D-Machine

CAR-T is not a cure in any sense of the word, in part because "cure" is just not a scientifically valid concept in oncology. I would know, CAR-T saved my life, but this was at great cost and is not even remotely close to a guarantee, and the side-effects can be beyond devastating https://news.ycombinator.com/item?id=49827669. At best, CAR-T is more like a tradeoff: often just a coin-flip's chance to live, for long-term—maybe even permanent—life-shattering consequences.

Correct. The people here saying CAR-T is a cure have no clue at all what they are talking about, and I say this as someone that is only alive right now because of CAR-T https://news.ycombinator.com/item?id=49827669

There is no world in which CAR-T is a "cure", especially since this isn't even a scientific term in oncology. We generally say if there is no relapse after 5 years post-treatment, then any cancer is a "new" cancer, so 5 years of remission is the closest thing to a "cure", but this isn't a scientific term.

Also, we barely have more than 3 years data for CAR-T for most cancers. And even still, the survival rates aren't great, in many cases 50% compared to e.g. ~20% for previous chemo-immunotherapies plus marrow transplants. And this ignores how massively immunocompromised (or so permanently brain-damaged you are effectively senile) CAR-T can leave you. You can be severely immunocompromised (literally identical to or worse than AIDS / late-stage HIV) for at least a year in close to half of cases, but maybe even permanently, in perhaps as high as 10% of cases (at least for lymphomas).

I say this as a person that is only alive because of CAR-T treatment 1.5 years ago. CAR-T is amazing, and a far better treatment than previous treatments, but calling it a "cure" is deeply misleading and mostly clueless. Currently, it is simply a much better last-ditch effort than the previous ones.

HN is now heavily astroturfed or overrun by bots (or, alternately, low-quality posters now indistinguishable from the previous), and this is especially so in AI-related threads.

One tell is that most comments barely exceed one or two sentences (because otherwise AI detection gets easier and much more reliable), when this was not as much the case many years ago. The drive-by comments are also low / zero quality, mostly expressing a feeling or agreement/disagreement, and primarily driven by ideology or pre-existing beliefs and commitments.

Look at non-AI-related threads and you'll notice a large distribution shift relative to AI-related ones.

EDIT: Basically HN is orange Plebbit now. If you doubt this, compare HN discussions to those on e.g. lobste.rs, LessWrong, The Motte, DSL, ACX, or other old obscure forums. You'll notice those places have their own very serious biases and problems, but it is obvious the vast majority of posters are nevertheless human and making some minimal efforts.

Now compare Reddit and 2026 HN to the above, and see if you can confidently say the same.

Falling into deranged ideological projection is also an interesting choice - one I chose to mostly ignore. You seem to really obsess a lot about this Yarvin fellow: I tried reading his stuff once and found it intolerable.

I imagine you think calling some of my language choice a "slur" here is some kind of gotcha, when the term I used is specifically one widely disputed as actually being offensive, given it is mostly used now to refer to normal people acting in intellectually deficient ways, and not generally to those with actual learning disabilities that deserve our sympathy. There are studies on this, which you surely are aware of.

If I had referred to your more deranged positions as "smooth-brained halfwit extremes", you likely wouldn't haven't tried to impotently pull this "slur" card, even though the semantics are basically identical. Which basically goes to show that you value irrelevant surfaces over substantial realities, and frankly is perfectly consistent with the midwit intelligence denialism on display in your posts in this exchange.

> Cognitive disparity is because of compounding investment of attention and metacognition in development [...]

Sure, but exclusively? There are no other factors that don't depend on effort / investment / social context?

I can't take you seriously when you take such an absolutist stance on these things when science has long since accepted nothing complex about humans is 100% nature or 100% nurture (really, shared vs. non-shared environment vs. genetics: but, surely you know this).

"Abstraction ceiling" is just a way to talk about intelligence at at the tails that reveals one of the difficulties / limitations you can encounter when it comes to compressing / abstracting complex mathematical objects. Your objections to the term are facile and clearly stem from an obviously unvocalized intelligence denialism that is simply indefensible today. Also, intelligence != IQ, obviously this is too simplistic.

Everything about your arguments and other posts is similar reductions to retarded extremes (our only options are "eugenics 2.0" or deranged intelligence denialism - there is no room for anything in between, e.g. the idea that base intelligence matters and sets a hard average ceiling on potential, but that effort and other factors might push one slightly above/below this ceiling relative to others with a similar intelligence, and etc). Or alternately you hallucinate things I never said or even remotely implied (e.g. we should gatekeep based on dumb psychology metrics or hand sizes).

Just be honest: you know intelligence is real and matters, but you want to dance around this fact because you find it ideologically inconvenient, or you can't admit you yourself have limits (and lack the courage to realize the obvious social broader consequences of this personal admission).

> But everyone is as a kid when they learn language and everything else

Also clearly false by almost all current research.

> So the task is just to get people back into that mindset.

Again, you have no evidence, and this is clearly wrong in cases of mental retardation or brain damage. Modern genetic studies also seem to suggest intelligence is related to a lucky absence of errors / genetic problems that are otherwise inconsequential (or even advantageous) in other domains, so really, your "everyone starts perfectly equal in intellectual ability" is just empirically disconnected and ignorant fantasy.

Uh huh. A bunch of quasi-mystical bullshit to justify utterly inept garden variety intelligence denialism (it also just shifts terminology: if we accept your metaphysics, it would still be strange to propose that everyone has exactly equal "hacking" ability).

The hubris and willful ignorance required to imagine that everyone is just equally and infinitely unbounded in their cognitive ability is simply mind-boggling in 2026.

IMO this would track, applied mathematics (even e.g. data science, though perhaps calling that applied math is a bit generous / insulting to more serious applied math) is in some ways more exciting now because it is far easier to surface complex / appropriate methods for the task at hand, and you can more confidently explore these methods because the AI sort of "has your back" in catching some of the more obvious beginner errors you make during these explorations. Plus, applied math feels roughly more results- than process-focused, compared to pure math.

IMO the divide here between pure vs. applied math feels a lot like the divide between those who enjoyed coding for the understanding it led to, i.e. the writing itself was the joy, vs. those that primarily coded for the results. I enjoy the creative part of coding, the thought of software jobs just devolving into writing specifications and doing code review very much kills it for me.

Find me the magical teaching method that can make anyone learn any kind of mathematics at nearly the same rate, and you have falsified the idea that anyone has mathematical limits.

Given we as a society can't even figure out how to do this for educating stuff involving simple fractions, my theory is far superior than whatever exactly it is you think.

> If a program always have the same behaviour, it's a pretty bad program

This is false in far more cases than the cases where it is true, and even in the cases where you are right, you still usually want similar behaviour.

> You need inputs and, as demonstrated by the recent LLM inputs, parameters you can tune to correctly address a specific context

You seem very confused about what "static" means here.

> I would also contest the point about science being social. To me, science is the interplay between social constructions and physical reality.

Well, almost all philosophers of science would disagree with you, and IMO this sentence immediately contradicts itself.

Frankly, you should really work on learning to write more coherently and carefully. You maybe have some good ideas, but they are being communicated extremely poorly and inconsistently.

Work on your reading comprehension, I made it clear that increased effort is what an abstraction ceiling feels like, but also made it clear that GP could have been talking about the effort of academic busywork.

Let's also not pretend that "you could have learned epsilon delta proofs, you just didn't try hard enough or your teachers weren't competent" or "you just didn't have enough time" and etc. is also not rude and presumptuous. Denying the existence of such limits is equally offensive.

Here I would maybe argue that the simplicity makes these kinds of tasks tedious (like doing taxes), so it also makes a lot of sense for people to want to just throw AI at it. Tedious, easy rote tasks can be much more unpleasant than engaging ones.

I think people massively over-rely on AI and I really worry about the consequences of this. But there is nothing baffling at all about the basic appeal, IMO.

You clearly don't understand the meaning of the term. Grothendieck was almost certainly wrong about his gifts here, and even if not, your mathematical ability and output isn't fully explained by your ability ceiling.

Honestly, the pushback on this post is utterly baffling. Clearly the human mind has limits on what it can comprehend and the rate at which it can learn difficult things. Clearly these limits differ among individuals and are related to intelligence broadly.

Huge proportions of the population struggle to ever even grasp simple fractions, and not for a lack of effort from them or society. Fourth-year undergraduate mathematics is another beast entirely. Pretending the world is otherwise is pure fantasy and also plainly harmful, to the world and people that are unfairly pushed beyond their capabilities.

Of course, every area has similar issues. The main unique problem with (contemporary) academia is the one I mentioned:

> tech and industry, where producing junk actually has real negative economic and personal consequences

In academia, you can just endlessly produce low-quality garbage, and basically make a career out of this. In industry, things more often eventually at least have to work and survive contact with reality. Academia mostly lacks this basic check.

The scientific method should be the north star, sure. Much of what is happening in academia is cargo-cult / degenerate / pathological science though.

Of course "burning it to the ground" is rhetoric and not meant literally. It is meant to convey though that "nice" and "gentle" solutions might not really be enough here.

Yes, for the most part the fixes have to be in terms of funding and incentives. Funding needs to be more careful, and more careful funding can be a carrot rather than a stick here.

Re: incentives, IMO we clearly need a stick: there need to be harsh negative consequences for engaging in degenerate research programs and methods that have clearly been shown to result in pathological or cargo-cult science. Null-hypothesis significance testing is one clear practice that needs to go, but building entire fields on phony / meaningless uncalibrated metrics (think: a lot of self-report instruments that are never properly calibrated to objective outcomes or real-world behaviours and/or consequences, with results being reported only as standardized effect sizes) are another more pernicious practice permeating far too many fields. Ideological bias also needs to have funding consequences. Replication issues are still only surface problems in many fields, where the research would still all be worthless even if it replicated 100% perfectly.

> Your analysis completely ignores the physical and biological sciences and the humanities

I admitted later to painting with a broad brush, and yes, it is always hard to generalize and cover everything fairly. But IMO humanities has serious ideological and methodological rigor problems as well, and is overdue for disciplining. I would tend to have stronger positive feelings toward the biological sciences generally, yes. Yes, the social sciences are the major source of the problem (in part because they are so bad they tarnish the reputation of all academia).

> These fields have gotten a lot better over the past decade in the wake of the replication crisis. Preregistration, publishing all code and data, reporting null findings, replicating results, etc. are becoming the norm.

IMO "a lot better" is subjective, and I don't see those things as being the norm yet (beyond as lip-service), and the rate is far too slow. I agree we'll get there eventually, but I am worried about the loss of public trust and thus the production of real knowledge if we don't try a bit harder at this. Plus, globally, countries like China do seem to be more willing to actively crack down on research misconduct, at least in the past years, and it might not be unrelated to them increasingly pulling ahead technologically in many areas.

We have mountains of evidence that humans differ dramatically in cognitive potential, and more again that often effort / practice can only explain a small amount of the variance in performance in a wide variety of fields. We have basically zero evidence at all that anyone can just learn anything if they try hard enough under the right teacher, and plenty of evidence to the contrary.

Abstraction ceilings are about rates and difficulty of learning, so even if we assumed the (absurd) claim that no one has any fundamental cognitive limits, until we are immortal, being slow enough still creates an effective ceiling.

Intelligence denialism is the incoherent and indefensible position here.

Writing is pretty hard for a lot of people, maybe especially so if they are more non-verbal thinkers, and then doubly so again if one must write not in one's native language.

AI is very bad at properly handling statements that make heavy use of vague quantifiers (e.g. "some", "most") and also commits a lot of pretty serious logical fallacies. It is also bad at handling subtle logical negation, generally.

One of the most egregious negation issues I run into a lot is when I (or someone) makes a statement of the form: "not X" or "X is thus not true", and the AI then proceeds to interpret or summarize this as 'whatever is the opposite of X is the case'". This will cause it to go down a useless path investigating or disputing the opposite of X, which generally has no relevance or bearing on anything.

It also often very harmfully will replace your carefully chosen words with weirdly specific academic operationalizations or formalisms, then again waste huge amounts of text refuting / showing "problems" that result from that formalism, all of which again have no bearing or relevance on the original statement. An example would be you saying something like "intelligence, generally, must surely explain some of the differences in X", and then it will go "actually IQ does not correlate with X", unless you specifically tell it not to conflate psychometric IQ with intelligence generally.

Sometimes this is helpful, but the more specific / technical the domain, the more often you specifically have to prevent it from going down stupid paths that should be obvious given the expert context and wording, because it can seem almost hungry to try to catch you in some kind of insipid 'gotcha'. Much of these issues often clearly arise immediately from the first-pass "reword what the user said" part, given the reasoning traces.

And also to think in different directions than you would have gone in isolation. Regardless, this is a great heuristic / rule that I will be sharing and keeping in mind.

This feels very related to the issues re: the presence or absence of world models in LLMs. Insofar as they have world models (or "intuitions"), these would seem to have to be primarily verbal-linguistic (or symbolic, when using math). LLM world models are not likely (currently) very spatial, in contrast to e.g. V-JEPA-2 models, which likely do have some basic spatial models (and perhaps "intuitions").

> Eh. I'm a math PhD who fled academia because it was too much for me. But I have never encountered this term "abstraction ceiling" nor did I succumb to it.

This sounds a lot like you may have in fact succumbed to your abstraction ceiling, because in practice, the ceiling manifests as not as it being impossible for you to learn something, but that it would take you years and inordinate effort to master what you notice others mastering easily in just a fraction of the time. You may have not heard the exact term (comes from Douglas Hofstadter), and you may be talking about just the academic busywork, but I find it hard to believe you never encountered discussions about this kind of stuff. I would also politely suggest that unless you are Terry Tao posting under some kind of alt, you most certainly do have an abstraction ceiling (or your own mathematical limits) too.

> It's a notoriously hard subject to teach, and with all the demands placed on e-d in so little time in your average curriculum, it doesn't require appeals to IQ to explain its infamy. With enough motivation and practice, the quantifier alternation is comprehensible to any sound mind

The latter statement is obviously false, but regardless, intelligence explains some of the difficulty, and much other difficulties far more parsimoniously than "everyone could just learn any math if they just tried hard enough and had good enough teachers". E-d is merely an obvious and generally familiar example, and nothing I said really relies on this very specific aspect of maths, obviously. We also shouldn't pretend your (almost certainly false) view of math and intelligence isn't also often harmful to struggling students in its own way.

There's an old joke about funding, goes something like:

"Why you are always demanding more funding? Why can't you be more like the mathematicians, all they need is a desk, some paper, and a pencil, and a garbage can, and they just do fine. Or how about philosophy, for that matter? They don't even need the garbage can"

I mean, obviously with modern computational mathematics, this doesn't hold so simply, but there is this confound about math research also not getting much funding also because much of it isn't that expensive, relatively speaking.

This is tricky, because, in fact, hard math having an intelligence floor is one of the nastier realities of the human condition. Anyone who is even quite intelligent but has really pursued the rigorous stuff, unless they are in fact a prodigy, eventually realizes they have an abstraction ceiling (and this term is a common one thrown around in people studying mathematics, because intelligence denial is so obviously false when you do hit your abstraction ceiling).

Most people are correct that they lack the intelligence / mind for a lot of hard math (even epsilon-delta proofs are enough to eliminate the majority of the population, no matter how good a teacher you are, and these are just basic undergrad calc).

And yeah, sure, people have different kinds of intelligence and such, but there is still a g-factor, and people of low intelligence almost universally can't do hard math, whereas most people who can do e.g. advanced undergrad math can generally do almost all other advanced undergrad fields reasonably well. The world isn't fair here.

Make analysis code available. Make anonymized data available for download without people having to jump through hoops to get it. If you have highly sensitive data, release only the variables or other statistics needed to reproduce core analyses. Don't only do garbage null-hypothesis significance testing or statistical analyses on the full data, also do ML approaches were you have to actually show your analyses replicate on held-out subsets, and report this. Make reviews open (anonymizing as needed) so we can see when biased or incompetent reviewers are blocking good publications. Allow public review (or at least broader academic open review, in some form), since it is no longer defensible to delegate review and decisions to one or two random people that just happen to be emailed and have the time / are on some editorial / review board. Also allow public post-publication review. Publish null findings / results, if only in minimal forms so we don't waste time and money trying to reproduce garbage. Make articles available and don't charge insane article processing fees or open access fees of thousands of USD (especially since hosting fees are not that crazy, and also because journals don't do any of the formatting work half the time anyway, and make academics or RAs or students do all the typesetting and formatting, even though now this could all be automated with template files, mostly).

Most of these things are easy to do for the majority of papers, especially in the past 20 years with the internet and modern tech and software. Plenty of frameworks exist already that have done most and/or at least some of these things, but, collectively, academia is decades behind overall.

> it's now widely expected that a high impact pub will make all data and code available for review, and then publicly available upon manuscript publication

I am also in academia and regardless, factually this is not true at all for data, not even remotely (less than like 10% of journals even have data availability policies which are recommendations, and in practice only a small percentage of papers actually make anything available), unless by "publicly available" you mean "available to some academics or academic labs after an often tedious and slow approval process requiring an academic email and various signed agreements". Maybe what you are saying is true in some very specific domains (e.g. machine learning research), but in general what you are saying here is IMO wildly out of touch with present realities in the vast majority of fields, but especially those involving human subjects.

> While I have some sympathy with a lot of your bitterness, this statement is insulting silliness that a quick look at the list of Nobel Prizes in physiology and medicine would prove wrong. Almost all major breakthroughs in the applied sphere stem from decades of basic research that happened just because it interested someone.

Nobel Prizes are so rare they don't speak at all to the generalizations I am making here. Also, much medical academic research is arguably successful because it is in fact ultimately industry-funded or tied to industry. It is of course though highly dependent on the academic subfield, for sure, and I was painting with a broad brush.

If I had to narrow things, STEM academic research isn't so bad, so long as we exclude social science from STEM. Much social science research needs to be defunded ASAP. And I'm not claiming industry research doesn't also have warped incentives. But, on balance, I'd wager outside of pure math/physics and certain more algorithmic/pure domains in comp sci, the smartest people today are going to choose (and be found in) industry, not academia.

Yup, strongly agree with all of this, especially the RCT stuff.

This has all been profoundly obvious for at least well over a decade or even two now. A consequence has been that too many serious people are driven away from academia and research, to the detriment of science generally.

I've no idea what to do about all this, because people have voiced obvious and easy solutions for decades, but they are all routinely ignored.

I think it is worse than that. Science is a process for resolving disagreements, ambiguity, and uncertainty, and also for discovering abstractions (patterns) among phenomena. It is social and can not be reduced to a binary / digital file, as it is dynamic and ongoing, and, fundamentally, exploratory.

Software is a static program and basically none of these things.

Software development is kind of like science, in some ways, in that you discover abstractions and patterns, and this requires resolving disagreements and ambiguity between you and your users, but in the end, the user demands are usually fairly concrete and specific (though no one may know how to express those demands precisely, initially), and the process is not really exploratory in the way science is.

It just really isn't a very good comparison IMO.

Science should be more like this, in current times, yes.

But until much of academia is burned to the ground, or until science can be properly separated from modern academia, this will never be so. The current academic incentives are all wrong: low-quality research is rewarded and results in publications, whereas high-quality research (that takes time, and usually reveals that most exciting publications depend on p-hacking or other highly data-dependent analyses and selective presentations) is not published or actively blocked during peer review.

So instead you get BS arguments about how data can't be released for various privacy concerns (when in reality the vast majority of most datasets are trivial to scrub of identifying factors, and even in more complex datasets where you need to consider k-anonymity, it is still trivial to release data that allows replication of core analyses), and academic science is increasingly irrelevant unless it is tied to tech and industry, where producing junk actually has real negative economic and personal consequences.

I don't know what world this article / post lives in, but it isn't the messy world of actual reality.