Tootfinder

Why AI can't possibly make you more productive; long
#AI and "productivity", some thoughts:
Edit: fixed some typos.
Productivity is a concept that isn't entirely meaningless outside the context of capitalism, but it's a concept that is heavily inflected in a capitalist context. In many uses today it effectively means "how much you can satisfy and/or exceed your boss' expectations." This is not really what it should mean: even in an anarchist utopia, people would care about things like how many shirts they can produce in a week, although in an "I'd like to voluntarily help more people" way rather than an "I need to meet this quota to earn my survival" way. But let's roll with this definition for a second, because it's almost certainly what your boss means when they say "productivity", and understanding that word in a different (even if truer) sense is therefore inherently dangerous.
Accepting "productivity" to mean "satisfying your boss' expectations," I will now claim: the use of generative AI cannot increase your productivity.
Before I dive in, it's imperative to note that the big generative models which most people think of as constituting "AI" today are evil. They are 1: pouring fuel on our burning planet, 2: psychologically strip-mining a class of data laborers who are exploited for their precarity, 3: enclosing, exploiting, and polluting the digital commons, and 4: stealing labor from broad classes of people many of whom are otherwise glad to give that labor away for free provided they get a simple acknowledgement in return. Any of these four "ethical issues" should be enough *alone* to cause everyone to simply not use the technology. These ethical issues are the reason that I do not use generative AI right now, except for in extremely extenuating circumstances. These issues are also convincing for a wide range of people I talk to, from experts to those with no computer science background. So before I launch into a critique of the effectiveness of generative AI, I want to emphasize that such a critique should be entirely unnecessary.
But back to my thesis: generative AI cannot increase your productivity, where "productivity" has been defined as "how much you can satisfy and/or exceed your boss' expectations."
Why? In fact, what the fuck? Every AI booster I've met has claimed the opposite. They've given me personal examples of time saved by using generative AI. Some of them even truly believe this. Sometimes I even believe they saved time without horribly compromising on quality (and often, your boss doesn't care about quality anyways if the lack of quality is hard to measure of doesn't seem likely to impact short-term sales/feedback/revenue). So if generative AI genuinely lets you write more emails in a shorter period of time, or close more tickets, or something else along these lines, how can I say it isn't increasing your ability to meet your boss' expectations?
The problem is simple: your boss' expectations are not a fixed target. Never have been. In virtue of being someone who oversees and pays wages to others under capitalism, your boss' game has always been: pay you less than the worth of your labor, so that they can accumulate profit and thus more capital to remain in charge instead of being forced into working for a wage themselves. Sure, there are layers of management caught in between who aren't fully in this mode, but they are irrelevant to this analysis. It matters not how much you please your manager if your CEO thinks your work is not worth the wages you are being paid. And using AI actively lowers the value of your work relative to your wages.
Why do I say that? It's actually true in several ways. The most obvious: using generative AI lowers the quality of your work, because the work it produces is shot through with errors, and when your job is reduced to proofreading slop, you are bound to tire a bit, relax your diligence, and let some mistakes through. More than you would have if you are actually doing and taking pride in the work. Examples are innumerable and frequent, from journalists to lawyers to programmers, and we laugh at them "haha how stupid to not check whether the books the AI reviewed for you actually existed!" but on a deeper level if we're honest we know we'd eventually make the same mistake ourselves (bonus game: spot the swipe-typing typos I missed in this post; I'm sure there will be some).
But using generative AI also lowers the value of your work in another much more frightening way: in this era of hype, it demonstrates to your boss that you could be replaced by AI. The more you use it, and no matter how much you can see that your human skills are really necessary to correct its mistakes, the more it appears to your boss that they should hire the AI instead of you. Or perhaps retain 10% of the people in roles like yours to manage the AI doing the other 90% of the work. Paradoxically, the *more* you get done in terms of raw output using generative AI, the more it looks to your boss as if there's an opportunity to get enough work done with even fewer expensive humans. Of course, the decision to fire you and lean more heavily into AI isn't really a good one for long-term profits and success, but the modern boss did not get where they are by considering long-term profits. By using AI, you are merely demonstrating your redundancy, and the more you get done with it, the more redundant you seem.
In fact, there's even a third dimension to this: by using generative AI, you're also providing its purveyors with invaluable training data that allows them to make it better at replacing you. It's generally quite shitty right now, but the more use it gets by competent & clever people, the better it can become at the tasks those specific people use it for. Using the currently-popular algorithm family, there are limits to this; I'm not saying it will eventually transcend the mediocrity it's entwined with. But it can absolutely go from underwhelmingly mediocre to almost-reasonably mediocre with the right training data, and data from prompting sessions is both rarer and more useful than the base datasets it's built on.
For all of these reasons, using generative AI in your job is a mistake that will likely lead to your future unemployment. To reiterate, you should already not be using it because it is evil and causes specific and inexcusable harms, but in case like so many you just don't care about those harms, I've just explained to you why for entirely selfish reasons you should not use it.
If you're in a position where your boss is forcing you to use it, my condolences. I suggest leaning into its failures instead of trying to get the most out of it, and as much as possible, showing your boss very clearly how it wastes your time and makes things slower. Also, point out the dangers of legal liability for its mistakes, and make sure your boss is aware of the degree to which any of your AI-eager coworkers are producing low-quality work that harms organizational goals.
Also, if you've read this far and aren't yet of an anarchist mindset, I encourage you to think about the implications of firing 75% of (at least the white-collar) workforce in order to make more profit while fueling the climate crisis and in most cases also propping up dictatorial figureheads in government. When *either* the AI bubble bursts *or* if the techbros get to live out the beginnings of their worker-replacement fantasies, there are going to be an unimaginable number of economically desperate people living in increasingly expensive times. I'm the kind of optimist who thinks that the resulting social crucible, though perhaps through terrible violence, will lead to deep social changes that effectively unseat from power the ultra-rich that continue to drag us all down this destructive path, and I think its worth some thinking now about what you might want the succeeding stable social configuration to look like so you can advocate towards that during points of malleability.
As others have said more eloquently, generative AI *should* be a technology that makes human lives on average easier, and it would be were it developed & controlled by humanists. The only reason that it's not, is that it's developed and controlled by terrible greedy people who use their unfairly hoarded wealth to immiserate the rest of us in order to maintain their dominance. In the long run, for our very survival, we need to depose them, and I look forward to what the term "generative AI" will mean after that finally happens.

Epstein shit and adjacent, Rural America, Poverty, Abuse
Everyone who's not a pedophile thinks pedophiles are bad, but there's this special obsessed hatred you'll find among poor rural Americans. The whole QAnon/Epstein obsession may not really make sense to folks raised in cities. Like, why do these people think *so much* about pedophiles? Why do they think that everyone in power is a pedophile? Why would the Pizzagate thing make sense to anyone? What is this unhinged shit? A lot of folks (who aren't anarchists) might be inclined to ask "why can't these people just let the cops take care of it?"
I was watching Legal Eagle's run down on the Trump Epstein thing earlier today and I woke up thinking about something I don't know if I've ever talked about. Now that I'm not in the US, I'm not at any risk of talking about it. I don't know how much I would have been before, but that's not something I'm gonna dig into right now. So let me tell you a story that might explain a few things.
I'm like 16, maybe 17. I have my license, so this girl I was dating/not dating/just friends with/whatever would regularly convince me to drive her and her friends around. I think she's like 15 at the time. Her friends are younger than her.
She tells me that there's a party we can go to where they have beer. She was told to invite her friends, so I can come too. We're going to pick her friends up (we regularly fill the VW Golf well beyond the legal limit and drive places) and head to the party.
So I take these girls, at least is 13 years old, down to this party. I'm already a bit sketched out bringing a 13 year old to a party. We drive out for a while. It's in the country. We drive down a long dark road. Three are some barrel fires and a shack. This is all a bit strange, but not too abnormal for this area. We're a little ways outside of a place called Mill City (in Oregon).
We park and walk towards the shack. This dude who looks like a rat comes up and offers us beer. He laughs and talks to the girl who invited me, "What's he doing here? You're supposed to bring your girl friends." She's like, "He's our ride." I don't remember if he offered me a beer or not.
We go over to this shed and everyone starts smoking, except me because I didn't smoke until I turned 18. The other girls start talking about the rat face dude, who's wandered over by the fire with some other guys. They're mainly teasing one of the 13 year old girls about having sex with him a bunch of times. They say he's like, 32 or something. The other girls joke about him only having sex with 13 year olds because he's too ugly to have sex with anyone closer to his own age.
Somewhere along the line it comes out that he's a cop. I never forgot that, it's absolutely seared in to my memory. I can picture his face perfectly still, decades later, and them talking about how he's a deputy, he was in his 30's, and he was having sex with a 13 year old girl. I was the only boy there, but there were a few older men. This was a chunk of the good ol' boys club of the town. I think there were a couple of cops besides the one deputy, and a judge or the mayor or some kind of big local VIP.
I kept trying to get my friend to leave, but she wanted to stay. Turns out under age drinking with cops seems like a great deal if you're a kid because you know you won't get busted. I left alone, creeped the fuck out.
I was told later that I wasn't invited and that I couldn't talk about it, I've always been good at compartmentalization, so I never did.
Decades later it occurred to me what was actually happening. I'm pretty sure that cop was giving meth he'd seized as evidence to these kids. This wasn't some one-off thing. It was regular. Who knows how many decades it went on after I left, or how many decades it had been going on before I found out. I knew this type of thing had happened at least a few times before because that's how that 13 year old girl and that 32 year old cop had hooked up in the first place.
Hearing about Epstein's MO, targeting these teenage girls from fucked up backgrounds, it's right there for me. I wouldn't be surprised if they were involved in sex trafficking of minors or some shit like that... but who would you call if you found out? Half the sheriff's department was there and the other half would cover for them.
You live in the city and shit like that doesn't happen, or at least you don't think it happens. But rural poor folks have this intuition about power and abuse. It's right there and you know it.
Trump is such a familiar character for me, because he's exactly that small town mayor or sheriff. He'll will talk about being tough on crime and hunting down pedophiles, while hanging out at a party that exists so people can fuck 8th graders.
The problem with the whole thing is that rural folks will never break the cognitive dissonance between "kill the peods" and "back the blue." They'll never go kill those cops. No, the pedos must be somewhere else. It must be the elites. It must be outsiders. It can't be the cops and good ol' boys everyone respects. It can't be the mayor who rigs the election to win every time. It can't be the "good upstanding" sheriff. Nah, it's the Clintons.
To be fair, it's probably also the Clitnons, a bunch of other politicians, billionaires, etc. Epstein was exactly who everyone thought he was, and he didn't get away with it for so long without a whole lot of really powerful help.
There are still powerful people who got away with involvement with #Epstein. #Trump is one of them, but I don't really believe that he's the only one.
#USPol #ACAB

“Family values do not stop south of the Rio Grande,” he told the crowd. “Why do you think they are here? It’s because they love their family. You and I would do the same thing if we were in their position.”
Guess who said that? Answer below
https://ww…

77-year-old poem explains protestors' ICE anger
Later set to music, Guthrie’s ode decries the dehumanization of immigrants that we’re seeing today

“Family values do not stop south of the Rio Grande,” he told the crowd. “Why do you think they are here? It’s because they love their family. You and I would do the same thing if we were in their position.”
Guess who said that? Answer below
https://ww…

77-year-old poem explains protestors' ICE anger
Later set to music, Guthrie’s ode decries the dehumanization of immigrants that we’re seeing today

Long; central Massachusetts colonial history
Today on a whim I visited a site in Massachusetts marked as "Huguenot Fort Ruins" on OpenStreetMaps. I drove out with my 4-year-old through increasingly rural central Massachusetts forests & fields to end up on a narrow street near the top of a hill beside a small field. The neighboring houses had huge lawns, some with tractors.
Appropriately for this day and this moment in history, the history of the site turns out to be a microcosm of America. Across the field beyond a cross-shaped stone memorial stood an info board with a few diagrams and some text. The text of the main sign (including typos/misspellings) read:
"""
Town Is Formed
Early in the 1680's, interest began to generate to develop a town in the area west of Natick in the south central part of the Commonwealth that would be suitable for a settlement. A Mr. Hugh Campbell, a Scotch merchant of Boston petitioned the court for land for a colony. At about the same time, Joseph Dudley and William Stoughton also were desirous of obtaining land for a settlement. A claim was made for all lands west of the Blackstone River to the southern land of Massachusetts to a point northerly of the Springfield Road then running southwesterly until it joined the southern line of Massachusetts.
Associated with Dudley and Stoughton was Robert Thompson of London, England, Dr. Daniel Cox and John Blackwell, both of London and Thomas Freak of Hannington, Wiltshire, as proprietors. A stipulation in the acquisition of this land being that within four years thirty families and an orthodox minister settle in the area. An extension of this stipulation was granted at the end of the four years when no group large enough seemed to be willing to take up the opportunity.
In 1686, Robert Thompson met Gabriel Bernor and learned that he was seeking an area where his countrymen, who had fled their native France because of the Edict of Nantes, were desirous of a place to live. Their main concern was to settle in a place that would allow them freedom of worship. New Oxford, as it was the so-named, at that time included the larger part of Charlton, one-fourth of Auburn, one-fifth of Dudley and several square miles of the northeast portion of Southbridge as well as the easterly ares now known as Webster.
Joseph Dudley's assessment that the area was capable of a good settlement probably was based on the idea of the meadows already established along with the plains, ponds, brooks and rivers. Meadows were a necessity as they provided hay for animal feed and other uses by the settlers. The French River tributary books and streams provided a good source for fishing and hunting. There were open areas on the plains as customarily in November of each year, the Indians burnt over areas to keep them free of underwood and brush. It appeared then that this area was ready for settling.
The first seventy-five years of the settling of the Town of Oxford originally known as Manchaug, embraced three different cultures. The Indians were known to be here about 1656 when the Missionary, John Eliott and his partner Daniel Gookin visited in the praying towns. Thirty years later, in 1686, the Huguenots walked here from Boston under the guidance of their leader Isaac Bertrand DuTuffeau. The Huguenot's that arrived were not peasants, but were acknowledged to be the best Agriculturist, Wine Growers, Merchant's, and Manufacter's in France. There were 30 families consisting of 52 people. At the time of their first departure (10 years), due to Indian insurrection, there were 80 people in the group, and near their Meetinghouse/Church was a Cemetery that held 20 bodies. In 1699, 8 to 10 familie's made a second attempt to re-settle, failing after only four years, with the village being completely abandoned in 1704.
The English colonist made their way here in 1713 and established what has become a permanent settlement.
"""
All that was left of the fort was a crumbling stone wall that would have been the base of a higher wooden wall according to a picture of a model (I didn't think to get a shot of that myself). Only trees and brush remain where the multi-story main wooden building was.
This story has so many echoes in the present:
- The rich colonialists from Boston & London agree to settle the land, buying/taking land "rights" from the colonial British court that claimed jurisdiction without actually having control of the land. Whether the sponsors ever actually visited the land themselves I don't know. They surely profited somehow, whether from selling on the land rights later or collecting taxes/rent or whatever, by they needed poor laborers to actually do the work of developing the land (& driving out the original inhabitants, who had no say in the machinations of the Boston court).
- The land deal was on condition that there capital-holders who stood to profit would find settlers to actually do the work of colonizing. The British crown wanted more territory to be controlled in practice not just in theory, but they weren't going to be the ones to do the hard work.
- The capital-holders actually failed to find enough poor suckers to do their dirty work for 4 years, until the Huguenots, fleeing religious persecution in France, were desperate enough to accept their terms.
- Of course, the land was only so ripe for settlement because of careful tending over centuries by the natives who were eventually driven off, and whose land management practices are abandoned today. Given the mention of praying towns (& dates), this was after King Phillip's war, which resulted in at least some forced resettlement of native tribes around the area, but the descendants of those "Indians" mentioned in this sign are still around. For example, this is the site of one local band of Nipmuck, whose namesake lake is about 5 miles south of the fort site: #LandBack.

Should we teach vibe coding? Here's why not.
Should AI coding be taught in undergrad CS education?
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I teach undergraduate computer science labs, including for intro and more-advanced core courses. I don't publish (non-negligible) scholarly work in the area, but I've got years of craft expertise in course design, and I do follow the academic literature to some degree. In other words, In not the world's leading expert, but I have spent a lot of time thinking about course design, and consider myself competent at it, with plenty of direct experience in what knowledge & skills I can expect from students as they move through the curriculum.
I'm also strongly against most uses of what's called "AI" these days (specifically, generative deep neutral networks as supplied by our current cadre of techbro). There are a surprising number of completely orthogonal reasons to oppose the use of these systems, and a very limited number of reasonable exceptions (overcoming accessibility barriers is an example). On the grounds of environmental and digital-commons-pollution costs alone, using specifically the largest/newest models is unethical in most cases.
But as any good teacher should, I constantly question these evaluations, because I worry about the impact on my students should I eschew teaching relevant tech for bad reasons (and even for his reasons). I also want to make my reasoning clear to students, who should absolutely question me on this. That inspired me to ask a simple question: ignoring for one moment the ethical objections (which we shouldn't, of course; they're very stark), at what level in the CS major could I expect to teach a course about programming with AI assistance, and expect students to succeed at a more technically demanding final project than a course at the same level where students were banned from using AI? In other words, at what level would I expect students to actually benefit from AI coding "assistance?"
To be clear, I'm assuming that students aren't using AI in other aspects of coursework: the topic of using AI to "help you study" is a separate one (TL;DR it's gross value is not negative, but it's mostly not worth the harm to your metacognitive abilities, which AI-induced changes to the digital commons are making more important than ever).
So what's my answer to this question?
If I'm being incredibly optimistic, senior year. Slightly less optimistic, second year of a masters program. Realistic? Maybe never.
The interesting bit for you-the-reader is: why is this my answer? (Especially given that students would probably self-report significant gains at lower levels.) To start with, [this paper where experienced developers thought that AI assistance sped up their work on real tasks when in fact it slowed it down] (https://arxiv.org/abs/2507.09089) is informative. There are a lot of differences in task between experienced devs solving real bugs and students working on a class project, but it's important to understand that we shouldn't have a baseline expectation that AI coding "assistants" will speed things up in the best of circumstances, and we shouldn't trust self-reports of productivity (or the AI hype machine in general).
Now we might imagine that coding assistants will be better at helping with a student project than at helping with fixing bugs in open-source software, since it's a much easier task. For many programming assignments that have a fixed answer, we know that many AI assistants can just spit out a solution based on prompting them with the problem description (there's another elephant in the room here to do with learning outcomes regardless of project success, but we'll ignore this over too, my focus here is on project complexity reach, not learning outcomes). My question is about more open-ended projects, not assignments with an expected answer. Here's a second study (by one of my colleagues) about novices using AI assistance for programming tasks. It showcases how difficult it is to use AI tools well, and some of these stumbling blocks that novices in particular face.
But what about intermediate students? Might there be some level where the AI is helpful because the task is still relatively simple and the students are good enough to handle it? The problem with this is that as task complexity increases, so does the likelihood of the AI generating (or copying) code that uses more complex constructs which a student doesn't understand. Let's say I have second year students writing interactive websites with JavaScript. Without a lot of care that those students don't know how to deploy, the AI is likely to suggest code that depends on several different frameworks, from React to JQuery, without actually setting up or including those frameworks, and of course three students would be way out of their depth trying to do that. This is a general problem: each programming class carefully limits the specific code frameworks and constructs it expects students to know based on the material it covers. There is no feasible way to limit an AI assistant to a fixed set of constructs or frameworks, using current designs. There are alternate designs where this would be possible (like AI search through adaptation from a controlled library of snippets) but those would be entirely different tools.
So what happens on a sizeable class project where the AI has dropped in buggy code, especially if it uses code constructs the students don't understand? Best case, they understand that they don't understand and re-prompt, or ask for help from an instructor or TA quickly who helps them get rid of the stuff they don't understand and re-prompt or manually add stuff they do. Average case: they waste several hours and/or sweep the bugs partly under the rug, resulting in a project with significant defects. Students in their second and even third years of a CS major still have a lot to learn about debugging, and usually have significant gaps in their knowledge of even their most comfortable programming language. I do think regardless of AI we as teachers need to get better at teaching debugging skills, but the knowledge gaps are inevitable because there's just too much to know. In Python, for example, the LLM is going to spit out yields, async functions, try/finally, maybe even something like a while/else, or with recent training data, the walrus operator. I can't expect even a fraction of 3rd year students who have worked with Python since their first year to know about all these things, and based on how students approach projects where they have studied all the relevant constructs but have forgotten some, I'm not optimistic seeing these things will magically become learning opportunities. Student projects are better off working with a limited subset of full programming languages that the students have actually learned, and using AI coding assistants as currently designed makes this impossible. Beyond that, even when the "assistant" just introduces bugs using syntax the students understand, even through their 4th year many students struggle to understand the operation of moderately complex code they've written themselves, let alone written by someone else. Having access to an AI that will confidently offer incorrect explanations for bugs will make this worse.
To be sure a small minority of students will be able to overcome these problems, but that minority is the group that has a good grasp of the fundamentals and has broadened their knowledge through self-study, which earlier AI-reliant classes would make less likely to happen. In any case, I care about the average student, since we already have plenty of stuff about our institutions that makes life easier for a favored few while being worse for the average student (note that our construction of that favored few as the "good" students is a large part of this problem).
To summarize: because AI assistants introduce excess code complexity and difficult-to-debug bugs, they'll slow down rather than speed up project progress for the average student on moderately complex projects. On a fixed deadline, they'll result in worse projects, or necessitate less ambitious project scoping to ensure adequate completion, and I expect this remains broadly true through 4-6 years of study in most programs (don't take this as an endorsement of AI "assistants" for masters students; we've ignored a lot of other problems along the way).
There's a related problem: solving open-ended project assignments well ultimately depends on deeply understanding the problem, and AI "assistants" allow students to put a lot of code in their file without spending much time thinking about the problem or building an understanding of it. This is awful for learning outcomes, but also bad for project success. Getting students to see the value of thinking deeply about a problem is a thorny pedagogical puzzle at the best of times, and allowing the use of AI "assistants" makes the problem much much worse. This is another area I hope to see (or even drive) pedagogical improvement in, for what it's worth.
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How popular media gets love wrong
Now a bit of background about why I have this "engineered" model of love:
First, I'm a white straight cis man. I've got a few traits that might work against my relationship chances (e.g., neurodivergence; I generally fit pretty well into the "weird geek" stereotype), but as I was recently reminded, it's possible my experience derives more from luck than other factors, and since things are tilted more in my favor than most people on the planet, my advice could be worse than useless if it leads people towards strategies that would only have worked for someone like me. I don't *think* that's the case, but it's worth mentioning explicitly.
When I first started dating my now-wife, we were both in graduate school. I was 26, and had exactly zero dating/romantic experience though that point in my life. In other words, a pretty stereotypical "incel" although I definitely didn't subscribe to incel ideology at all. I felt lonely, and vaguely wanted a romantic relationship (I'm neither aromantic nor asexual), but had never felt socially comfortable enough to pursue one before. I don't drink and dislike most social gatherings like parties or bars; I mostly hung around the fringes of the few college parties I attended, and although I had a reasonable college social life in terms of friends, I didn't really do anything to pursue romance, feeling too awkward to know where to start. I had the beginnings of crushes in both high school and college, but never developed a really strong crush, probably correlated with not putting myself in many social situations outside of close all-male friend gatherings. I never felt remotely comfortable enough to act on any of the proto-crushes I did have. I did watch porn and masturbate, so one motivation for pursuing a relationship was physical intimacy, but loneliness was as much of a motivating factor, and of course the social pressure to date was a factor too, even though I'm quite contrarian.
When I first started dating my now-wife, we were both in graduate school. I was 26, and had exactly zero dating/romantic experience though that point in my life. In other words, a pretty stereotypical "incel" although I definitely didn't subscribe to incel ideology at all. I felt lonely, and vaguely wanted a romantic relationship (I'm neither aromantic nor asexual), but had never felt socially comfortable enough to pursue one before. I don't drink and dislike most social gatherings like parties or bars; I mostly hung around the fringes of the few college parties I attended, and although I had a reasonable college social life in terms of friends, I didn't really do anything to pursue romance, feeling too awkward to know where to start. I had the beginnings of crushes in both high school and college, but never developed a really strong crush, probably correlated with not putting myself in many social situations outside of close all-male friend gatherings. I never felt remotely comfortable enough to act on any of the proto-crushes I did have. I did watch porn and masturbate, so one motivation for pursuing a relationship was physical intimacy, but loneliness was as much of a motivating factor, and of course the social pressure to date was a factor too, even though I'm quite contrarian.
I'm lucky in that I had some mixed-gender social circles already like intramural soccer and a graduate-student housing potluck. Graduate school makes a *lot* more of these social spaces accessible, so I recognize that those not in school of some sort have a harder time of things, especially if like me they don't feel like they fit in in typical adult social spaces like bars.
However, at one point I just decided that my desire for a relationship would need action on my part and so I'd try to build a relationship and see what happened. I worked up my courage and asked one of the people in my potluck if she'd like to go for a hike (pretty much clearly a date but not explicitly one; in retrospect not the best first-date modality in a lot of ways, but it made a little more sense in our setting where we could go for a hike from our front door). To emphasize this point: I was not in love with (or even infatuated with) my now-wife at that point. I made a decision to be open to building a relationship, but didn't follow the typical romance story formula beyond that. Now of course, in real life as opposed to popular media, this isn't anything special. People ask each other out all the time just because they're lonely, and some of those relationships turn out fine (although many do not).
I was lucky in that some aspects of who I am and what I do happened to be naturally comforting to my wife (natural advantage in the "appeal" model of love) but of course there are some aspects of me that annoy my wife, and we negotiate that. In the other direction, there's some things I instantly liked about my wife, and other things that still annoy me. We've figured out how to accept a little, change a little, and overall be happy with each other (though we do still have arguments; it's not like the operation/construction/maintenance of the "love mechanism" is always perfectly smooth). In particular though, I approached the relationship with the attitude of "I want to try to build a relationship with this person," at first just because of my own desires for *any* relationship, and then gradually more and more through my desire to build *this specific* relationship as I enjoyed the rewards of companionship.
So for example, while I think my wife is objectively beautiful, she's also *subjectively* very beautiful *to me* because having decided to build a relationship with her, I actively tried to see her as beautiful, rather than trying to judge whether I wanted a relationship with her based on her beauty. In other words, our relationship is more causative of her beauty-to-me than her beauty-to-me is causative of our relationship. This is the biggest way I think the "engineered" model of love differs from the "fire" and "appeal" models: you can just decide to build love independent of factors we typically think of as engendering love (NOT independent of your partner's willingness to participate, of course), and then all of those things like "thinking your partner is beautiful" can be a result of the relationship you're building. For sure those factors might affect who is willing to try building a relationship with you in the first place, but if more people were willing to jump into relationship building (not necessarily with full commitment from the start) without worrying about those other factors, they might find that those factors can come out of the relationship instead of being prerequisites for it. I think this is the biggest failure of the "appeal" model in particular: yes you *do* need to do things that appeal to your partner, but it's not just "make myself lovable" it's also: is your partner putting in the effort to see the ways that you are beautiful/lovable/etc., or are they just expecting you to become exactly some perfect person they've imagined (and/or been told to desire by society)? The former is perfectly possible, and no less satisfying than the latter.
To cut off my rambling a bit here, I'll just add that in our progress from dating through marriage through staying-married, my wife and I have both talked at times explicitly about commitment, and especially when deciding to get married, I told her that I knew I couldn't live up to the perfect model of a husband that I'd want to be, but that if she wanted to deepen our commitment, I was happy to do that, and so we did. I also rearranged my priorities at that point, deciding that I knew I wanted to prioritize this relationship above things like my career or my research interests, and while I've not always been perfect at that in my little decisions, I've been good at holding to that in my big decisions at least. In the end, *once we had built a somewhat-committed relationship*, we had something that we both recognized was worth more than most other things in life, and that let us commit even more, thus getting even more out of it in the long term. Obviously you can't start the first date with an expectation of life-long commitment, and you need to synchronize your increasing commitment to a relationship so that it doesn't become lopsided, which is hard. But if you take the commitment as an active decision and as the *precursor* to things like infatuation, attraction, etc., you can build up to something that's incredibly strong and rewarding.
I'll follow this up with one more post trying to distill some advice from my ramblings.
#relationships #love

AI, AGI, and learning efficiency
My 4-month-old kid is not DDoSing Wikipedia right now, nor will they ever do so before learning to speak, read, or write. Their entire "training corpus" will not top even 100 million "tokens" before they can speak & understand language, and do so with real intentionally.
Just to emphasize that point: 100 words-per-minute times 60 minutes-per-hour times 12 hours-per-day times 365 days-per-year times 4 years is a mere 105,120,000 words. That's a ludicrously *high* estimate of words-per-minute and hours-per-day, and 4 years old (the age of my other kid) is well after basic speech capabilities are developed in many children, etc. More likely the available "training data" is at least 1 or 2 orders of magnitude less than this.
The point here is that large language models, trained as they are on multiple *billions* of tokens, are not developing their behavioral capabilities in a way that's remotely similar to humans, even if you believe those capabilities are similar (they are by certain very biased ways of measurement; they very much aren't by others). This idea that humans must be naturally good at acquiring language is an old one (see e.g. #AI #LLM #AGI