc. etc. It is wholly possible for companies to go from investing into something that created hundreds of thousands of jobs to invest into something that only creates thousands. That’s one of the ways you can get medium-term systemic unemployment.
I didn’t say labor share can’t be measured for a nation. I said it doesn’t measure wages or jobs. Obviously the same metric can be aggregated for all companies not just one company. But it’s still the same thing. The rest follows (including the laughable effect size etc.).
Hence there is no evidence here that AI is killing jobs or lowering wages.
Correct. The reduced labor share and the switch of pay from high to low skill work do not necessarily mean a reduction in wages… but if the labor share goes down and the population hasn’t changed (or if profits/GDP have not increased absolutely massively), it inherently will mean reduction in wages. More importantly, what Minnitti et al. are identifying (and not just from “AI” in the sense of LLMs but decades of various forms of automation – which makes citing them as a study for AI in the current sense actually even more useless) is, effectively, an increase in inequality and a decrease in skills, and at the scale of huge regions. This is a serious concern (we should be trying to reduce not increase inequality), and the magnitude of that shift from higher and medium skilled to lower skilled workers (obscured by the relatively small magnitude of the general labor share decline) is worth addressing. Now, again, we agree that the concern isn’t automation per se but its implementation and specifics, as well as the broader political climate.
Cartesian certainty isn’t a game. It’s literally viscerally, obviously true. Yes, you can put into the gamefied “language” of logic, but logic is a language designed to effectively be a meta-language and determine what is even comprehensible and determinable. If you find something uncontestably obvious under logic, it’s perfectly fine to take it as something that is literally incoherent to deny.
The problem with all pragmatic approaches is that you need to ask, “But why should something be useful to me? And what makes propositions useful?” And the only reasonable inferences are, “Because I am a being with a set of facts I do not control about my needs and what will fulfill me, thus leading me to needing to engage with what appears to be an external world; and because of the coherence, scope and consistency of what appears to be external makes it actually being external the best hypothesis, and that allows me to make useful decisions”. I agree that the pragmatist getting you to stop worrying about “But is it really, really true?” is, again, a practically good set of priorities.
Rorty is a deeply flawed philosopher, and I think Michael Albert engaged with him quite effectively.
Yeah, the AI doesn’t understand what it’s talking about. It just finds these words are found together more often and so puts them together in the blind hope that it will match how a human who knew what they were talking about would arrange them. But the internet is full of mostly idiots and poor contextualization or quality control, so its guesses based on frequencies there will fail the more complex the subject.
Hence, for example, it fails to understand the difference between beliefs (which are phenomena) and propositions (which are the units of logic), and then based on that mistake, it looks for all the wrong frequencies of juxtaposition for the remainder of the text.
Likewise that it thinks Rorty even is relevant to this article is a classic AI screw-up that sets it on completely the wrong word frequency game. It made that mistake because the word “Cartesian” appears in relation to Rorty’s epistemology, but never in the way I am using it. He uses the concept as referring to “certain knowledge” (i.e. not uninterpreted but interpreted sense experience), which I am denying as much as he did. The AI is dumb so it cannot tell that this is the case. It just sees words juxtaposed and blindy juxtaposes more words, getting everything so wrong it’s kind of like what is often called “not even wrong.”
And because only experts can see this, AI is pernicious. People think it can replace experts—but they only think that because they aren’t experts and thus can’t tell when it’s gaslighting them with mistakes, conflations, and hallucinations.
To be fair to the AI (gag), I actually think Rorty would say nonsense like what is being said here. Perhaps I am being too unfair to him, but having had to debate him in high school (he’s the default Kritik answer if you want to attack Foucault or some other postmodern critique from “within the club” to speak), he really does make errors this basic. At least Derrida wanted the reader to interrogate the text and understand all of its motives and incompleteness, and other postmodernists have an ethos of critical thought and thinking about concepts and rhetoric. Rorty can’t even do that.
Oh yes, for sure.
Indeed, Rorty’s incoherent and incorrect use of “Cartesian” as an adjective is itself an example of what you are saying here.
But unlike high school kids, AI will never be able to understand that or even notice it, much less explain it.
You referenced your article on the ontology of logic here a few times. I was wondering if you are considering responding to Dr Alex Malpass and his recent review/critique of that article?
I’m a fan of both you and him so it’d be interesting to see you discuss it.
Definitely. Expect something sometime this month (exactly what is still being discussed).
Awesome. Looking forward to it.
Also I had been meaning to ask for a little while and I think it’s relevant to this discussion but would you consider writing about your thoughts on the distinction between logic, metaphysics, and physics? I think I recall you saying that you find metaphysics to be a problematic or unnecessary category and you disagree with Kripke on a posterior metaphysical truths as being distinct from logically necessary or physically necessary truths.
I find myself leaning towards the conclusion that what people mean when they say metaphysical is either just logical or physical and so it’s not a useful third category.
Maybe this will come up in your response to Dr Malpass.
Thanks again
Maybe (I haven’t listened to it yet; there are now two videos, so about four hours to get to!). But that is an applicable example (The Ontology of Logic is, in effect, exactly about that, it just doesn’t use those exact words or name drop Kripke).
Note that I have already covered this in a de facto sense in Sense and Goodness without God (and I am working on a new edition of that for next year). Metaphysics is there described as just physics with less data. There is no categorical or ontological but only an epistemic distinction there. As for where logic relates to those, that’s described in SAG in the same way elaborated in The Ontology of Logic.
But as for Kripke specifically, see the comment thread here.
The point I always liked in Hume’s work was that your need for certainty is ultimately one that needs to be set to your application.
Ultimately, the answer to “How do you know that you’re not a brain in a jar?” is “It’s exceedingly unlikely and if it were true it would have no impact on my life so I’m not going to expend cognitive and physical resources investigating it until such time as it actually seems to have a vanishing possibility of both being true and mattering”.
We care a lot more about knowing and testing the failure rate of airplanes than spoons. That’s wholly appropriate.
You achieve a level of certainty appropriate for the context and then make decisions based on that appropriate level of certainty. Anything beyond that, while interesting philosophically, is not practically necessary or even useful.
That’s a good way of putting it.
In this article I didn’t go into risk theory, that epistemology is actually modulated to risk, so that limited epistemic resources are only rationally proportioned to the cost of being wrong. This is true at all levels of analysis, e.g. even at whether I should believe the Brewers beat the Braves (or doubt it) or believe the earth is round (or doubt it) or believe my car is safe to drive (or doubt it), the amount of time and other resources I should spend vetting those beliefs is a function of the cost of being wrong, which is greater in respect to my car than the Earth, and greater in respect to the Earth than the Brewers. But this carries all the way down to radical skepticism, i.e. the cost of misbelieving you aren’t a brain in a vat is actually phenomenally low as well as it being extremely improbable. So the total risk of being wrong about that is functionally zero.
I suggest that before any rational argument can begin, participants must define all terms. What do,we all mean by “God”? I think Anselm of Canterbury had the best definition. He wrote in Latin, but I guess the customary English version is as accurate as we can get in that language with our customary usage: “a being none greater than which none can be conceived.”
But what is a “being”? Here we have the issue of the difference between being and existence. Did Anselm recognize that (in Latin)? Let’s say “maybe. If so, we must assume that God “is” (Descartes’ “sum”) and the world “exists” in these terms.
Interestingly, the sages in Kashmir around the same time as Anselm had a similar understanding. Aphorism 1 of the Shiva Sutras: Chaitonyam Atman. God is Consciousness.
Nothing exists that is not that Consciousness. Anselm would have agreed. Holographic theory would complete the view.
—Jack Hill
Sorry, I do not understand the relevance of this remark to the article here or even what point you mean to be making.