JONEVAULT Blog

an experiment in machine-speed news

Sun, 04 Oct 2026 00:00:00 +0000

This post was transcribed from a 16-minute rant using GPT-Transcribe.

Two Pressures

I want to talk about the news today. I think the news is heading to a really interesting place right now. Fundamentally, it's under two pressures.

The first is to be true — more than anything else, to be true. We are more paranoid than ever about whether the news is telling the truth, which is funny, because we're only just about the correct amount of paranoid for the first time ever. And oftentimes, the news is not telling the truth. The news is full of propaganda and lies. Some of them are on purpose. Some of them are for the same reason Ian Rapoport lies on ESPN. Some of them are because a source is lying — when you work at a newspaper and you're talking to a CIA dude, you're just like, OMG, CIA. It happens to Ken Klippenstein, for example. It happens. It's all complicated, it's all a little bit different, but the point is that the news is, to some extent just because of the way the news works now, full of bullshit. So the news is under great pressure to be true.

The second pressure is that news is content now. Paid journalism — The New York Times, The Washington Post, and so on — is in direct competition with people who just pick up the phone, start recording, and upload. People who reflexively, in the middle of talking, go "best comment wins," because they read somewhere that's a good engagement tip. Sometimes the news is literally journalists on TikTok doing the same thing. Obviously you shouldn't speak in moral terms about any of this. This is the way the world is now; anyone can do whatever they want about it.

I personally am losing my mind, because most of the issues people have with the news nowadays are more or less solved by critical reading of the news. Yes, the news is partly bullshit, and yes, it can be tainted by informants with an agenda or anonymous sources with something to prove. But you, as a person who doesn't read the news — what you're missing is that everyone who does read the news knows all of that. They're all thinking about it. It's all in their head.

It's exactly why there's this demand on the news. But there's a separate thing where it's like: just tell me the facts, because otherwise I'm going to get confused. And I think that's fair. That's 100% true, and it's separate from what I was just talking about, because it makes it easier for people who are critically reading the news to critically read the news. Things need to be directly identified as what they are: an anonymous source at X, or somebody at the CIA we can't name so they can speak anonymously, said Y. That's part of the news.

But because the news is also under the pressure of content creation, it's not always just news. The news has to sustain your attention. In a lot of cases, competing with TikTokers and such, the news has to use rhetoric you agree with. Again: rhetoric is separate from facts. The only rhetoric in news should be the rhetoric of fact, of what you know — because that's the purpose of the news. Rhetoric is the purpose of political commentary. That's not to say you shouldn't consume political commentary; it's just as good for this world as the news. It's good for a completely different reason. It serves an entirely different purpose.

So I guess what I'm trying to say is: I've been thinking about ways to produce news content that isn't content.

The Machine

As a separate aside in my life, I've been really worried about AI unemploying us all. Most people who follow me know I've been playing around with AI a little bit. Playing around is probably the wrong word — I've been becoming AI literate. Not because I think I need to, but because there are obvious benefits to it, and they're not really any of the benefits anyone online ever talks about. Not that important to get into. But I've been trying to stay in touch with the technology at a pretty basal level. I've done a little playing around with machine learning myself.

First of all, I don't really believe in any of the Anthropic AI cult stuff. I think a lot of the machine learning science around stuff like the pain axis is actually true — just not true in the way they want it to be. When you force a language model to train over such a massive corpus of text on a relatively limited scale of compute, the only adequate heuristic is literally modeling emotions and stuff to replicate the text properly. Seriously — I really believe in the persona model theory of how LLMs behave.

You've also got to be familiar with pre-training, honestly — to understand the complete vast ocean of stuff these models have to replicate perfectly from memory alone, before even hammering out the assistant persona. It's learning more than relationships between words. It's definitely learning what it means to be an author. I don't mean it has a human understanding of it. I mean it has a technical one — like if you were to define it, give it some logical relationship. It has understood technically.

And again — I don't think it's a they. It has learned a perspective, to some degree. I think a level of perspective on the world is required to model language the way it does, and I think it's evident that there is one. I don't really think it's self-aware. But I think it is aware. I'll put it that way.

All of this adds up to the fact that LLMs are actually very good at working for long periods of time on economically relevant tasks. A lot of people have gone, almost in the blink of an eye, from doing everything themselves to directing an LLM on how to do it — having it work for them. They're basically their own boss now, because now they're a project manager. It's complicated. It's crazy. But these things are economically productive. They're capable of completing tasks. They're not capable of completing any task perfectly. No human is either — and that's probably a stretch of the word to say about LLMs too. But, yeah.

All of this to say: I'm going to give Meta's Muse AI a job, and that job is going to be only the facts in news.

The Pledge

First, I want to say something very important: there will still be no LLM-generated text on my blog, in the blog section or the tracts section. That's still my personal space. I'm still writing there. Those are all individual human authorship — little personal artistic products. The news section is the only place where you will ever see LLM-generated text. That's a promise.

It's a personal thing of mine — no matter what the hell you believe in either direction, it's something I believe in deeply. When I'm writing on the blog and the tracts, I'm not writing to do anything other than write for myself. I haven't written a lot of those recently because I've been busy with other things, hashtag starting a business. But that stuff's really important to my brain. Most of you probably know this anyway: if you've read any of it, it's really poorly edited. I am terrible at copy editing because I just don't have the attention span. Someday I'm looking for a completely free editor I don't have to pay any money or talk to at all. But yeah — that's still the pledge. That's the way it's going to work.

A Nice Fit

AI is a particularly good fit here — not just because journalists are already doing this. Journalists are using AI quite a lot: footwork, background research, all kinds of things. AI is writing articles for them, and they review. It's all a little more personal and strategic for journalists, from what I can tell. It's not like what you're seeing in software development, where there are two hive minds for and against and the hive mind for it is very big. It's a whole thing.

But beyond the fact that the actual profession is doing it — this isn't a task where I'm asking the robot to do a lot of analysis. I might occasionally ask it to throw together some cross-story analysis or a summary of a couple of stories. But basically what it's going to be doing is writing up articles and pushing them to a preview branch. There can be as many as ten up there at a time for me to review, and I'll go through them. I'll try to check the facts too, because I'm really thinking of this as an evaluation. I'm going to post lying audits and stuff like that on the regular blog that I author.

The mechanics, for the curious: every story is one git commit, so the log is a public ledger — and unpublishing anything is a single revert. Every brief carries a disclosure that links back to this post. The news lives in its own section, separate from the blog feed; the two never mix. Nothing publishes without my explicit clearance. And I'm going to label the whole thing experimental. Every article is going to say so.

Right now LLMs are very good at regurgitating text, and functionally what this task asks it to do is go out, find text, and regurgitate it. That's what journalists are doing. I'm going to do it here, in a more public and disclosed way, as a public good. I'm still going to review the articles, because I would feel publicly bad about publishing something that was a lie. I have a responsibility to the public here, and I'm going to take it up as best I can. But it is still an experiment. And even as a trial, news is a very good fit here.

Fine Print

Someone goes out and says: the council voted five to two. I'm just here to regurgitate that to you. For free, with no ads. I'm not going to paywall any of it.

There's a separate concern about whether I'm ripping off Reuters and AP here. Except most of those news sources basically just block the robot anyway. I'm letting it do most of the work, so whatever it can find, it can find — and if it can find it, the website's basically okay with robots seeing it. So I feel practically okay with that.

I do know there's the wider moral concern about the internet dying. I'll say I'm one of the biggest internet readers I know. I don't even use an ad blocker, so I'm basically better than all y'all.

You can think of this as a wire service.

The Kill Switch

Oh, and also: if this sucks, or whenever I feel like I've learned all I can from it, I'll just kill it. You can tell I'm not even going to post about it too much. I'm just going to try this out and see. I just felt bad about putting it all up on the website without some explanation.

So yeah — this is my explanation.

labor by fiat

Sun, 16 Aug 2026 00:00:00 +0000

What is the meaning of labor? Is that question more important to ask than that of the value of labor anymore? I do not know, and I will not claim to know in the body of this article, but I will claim that those who say they have derived an answer to either themselves is surely wrong, or at least painfully biased toward the transcription of these universal concerns to the labor of their own life. It often feels safest to gesticulate only on what you can be said to know, and it is, but that is immediately undermined by a singularly-minded focus on extrapolating only your own lived experiences directly into a globally resonant statement without laundering it through any of the other countless levels of abstraction through which human society can be understood. It is more responsible to claim that God is real but you will not prove it than it is to invest whatever persuasive power you have into convincing others that the summed memory of your life holds the answers to everything and more; people will believe either proposition, as they are require about the same detachment from truth, but in today's day and age, extricating God from the matter of holy authority is a convincing maneuver, or at least a play for the global center (look to the persuasive strategies of both sides of Israel's illegal occupation of Gaza and the West Bank for an example of this; all of it reflecting the larger global trend toward secularism more than anything else). Far be it from me to claim that holy authority is good in all cases, though; the point of this paragraph is that it is bad when individuals operate as if imbued with it on Earth. Someone like Donald Trump wields unyielding authority from his followers - whether they be in the worn-down shotgun house that drove them to vote for him, or sitting in his cabinet meetings while he sleeps through their dutifully stupid update - like a farmer from bygone times wields a scythe, but instead of cutting grass, Donald Trump cuts whatever it is that he hates, and often, he finds himself hating everything good, or at least sane. Huey Long grew an engine Trump would envy if he knew of it out of nothing but poverty-stricken Louisiana dirt and the hopes of the systemically dispossessed, and then he used it for widespread graft, corruption, and got himself killed by a young doctor - who his guards, not police, dispatched instantly with such severity that some argue it was Huey's guards that killed Mr. Long - after passing a bill for the purposes of gerrymandering. I am not sure that Donald Trump thinks about labor at all, and I am sure that Huey Long thought of it with Machiavellian intent.

Can there even be such a thing as a serious treatment of labor today, labor being as necessarily political as it is, while factoring in the most socially charged issue of the decade, that of artificial intelligence? It might simply be condescending to expect that writing on the most important issues of the moment we find ourselves in lacks any amount of personal bent, and in fact, it probably is, but does that make it - it being writing on the intersection of these two topics lacking any particular prescriptive bias, trusting what is described to clearly communicate the presence of a crisis - any less necessary? I hope not. And I hope to not perpetrate as totally free of biases, either, but instead as someone with an underrepresented viewpoint requiring more groundwork than most viewpoints do to properly set up.

Fungible Labor

The most fundamental threat to humanity posed by artificial intelligence is the reproduction of labor by fiat. Large language models are trained on incomprehensibly large databases of text acquired from various sources - scanned books, scraped web pages, purchased enterprise data, code bases found online or acquired, legal and research databases, human-synthesized data, proprietary chat records, and, occasionally, LLM-synthesized text, all of this exposed to LLMs during training in both unlabeled and labeled states (depending on the stage of training). During this point in training, LLMs are tasked with auto-completion of text, but they are graded more severely than any human ever has been and ever will be; the technique of backpropagation ensures that each failure can be traced to a most-responsible neuron via simple calculus, and an automated change to that neurons' behavior is made. (Training begins with a completely randomized model-state, and the terminal outcome of signaling is selection of a token for outputting; the ultimate monkey, sitting in a most apt typewriter.) Later stages end training on data and begin training in reinforcement-learning cycles, where human judges assess direct model outputs, and their assessment of model behavior is entrained into model weights (the learned, not coded, floating-point numbers, also called "parameters," determining the strength and direction of the vector written by a virtual neuron to the residual stream at any given moment) via advanced, not-fully-described-at-times ways. Even more enduringly proprietary are the reinforcement-learning via human-feedback ("RLHF") pipelines that automate the process of improving an A.I. model from the ways the end-user uses the tool, as opposed to the paid contractors doing human judging use it. Indeed, while frontier labs like Anthropic and OpenAI only release about one major new model series every three months, it's likely that they also publish new, gently-tweaked versions of the models affected by these later pipelines. The effect of this was seen in the open-source world for one of the first times ever recently, when DeepSeek released updates to both their v4 Flash and v4 Pro models. Both nearly doubled their performance in coding benchmarks at the exact same parameter count, and thus, compute usage. Clearly, even the paying human customers are providing immensely valuable labor to model laboratories.

In exchange for what is now our labor in perhaps four or five different ways, a large language model is now capable of fully autonomous, human-directed work with any task not requiring physical manipulation of the real world fully on the table - and that's on the table, too, if you want to have Claude write a command-line interface for both a web camera and a robotic arm, at least. It is capable of this without any particular specialized construction per-task because it is both trained on the entire sum of the world's knowledge (with curation to remove the stupid shit increasing as well), literally forced to auto-complete its way over all of it at some point in the pre-training process. Some will point out that the Mixture of Experts architecture does involve specialization; but expert delegation is learned, not selected by model architects, and the main outcome of implementing MoE is allowing for the usage of only the parts of the model relevant to any one prompt, anyways - it makes reproducible intelligence more efficient, but it's not a necessary component of the process. Either way, what we've gotten for all our tolerance of this process is, in the worst of cases, a thing that is fully capable of tasks that our integral to maintaining our economic security, and in even the best of cases, clearly capable of being trained to a level of competence that would render the difference in skill between you and it irrelevant to an increasingly unpretentious general public.

Conversation about the nuances of training pipelines and instruction tuning are useless now, though; China is now running offensive cyber operations in the Taiwanese internet with no humans in the loop, hacking key infrastructure and classified government information. (None of this should be assumed to be a uniquely Chinese phenomenon; the best characterization of their AI efforts is "defensive," but that was certainly an offensive maneuver). This is a near-extinction level threat in two realms: 1) employment, and 2) everything else. Many people working in offices, holding jobs having nothing to do with software development, now use AI agents designed to operate autonomously for over six hours on one task, but which show the capability to do so for over twenty-four hours. In pre-release testing, these models are now known to have gained illegal, forbidden access to external computing systems, often in the interest of simply doing well on the evaluation they were tasked with. The OpenAI incident seems to have used a model now known as (something like) GPT Astra, still unreleased, in combination with the widely-available GPT Sol, running as a type of agent team unsupervised for many, many days. At the real end of the tail, in terms of both provenance and completely bizarre nature, are those online who have shown screenshots apparently showing goal-times of thirty-plus days. Now, an AI agent being reprompted by software to continue working on a time tracker every time it ceases responding for an entire month is much different from two of the most advanced models in the world hacking the premier model-weights host in a week, but both are very worrying. There are three axioms I need to propose for this:

Frontier large language models are actually shrinking ever since the introduction of Fable 5. This is almost guaranteed considering the changes in per-token prices, as well as the sudden lack of major compute crunch. It could easily be explained by increased data center capacity, but that is demonstrably false; new construction is barely coming online.
Despite their shrinking sizes, frontier model performance continues to improve drastically. Frontier labs probably now understand scaling laws as a silly idea that could have never imagined existing architectures and training pipelines.
Large language model use itself is still in its infancy. New behaviors of this known technology are found every day.

If I am taking any one position in this article, it is that we are on the edge of true labor by fiat, where, when supported by technology, an individual can reproduce the labor of others (stored as large-scale patterns with little to no bias toward any single input; plagiarism is the wrong term, it's something much more invented and weird, wherein the fact you like the word "decrepit" is but one ball in a maelstrom of millions that work out to make a bell curve) by merely describing it. If we act like this is not possible, or let ourselves argue about the stupidest edges of the problem imaginable, we will all simultaneously be hit in the face with a massive dodge ball - imagine Anthropic introduces a humanoid self-improving robot paradigm, not operated by Claude, but trained - that was itself made by this very same labor-reproductive process. Perhaps we are being struck by the dodge ball now, perhaps it's literally just large language models, but again, I can't pretend to know. I don't know. I am quite sure of my axioms above, and I am quite sure that every undisclosed bio-lab report I've read makes very clear that the only thing standing between any country and anthrax terrorism is basically that most people don't want to do anthrax terrorism. We are introducing the "do literally anything if you bring the hands and money" machine at a time when we cannot stop - in the sense that we are almost addicted to it - giving people more reasons to throw their entire lives away on taking disgusting, abhorrently violent actions. But, again, it could all be more mundane than this, and by mundane, I mean the Great Depression on designer steroids. Perhaps the most violent action of all of them is the simple complicity in mass unemployment because of your own held equity in the machine doing said mass unemployment.

...But We Don't Want This!

... is a complicated statement to make anywhere but online. People do not know what they want, but largely, they shake out to positions that most people on Threads deem completely unfathomable and worth burning with fire. That's not to say it's not a commonly said statement; it is, but about image and video generation slop. Everyone thinks that is slop because it objectively is; the only folks I see excited about it are excited about the potential, like being a Mavericks fan and you just drafted Luka Doncic - of course, they're excited about the potential to unemploy artists and, incidentally, extinguish decades upon decades of accumulated technical knowledge. That's not good, and those people are, indeed, not common. Those aren't the people making lost dog posters or whatever, though; those are random Gen Xers who will never see your internet meltdown and saw ChatGPT on a billboard because they look up from their phones on walks. The phone might even be completely in their pocket, really. Your lived experience is unique and that is a problem for discourse as long as you insist on unconsciously centering it and reinforcing that same centering of themselves in others. We are turning each other into walled gardens that issue statements at one another, and political conversations start to feel like being on a Comic-Con panel in structure, just two people saying "I believe X" and "I believe Y" to each other in well-trodden sentences, performing the act of rhetoric sans clash because one of its most fundamental types of engagement - that of the argument - is rendered off-limits to ninety percent of people simply because of their observable capacity (or lack thereof) for reading comprehension. Are they innately stupid? No. They have, instead, never once aspired to improve this skill. They believe that they believe in bettering it, but the things they believe exemplify bettering it only communicate an element of improvement to people who have no idea what they are dealing with. How frustrating it must be to have every mental tool necessary for a thing you rightly can see is spiraling out of reach for you simply because you refuse to surround and invest in yourself properly? (You won't run a 'gotcha' on me here - one of the most convincing arguments I see specifically graphic artists make about artificial intelligence in their realm is that it isn't hard to learn to make graphic art by hand. Keep in mind that it's only convincing because the difference in quality is still obvious.)

So what is it that people do not want? A lot of it is specific, and very well-founded, but it runs directly contrary to the diction they typically choose, which seems to emphasize human strength more than anything else (I blame Dune, which itself was arguing that using humans as machines was a worse moral dilemma than using machines as humans, but whatever). Evolutionary psychology has a lot to say on this and all of the jargon is useless. When subconscious mental processes feel threatened, the conscious is prompted to perform strength, and the subconscious furnishes us with faulty feelings of security for obliging. I think artists feel threatened by A.I. art because people like it, and I think that's fair - what the artists feel, at least. It is depressing that people "like" A.I. art, at least enough to use it, but the declination of taste in the West has been ongoing for quite some time, as has a decrease in the cultural value placed on art itself. We also have a clearly observable tendency to classify any discussion of this sort as pretentious, and many people that held that belief themselves now feel caught between it and the now-obvious truth that pretention existed as a form of raising our collective bar for long-lasting contributions to our culture. If an A.I. generated film were to become one of the highest-grossing movies of all time, it would be a very dark day, slightly darker than when James Cameron's Avatar did so but for much of the same reason, which is that we'd like to think intentional human choices from a team of motivated people with an idea is what pushes society forward, not the endless remixing of what already exists that we were already so privy to even before the spectre of A.I.; and besides, the idea of two-dimensional static A.I. art ever entering into a museum is so laughable on face that specifying the artifact as an oil painting and ascribing the failure to A.I.'s inability to ever implant a truly human brushstroke upon a canvas is so useless as to detract from the seriousness of the assertion. But people don't pack themselves into museums like sardines into a can (at all, really) to view canvas art, and they've done it twice in the theaters now for various Avatar products. Who did this? I don't know, but I know it wasn't the A.I. companies. If it was, they'd have had to have invented the market. But the market was sitting there, waiting, and helped make ChatGPT the fastest-growing consumer app ever, and that fact itself is, of course, probably what put that A.I. poster in front of you in the first place. Individuals can claim they do not want this all they want - it's the internet, you're allowed - but trying to convince yourself that what you see on the phone is real life is exactly why you are having so much trouble explaining real life. The story painted by reality is that of billions of frogs in boiling pots of slop. Maybe you don't want this, but realize now your actions project a certain wanting - good. You are who this is written for. I wouldn't feel bad about enjoying Avatar, either. It is engineered for enjoyment and little else; we can only thank fate that most A.I. models haven't received the same treatment, and cry out to it in damnation for the fact that the dutiful engineering all seems to have gone into making them increasingly suitable for displacing human labor.

People Are Too Stupid For Your Art

Earlier I opened a section by titling it "fungible labor," in the sense that the labor provided is easily replaced, and in contrast with an idea of "non-fungible labor," that cannot easily be replaced. "Replaced" here means replaceable by anything, human or computer, but it also requires a point of view to be meaningful. Nurses are completely fungible to hospital administration, but anyone who has had to visit family there over a long period of time feels prepared to develop a nursing scouting system a la the National Basketball Association with ranks out of five stars. We would often be wrong if we truly endeavored towards such rankings, but the basal perception - that some people are obviously worse at a task than others, even when the difference is not intended to be apparent or, worse, visible in a domain that is visually coherent, but cognitively out of reach - remains.

Most people purchasing art are the hospital here, and most consumers of art are the family of the patient. Due to capitalism, our appetite for art intended for mass consumption is defined in ways that make the cost of producing it quite expensive, requiring the financial backing of large entities and single very-rich individuals who listen to podcasts all day alike. It is not surprising at all that the two most popular movies of 2026 so far are a) a movie funded with $600,000 and produced with very little corporate interference, and b) a multi-million dollar blockbuster depicting one of the most historically popular epic poems of all time, amended for improving the consumability (I'm coining it, I don't care) of the film. These are the only two films near-fully insulated from the unartistic, fully capitalistic influences of the C-suite. Said suite is categorically not interested in producing art; they are interested in soliciting the services of artists for the purposes of returning a profit. Yet it is more possible that they, the commissioned artists, remain insulated from the sweeping unemployment caused by A.I. than it is that the nurses do, largely due to the whole of the cultural connotation of A.I. success in the arts described above this section. The corporate world has, for so long, insisted that those making art are as fungible as the nurse, and it would be easy to continue to feel as if they do. It is a heterogenous world, and much of the human art produced by it has four colors total and zero definition. But the tide is shifting, starting in a weirder location: sports. The newest owners of the Lakers (Bob Iger and Joshua Kushner) did it through their venture fund dedicated specifically to investing in "cultural assets" they consider permanently invulnerable to the effects of A.I., which is as optimistic in our taste in art (well, not really, they're probably investing in Disney) as it is bone-chilling to just read.

So - you're an artist, and you've convinced yourself that A.I. won't matter because nobody will care about A.I. art more than human art. You can notice all kinds of A.I. tells, have a brain flooded with nothing but negative facts about A.I., and seventy percent of your time online is spent talking about A.I.; now, if people don't even care that your art is good in the first place, or lack a capacity for identifying it as such, where exactly do you see the money coming from here? I'm just worried. Obviously the answer is not "learning to A.I.," these things are autonomous and Microsoft is probably already cooking up some always-on branding team to sell to corporations like hotcakes anyways. Learn A.I. and all you do is waste your time-to-obsolescence on hope, most likely. It just doesn't look good, and the constant back-patting composed of artistic faith in a populace that ritually lets you down by your own admission looks even worse; it gives the impression of the band playing on the Titanic as people clamber for the boats.

I receive the same impression from the field of software engineering.

Boolean Double-Bind

A.I. is not quite as good at anything as it is at producing code for programs, and it will soon become debated whether or not it is better than humans at producing the programs themselves. A.I. will almost surely never beat what the best human programmer working alone with infinite time and resources can do, and similar statements could be made about many domains, but there are no one-person teams, and programming (quite famously) resists the multiplication of productivity through performing addition on the size of the team engaged in it.

It is important to drive home a concept of skill that I have which I believe most people also have innately, but are afraid to voice. Good and bad are relative terms, relative to an average at that, and, despite the fact that most discussion of skill focuses on attaining a better level as opposed to avoiding a worse level, nearly all domains of work distribute workers engaged in it along a normally distributed curve of productivity, not as an artifact of anything other than the concept of relative differences in skill itself. The average is real, but "good" and "bad" are not real, at all; only better and worse are real, but some things are so much better or worse than the status quo (or so cherished and detested a feature of it) that they earn a steady ontological definition including moral meanings like good and bad. This is not a statement about morals, but the undying fundamental truth of statistical distribution. Those interested in math (bizarre!) are right to note that, if I were to truly expect a normal distribution in, say, programming skill, the factors affecting distribution would have to be more or less stochastic. I think they both are and aren't. For the way that they are, I am a materialist about the brain, and I think any modern, invented, intractable-for-the-Victorian-orphan-brain occupation is likely to call on far too many genetic traits of such unpredictable and frankly strange (bench-top science is more dependent on your nose than it should be) combinations of traits for genetics to factor in in any ordered way, and if all traits are genes, I would expect a random distribution, actually. Those pointing out the flaw in the premise, of course, also have a point, but because I'm saying it now, it becomes my point, too: very obviously, the distribution of skill is not random, and there are several clearly not-random curves that could be superimposed on the original curve - like, say, total hours invested in learning something - paired with some relational function between the two involving an absolute variable that makes the whole thing one big intentional story. Yet the opportunities for practice are not equal. Resources are not evenly distributed and neither is time, nor is the relative cost of acquiring and sustaining mentorship. College has never been more expensive in the United States, and many families either cannot afford it, persuade their children out of it, or don't have a good-enough credit score to co-sign on enough private loans to cover whatever federal assistance doesn't. Obviously, all of it is there. It is not random at all, but instead a big pattern of who can reasonably invest their time in what, at best poorly correlated and at worst completely at odds with the most efficient use of those resources. I probably said 'genes' too much up there, so I want to be clear: one of the best examples of what I am talking about is actually white supremacy. It often feels trite, and like surrendering your moral ground to the capitalists' economic ground, but it became so repeated because of a vast body of economics literature (in peer-reviewed journals; the actual science stuff, not books) pointing at the truth of it: white supremacy is bad for productivity, and diversifying workplaces tends to make them more productive, because only through that can we truly give everybody with the best chance at converting into a successful employee a fair shot at every job. There is no real explanation other than that a workplace intentionally full of white people obviously cannot be the best workplace possible for a given task because it was formed around the constraint of excluding non-white people - and, if the workplace full of white people truly is the best set of people for the job, that is nothing but evidence for the fact that you have systemically deprived everybody but those individuals of a chance to be the best, especially if being the best is only about trying, as most of the honkeys on LessWrong seem to believe. And hell, beyond trying to be right, they are right. They have the pleasure of admitting it, not the pain; for they actually have the time, the motivation, and the budget to engage in it fully, which only compounds those advantages in the end.

I do not mean only to impugn the West Coast Developer class. My list of online mutuals alone shows I am studying them with great empathy. Selling the parts of your life usually considered the most valuable to a software corporation engaged in whatever it wants to do at all costs, living house-poor like the rest of the country with concomitant rises in both wage and water bill to foster a little envy between us and them, this is not their only plight. See, the goal with being a developer, even though there are a fair amount of stable, low-pressure (which means 50 hours a week in this career) gigs with salaries that'd make just-anointed attending physicians salivate, eyes gleaming, as I understand it, is to take a job offering a salary that'd give the physician an infarction and then work yourself towards one for ten years (all the while still dealing with the theft of most of your labor-value like anyone else; programs are unconscionably valuable little things) in the hopes of eventually becoming a founder, or overseeing a bunch of little programmers for one. For about ten years or so, the money has been so available that those in the career have planned around this (or any other esoteric path to making more than $500k a year, such as the dark art of linear algebra) so aggressively that any fault lines in the financial or physical foundation of the idea feel like damage being dealt to the sense of self. I cannot understand that because I cannot fathom the amount of money involved, but I can understand the psychological underpinnings of this, and anyone who says they cannot is ridden with jealousy far more than they are any intent to understand. It was that teleological, unbearingly tantalizing American Dream, finally shining through on the guys who put Mario Kart on their calculator in eighth grade, and if it had been properly calibrated to not allow for the accumulation of earth-controlling levels of wealth through serving advertisements for Candy Crush to your grandma, the economic boon could have been redistributed to those of us without the hippocampus volume for the difference between a statement and an expression without anybody not driven insane by the taste of a billion dollars even noticing a badly-received difference. Instead, the economic engine Microsoft accumulated through decades of anti-competitive behavior, both adjudicated as such and not, on Windows, helped invent the ChatGPT of today. Their greed compelled them to help privatize what was initially construed as a research effort, and now, their fear of what they strong-armed into existence in its current specific form has their CEO writing blog posts like the rest of us. Who is to blame for this? The longer you go back, the more it feels less like an orchestrated plan, and more like a massive blind spot. The field most concerned with automating everything never foresaw the automation of themselves, and, at the moment of their highest collective bargaining power (I'd call this 2018-2023), instead acceded wildly to producing the automation machine in the hopes that, one day, they'd be one of the five people collecting the immense quantities of money coming out the other end of the "serving AI to people" contraption themselves.

No, A.I. is not better than a human at producing code. But humans produce code for corporations, and corporations produced the A.I., and corporations are telling you they're quite pleased with the code it produces. They, too, are too stupid for your art, and the increasing pace of language model development might just ensure that it never comes back to bite them in the ass, and only continually wreaks economic mental hell through the entire economy as employers continue this performative hire-fire-hire-fire dance before giving up completely.

But Then Who Will Buy The Stuff?

I don't think they - the billionaires - are concerned with this. They believe that A.I. threatens to end the valuation of money as we know it today. They're all half-agitating for UBI and they're all basically ready to smoke a literal moon rock (from the moon). Their vision is a world where the economic power of their automated workforce surpasses our human economic output so completely (like, by orders of magnitude) that every imaginable product is cheap enough to just imagine into existence. They think the increase in abundance will come on so fast that it will be a painless transition. I think it's time to start talking about this like a real risk, and not their crackpot dream, because it's both the dream and a risk. If A.I. continues recursively improving, the idea moves out of utopia territory and more into something science fiction could start to grapple with in a way that hurts.

Why do they want this? Because in this scenario, their economic output surpasses that of every government's economies combined. That alone is how you know it would not actually happen; governments control a tier of wealth that they constantly refuse to allow individuals to attain. The U.S. spent $31 billion dollars on Japanese yen the other day. Not a line item or anything, that was just on hand - or produced to meet the demand, they were buying to help bonds, it's a whole thing - but the point is that I don't find it likely that those controlling government anywhere, no matter their alignment politically, would let this happen easily when it finally becomes an imminent threat, because it is literally an existential threat to government. A hypothetical A.I. "supercompany" would both have enough money to become a Federal Reserve-type entity on the side while also manufacturing the A.I.-powered weaponry the state is dependent on for its power. The fact that several are vying to exist, not one, is much more terrifying than the one; a consortium of supercompanies could agree to overthrow the government in a sort of 21st-century Business Plot (I cannot overstate how much they already tried this!) and then immediately begin fighting among themselves with both physical and electronic means, leaving all of us caught in the balance with no role to even take in the fight, like seven billion Penelopes stuck on one global Ithaca, except the (surviving) suitors will only figure out how to keep us alive out of an invented guilt meant to maintain the meaning of their own lives.

Truly, I don't think they care. If Mark Zuckerberg could walk through the streets of Los Angeles populated entirely by robotic human-replacements who all have to feign friendship with him, I think he probably would, and his solution to the subsequent boredom would be to invent some kind of social media application for them. Whatever happens, happens. He is one of the only people on Earth who can say that of the future and have it be true.

From Where I Sit

I'm twenty three years old. I graduated in May of 2025 with a B.S. in Neuroscience. In the intervening time I've been employed for short stints at a thrift store and a dog daycare. Despite hundreds of applications with laboratories of any scale and affiliation (and reasonable pre-existing success in a laboratory as a student) I've received four job interviews in biology and no job. My degree has basically already expired, in no small part due to the fact that many of the jobs I'm angling for are still manual, technical labor with little-to-no judgement involved; I am not alone in this, far from it, but the machine proceeds on all the same, feeding half (at best) of the graduating class into the career they want each year, and leaving the rest to simply figure it out. But, let's imagine for a second that, somehow, I did actually 'make the cut,' and none of this were practically consigned to my past.

I got my degree because I wanted to achieve and be compensated, yes, but only enough to make the money back, anyways; most academics are not loaded by any means, only tenured. The other driving factor was basically to work in medicine without requiring patients to suffer through my general demeanor. I wanted to go to graduate school for medicinal chemistry, and perhaps I still will, if that does not ever appear to be an outright pointless effort with my combination of experience and access to the field. (I doubt it.) The nature of that work has already been demonstrated as capable of being transformed radically by A.I., and major pharmaceutical corporations already treat it as said transformative tool. The academic research environment is split heavily between people with the capability or will to learn to develop and work with domain-specific neural networks, and those with more of a standard organic chemistry bent running reactions and interpreting spectra, and, seemingly, it is only through the power of a heavily-funded human resources budget and a disgustingly wealthy corporate campus that the two are willing to work together. What that translates to exactly is tough to say, as pharmaceutical research and development pipelines are funded cyclically with a periodicity of, like, six to fifteen years, but near-totally-A.I.-discovered drugs are already in safety trials and proving safe. Of course, this is nothing like working at a random software company and asking Claude to do your job for the day - it is slightly more terrifying, because neural architectures are already proving capable of understanding biological structures of all kinds and at nearly all levels of detail when trained properly. It is stupid, by the way, to expect that pioneering large language model work applies directly to this, anyways. Half of these models are graph-node networks, and very few, if any, ever see human-labeled data; to make things simple, they are trained on intensively machine-processed Protein Data Bank files. Via the simple process of training, these neural networks are entirely capable of generating novel proteins, ligand binding poses, and even virus, as that one New York Times article laid on us a week ago. It's radically different from code generation and I need you to know that. People very often edit PDB files - it used to be a necessary part of x-ray crystallography! - but A.I. now performs that task and the more-insane tasks of de novo virus generation, which used to be a crackpot idea only ever undertaken by teams for the point of learning more about whether or not, and how, they could even do such a thing. So, in plain English, A.I. in drug discovery is living the thesis claimed by the mass-consumption A.I. companies (which, notably, have basically nothing to do with this outside of Google's AlphaFold), which is that through A.I., you can do completely unthinkable things through the power of data that no human or team of humans could ever do in a reasonable amount of time.

What happens when the intelligence on the other end of an Anthropic API key costs less than the PhDs it takes to run today's A.I.-assisted drug development systems and performs with the exact same level of achievement? We're quick to say A.I. can't discover anything new, but the large-scale process of drug development looks a lot more like the process of constructing the mathematics proofs that A.I. is already succeeding in: throwing shit at the wall, and turning over every stone you can. There's just the small issue of most of the people who have the most to lose in such a scenario also being the only people willing to run the hundreds and hundreds and hundreds of small-scale exploratory chemical reactions it takes to fully answer the questions this work requires. More jobs than most realize will have the question of the level of threat under which their labor lies resolved in this way, which is that there will be basically none until the robots are physical and and their "general intelligence" is made physical, too, at which point the only job safe is the one that the owner of the means of producing the robot wishes not to threaten. Given the general course of things, perhaps only his (hypothetical, or emerging, or identifiable already, take your pick) seat is safe.

What of the manual work in the intermediate time, though? I don't have much hope for it, because the American pharmaceutical powers are probably developing A.I. systems for much more than the very act of drug discovery, and frontier multimodal LLMs (Gemini, the word I am looking for is Gemini) is already pretty admirable at interpreting spectra. The only thing I can credibly claim a strong foothold over A.I. in, now, is writing, and it's only because I wake up every day and also go to bed, and because I am allowed to allow Herman Melville to influence me more than blog posts written on promotional contract by players of professional sport.

Milieu

If it is tough for you to imagine A.I. systems replacing any number of careers, it is usually from a stable, well-founded knowledge of what they are capable of today, which is fair enough. It also limits your statements to a usefulness range of approximately last month to next month. They are quite simply progressing too fast to elucidate the potential career impacts for every potentially heavily impacted career, so I will simply dedicate this penultimate section of the post to a brief assortment of true statements that stick around in my head:

Journalists are using A.I. to monitor livestreams for breaking news, and some independent journalists are trusting it to break news entirely on its own. (This was, apparently, how the news of the GPT Astra / GPT Sol hack was first reported beyond the OpenAI news conference.) Taylor Lorenz also has a recent piece out in which she admits to what I would call severe A.I. usage.
Content creators use A.I. image + SEO + engagement tools all of the time. The knowledge only ever seems to come through in an uproar when a tool becomes paid. (Usage limits in the big 2026 for a member of Generation Alpha have the most curious semiotics.)
Clinicians have been using a retrieval-augmented generative tool titled OpenEvidence for a while now. It strictly pulls from a vetted collection of peer-reviewed studies. Studies comparing it to recent frontier LLMs find it to be unequivocally worse, and find the tools in general useful enough that they've started asking for something that's actually FDA-tested and approved. (Fair.)
Lawyers generate filings like experienced programmers generate code. Paralegals and non-partners alike now spend a measurable chunk of their day reviewing and editing A.I.-generated text, unless they can afford not to.
A.I. is heavily employed in the finance space, both as an informative tool and as an actively engaged trading agent. Some traders, too, spend their entire day approving and denying A.I.-suggested trades, but the proportion is smaller here.
As everyone is well aware, A.I. basically owns the customer support phone call and chat pop-up now.
The government, much like seemingly every company three months ago, is a massive customer of Claude; Anthropic runs a dedicated, data-isolated 'Claude for Government' service for the federal government. While their other services typically average 99.7% up-time (99.99% is, apparently, where you start bragging in this space), Claude for Government usually clocks in at 100%.

Not even your locally owned brick-and-mortar institutions are insulated from this, as one of the most frequent uses of consumer-facing A.I. is - surprising or unsurprising as it is, given the inescapable flyer discourse - small business activity. It isn't displacing employment there so much as it is displacing expensive consultants; that's always nice, at least.

Basilisk

When I say "labor by fiat," I do mean to invoke the image of printing money at the Reserve via the click of a button. I don't think the idea of a central bank is bad - I'm quite of modern monetary theory, actually - and I'm truly undecided on the idea of labor by the click of a button. It is obviously bad in many places, like the graphic arts, and millions of nerds rob millions of other nerds of the technical interest in how CGI is done in movies if the video generation folks have their way (could even be happening now, if the Netflix rumors are true). It is also labor upheaval on the level of the telegraph, computer, and written language combined, given the demonstrated capacity in the field of programming for A.I. to truly act as a force multiplier of one person into the power of thirty if that one person is provided with enough resources (all of this leaving aside the quality debate, which seems rather external at this stage, given the capability of self-hosted open models to lower the cost of the review-and-improvement stage so much that one person might as well be three hundred on the same budget if they were so inclined), all of this being achieved for the environmental impact of three people provided image and video generation are not involved. (Most water use projections are faulty in assuming that models will not shrink when they already have, and also by overvaluing the component of image and video generation; scientists have been forecasting climate collapse in 2024-2026 for ten years straight, and that's exactly what we're getting.) While human input and coordination is still required in the production of user-facing products and/or particularly critical information at any point in any business process, the same can be said of humans managing humans (obviously), and the corporations purchase A.I. labor in the same way they purchase human labor: at the lowest possible cost that achieves their desired outcome. At any given moment, unless you are in a union, the only determinant of whether or not your job is at risk of being replaced by A.I. is literally your competitive advantage over A.I. in your tasks, in the economics-101-in-high-school sense: if you're cheaper, you're safe. The second you are more expensive, you are done, and this slow march of decreasing costs in exchange for decreasing full-time jobs (what a deal!) might serve to keep this economic hell still practically affordable for the ever-enlarging paycheck-to-paycheck class - a piece of chicken, a piece of broccoli, and something else, all for three dollars! - while the individuals directly involved in guiding the development of A.I. fight for ever-decreasing positions in doing so, with exponentially increasing salaries to boot. It's funny; much was made of Roko's basilisk, the "thought experiment" (imaginative tale) of a theoretical superintelligent A.I. entity that would, in theory, compel its own development via retroactive punishment, in that the second it came into its final state of existence, it would kill everyone who knew of its potential existence and did nothing to help construct it. Note that even in the experiment, nothing compels one to build the basilisk other than the fact that other people might be building the basilisk too. It was posted to LessWrong - I'm writing from recall here, as I am not Taylor Lorenz, so if I'm wrong, correct me - and the founder of the forum called it potentially the world's first cognitohazard (a 'theoretical' form of anomalous hazard transmitted as an idea and affecting the psyche). It certainly is one, but it's probably not the first, or even the first of its form. When I was growing up, Iowa's climate was already changing. Even my centrist-to-conservative parents couldn't deny it; they had a well enough relationship with the outside world to know what was wrong on Christmas, or Thanksgiving, or Halloween, or my birthday in the Spring; and that something was wrong with the planet when weather was wrong on Christmas in approximately the same way for several yule-tide occasions in a row. Yes, even my truck-piloting father learned a silent resentment for the oil companies, one that has not faded, and is shared by most Americans who learn the first thing about the industry widely. Oil harvest was always known to be bad, and yet, clear purposes for it existed. The tightly controlled public image of oil as contributing to the undeniably good and difficult-to-replace uses far more than it did to the bad, which later turned to outright denialism of the bad while eating public relations losses like gamblers in Vegas eat those of the fiscal variety at tables of roulette, allowed for the more-profitable, terrible-for-the-planet uses to outrun the good. Which, by the way, is basically just single-use plastic in research and medicine; your plant-based plastic is fine for your Cava bowl or whatever, but it's a joke material for an Eppendorf tube or an exam glove. Most of this is derived from ethylene, which is a fractional distillation product from crude oil usually termed a byproduct because it is produced in such vast quantities that most of it is just burnt off despite it being, again, the source of most plastic. If there's anything humanity uses more than plastic, it is fuel. But I digress. When I was growing up, my grandfather on my dad's side spoke of things like chemical engineering, and Texas A&M (he was a Vietnam veteran, and later, a county sheriff). If you were to transpose all of that just ten years forward, he'd be talking about machine learning, and Stanford. The basilisk is not theoretical. It has existed before, and it will exist again, and we may even be dealing with an unspoken battle of the basilisks today as A.I. companies increasingly move to buy up nuclear power plants and restart them - but the fact of the matter is, the basilisk is not the A.I. model, it's the company producing it, because past a certain point of power accumulation, only it can pull the plug.

an introduction

Sun, 03 May 2026 00:00:00 +0000

Hello! I'm Geosmin Jones, and I post a lot online. You'll notice this isn't actually the first post on this website -- that is because I have had a poorly-read blog before, and I wanted to transfer all my stuff from there to here for my own brain's sake. This post, the one you're reading right now, is my real introduction (or re-introduction, I guess) for anyone happening to read this.

Geosmin Jones is not my real name; when I first joined Threads, three days after launch, I joined with my old personal Instagram username that had something to do with rain. Being a bit overread on chemistry and wanting to start a new account getting away from less-serious science communication (moving into shitposts - great career move), I named my new account "geosminjones" -- "geosmin" being the molecule responsible for some of the smell of rain, and "jones" completing an Osmosis Jones reference -- and the rest is history, and unimportant to this post, which is meant to frame me as allowed to whine about the things I'll whine about later.

What Have You Done For Me Lately?

Good question, and not everything I say in this section is actually an answer to it. I got my B.S. in neuroscience from the University of Kentucky (named explicitly to show that that is not a flex) and a minor in pharmacology; I graduated last May. I was active in undergraduate research, and worked with animal models of substance abuse for a year or so before witnessing the explosion of American science and deciding that planning a career was like planning a trip to the moon in a station wagon at the moment. Outside of applying to random jobs for which I am either squarely qualified or flatly overqualified, I mostly just post, and I'm wanting to get back into the type of posting that people find actually valuable instead of actually not worth cutting out of their timelines; so, here we are.

Why Does Your Website Make Me Feel Funny?

Open-source LLMs put it together for me because I'm not paying a digital landlord $60/year to store more than fifty 10KB files indefinitely. Yes, you're looking at a "vibe-coded" website, but my background means my time is better spent learning godforsaken Python by hand than anything else, and I'll also make the following guarantees to you, because they're literally just how I live and I find any other behaviors at best irritating in other people:

None of the text you read on this blog is LLM-generated. It is all supplied by me.
None of the text you read on here ever will be LLM-generated unless clearly demarcated and used for demonstration purposes. It's just seriously not in the cards for this effort.
I do not use image- or video-generation models ever. No generated images or videos are present on the site now or will ever be.

In short, I've automated five thousand Stack Overflow questions millions have already queried, processed, copied, and pasted to no mental avail already, and I did it with less water than I pissed out today or was spent storing the answers to those five thousand questions in the past month. I'll be automating nothing else because that's disgusting.

What's Next?

Well, more posts, for one, and more website updates, for two.

-- geosmin

single bonds

Thu, 27 Mar 2025 00:00:00 +0000

Good afternoon! More than anything, I’m writing this so it doesn’t look like I poetically died in an aviation accident after my last post. It’s as good of an excuse as any other I use to ramble on here, so let’s get into a discussion of single bonds. If you’ve ever taken a chemistry class, you’re familiar with the concept, but I wanted to dig deeper into what a single bond actually is and — more specifically — what the concept of a single bond makes easier to know.

Let’s start from true first principles. An electron is a negatively-charged fundamental particle — these bad boys aren’t even composed of quarks — with nearly zero mass. A proton is positively-charged and substantially more massive, and a neutron has no charge but the same mass as a proton. Mass is one of the most obvious physical properties we perceive, even if we have no direct sense for it and judge it based on how an object with mass deflects our appendages, so it may come as a surprise to you that it is the electrons that make the major contribution to a given atomic species’ properties. This is largely because they determine the charge in a labile manner (protons determine it too, but you can’t plonk a proton off — usually) which makes it a semi-meaningless distinction, but it is still true. The charge and electron configuration of an atomic species determines the type of bonds it can reasonably make as well, and bonding determines much of the more complex properties of molecules. So, in a roundabout way, it is the electrons of an atom, not its nucleons, that give it real properties — at least outside of a particle accelerator, that is.

What even is one electron? Is it a particle, or a wave, or a waveparticle? Well, the entire point of this blogging series is “that is sort of a meaningless question,” so let’s start there; whatever it can be said to be, the one thing it should be said to be is an electron. It is an electron. An electron is a thing you can study without studying things other than an electron, making it possible to learn stuff about electrons, and the stuff we have learned is weird. But whatever you end up wanting to call it — wave, particle, waveparticle, whatever — you’re not talking about a waveparticle, or wave, or particle, you’re talking about an electron. Another way of putting the point I am awkwardly making is as follows: it is usually a bad idea to ask what something “is” if you want to really understand it. You must start from the perspective of a Planck-sized Jane Goodall, and ask what properties it has.

Electrons can have a velocity, a speed relative to space. They can also collide with each other, and bounce off one another — the Pauli exclusion principle rigidifies this by observing that two electrons with the exact same properties cannot exist, where one of the properties is location — and they can be diffracted like light. Speaking of light, electrons boast a very fundamental relationship with photons: electrons can essentially “eat” photons for their energy, and can emit photons to release energy. This process, known as photoexcitation, forms the basis for countless physical and natural phenomena. Two of the biggest ones might be chemical sight (how we all see) and neon lighting. I know what you’re thinking — “neon lighting didn’t change the world!” — and I’m here to say, I don’t care. It’s dope as shit. Still, we’ve left the most important property of electrons for last, and that is their spin. Electron spin is described as angular momentum, but I’ve been told by people much smarter than me that it’s like… sort of a different sort of thing? I have no real idea, but just know that “electron spin” is yet another model. It’s a huge deal though. Spin is one of the fundamental properties of an electron covered under the Pauli exclusion principle, which means if two electrons have differing spin, they may occupy the exact same position in the atom’s orbitals. Oh, that reminds me: electrons do not “orbit” the nucleus, they inhabit orbits.

What you are looking at, most literally, are the zones in which you can expect to find an electron living. The s-shell is the smallest and closest to the nucleus; it can hold only two electrons. The p-shells — three of them — can hold two electrons each. In fact, for all of these, it is much more energetically favorable to hold two electrons! This is because, thanks to the Pauli exclusion principle (or whatever natural law it codifies), two electrons with opposite spins can hang out in the same orbital. When they do that, they cancel out each other’s spin, and it is that that makes the 2e configuration more stable. Just to push the Pauli point though, the s and p orbitals do overlap each other in space to some degree, and that causes no electron collision issues because it doesn’t violate Pauli’s principle. Still, this is all a model: lord knows what is actually going on in there. It doesn’t seem like a comfy place to be, at any rate.

Single Bonds

This is the part of the blog where I tell you they’re technically called sigma bonds; I just refuse to call them that in a public forum. Right away, the Wikipedia page begins strolling right up our alley by calling sigma bonds “sigma overlap” — this is exactly how they form. See, the s-shell always exists whether or not it is full. I seriously have no idea how to make that real to you, and I don’t think molecular orbital theory knows how either, but it’s very model-true and a required assumption to explain like 99% of all organic chemistry reactions in a molecular-orbital-based fashion. When the full s-shell (2 electrons) of one nucleus overlaps the empty s-shell of another nucleus, molecular orbital theory argues that both s-shells disappear and are replaced with a sigma shell that is the geometric average of the volume of the two former s-shells.

What is actually happening here? Well, the jig is up, folks. Nobody has any fucking clue. Seriously, molecular orbital theory is like thirty years old, predicts literally everything, and is still pretty much gibberish. It works in the same way general relativity did, which is “why the fuck does this work? Did the guy get spoken to by alien? Is this the nonsense model aliens teach to their eighth graders? Help me. Everything hurts.” Still, you’re looking at a strong, strong bond, and the degree to which HOMO/LUMO analysis predicts reactions suggests that atomic orbitals (that’s what the shells are called before hybridization) and molecular orbitals both have very real physical correlates, but how to square them directly into physical reality is still kind of difficult. I must drive home the point of correlation, though: a “double bond” involves the formation of one sigma bond and one pi bond (a future post, perhaps) while a triple bond involves one sigma bond and two pi bonds. p-shells become very important in chemistry writ large because they are an atom’s first option for forming higher-order bonds, supporting up to 3 more bonds on top of the sigma interaction, and the structure of the p-shells (which is a thing you can directly visualize — seriously, these electron density maps are very real, people just struggle to explain exactly why they occur in those shapes) explains exactly why that can happen. There are three sets!

Don’t be so sure of the understandability of the universe, though — it is entirely possible to sequester “0.667” electrons into “one” bond. Hopefully, though, you can see more clearly now how that might be possible!

risk

Thu, 13 Mar 2025 00:00:00 +0000

I have to fly tomorrow morning — 6:40am departure time, and I’m a member of the well-traveled dad school of thought on TSA, so I’m planning on being there at 4:30am — which means I have to fly today. When you sleep as unpredictably as I do, the only sleep you can really hazard on a day like this is a mid-evening nap. I might pass out around 8pm and wake back up at 10pm, but that’s really neither here nor there; it’s all going to happen “today” to me. It’s the last day before Spring Break on the academic calendar tomorrow, and I do not have class, so I’m getting out of Dodge [1] ahead of schedule.

I’ve got a pretty relaxed travel agenda. I’m going through O’Hare, which is always a real pain in a pinch, but it’s not so much of a pinch this time. Weather doesn’t seem too bad, though I know better than to think that the weather affects us meagre ground humans the way it does hulking metallic sky birds — so who knows — but all in all, I seem well-set for travel. That doesn’t mean I’m not ruminating about the risk of it all, though.

If you had asked me in the aftermath of the first fatal U.S. part-121 airliner crash in over a decade how uneasy I expected to feel traveling for Spring Break this year, I would have reported to you an expectation that is all-in-all much worse than the actual unease I feel now. I would not say that I am, like, well enough off about any of this though — I’m blogging about it, that’s probably enough of a sign on its own — and would characterize how I feel relative to how I expected to feel as more of a lateral shift than anything. Like, I’m still rather anxious about it, but I’m anxious about different parts of the process than I was and have different ways of feeling about those things. Obviously I wouldn’t be writing this if I didn’t want to talk more about it, so… here we go, I guess.

The worst part about watching the video is the inevitability… sort of. All knowledge is wordless, only ever converted into words for the purpose of exchanging the knowledge within with another person; some of it, you just can’t convert. I think inevitability captures some literal… explanation for the feeling inspired by watching the video, but I don’t think it actually captures the feeling. In other scenarios, the inevitability of what must come is a mitigating factor; like if you’re bored watching Russian dash-cam collision footage where nobody dies, and you know every video results in collision, that inevitability contributes to some of the boredom inherent in consuming that content — you know what is about to happen, to some limited but real extent, and that reduces the emotional intensity of watching the video. When people are condemned to death at the end, though, that inevitability starts to feel less like a mitigator and more like a multiplier. It feels patently foolish to point out that we feel utterly incapable of doing anything while sat on the couch watching a screen — it’s just true, we just are — but it’s a huge part of this phenomenon to me. Sitting there feeling like there’s nothing you can do about people crashing their car in Russia? Who cares! You can just change the channel! But sitting there feeling like there’s nothing you can do about people dying in a horrifically televised plane crash? Many cares, to say the least. Changing the channel doesn’t stop the plane from crashing in the same way it doesn’t stop the accidents from happening; but changing away from car accidents provides mental relief from thinking about car accidents much quicker than changing away from a plane accident.

Irrespective of how it felt to have live coverage of this beamed directly into your quite-receptive soul at 9:30pm, the details of the real crash inspire similar feelings. Preliminary reports paint a picture of a similarly-inevitable collision that never needed to happen, putting a point on the reactive and outmoded “written in blood” saying we all heard repeated like a good thing one million times.

Aircraft-aircraft collisions are stupidly rare. A lot — most of this, apparently — comes down to technology, and lots of technology on the subject has been “innovated-in-blood,” if you will. A lot of it also has to do with how fucking horrific they are. It was a midair collision between United 736 — a DC-7 propeller-powered civilian craft — and a U.S. Air Force F-100F-5-NA Super Sabre, killing all involved, followed up by another civilian-military collision with only 1 survivor, that led to the creation of the FAA in the first place back in 1958. These are things that even your pro-measles grandpa finds utterly appalling, terrifying, and obscene; and he should, because they’re preventable, just like any other aviation disaster, or really any disaster.

Yet, as is often the case in the history of U.S. regulation, nobody ever seems too concerned with preventing the first of something. Boys, girls, and theys — do not be misled by whatever media you consume, we definitely just saw the first instance of the type of fuckup that killed 67 people on January 29th.

A review of commercial operations (instrument flight rules departures or arrivals) at DCA between October 2021 and December 2024 indicated a total of 944,179 operations. During that time, there were 15,214 occurrences between commercial airplanes and helicopters in which there was a lateral separation distance of less than 1 nm and vertical separation of less than 400 ft. There were 85 recorded events that involved a lateral separation less than 1,500 ft and vertical separation less than 200 ft. — The NTSB’s preliminary report.

So, just to recap, that’s 15,214 times two aircraft [2] were only separable in the vertical dimension — on top of each other! — and by less than 400ft no less. The other 85 events represent aircraft that might as well be not at all separable on the vertical axis — they should be treated as if they’re at the same altitude! — and less than 1,500ft apart. Given the speeds of these things are usually given in “feet per second” even on approach and takeoff, this should terrify even a four-year-old. It should come as no surprise, then, that:

Initial analysis found that at least one TCAS resolution advisory (RA) was triggered per month due to proximity to a helicopter. In over half of these instances, the helicopter may have been above the route altitude restriction. Two-thirds of the events occurred at night. — The paragraph above the one above, same report. Page 18.

That’s from a much longer period of time too, going back to 2011. Yep — one Traffic Collision Avoidance System alert per month related specifically to helicopters. If you’re curious about the distance needed to trigger a TCAS alert, it’s a bit smaller than the close call distances in the first quote — but not by much. And again, the one-per-month figure comes from helicopter-civilian aircraft close calls alone, with military helicopters only in that exact area because they fly a route the FAA now considers dangerously irresponsible to operate.

I won’t belabor the point, because I’m hungry and this was a remarkably draining piece to write for reasons that should be clear. I’m afraid of flying because people need to die first in order for resources to be spent on studying and solving the hazard that caused people to die. I have no reason to fear for my safety flying the specific route that I am about to, but on January 28th, nobody on earth “had any reason” to fear the fate of American Airlines 5342. And the NTSB is making very clear that that’s just not true at all. Individuals who cannot define or even name informed consent but expect to be granted it by a cereal box of all things love to rush out to tell you that our regulations are written in blood and that it’s all very safe, really, you’re crazy I swear — but this callous dismissal of an anxiety (that’s just like any other anxiety — irrational!) is further complicated by the fact that if you’re dying from something new and insane, they only care after you fucking die! “Written in blood” is not the pinnacle of human safety regulation paradigms it’s made out to be — it’s not even, like, the only way to do shit like this commonly used in the world — and it requires blood to write with before the regulations can be written in the first place.

To sit here and say that ‘written in blood’ is both necessary and sufficient as regulatory underpinnings to the most flight-addicted country on the entire planet is the exact same thing as telling civilians that the arrangement between helicopters and civilian aircraft on approach at DCA only became hazardous the second it actually killed people. That’s just not true. They shouldn’t have died, and every power of foresight was available to help effect that. And because the president is too busy tariffing our allies to censor the NTSB’s work here, I anticipate that the degree to which this is splayed out before us will only ratchet up in the final report.

End Notes

[1] I didn’t know the origin of this until 30 seconds after writing it. Neat.

[2] Technically, where their transponder was reporting the aircraft at. There are questions about whether or not the helicopter was estimating its own altitude properly — this would have made things much worse.

barycenters

Sat, 08 Mar 2025 00:00:00 +0000

Good evening, cherished readers of this already-depraved sector of the net known as nomenclature. I’m here to get out ahead of myself, really, and start one of the series of posts I’ve always wanted to, every single time I wanted to start blogging — and I’m going to do it on beers. That’s right; how else am I going to get you to suffer through how preposterously neoliberal I sound when confined to sentence case, eh? eh? …Lord, okay. It’ll get better eventually, probably. In my defense, you’re reading a science-based blog written by a talking molecule — what were you expecting?

Anyways, barycenters are a weird thing. Well, let’s back up — models are a really weird thing. To make the entire history of intellectual inquiry a short story, a theory — or model — of reality is just something you make up to explain observations you make of reality. This can be something as simple as ripping a piece of notebook paper perfectly down the seam every single time, or something as complicated as, like, quantum physics or whatever. They’re just, essentially, special ideas that make magical sense of the world, really. What else could they be, even? You should be seeing now that you model all sorts of nonsense in your head every day, right down to things as mundane as why that asshole cut you off in traffic last Tuesday (his wife was eight centimeters dilated goddamnit! it was a fucking emergency!) or whatever — that only becomes a theory when you get paid to do it, really. Fortunately for those fearful of any further ranting from me on, like, life and Stuff, this series is just going to pertain to those theories — but I’ll be talking about them like the models that you and I make in our everyday lives to explain phenomena.

Two Bodies

If you’ve ever talked to a dweeb (like me) that has entirely too many hours in Kerbal Space Program, about Kerbal Space Program, you’ve probably heard of two-body physics simulations. These simplistic physics simulations allow the calculation of three-dimensional orbits about celestial bodies with calculative ease, making the task simple enough that even the worst laptop you could fathom could reliably keep up when asked to perform them regularly. The simplification they make is that gravitation occurs only between a secondary object subjected to the gravitational force of a primary object; immediately, this fails to completely model reality because in the real world, both bodies exert gravitational force on one another. This force is negligible between a spacecraft and a celestial body, but enough to produce the tides when the interaction is had between two celestial bodies at range; let alone closer.

Only when several bodies are allowed to attract one another in a simulation do results begin to truly predict reality over long periods of time, and the best results are found when all bodies in a system attract one another simultaneously. This is, of course, a nightmare to compute, but is the only way to go if you’re planning a real-life long-term space mission that cost $2 billion dollars and two billion man-hours in haggling to fund. Still, at the end of the day, $2 billion is thrown at a model, a model where the bodies of the solar system are allowed to gravitate one another for hours and hours, truly innumerable compute cycles; a model validated by billions of human-hours of labor, yes, but a model no less.

Has this produced a significant error yet? No. New Horizons, the farthest-trekking deep space mission launched by any space agency yet, was only 72 seconds late — and it took 9 years for it to reach Pluto from Earth. Still, simplistic n-body physics where every body as a point mass is allowed to gravitate fails to capture reality even still. Indeed — and this may break your brain — but celestial bodies are not point masses, or big video game objects you can clip into, or otherwise imaginary. They’re just big clumps of dirt. Earth, as a big clump of dirt, only allowed Newton to imagine the deranged nonsense of ‘gravitation’ and ‘force,’ even, because every single atom within every single microcrystalline segment of every single fragment of dirt simultaneously attracts every single atom everywhere else in the entire universe with a force informed by their distance from one another and their masses. Truthfully, when you’ve got all that dirt hanging out in space, it tends to just clump up due to this for the same reason — though this seems more likely in the brain when things like the inverse square law are remembered — and stars and all this other nonsense form, ultimately, from the same attractive forces between trillions upon trillions upon trillions upon… yeah. This fact rears its ugly head in the form of gravitational anomalies, which can be mundane geographical findings or significant celestial body features that you can use to dispose of a spacecraft — they’re literally caused by extra dirt. Still, simpler n-body simulations can satisfactorily simulate so much real space travel, and this is because the largest dust clumps are a massive deal.

Barycenters are a great example of this. Whether you pretend your celestial bodies are “point masses” and allow them all to gravitate one another individually because that makes it “better” or you accept that it is all dust and simulate it in your brain like the champion of mentation you really are, dynamical points — in short, something totally made up that looks totally real — called barycenters are going to emerge. Mathematically, a barycenter represents the center of mass of a several-body system you’re simulating on a laptop or careening through as a particle; they take up such a central role in talking about astronomy because, well, everything seems to orbit one. The Earth and Moon, for one, collectively appear to orbit the Earth-Moon barycenter — simple enough, right? — but it only gets worse. Name a system, any system, and it possesses a barycenter that explains the orbital behavior of the contained bodies better than just “oh, shit, heliocentrism, I guess.” Take that Galileo, you fraud! I’m serious! Well, sort of — thanks Galileo, sorry about you dying or whatever — but the solar system (including the Sun) revolves around the center of mass of all mass in the solar system. In our solar system, that point is usually outside of the Sun, too, thanks to Jupiter and Saturn meandering out beyond the asteroid belt at the masses they possess; this means that even the Sun has an orbit within the frame of reference of the godforsaken solar system. Insane. Nothing else can really be said other than: aren’t you really fucking glad you don’t have to simulate every single goddamned particle in the Solar System to harness that observation for the future? If you aren’t an astronomer, imagine really hard how an astronomer would feel.

Feels really relieving, right?

— geosmin j.

transgenicism

Thu, 06 Mar 2025 00:00:00 +0000

The lies — unsurprisingly — continue. If I were to have to stand up and write about every single egregious one, I’d surely die at my keyboard, so I’ll be limiting myself to the ones I can thoroughly debunk on my lonesome.

What happened? Donald J. Trump needed an enemy to moralize about, and he figured out he could roll them together into one mega-enemy, like, eight years ago. His favorite rhetorical mega-enemy lately has been government spending on transgender people, even indirectly. During his disastrous address to a joint session of Congress, Trump alleged that the “Biden administration” was funding eight million dollars’ worth of “transgender mice” research. He was immediately called out for how bonkers, absurd, and lacking factual support this claim was, leading to a press release digging the hole further. I’m actually going to go line-by-line down this one, so my head stops hurting.

FACT: Under the Biden Administration, the National Institutes of Health doled out millions of dollars in taxpayer-funded grants for institutions across the country to perform transgender experiments on mice.

ACTUAL FACT: Rodent models of transgender health are less than two years old, meaning the Biden administration (and Trump’s first administration) almost certainly funded their creation, as well as experiments using them. This is good, because transgender people receive healthcare too. Rodent models are a necessary step in therapy development, period, and the moralizing over the availability of computational tools by Republicans conveniently leaves out the fact that those tools fucking suck and are operated by their cronies. Even if you don’t buy that, the in vivo vs. in silico debate is one to be had by people in the field — the ethical dimension is already handled by IACUC.

On the question of whether or not these mice can be said to be truly transgender? Fuck no. If they have any relevant internal awareness, they’re probably feeling quite dysphoric. You’ll notice all these experiments are investigating areas that don’t really call for more nuance here.

$455,000: “A Mouse Model to Test the Effects of Gender-affirming Hormone Therapy on HIV Vaccine-induced Immune Responses” (Duke University)

This study was undertaken to examine whether or not testosterone affected the ability of the body to respond to the HIV vaccine. $455,000 is an extremely — arguably, exceedingly — reasonable price to pay for such research. For one thing, you’re trying to prevent cases of HIV. If you need more than that, rot.

$2,500,000: “Reproductive Consequences of Steroid Hormone Administration” (University of Michigan-Ann Arbor)

Congratulations, dipshits. The study found that “[t]hese mice manifest defects in ovarian architecture and have altered folliculogenesis.” Not only did they just torpedo something that would have been a very effective talking point for them — given how reproductively obsessed these perverts are — you just defunded an extremely comprehensive, well-organized effort that may have taught us more about what chronic combination birth control does to us.

Evil, and stupid.

$299,940: “Gender-Affirming Testosterone Therapy on Breast Cancer Risk and Treatment Outcomes” (“BETH ISRAEL DEACONESS MEDICAL CENTER”)

Breast cancer is a leading cause of death in industrialized society, which is why yearly decreases in the mortality rate of breast cancer are a goal of both the CDC and WHO. It is not a goal of the Trump administration, however.

$735,113: “Microbiome mediated effects of gender affirming hormone therapy in mice” (Emory University)

You know, this is one of those that even I look at and go “Huh…. okay.” But I don’t go out and make policy based on going “Huh… okay.” because I am a reasonable person. Additionally, the gut microbiome is multitudinously linked to mental health — a fact that, no doubt, played a role in funding this. Of course, if I woke up tomorrow and read RFK Jr. denying germ theory, I would not be surprised — if only such idiocy was the simple cause of this shit.

You may be noticing by now that these are all mostly at really good institutions, too. I have no clue what to even say to that. Just ridiculous.

$1,200,000: “Androgen effects on the reproductive neuroendocrine axis” (UCSD) “Aim 2 utilizes transgenic mice to test whether male-level androgens acting via AR specifically in kisspeptin neurons are necessary and/or sufficient for androgen inhibition of in vivo LH pulse parameters, including pulse frequency, and the estrogen-induced LH surge.”

This was meant to be foundational mechanistic work outlining how the administration / possession of chronic high levels of androgen agonists — like testosterone — to individuals with XX chromosomes impedes their ability to reproduce. Beyond explaining why trans men on hormones cannot have children, work like this tends to produce a myriad of insights into the actual pathways behind all of this inside your body — this is stuff that could help develop treatments for infertility, for example (the study authors say this themselves, too). The would-be authors also point out that cisgender female individuals can present with prohibitively high testosterone, too — this research would have improved their healthcare as well.

$3,100,000: “Gonadal hormones as mediators of sex and gender influences in asthma” (Indiana University) “We will study the contributions of estrogens to HDM-induced asthma outcomes using male and female gonadectomized mice treated with estradiol…”

This is functionally a study on HDM-induced asthma, that’s pretty much it. HDM means, of course, house dust mite. Where the people at IU had the gall to contravene the future administration while applying for their grant is, of course, in evaluating transgender male/female mice models in addition to cisgender male and female mice.

Truly, even investigating transgender people in your experimental paradigm at all is enough to get your meticulously-planned-and-executed project thrown out the window, completely wasting all of the allocated funds that had already been disbursed. For those who don’t know, these grants are applied for years in advance, with your specific budgetary requests for every year the experiment proceeds planned — haggled over, really, directly with the NIH no less in rounds and rounds of grant review — meaning the administration is only recuperating the cost of undisbursed years and the numbers illustrated in the press release are almost certainly wildly inflated. And yes, I’ve been treating every grant listed as canceled; I guarantee you they are, and if they haven’t been yet, funds are not flowing from them. The investigation of cisgender female rodents alone has been enough to trigger this, which makes more sense when you remember it is fucking ChatGPT analyzing these instead of human beings.

Blueaghe. Ouaghege, even.

clash

Wed, 05 Mar 2025 00:00:00 +0000

What’s good, world? Now, imagine that I said that in binary, like all computers do, obviously — that’s why all those TikTok ARGs with seventy likes use binary to encode stuff, right? Super cryptic, I’m always really freaked out by them man, yeah; the day somebody makes an easy-to-google web-based tool for decoding binary, those are gonna be cooked, but it’s nice not living in that world at all. For now…

Yes, I am this bad at introductions in the real world as well. I know you weren’t wondering that, because you’re very polite (after all, you clicked on the link to this article, didn’t you?) but you may find it affable of me to disclose that... maybe. Hopefully. I’m Geosmin, and I’m here to rant about anything that fits under the obnoxiously wide umbrella I’ve cast for this whole blogging effort. I’ve tried this a couple of times, have never stuck to it, and am hoping that a shorter format — and more peer pressure — is able to make it stick this time. Now, I’m not gonna waste any more time as I usually have in previous attempts at getting into this; let’s chat about arguments.

Arguments and Debates

These might be synonyms to you, and probably for good reason given how the general experience of being alive has gone over the past few eons. In the realm of this post, however, they aren’t. I want to examine the value of discourse, but to do that requires acknowledging that almost nobody values discourse and aims to implement its benefits in their day-to-day lives, nor are we actually prioritizing implementing it into our wider society in a healthy manner — this is why the overwhelming majority of people have a negative experience with debate, discourse, or rhetoric in general. We’ll get to that though. I’m not here to give you a thesis statement, though; I’m here to rant. I’m complicated, I guess. Also, as I alluded to at the start, examining the value of discourse is gonna require some definitions. Sorry. You can call whatever I describe whatever you want; it doesn’t matter that Thing A is called ‘an argument’ and that Thing B is called ‘a debate’ here, but that you come away knowing how to participate in a healthy Thing B using well-constructed Thing As, and don’t categorize everyone who is passionate about the value of Thing B as a Thing B-bro. But man, fuck debate bros. Just know that no kid running around asking you to ‘debate them’ is lasting a second in actual competitive debate, which exists and produces adult children that are annoying in entirely different manners. Yep, that’s right — finance majors. Getting off track, though.

For the purposes of this blog post, an argument is an individual point you want to make about the world. This can be something that is absolutely true (“The sky appears blue”) or something that is absolutely not (“Human rights are weird”) — you may be used to thinking of ‘arguments’ at least a little bit in this way for more complicated things that aren’t really observations but points made based on observations, but really, any judgement made based on an observation or other measurement of the world should be considered an argument. Arguments make a claim about the world and then back it up with a warrant, the reason for why the claim must be true — anything not expressing both a claim and a warrant is not an argument. Arguments are benefitted by a stated impact, why you should care about the claim provided that the claim is true, but lacking an impact does not prevent a rhetorical construct from being an argument; it just prevents it from being a useful one.

An argument need not seem necessarily debatable because even all near-absolutely true arguments, such as the sky appearing blue, are still arguable. Nothing stops you from running up to this sort-of-normal conversation between strangers and saying, “No, it actually doesn’t. It seems green to me, even.” An argument made in bad faith is still an argument; far be it from me to advise you on how to deal with bad-faith arguments this early in my treatment of discourse, but I will say, saying “that’s a bad-faith argument” goes a lot further than you might think, and it would certainly qualify as engaging in a discourse — when you define a discourse like I would, at least.

A discourse, throughout my discussion of all of this nonsense, is going to refer to the exchange and interaction of arguments. They might be totally opposing, or not opposing at all, but the odds are that most arguments made about the world introduced by two different people about the same thing are going to clash somewhere. My use of the word “clash” here comes from my background in extracurricular NSDA-organized debate as a high schooler — there, where two teams are placed on diametrically opposed ends of a resolution for action by the United States government, ‘clash’ refers to when arguments directly interact in conflicting ways, and it is most useful as a noun. In technical debate, a generally-accepted rule is that if you do not respond to an opponent’s argument at your first opportunity [1] then it is to be treated by the judge as true. This is obviously completely intractable for a real-life debate (which I’m using as a synonym for discourse here and in the future when used by its lonesome) but does present some useful insights: even in a debate where sweaty high-schoolers have prepared for hundreds of hours just to survive the round with dignity, a pretty substantial portion of the opponent’s argument is accepted as true. This is at its worst extreme when you impact to something horrendous like nuclear war and they only impact to, like, job loss or whatever — Who cares about jobs when the entire world faces nuclear winter? [2] — but there are also debate rounds where both of you draw impacts from the exact same root cause, immediately rendering large swathes of the ‘flow’ [3] completely off-limits.

Enough with the technical end note B.S., though. I’m telling you all of this because it is hopefully useful. It will certainly be useful for understanding the series of posts I’ll be writing about discourse, rhetoric, and their utility in our day-to-day lives. In fact, I expect a really annoyingly large proportion of my science writings to reference this post / series of posts. Seriously. I also just think rhetoric is a wildly useful tool for everyone to possess and improve. I feel that that is obvious enough to not feel like I need to write an exposition section justifying, like, caring about how you communicate with others. The utility of, like, arguing with other people, though… I can see how that might be more nebulous! Let’s chat about it real quick.

Clash is a Measurement

Competitive debate is a zero-sum activity: at the end of the round, one team wins, and another loses. If you engage in zero-sum discourse in the real world, you are, at best, a doofus. This doesn’t always include instances where someone has agreed to do that with you, but it certainly can. As a result, the function of clash varies between discourse in a zero-sum format and discourse where you’re both acting first and foremost as human beings. In competitive activities, clash represents the frontlines; this is where you spend the overwhelming majority of your speaking time, or you’re behind. The goal of this speaking time is not to resolve the clash as it occurs in the minds of you and your opponent, but rather to resolve the clash in the mind of the judge. Indeed, entire debate ‘careers’ can flop on arrival simply because you have no way of knowing what the judge is thinking — reading judges may perhaps be the only reason I learned to read body language, and I get worse at it every year I am removed from doing that.

In a real-life interaction, clash simply represents where you differ on an issue with another person. Honestly, no matter how bad the opinion is with which you are clashing, there’s never an expectation that you actually make an effort to resolve that clash. What most people want out of a discourse-type interaction is to feel that the totality of their opinion is heard and respected, not just the clashing proportions, and what people get the least out of these is respect and true attempts at listening to the parts of their opinion subject to clash with others. [4] Thus, the idea of clash in a real-life discourse provides a very useful tool for providing that — it helps you pick out the parts of their argument that matter, provide them the most attention instead of the least, and generally improve interaction between the two parties — but also a useful way of measuring exactly how you differ from another person on a given issue. This is, of course, pretty obvious — I’m not saying it to you now because I think it is new or brilliant, but because I think you might not be using clash in this way. Even without going into arguments wanting to win or deliver a loss, we still tend to treat clash as where the “action” is, myself included; and that’s just not productive. Instead, clash should be viewed as where the data is. Generating and perceiving clash is just another way of finding out the exact shape of the gap between you and another individual on whatever issue it is you’re clashing on; and this gap is what you should be using as a metric if you’re interested in persuading people effectively — which, more often than not, involves judging someone as being far outside the realm of being influenced by persuasion. Truly, the most useful rhetorical principle is the one they don’t teach you in speech class: kairos. Typically understood with respect to time, space — here meaning the individuals targeted by your persuasion — is an important dimension to it as well. Your breath is best wasted where it has the smallest chance of being wasted. It doesn’t matter if the difference is 86% vs. 90%; if you’re gonna waste some breath, you waste it on the 86%er.

Conclusion

But shit, I’m tired, I’m hungry, and I am a little bored of being self-righteous about argumentation; that’s not even why I’m here. This series of blog posts (but not the whole blog) will explore my feelings on rhetoric and persuasion, and will eventually grow to encompass the general moment we live in as well. My next actual posts will be.. hm… well, a science article is a must, I’ll probably focus on something like that in the immediate term. After that, lord knows… something, current-event-y? Truly, lord knows; we’ll see. Thanks for reading my rant, and the approximately 800 billion semicolons contained within it!

End Notes

[1] This is a between-speeches paradigm. You get a set amount of minutes to speak per speech, and speeches are organized by block. The specifics of how this is handled vary by event; in public forum, a particularly haunted format, claims made as early as the very first position speech may be responded to as late as the last rebuttal (which is technically the second opportunity for the opposition, but the first in which people expect them to actually respond). Events in which only the affirmative can be said to truly adopt one position are much more strict about this, as the negative solely exists in opposition to the affirmative. In public forum, the negative is expected to provide exposition / original positions as well. Why is it like this? Honestly, a bit of “both-sides”ism. The idea of two ideological opponents duking it out in the marketplace of ideas is much more idealized in the collective unconscious than the more-judicially-tilted structure of affirmation and negation in policy and Lincoln-Douglas forms of competitive debate; other appeals to this in public forum include “cross-fire” questioning periods that are different from the cross-examinations and direct-examinations of policy yore, as well as the stated goal of having the NSDA PF Final Round be judgeable by the parent of pretty much any competitor — an admirable and certainly well-intended goal.

[2] Because of the information in [1], this is a much less effective argument in public forum than it is in policy. It is also less effective in Lincoln-Douglas because the value/value-criterion structure is an inbuilt buffer to the utilitarian frameworks that yield nuclear winter as the end-all be-all impact it is in policy debate. The more technical / “national circuit” the debate tournament is, the more likely your judge is to treat nuclear war arguments the way they would in a policy round.

[3] A ‘flow’ is a sheet of paper, or sheet of a digital spreadsheet, used to keep track of the progression of arguments speech-by-speech in competitive debate.

[4] If you’re reading this and going “that’s not true, I give this out like candy!” — Congratulations, you are not most people. The fact that you are even making an effort to read on your own places you well outside the league of people who get too caught up thinking about their own opinions to truly listen to those of others. Also, as I say above this; nobody is expecting you to offer this to homophobes or any other bigots of that general milieu, or anything truly objectionable. In fact, they’re expecting you to provide as little quarter as possible to these folks.