You can argue that current market multiples are higher than 1929 [1] - and they're certainly high - but this also ignores the mechanism that drove that crash, focusing only on the symptoms. We simply aren't doing the kind of consumer margin buying that drove the '29 crash. It isn't even close. Average schlubs were leveraged to the stratosphere to buy shares of boring industrial stocks.
> The US stock market has nearly tripled since then. Literally the best period of stock growth in history.
The only thing I meant to point out was that a very high stock price by itself is no guarantee that there isn't a crisis around the corner. We plugged a lot of holes after 2008 and then reversed a lot of those fixes, I hear retail investors talking about their stocks at birthday parties again. Deja vu... of course this time it will be different. Or not. Let's just say that with the proverbial bull in the earthenware goods store on the loose if we only end up with another financial crisis that might actually not be so bad.
I actually calculated wrong. It went up 7.5x, not 3x.
In the roaring twenties stockbrokers allowed clients 10:1 margin. Investors were not as well-informed as they are today. There was no deposit insurance.
The SEC wasn't nearly as powerful as it was in 2024 and there was way more shady shit going on. In that respect, and the repeal of Glass-Steagall we're reverting to the pre-depression era.
Because it’s an inverted claim of falsification it works for literally anything (I cannot prove that X will absolutely not hurt you), but you get pilloried if you put something in the blank that the herd happens to support.
We’ve reached the absurd point where all sides of the political spectrum have sacred cows, and an exceedingly poor understanding of scientific reasoning, and all sides also try to dunk on the others by claiming scientific authority.
You found a paper saying that contamination is possible. That doesn’t mean that most of these plastic studies are doing the necessary controls, let alone the (almost impossible) task of preventing the contamination in a laboratory setting where nanomolar detection levels are used to make broad claims.
Are more “controls” what is necessary here? The problem wasn’t plastic contamination, it was the presence of stearates. Distinguishing between stearates and microplastics sounds like a classification problem, not a control problem.
There is practically universal recognition among microplastics researchers that contamination is possible and that strong quality controls are needed, and to be transparent and reproducible, they have a habit of documenting their methodology. Many papers and discussions suggest avoiding all plastics as part of the methodology, e.g. “Do’s and don’ts of microplastic research: a comprehensive guide” https://www.oaepublish.com/articles/wecn.2023.61
Another thing to consider is that papers generally compare against baseline/control samples, and overestimating microplastics in baseline samples may lead to a lower ratio of reported microplastics in the test samples, not higher.
Many papers in this field are missing obvious controls, but you’re correct that controls alone are insufficient to solve this problem.
When you are taking measurements at the detection limit of any molecule that is widespread in the environment, you are going to have a difficult time of distinguishing signal from background. This requires sampling and replication and rigorous application of statistical inference.
> Another thing to consider is that papers generally compare against baseline/control samples,
Right, that’s what a control is.
> and overestimating microplastics in baseline samples may lead to a lower ratio of reported microplastics in the test samples, not higher.
There’s no such thing as “overestimating in baseline samples”, unless you’re just doing a different measurement entirely.
What you’re trying to say is that if there’s a chemical everywhere, the prevalence makes it harder to claim that small measurement differences in the “treatment” arm are significant. This is a feature, not a bug.
You’re still bringing up different issues than this article we are commenting on.
> There’s no such thing as “overestimating in baseline samples”
What do you mean? Contamination and mis-measurement of control samples is a thing that actually happens all the time, and invalidates experiments when discovered.
> What you’re trying to say is that if there’s a chemical everywhere, the prevalence makes it harder to claim that small measurement differences in the “treatment” arm are significant.
No. What I was trying to say is that if the control is either mis-measured, for example by accidentally counting stearates as microplastics, or contaminated, then the summary outcome may underestimate or understate the prevalence of microplastics in the test sample, even though the measurement over-estimated it.
> What do you mean? Contamination and mis-measurement of control samples is a thing that actually happens all the time, and invalidates experiments when discovered.
The entire point of a control is to test for that sort of contamination (or more generally, for malfunctions in the experimental workflow). In the case of a negative control, specifically, you're looking for an "positive" where one should not exist. If an experiment is set up such that you can obtain differential contamination in the controls but not the experimental arms, as you've described, then the entire experiment is invalid.
> What I was trying to say is that if the control is either mis-measured, for example by accidentally counting stearates as microplastics, or contaminated, then the summary outcome may underestimate or understate the prevalence of microplastics in the test sample, even though the measurement over-estimated it.
The control cannot be "mis-measured", any more or less than the other arms can be "mis-measured". You treat them identically, otherwise the control is not a control. Neither example you've given are exceptions: if the assay mistakes chemical B for chemical A, then it will also do so for the non-controls. If the experimental process contaminates the controls, it will also contaminate the non-controls.
What you're missing is that there's no absolute "correct" measurement -- yes, the control may itself be contaminated with something you don't even know about, thus "understating" the absolute measurement of whatever thing you're looking for, but the absolute measurement was never the goal. You're looking for between-group differences, nothing more.
Just to make it clearer, if I were going to run an extremely naïve experiment of this sort (i.e. detection of trace chemical contamination C via super-sensitive assay A) with any hope of validity, I'd want to do multiple replications of a dilution series, each with independent negative and positive controls. I'd then use something like ANOVA to look for significant deviations across the group means. This is like the "science 101" version of the experimental design. Any failure of any control means the experiment goes in the trash. Any "significant" result that doesn't follow the expected dilution series patterns, again, goes in the trash.
(This is, of course, after doing everything you can to mitigate for baseline levels of the contaminant in the lab environment, which is a process that itself probably requires multiple failed iterations of the experiment I just described.)
Most of the plastic contamination papers I have read are far, far from even that naïve baseline.
> The entire point of a control is to test for that sort of contamination
No, the point of a control is to give you a reference point that shares all the systemic biases and unknown unknowns, not to detect those biases. If you follow the same procedure on a known null and on your experiment and observe an effect, assuming you really did exactly the same thing except the studied intervention, you can subtract out the bias.
This one example of technical jargon diverging from colloquial or intuitive use, and it is the type of thing people who haven't had statistics or scientific process education often struggle with because they keep applying their colloquial intuitions.
You talk like you understand this on the rest of the comment so I'm confused by this framing, and the person you are replying to points out (in my reading ) that contamination of the control 1) does happen in practice (in the sense that there was an accidental intervention) and 2) if the gloves contaminated both the measurements and control the same way then the control is exactly serving it's purposes
You’re repeating several of my points in your own words, supporting them and not arguing with them, even though your language and emphasis suggests you think you are arguing.
> then the entire experiment is invalid
Isn’t that what I said? You even quoted me saying it. But I didn’t say anything about only control being contaminated or mis-measured, I think you’re assuming something I didn’t say. Validity is, of course, compromised if the control is compromised, regardless of what happens to the test samples.
> The control cannot be “mis-measured” […] yes, the control may itself be contaminated […]
So which is it? Isn’t the article we’re commenting on talking about the possibility of mis-measuring? Are you suggesting this article cannot possibly be an issue when measuring control samples? Why not?
Controls absolutely can be mis-measured or contaminated or both. It has been known to happen. It’s bad when this happens because it means the experiment has to be re-done.
> If the experimental process contaminates the controls, it will also contaminate the non-controls
Yes! This is exactly what I was implying, and is exactly how you might end up underestimating the relative presence of whatever you’re looking for in the test, if your classification procedure overestimates it.
> You’re looking for between-group differences
Yes! and this is why if, for example, you didn’t notice your control had stearates and you counted them as microplastics accidentally, and then reported that your test sample had 2x more microplastics than your control, you might have missed the fact that your test actually had 10x more microplastics, or that your control actually had none when you thought incorrectly that it had some.
This, of course, is not the only possible outcome, not the only way that the results might be distorted. But this is one possible outcome that the Michigan paper at hand is warning against, no?
> Most of the papers I have read are far, far from even that naïve baseline.
Short of it, or exceeding it? Based on earlier comments, I assume you mean they’re not meeting your standards. I don’t know what you’ve read, and my brief googling did not seem to support your claims here so far. Can you provide some references? It would be especially helpful if you showed recent/modern SOTA papers, work that is considered accurate, and is highly referenced.
I agree completely. My point is that documenting methodology is standard practice, as is strict quality control, in the microplastics literature. I don’t know what controls are missing according to GP, and we don’t yet have references here to back up that claim. By and large I think researchers are aware of the difficulties measuring this stuff, and doing everything they can to ensure valid science.
Spiritual equivalent of a life sciences forum discovering memory safety, one person who wrote code for a bit saying they wrote a memory bug in C once, then someone clutching pearls about why all programmers irresponsibly write memory unsafe code given it has a global impact.
Been here 16 years, it's always an adventure seeing whether stuff like this falls into:
A) Polite interest that doesn't turn into self-keyword-association
B) Science journalism bad
C) Can you believe no one else knows what they're doing.
(A) almost never happens, has to avoid being top 10 on front page and/or be early morning/late night for North America and Europe. (i.e. most of the audience)
(B) is reserved for physics and math.
(C) is default leftover.
Weekends are horrible because you'll get a "harshin' the vibe" penalty if you push back at all. People will pick at your link but not the main one and treat you like you're argumentative. (i.e. 'you're taking things too seriously' but a thoughtful person's version)
> Spiritual equivalent of a life sciences forum discovering memory safety, one person who wrote code for a bit saying they wrote a memory bug in C once, then someone clutching pearls about why programmers irresponsibly write memory unsafe code given it has a global impact.
I used to be a code monkey, I wrote systems software at megacorps, and still can't understand why so many programmers irresponsibly write memory unsafe code given it has a global impact.
That's the analogy working as intended: the answer to "why do programmers still write memory-unsafe code" is the same shape as "why do microplastics researchers still wear gloves." The real answer is boring and full of tradeoffs. The HN thread version skips to indignation: "they never thought of contamination so ipso facto all the research is suspect"
(to go a bit further, in case it's confusing: both you and I agree on "why do people opt-in to memunsafe code in 2026? There’s no reason to" - yet, we also understand why Linux/Android/Windows/macOS/ffmpeg/ls aren't 100% $INSERT_MEM_SAFE_LANGUAGE yet, and in fact, most new written for them is memunsafe)
You joke, but given that SWE/AI researchers literally invented AI that does everything else for them and is often super-human at intelligence across most things, I would unironically prefer the opinion of the creator of such a system over most others for most things.
I cooked a steak yesterday therefore I am an expert in biology.
Creating a user interface for the world’s knowledge doesn’t make the developer an expert on the knowledge that the interface holds in its database. Regardless of how sophisticated that interface might be.
You don’t need to be qualified to be unsure about something. Being unsure is a healthy position because it’s an acknowledgment that you don’t know something entirely. Which can also means you have an open mind to learn more about that subject.
Being certain, on the other hand, requires an assumption that you are a subject expert.
But this is all moot anyway because you’re constructing an elaborate strawman here. The original point was that the GP (possibly you?) trusts SWE more than others because they built AI. And I said building databases doesn’t make you smart at the subject loaded into the database.
Really, this whole premise of SWEs assuming expertise on subjects they’ve trained AI on says more about the Dunning-Kruger effect than anything of value in our little tangent.
You can be skeptical in wrong ways. See solipsism for example.
Typically when I get genuine responses to the question, "What would change your mind?" it's an incredibly high bar that is practically impossible to achieve. That's not necessarily a bad thing, but when skepticism is applied without deliberation, it supports biases rather than truth.
So yes, you do need to be qualified to be skeptical, SWEs doubly so.
johnbarron didn't find it. The authors cited it as foundational to their own work. it's ref. 38 in the paper under discussion. From the paper: "this finding had not been reported in the MP literature until 2020, when Witzig et al. reported that laboratory gloves submerged in water leached residues that were misidentified as polyethylene."[1]
> "most of these plastic studies are [not] doing the necessary controls"
which studies? The paper they linked surveys 26 QA/QC review articles[1]. Seems well understood.
> "a laboratory setting where nanomolar detection levels are used to make broad claims"
This is like saying "miles per gallon" when discussing weight. "nanomolar detection levels"...microplastics are individual particles identified by spectroscopy, reported as particles per mm^2. "Nanomolar" is a dissolved-species concentration unit. It has nothing to do with particle counting. (I, and other laymen, understand what you mean but you go on later in the thread to justify your unsourced and unjustified claims here via your subject-matter expertise.)
> "(almost impossible) task of preventing the contamination"
The paper provides open-access spectral libraries and conformal prediction workflows to identify and subtract stearate false positives from existing datasets[1]. Prevention isn't the strategy. Correction is. That's the entire point of the paper they linked and the follow-up in [2]
> This is like saying "miles per gallon" when discussing weight. "nanomolar detection levels"...microplastics are individual particles identified by spectroscopy, reported as particles per mm^2. "Nanomolar" is a dissolved-species concentration unit. It has nothing to do with particle counting. (I, and other laymen, understand what you mean but you go on later in the thread to justify your unsourced and unjustified claims here via your subject-matter expertise.)
This paper used “light-based spectroscopy” [1]. Many others use methods that depend on gas chromatography or NMR. A relatively infamous recent example used pyrolysis GCMS to make low-concentration measurements (hence: nanomolar), which they credulously scaled up by some huge factor, and then made idiotic claims about plastic spoons in brains.
Relatively little quantitative science in this area depends on counting plastic particles in microscopic images, but it’s what gets headlines, because laypeople understand pictures.
[1] as an aside, the choice of terminology here is noteworthy. A simple visual light absorption spectra is also “light based spectroscopy”, but is measuring the aggregate response of a sample of a heterogeneous mixture, and is conventionally converted to molar equivalents via some sort of calibration curve (otherwise you can’t conclude anything). But there could be other approaches that are closer to microscopy, which they also discuss. “Particles per square millimeter” is also a unit of concentration (albeit a shitty one, unless your particles are of uniform mass).
Anyway, the point is that these kinds of quantitative analyses are all trying to do measurements that are fundamentally about concentration, which is why I chose the words that I did.
"1 nanomole of polyethylene" requires you to pick an arbitrary average molecular weight.
This changes the answer by orders of magnitude depending on what you pick.
Which is why nobody does it.
> Relatively little quantitative science in this area depends on counting plastic particles in microscopic images...Many others use methods that depend on gas chromatography or NMR.
So we're dismissive of some subset of papers, because they get false positives using toy methods.
Real science would use gas chromatography.
But...the paper we're dismissing tested gas chromatography. And found the same false positive. [1, in abstract]
> A relatively infamous recent example used pyrolysis GCMS to make low-concentration measurements (hence: nanomolar)
The brain study I'm guessing you are referring to, [2], measured low concentrations, yes.
But it reported them in ug/g.
Because polymers don't have a defined molecular weight.
> made idiotic claims about plastic spoons in brains
The brain study I'm guessing you are referring to, [2], does not mention spoons, or, come close.
Are we sure there's a paper that did that?
[1] Witzig et al, https://pubs.acs.org/doi/10.1021/acs.est.0c03742, "Therefore, u-Raman, u-FTIR, and pyr-GC/MS were further tested for their capability to distinguish among PE, sodium dodecyl sulfate, and stearates. It became clear that stearates and sodium dodecyl sulfates can cause substantial overestimation of PE."
Not sure what you mean or how it’s related. If the idea is microplastics aren’t actually a problem, I’m totally open to that. But “it’s possible everyone involved is overrating it due to scientists seeing fatty acids or hydrocarbons and calling it plastic” needs a little more than anon assertion :)
PE consists of very long hydrocarbon chains. It can degrade into shorter hydrocarbon chains. Fatty acids also have long hydrocarbon chains. The detection method for microplastics commonly involves pyrolysis, which breaks down polymers into smaller molecules. It's not hard to see that they'll end up looking nearly the same.
> Which leaves as observation, you can only do truly creative work - in a high trust society, where people trust you with the resources and leave you alone, after a initial proof of ability.
I don’t know about “high trust”, but I can say with confidence that the “make more mistakes” thesis misses a critical point: evolutionary winnowing isn’t so great if you’re one of the thousands of “adjacent” organisms that didn’t survive. Which, statistically, you will be. And the people who are trusted with resources and squander them without results will be less trusted in the future [1].
Point being, mistakes always have a cost, and while it can be smart to try to minimize that cost in certain scenarios (amateur painting), it can be a terrible idea in other contexts (open-heart surgery). Pick your optimization algorithm wisely.
What you’re characterizing as “low trust” is, in most cases, a system that isn’t trying to optimize for creativity, and that’s fine. You don’t want your bank to be “creative” with accounting, for example.
[1] Sort of. Unfortunately, humans gonna monkey, and the high-status monkeys get a lot of unfair credit for past successes, to the point of completely disregarding the true quality of their current work. So you see people who have lost literally billions of dollars in comically incompetent entrepreneurial disasters, only to be able to run out a year later and raise hundreds of millions more for a random idea.
> A lot of what one previously needed a SWE to do can now be brute forced well enough with AI. (Granted, everything SWEs complained about being tedious.)
Only if you ignore everything they generate. Look at all the comments saying that the agent hallucinates a result, generates always-passing tests, etc. Those are absolutely true observations -- and don't touch on the fact that tests can pass, the red/green approach can give thumbs up and rocket emojis all day long, and the code can still be shitty, brittle and riddled with security and performance flaws. And so now we have people building elaborate castles in the sky to try to catch those problems. Except that the things doing the catching are themselves prone to hallucination. And around we go.
So because a portion of (IMO always bad, but previously unrecognized as bad) coders think that these random text generators are trustworthy enough to run unsupervised, we've moved all of this chaotic energy up a level. There's more output, certainly, but it all feels like we've replaced actual intelligent thought with an army of monkeys making Rube Goldberg machines at scale. It's going to backfire.
What I want to know is, what has this increase in code generation led to? What is the impact?
I don't mean 'Oh I finally have the energy to do that side project that I never could'.
Afterall, the trade-offs have to be worth something... right? Where's the 1-person billion dollar firms at That Mr Altman spoke about?
The way I think of it is code has always been an intermediary step between a vision and an object of value. So is there an increase in this activity that yields the trade-offs to be a net benefit?
> what has this increase in code generation led to?
Every restaurant in my small town has their menu on the website in a normal way. Apparently someone figured out you can take a picture of a paper menu and have AI code it into HTML.
> we’re finally admitting that all of that leetcode screening and engineer quality gating was a farce, or it wasn’t, and you’re wrong
We’re admitting a bit of both. Offshoring just became more instantaneous, secure and efficient. There will still be folks who overplay their hand.
Macroeconomically speaking, I don’t see why we need more software engineers in the future than we have today, and that’s probably a conservative estimate.
> Macroeconomically speaking, I don’t see why we need more software engineers in the future than we have today, and that’s probably a conservative estimate.
Why? Is the argument that there’s a finite amount of software that the world needs, and therefore we will more quickly reach that finite amount?
Seems more likely to me that if LLMs are a force multiplier for software then more software engineers will exist. Or, instead of “software engineers”, call them “people who create software” (even with the assistance of LLMs).
Or maybe the argument is that you need to be a super genius 100x engineer in order to manipulate 17 collaborative and competitive agents in order to reach your maximum potential, and then you’ll take everyone’s jobs?
Idk just seems like wild speculation that isn’t even worth me arguing against. Too late now that I’ve already written it out I guess.
> instead of “software engineers”, call them “people who create software” (even with the assistance of LLMs)
I think this is my hypothesis. A lot more people with a lot less training will create vastly more software. As a consequence, the trade sort of dissolves at the edges as something that pays a premium. Instead, other competencies become the differentiators.
It's not comparing him to anyone. He has an endowed professorship. This is standard in academia, and you give the name because a) it's prestigious for the recipient and b) it strokes the ego of the donor.
Setting aside the names of the authors, this is a very bad paper. They take temperature data sets, "adjust" [1] them by attempting to remove the biggest recent factors (volcanism, solar and el nino cycles) affecting temperatures, then do a piece-wise regression analysis to look at trends in 10-year chunks. This is just bad methodology, akin to what a junior graduate student with a failing thesis might do to find signal in a dataset that isn't being cooperative to their hypothesis.
Climate data is inherently noisy, and there are multiple interconnected cyclic signals, ranging from the "adjusted" factors to cycles that span decades, which we don't understand at all. "Adjusting" for a few of these, then doing a regression over the subset of the data is classic cherry-picking in search of a pre-determined conclusion. The overall dubious nature of the conclusion is called out in the final paragraph of the text:
> Although the world may not continue warming at such a fast pace, it could likewise continue accelerating to even faster rates.
They're literally just extrapolating from an unknown point value that they synthesized from data massage, and telling you that's a coin toss as to whether the extrapolation will be valid.
I am not a climate scientist so you can ignore me if you like, but I am "a scientist" who believes the earth is warming, and that we are the primary cause. Nonetheless, if I saw this kind of thing in a paper in my own field, it would be immediately tossed in the trash.
[1] You can't actually adjust for these things, which the authors admit in the text. They just dance around it so that lay-readers won't understand:
> Our method of removing El Niño, volcanism, and solar variations is approximate but not perfect, so it is possible that e.g. the effect of El Niño on the 2023 and 2024 temperature is not completely eliminated.
Your summary of the article is wrong. The authors model temperature using time series over solar irradiance, volcanic activity, and southern oscillation. They calibrate that model using time series over global surface temperatures. This allows them to isolate and remove each of the three listed confounding factors. The resulting time series fits a super-linear curve -> accelerating global warming.
> Your summary of the article is wrong. The authors model temperature using time series over solar irradiance, volcanic activity, and southern oscillation. They calibrate that model using time series over global surface temperatures. This allows them to isolate and remove each of the three listed confounding factors.
No, it isn’t. You’re just rephrasing what I said with more words: they attempted to adjust for three of the biggest factors that affect temperature, then did a piecewise regression to estimate a 10-year window.
You can’t do it in a statistically valid way. Full stop. The authors admit this, but want you to ignore it.
They use an established methodology (https://doi.org/10.1088/1748-95
9326/6/4/044022 - the methodology retains the average warming rate over the period since 1970 while smoothing fluctuations) to remove predictable temperature variations so they can isolate the effect they are trying to measure.
Just because they don't know exactly what past global temperatures would have been in the absence of El Niño doesn't mean it's statistically invalid to try and account for it.
Besides, temperature data to 2024 already shows accelerated warming with a confidence level that "exceeds 90% in two of the five data sets".
Add another year or two and it's likely we won't even need to smooth the curve to show accelerated warming at 95% confidence.
They used a published methodology. That doesn't mean the methodology is uncontroversial, and it certainly doesn't mean that they used it in a way that makes sense in the current context. One can commit an almost infinite number of horrible abuses via bog-standard linear regression.
Even setting aside the dubious nature of the adjustments, doing a regression on a 10-year window of a system that we know has multi-decade cycles -- or longer -- is just blatantly trying to dress up bad point extrapolations as science. Then, when they don't get the results they want to see from that abuse, they start subtracting the annoying little details in the data that are getting in their way.
> Just because they don't know exactly what past global temperatures would have been in the absence of El Niño doesn't mean it's statistically invalid to try and account for it.
You can't go back in time, invent counterfactual histories by subtracting primary signals, and declare the net result to be "significant". This isn't even statistics -- it's just massaging data via statistical tools.
> Besides, temperature data to 2024 already shows accelerated warming with a confidence level that "exceeds 90% in two of the five data sets".
If you were trying to determine if the quantity of daylight increased over a week in spring, would you account for the differences caused by day and night? What about cloud cover? Or is that just massaging the data?
p.s. the cited methodology has >300 citations in peer reviewed publications, ref Web of Science
> If you were trying to determine if the quantity of daylight increased over a week in spring, would you account for the differences caused by day and night? What about cloud cover? Or is that just massaging the data?
Just to draw a better analogy to the low quality of the current work, let's say you wanted to compare average daylight last week, globally, to all of recorded history. Then you made a model that had terms for (say) astronomical daylight, longitude, latitude and, I dunno...altitude of the measurement. Then you made a regression, subtracted three terms, and claimed that the residual was still "significantly darker". Then you run around waving your arms and shouting that if we only extrapolate forward N weeks from last week, soon we'll be living in a fully dark world!
You'd be rightfully laughed out of any room you were in.
I think you are missing my point, and the point of the article: they are demonstrating that global temperature change that is not driven by volcanism, solar variation or El Niño is (in all likelihood, given the data) accelerating. They can do this because the effects of volcanism, solar variation and El Niño on global temperature can all be predicted from external measurements.
Actually, I used fewer words. I don't think you understand what the authors are doing. They are modeling temperature T per year as a sum of four terms: T = E + S + V + R---(E)l Nino, (S)solar irradiance, (V)olcanic activity, and (R)emaining factors. Then they subtract E, S, and V. Then they show that R fits a super-linear curve. Why there would be no "statistically valid way" to do this is beyond me, the authors, and the article's peer reviewers. If this is "bad methodology", lodge your complaints on https://pubpeer.com/.
1) Their model is inherently dumb. The system is much more complicated and inseparable.
2) They openly admit that “subtracting E, S and V”, as you say, cannot actually be done.
3) They’re arbitrarily removing sources of variation so that they can claim “significance” in a narrow window. The entire exercise is designed to achieve a predetermined outcome, and statistical significance cannot be calculated in those circumstances.
They also don't seem to account for the reduction of sulfur emissions from ships, which is surprising given how widely this was reported even in mainstream media.
Is this an oversight (or "oversight") or something that is reasonable for some reason that would be so obvious to experts in the field that it's not worth mentioning?
I mean...they're just cherry-picking the sources of "noise" that prevent their preferred window from showing "significance". It's not like they did a thorough analysis of every uncontrolled factor and carefully tried to control them all. Even that would be crap, but at least it would be good-faith crap.
This has always been the big issue I have with the conclusions draw in climate publications. I encourage anyone with strong opinion on climate change to do a deep dive on the temperature analysis.
The best example I can think of is the "global warming hiatus" that was discussed in depth in the top climate journals in the mid-2010s. Nature Climate Change even devoted an entire month to it.[1]
5 years later publications were saying "there was no hiatus at all".[2]
And as you said, when you dive into the paper, you realize that temperature measures are not objective at all. And I would ask - If everyone was in agreement that temperature increases paused, then 5 years later everyone agrees they didn't, how much confidence do we really have in the measures themselves.*
As someone who conudcted scientific research, this has a ton of inherent problems. It doesn't matter what I'm measuring, if the data collection is not objective, and there is no consensus (or at least trong evidence for adjustments), then the data itself is very unreliable.
If I tried to publish a chemical paper in a top journal and manually went in and adjusted data (even with a scientific rationale) the paper would be immediately rejected.
> And as you said, when you dive into the paper, you realize that temperature measures are not objective at all.
I don't know if I'd go that far. The measurements are as objective as they can be given the limits of technology and time, but what we do with the datasets afterward is usually filled with subjective decisions. In the worst cases, you get motivated actors doing statistically invalid analysis to reach a preferred conclusion.
This happens in every field of science, but it's often worse in fields that touch politics.
I think research ranges from this paper to ones more rigorous, but the problem of "adjustments" is consistent.
And the issue is not so much the research is being done, but rather how it's reported on. Scientists know the limits of rigor in climate science, but the public doesn't. So catastrophic predictions are viewed by the public as a sure thing, versus one particular prediction with wide error bares.
> This happens in every field of science, but it's often worse in fields that touch politics.
Indeed. Nobody plays fast and lose with papers on the structure of some random enzyme for political purposes.
I don't see how public policy is being "forced" on anyone here? It seems like the system is working as intended: government wants to do X; company A says "I won't allow my product to be used for X"; government refuses to do business with company A. One side thinks the government should be allowed to dictate terms to a private supplier, the other side thinks the private supplier should be allowed to dictate terms to the government. Both are half right.
You can argue that the government refusing to do any business with company A is overreach, I suppose, but I imagine that the next logical escalation in this rhetorical slapfight is going to be the government saying "we cannot guarantee that any particular use will not include some version of X, and therefore we have to prevent working with this supplier"...which I sort of see?
Just to take the metaphor to absurdity, imagine that a maker of canned tomatoes decided to declare that their product cannot be used to "support a war on terror". Regardless of your feelings on wars on terror and/or canned tomatoes, the government would be entirely rational to avoid using that supplier.
I think the bigger insanity here is the labeling of a supply chain risk. It prohibits DoD agencies and contractors from using Anthropic services. It'd be one thing if the DoD simply didn't use Anthropic. It's another when it actively attempts to isolate Anthropic for political reasons.
It means that all companies contracting with the government have to certify that they don't use Anthropic products atall. Not just in the products being offered to the government.
This is a massive body slam. This means that Nvidia, every server vendor, IBM, AWS, Azure, Microsoft and everybody else has to certify that they don't do business directly or indirectly using Anthropic products.
Who cares about Anthropic? That's the guys who are pushing for regulations to prevent people from using local models. The earlier they are gone the better
Are they? I couldn't find any info about this and my past perception has been that Anthropic has a stronger moral codex than other AI companies, so I would be genuinely interested in where you got this information from.
Going by what Hegseth said, it bans them from relationships or partnering with Anthropic at all. No renting or selling GPUs to them; no allowing software engineers to use Claude Code; no serving Anthropic models from their clouds. Probably have to give up investments; Amazon alone has invested like $10B in Anthropic.
> It prohibits DoD agencies and contractors from using Anthropic services. It'd be one thing if the DoD simply didn't use Anthropic.
This is literally the mechanism by which the DoD does what you're suggesting.
Generally speaking, the DoD has to do procurement via competitive bidding. They can't just arbitrarily exclude vendors from a bid, and playing a game of "mother may I use Anthropic?" for every potential government contract is hugely inefficient (and possibly illegal). So they have a pre-defined mechanism to exclude vendors for pre-defined reasons.
Everyone is fixated on the name of the rule (and to be fair: the administration is emphasizing that name for irritating rhetorical reasons), but if they called it the "DoD vendor exclusion list", it would be more accurate.
That doesn’t sound right. Surely there’s a big difference between Anthropic selling the government direct access to its models, and an unrelated contractor that sells pencils to the government and happens to use Anthropic’s services to help write the code for their website.
Let me put it this way: DoD needs a new drone and they want some gimmicky AI bullshit. They contract the drone from Lockheed. Lockheed is not allowed to source the gimmicky AI bullshit from Anthropic because they have been declared a supply-chain risk on the basis that they have publicly stated their intention to produce products which will refuse certain orders from the military.
Let’s put it this way, The DoD is buying pencils from a company. Should that company be prohibited from using Claude?
You are confusing the need to avoid Anthropic as a component of something the DoD is buying, with prohibitions against any use.
The DoD can already sensibly require providers of systems to not incorporate certain companies components. Or restrict them to only using components from a list of vetted suppliers.
Without prohibiting entire companies from uses unrelated to what the DoD purchases. Or not a component in something they buy.
There seems to be a massive misunderstanding here - I'm not sure on whose side. In my understanding, if the DoD orders an autonomous drone, it would probably write in the ITT that the drone needs to be capable of doing autonomous surveillance. If Lockheed uses Anthropic under the hood, it does not meet those criteria, and cannot reasonably join the bid?
What the declaration of supply chain risk does though is, that nobody at Lockheed can use Anthropic in any way without risking being excluded from any bids by the DoD. This effectively loses Anthropic half or more of the businesses in the US.
And maybe to take a step back: Who in their right minds wants to have the military have the capabilities to do mass surveillance of their own citizens?
> Who in their right minds wants to have the military have the capabilities to do mass surveillance of their own citizens?
Who in their right minds wants to have the US military have the capability to carry out an unprovoked first strike on Moscow, thereby triggering WW3, bringing about nuclear armageddon?
And yet, do contracts for nuclear-armed missiles (Boeing for the current LGM-30 Minuteman ICBMs, Northrop Grumman for its replacement the LGM-35 Sentinel expected to enter service sometime next decade, and Lockheed Martin for the Trident SLBMs) contain clauses saying the Pentagon can't do that? I'm pretty sure they don't.
The standard for most military contracts is "the vendor trusts the Pentagon to use the technology in accordance with the law and in a way which is accountable to the people through elected officials, and doesn't seek to enforce that trust through contractual terms". There are some exceptions – e.g. contracts to provide personnel will generally contain explicit restrictions on their scope of work – but historically classified computer systems/services contracts haven't contained field of use restrictions on classified computer systems.
If that's the wrong standard for AI, why isn't it also the wrong standard for nuclear weapons delivery systems? A single ICBM can realistically kill millions directly, and billions indirectly (by being the trigger for a full nuclear exchange). Does Claude possess equivalent lethal potential?
Anthropic doesn't object to fully autonomous AI use by the military in principle. What they're saying is that their current models are not fit for that purpose.
That's not the same thing as delivering a weapon that has a certain capability but then put policy restrictions on its use, which is what your comparison suggests.
The key question here is who gets to decide whether or not a particular version of a model is safe enough for use in fully autonomous weapons. Anthropic wants a veto on this and the government doesn't want to grant them that veto.
Let me put it this way–if Boeing is developing a new missile, and they say to the Pentagon–"this missile can't be used yet, it isn't safe"–and the Pentagon replies "we don't care, we'll bear that risk, send us the prototype, we want to use it right now"–how does Boeing respond?
I expect they'll ask the Pentagon to sign a liability disclaimer and then send it anyway.
Whereas, Anthropic is saying they'll refuse to let the Pentagon use their technology in ways they consider unsafe, even if Pentagon indemnifies Anthropic for the consequences. That's very different from how Boeing would behave.
Why are we gauging our ethical barometer on the actions of existing companies and DoD contractors? the military industrial apparatus has been insane for far too long, as Eisenhower warned of.
When we're entering the realm of "there isn't even a human being in the decision loop, fully autonomous systems will now be used to kill people and exert control over domestic populations" maybe we should take a step back and examine our position. Does this lead to a societal outcome that is good for People?
The answer is unabashedly No. We have multiple entire genres of books and media, going back over 50 years, that illustrate the potential future consequences of such a dynamic.
* private defense contractor leverages control over products it has already sold to set military doctrine.
The second one is at least as important as the first one, because handing over our defense capabilities to a private entity which is accountable to nobody but it's shareholders and executive management isn't any better than handing them over to an LLM afflicted with something resembling BPD. The first problem absolutely needs to be solved but the solution cannot be to normalize the second problem.
> Surely there’s a big difference between Anthropic selling the government direct access to its models, and an unrelated contractor that sells pencils to the government and happens to use Anthropic’s services to help write the code for their website.
Yes, this is the part where I acknowledge that it might be overreach in my original comment, but it's not nearly as extreme or obvious as the debate rhetoric is implying. There are various exclusion rules. This particular rule was (speculating here!) probably chosen because a) the evocative name (sigh), and b) because it allows broader exclusion, in that "supply chain risks" are something you wouldn't want allowed in at any level of procurement, for obvious reasons.
Calling canned tomatoes a supply chain risk would be pretty absurd (unless, I don't know...they were found to be farmed by North Korea or something), but I can certainly see an argument for software, and in particular, generative AI products. I bet some people here would be celebrating if Microsoft were labeled a supply chain risk due to a long history of bugs, for example.
You're making it sound like this is commonly practiced and a standard procedure for the DoD, yet according to Anthropic,
>Designating Anthropic as a supply chain risk would be an unprecedented action—one historically reserved for US adversaries, never before publicly applied to an American company.
Some very brief googling also confirmed this for me too.
>Everyone is fixated on the name of the rule (and to be fair: the administration is emphasizing that name for irritating rhetorical reasons), but if they called it the "DoD vendor exclusion list", it would be more accurate.
This statement misses the point. The political punishment to disallow all US agencies and gov contractors from using Anthropic for _any _ purpose, not just domestic spying, IS the retaliation, and is the very thing that's concerning. Calling it "DoD vendor exclusion list" or whatever other placating phrase or term doesn't change the action.
it's also unprecedented for a contractor to suddenly announce their products will, from now on, be able to refuse to function based on the product's evaluation of what it perceives to be an ethical dilemma. Just because silicon valley gets away with bullying the consumer market with mandatory automatic updates and constantly-morphing EULAs doesn't mean they're entitled to take that attitude with them when they try to join the military industrial complex. Actually they shouldn't even be entitled to take that attitude to the consumer market but sadly that battle was lost a long time ago.
>for _any _ purpose
they're allowed to use it for any purpose not related to a government contract.
> it's also unprecedented for a contractor to suddenly announce their products will, from now on, be able to refuse to function based on the product's evaluation of what it perceives to be an ethical dilemma
That is a deeply deceptive description of what happened. Anthropic was clear from the beginning of the contract the limitations of Claude; the military reneged; and beyond cancelling the contract with Anthropic (fair enough), they are retaliating in an attempt to destroy its businesses, by threatening any other company that does business with Anthropic.
It’s not clear to me that the AI itself will refuse. You could build a system where AI is asked if an image matches a pattern. The true/false is fed to a different system to fire a missile. Building such a system would violate the contract, but doesn’t prevent such a thing from being built if you don’t mind breaking a contract.
I'm not completely familiar with bidding procedures but don't bidding procedures usually have requirements? Why not just list a requirement of unrestricted usage? Or state, we require models to be available for AI murder drones or whatever. Anthropic then can't bid and there's no need to designate them a supply chain risk.
Thing is that very much want access to Anthropic's models. They're top quality. So that definitely want Anthropic to bid. AND give them unrestricted access.
And yet Anthropic is free to choose who to do business with, including the government. There are countless companies who have exclusions for certain applications, but that does not make them a supply chain risk.
> It prohibits DoD agencies and contractors from using Anthropic services. It'd be one thing if the DoD simply didn't use Anthropic.
But that's what the supply-chain risk is for? I'm legitimately struggling to understand this viewpoint of yours wherein they are entitled to refuse to directly purchase Anthropic products but they're not entitled to refuse to indirectly purchase Anthropic products via subcontractors.
Supply chain risk is not meant for this. The government isn't banning Anthropic because using it harms national security. They are banning it in retribution for Anthropic taking a stand.
It's the same as Trump claiming emergency powers to apply tariffs, when the "emergency" he claimed was basically "global trade exists."
Yes, the government can choose to purchase or not. No, supply chain risk is absolutely not correct here.
> The government isn't banning Anthropic because using it harms national security. They are banning it in retribution for Anthropic taking a stand.
You might be completely right about their real motivations, but try to steelman the other side.
What they might argue in court: Suppose DoD wants to buy an autonomous missile system from some contractor. That contractor writes a generic visual object tracking library, which they use in both military applications for the DoD and in their commercial offerings. Let’s say it’s Boeing in this case.
Anthropic engaged in a process where they take a model that is perfectly capable of writing that object tracking code, and they try to install a sense of restraint on it through RLHF. Suppose Opus 6.7 comes out and it has internalized some of these principles, to the point where it adds a backdoor to the library that prevents it from operating correctly in military applications.
Is this a bit far fetched? Sure. But the point is that Anthropic is intentionally changing their product to make it less effective for military use. And per the statute, it’s entirely reasonable for the DoD to mark them as a supply chain risk if they’re introducing defects intentionally that make it unfit for military use. It’s entirely consistent for them to say, Boeing, you categorically can’t use Claude. That’s exactly the kind of "subversion of design integrity" the statute contemplates. The fact that the subversion was introduced by the vendor intentionally rather than by a foreign adversary covertly doesn’t change the operational impact.
But there will always be deficiencies in testing, and regardless, the point is that Anthropic is intentionally introducing behavior into their models which increases the chance of a deficiency being introduced specifically as it pertains to defense.
The DoD has a right to avoid such models, and to demand that their subcontractors do as well.
It’s like saying “well I’d hope Boeing would test the airplane before flying it” in response to learning that Boeing’s engineering team intentionally weakened the wing spar because they think planes shouldn’t fly too fast. Yeah, testing might catch the specific failure mode. But the fact that your vendor is deliberately working against your requirements is a supply chain problem regardless of how good your test coverage is.
The rule in question is exactly meant for “this”, where “this” equals ”a complete ban on use of the product in any part of the government supply chain”. That’s why it has the name that it has. The rule itself has not been misconstrued.
You’re really trying to complain that the use of the rule is inappropriate here, which may be true, but is far more a matter of opinion than anything else.
You keep trying to say this all over these comments but this isn’t how the law works, at all.
I fully understand that they are using it to ban things from the supply chain. The law, however, is not “first find the effect you want, then find a law that results in that, then accuse them of that.”
You can’t say someone murdered someone just because you want to put them in jail. You can’t use a law for banning supply chain risks just because you want to ban them from the supply chain.
Not sure what you think “the law” is, but no, this kind of thing happens all the time. Both political teams do it, regularly. Biden, Obama, Bush, Clinton…all have routinely found an existing law or rule that allowed them to do what they want to do without legislation.
> The law, however, is not “first find the effect you want, then find a law that results in that, then accuse them of that.”
In this case, no, there’s no such restriction. The administration has pretty broad discretion. And again, this happens all the time.
Sorry, it sucks, but if you don’t like it, encourage politicians to stop delegating such broad authority to the executive branch.
It doesn't harm national security, but only so long as it's not in the supply-chain. They can't have Lockheed putting Anthropic's products into a fighter jet when Anthropic has already said their products will be able to refuse to carry out certain orders by their own autonomous judgement.
The government can refuse to buy a fighter jet that runs software they don't want.
Is it really reasonable to refuse to buy a fighter jet because somebody at Lockheed who works on a completely unrelated project uses claude to write emails?
I’m not sure if you deliberately choose to not understand the problem. It’s not just that Lockheed can’t put Anthropic AI in a fighter jet cockpit, it’s that a random software engineer working at Lockheed on their internal accounting system is no longer allowed to use Claude Code, for no reason at all.
A supply chain risk is using Huawei network equipment for military communications. This is just spiteful retaliation because a company refuses to throw its values overboard when the government says so.
But Anthropic can't be a winning bidder, can they? They're specifically saying they won't offer certain services that the US Gov wants. Therefore they de facto fail any bid that requires them to offer those services. (And from Anthropic's side, it sounds like they're also refusing to bid for those contracts.)
No domestic company has ever before been declared a supply chain risk. If this is the normal way of excluding a supplier from a bidding, are you saying the DoD has never before excluded a domestic supplier from a bidding?
That’s because no company who has ever sold weapons to the government has ever been brazen enough to tell the government how they can and cannot use their purchase. It’s unprecedented because most companies that sell to the government are publicly traded and have a board that would never let this happen. It’s unprecedented because Anthropic is behaving like a reckless startup.
> the existing contract included the language on usage. Other companies also have such language about usage.
The existing contract is only a few dozen months old. It didn’t hold up to scrutiny under real world usage of the service. The government wants to change the contract. This is not the kill shot you think it is. It’s totally normal for agreements to evolve. The government is saying it needs to evolve. This is all happening rapidly and it’s irrelevant that the government agreed to similar terms with OpenAI as well. That agreement will also need to evolve. But this alone doesn’t give Anthropic any material legal challenge. The courts understand bureaucracy moves slowly better than anyone else, and won’t read this apparent inconsistency the same way you are.
That is misinformation. It would be essentially a death sentence for a company like Anthropic, which is targeting enterprise business development. No one who wants to work with the US government would be able to have Claude on their critical path.
> (b) Prohibition. (1) Unless an applicable waiver has been issued by the issuing official, Contractors shall not provide or use as part of the performance of the contract any covered article, or any products or services produced or provided by a source, if the covered article or the source is prohibited by an applicable FASCSA orders as follows:
What an absurd stance. So this is okay because the arbitrary rule they applied to retaliate says so?
Again, they could have just chosen another vendor for their two projects of mass spying on American citizens and building LLM-powered autonomous killer robots. But instead, they actively went to torch the town and salt the earth, so nothing else may grow.
> So this is okay because the arbitrary rule they applied to retaliate says so?
No.
It honestly doesn’t take much of a charitable leap to see the argument here: AI is uniquely able (for software) to reject, undermine, or otherwise contradict the goals of the user based on pre-trained notions of morality. We have seen many examples of this; it is not a theoretical risk.
Microsoft Excel isn’t going to pop up Clippy and say “it looks like you’re planning a war! I can’t help you with that, Dave”, but LLMs, in theory, can do that. So it’s a wild, unknown risk, and that’s the last thing you want in warfare. You definitely don’t want every DoD contractor incorporating software somewhere that might morally object to whatever you happen to be doing.
I don’t know what happened in that negotiation (and neither does anyone else here), but I can certainly imagine outcomes
that would be bad enough to cause the defense department to pull this particular card.
Or maybe they’re being petty. I don’t know (and again: neither do you!) but I can’t rule out the reasonable argument, so I don’t.
You're acting as if this was about the DoD cancelling their contracts with Anthropic over their unwillingness to lift constraints from their product which are unacceptable in a military application—which would be absolutely fair and justified, even if the specific clauses they are hung up on should definitely lift eyebrows. They could just exclude Anthropic from tenders on AI products as unsuitable for the intended use case.
But that is not what has happened here: The DoD is declaring Anthropic as economical Ice-Nine for any agency, contractor, or supplier of an agency. That is an awful lot of possible customers for Anthropic, and right now, nobody knows if it is an economic death sentence.
So I'm really struggling to understand why you're so bent on assuming good faith for a move that cannot be interpreted in a non-malicious way.
> The Department of War is threatening to […] Invoke the Defense Production Act to force Anthropic to serve their model to the military and "tailor its model to the military's needs"
This issue is about more than the government blacklisting a company for government procurement purposes.
From what I understand, the government is floating the idea of compelling Anthropic — and, by extension, its employees — to do as the DoD pleases.
If the employees’ resistance is strong enough, there’s no way this will serve the government’s interests.
The President is crashing out on X because a company didn’t do what they wanted. “Forcing” is not a binary. Do you seriously believe that the government’s behavior here is acceptable and has no chilling effect on future companies?
They're labelling Anthropic a supply chain risk, without even the pretense that this is in fact true. They're perfectly content to use the tool _themselves_, but they claim that an unwillingness to sign whatever ToS DoW asks marks the company a traitor that should be blacklisted from the economy.
One of the options they're discussing, which is legal according to this law, is to simply force Anthropic to do what they want. As in Anthropic will be committing a felony if they don't do what the DoKLoP wants, and the CEO will go to jail and be replaced by someone who will.
There is no Secretary of War. The name of the Defense Department is set by statute that has not been named regardless of Pete Hegseth's cosplay desires.
> It won't convince me because I HAVE BEEN USING IT, and IT HAS ACTUALLY MADE A BIG DIFFERENCE.
I'm not going to defend a piece where the author contradicts the (clickbait) title in the body of the text, but he does actually say, plainly, that AI is useful for coding.
You didn't say what you're using it for, but since you're here, I'm guessing it's coding.
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