The Problem

Like many other self-styled thinky programmer guys, I like to imagine myself as a sort of Holmesian genius, making trenchant observations, collecting them, and then synergizing them into brilliant deductions with the keen application of my powerful mind.

However, several years ago, I had an epiphany in my self-concept. I finally understood that, to the extent that I am usefully clever, it is less in a Holmesian idiom, and more, shall we say, Monkesque.

For those unfamiliar with either of the respective franchises:

• Holmes is a towering intellect honed by years of training, who catalogues intentional, systematic observations and deduces logical, factual conclusions from those observations.
• Monk, on the other hand, while also a reasonably intelligent guy, is highly neurotic, wracked by unresolved trauma and profound grief. As both a consulting job and a coping mechanism, he makes a habit of erratically wandering into crime scenes, and, driven by a carefully managed jenga tower of mental illnesses, leverages his dual inabilities to solve crimes. First, he is unable to filter out apparently inconsequential details, building up a mental rat’s nest of trivia about the problem; second, he is unable to let go of any minor incongruity, obsessively ruminating on the collection of facts until they all make sense in a consistent timeline.

Perhaps surprisingly, this tendency serves both this fictional wretch of a detective, and myself, reasonably well. I find annoying incongruities in abstractions and I fidget and fiddle with them until I end up building something that a lot of people like, or perhaps something that a smaller number of people get really excited about. At worst, at least I eventually understand what’s going on. This is a self-soothing activity but it turns out that, managed properly, it can very effectively soothe others as well.

All that brings us to today’s topic, which is an incongruity I cannot smooth out or fit into a logical framework to make sense. I am, somewhat reluctantly, a genAI skeptic. However, I am, even more reluctantly, exposed to genAI Discourse every damn minute of every damn day. It is relentless, inescapable, and exhausting.

This preamble about personality should hopefully help you, dear reader, to understand how I usually address problematical ideas by thinking and thinking and fidgeting with them until I manage to write some words — or perhaps a new open source package — that logically orders the ideas around it in a way which allows my brain to calm down and let it go, and how that process is important to me.

In this particular instance, however, genAI has defeated me. I cannot make it make sense, but I need to stop thinking about it anyway. It is too much and I need to give up.

My goal with this post is not to convince anyone of anything in particular — and we’ll get to why that is a bit later — but rather:

1. to set out my current understanding in one place, including all the various negative feelings which are still bothering me, so I can stop repeating it elsewhere,
2. to explain why I cannot build a case that I think should be particularly convincing to anyone else, particularly to someone who actively disagrees with me,
3. in so doing, to illustrate why I think the discourse is so fractious and unresolvable, and finally
4. to give myself, and hopefully by proxy to give others in the same situation, permission to just peace out of this nightmare quagmire corner of the noosphere.

But first, just because I can’t prove that my interlocutors are Wrong On The Internet, doesn’t mean I won’t explain why I feel like they are wrong.

The Anti-Antis

Most recently, at time of writing, there have been a spate of “the genAI discourse is bad” articles, almost exclusively written from the perspective of, not boosters exactly, but pragmatically minded (albeit concerned) genAI users, wishing for the skeptics to be more pointed and accurate in our critiques. This is anti-anti-genAI content.

I am not going to link to any of these, because, as part of their self-fulfilling prophecy about the “genAI discourse”, they’re also all bad.

Mostly, however, they had very little worthwhile to respond to because they were straw-manning their erstwhile interlocutors. They are all getting annoyed at “bad genAI criticism” while failing to engage with — and often failing to even mention — most of the actual substance of any serious genAI criticism. At least, any of the criticism that I’ve personally read.

I understand wanting to avoid a callout or Gish-gallop culture and just express your own ideas. So, I understand that they didn’t link directly to particular sources or go point-by-point on anyone else’s writing. Obviously I get it, since that’s exactly what this post is doing too.

But if you’re going to talk about how bad the genAI conversation is, without even mentioning huge categories of problem like “climate impact” or “disinformation”¹ even once, I honestly don’t know what conversation you’re even talking about. This is peak “make up a guy to get mad at” behavior, which is especially confusing in this circumstance, because there’s an absolutely huge crowd of actual people that you could already be mad at.

The people writing these pieces have historically seemed very thoughtful to me. Some of them I know personally. It is worrying to me that their critical thinking skills appear to have substantially degraded specifically after spending a bunch of time intensely using this technology which I believe has a scary risk of degrading one’s critical thinking skills. Correlation is not causation or whatever, and sure, from a rhetorical perspective this is “post hoc ergo propter hoc” and maybe a little “ad hominem” for good measure, but correlation can still be concerning.

Yet, I cannot effectively respond to these folks, because they are making a practical argument that I cannot, despite my best efforts, find compelling evidence to refute categorically. My experiences of genAI are all extremely bad, but that is barely even anecdata. Their experiences are neutral-to-positive. Little scientific data exists. How to resolve this?²

The Aesthetics

As I begin to state my own position, let me lead with this: my factual analysis of genAI is hopelessly negatively biased. I find the vast majority of the aesthetic properties of genAI to be intensely unpleasant.

I have been trying very hard to correct for this bias, to try to pay attention to the facts and to have a clear-eyed view of these systems’ capabilities. But the feelings are visceral, and the effort to compensate is tiring. It is, in fact, the desire to stop making this particular kind of effort that has me writing up this piece and trying to take an intentional break from the subject, despite its intense relevance.

When I say its “aesthetic qualities” are unpleasant, I don’t just mean the aesthetic elements of output of genAIs themselves. The aesthetic quality of genAI writing, visual design, animation and so on, while mostly atrocious, is also highly variable. There are cherry-picked examples which look… fine. Maybe even good. For years now, there have been, famously, literally award-winning aesthetic outputs of genAI³.

While I am ideologically predisposed to see any “good” genAI art as accruing the benefits of either a survivorship bias from thousands of terrible outputs or simple plagiarism rather than its own inherent quality, I cannot deny that in many cases it is “good”.

However, I am not just talking about the product, but the process; the aesthetic experience of interfacing with the genAI system itself, rather than the aesthetic experience of the outputs of that system.

I am not a visual artist and I am not really a writer⁴, particularly not a writer of fiction or anything else whose experience is primarily aesthetic. So I will speak directly to the experience of software development.

I have seen very few successful examples of using genAI to produce whole, working systems. There are no shortage of highly public miserable failures, particularly from the vendors of these systems themselves, where the outputs are confused, self-contradictory, full of subtle errors and generally unusable. While few studies exist, it sure looks like this is an automated way of producing a Net Negative Productivity Programmer, throwing out chaff to slow down the rest of the team.⁵

Juxtapose this with my aforementioned psychological motivations, to wit, I want to have everything in the computer be orderly and make sense, I’m sure most of you would have no trouble imagining that sitting through this sort of practice would make me extremely unhappy.

Despite this plethora of negative experiences, executives are aggressively mandating the use of AI⁶. It looks like without such mandates, most people will not bother to use such tools, so the executives will need muscular policies to enforce its use.⁷

Being forced to sit and argue with a robot while it struggles and fails to produce a working output, while you have to rewrite the code at the end anyway, is incredibly demoralizing. This is the kind of activity that activates every single major cause of burnout at once.

But, at least in that scenario, the thing ultimately doesn’t work, so there’s a hope that after a very stressful six month pilot program, you can go to management with a pile of meticulously collected evidence, and shut the whole thing down.

I am inclined to believe that, in fact, it doesn’t work well enough to be used this way, and that we are going to see a big crash. But that is not the most aesthetically distressing thing. The most distressing thing is that maybe it does work; if not well enough to actually do the work, at least ambiguously enough to fool the executives long-term.

This project, in particular, stood out to me as an example. Its author, a self-professed “AI skeptic” who “thought LLMs were glorified Markov chain generators that didn’t actually understand code and couldn’t produce anything novel”, did a green-field project to test this hypothesis.

Now, this particular project is not totally inconsistent with a world in which LLMs cannot produce anything novel. One could imagine that, out in the world of open source, perhaps there is enough “OAuth provider written in TypeScript” blended up into the slurry of “borrowed⁸” training data that the minor constraint of “make it work on Cloudflare Workers” is a small tweak⁹. It is not fully dispositive of the question of the viability of “genAI coding”.

But it is a data point related to that question, and thus it did make me contend with what might happen if it were actually a fully demonstrative example. I reviewed the commit history, as the author suggested. For the sake of argument, I tried to ask myself if I would like working this way. Just for clarity on this question, I wanted to suspend judgement about everything else; assuming:

• the model could be created with ethically, legally, voluntarily sourced training data
• its usage involved consent from labor rather than authoritarian mandates
• sensible levels of energy expenditure, with minimal CO2 impact
• it is substantially more efficient to work this way than to just write the code yourself

and so on, and so on… would I like to use this magic robot that could mostly just emit working code for me? Would I use it if it were free, in all senses of the word?

No. I absolutely would not.

I found the experience of reading this commit history and imagining myself using such a tool — without exaggeration — nauseating.

Unlike many programmers, I love code review. I find that it is one of the best parts of the process of programming. I can help people learn, and develop their skills, and learn from them, and appreciate the decisions they made, develop an impression of a fellow programmer’s style. It’s a great way to build a mutual theory of mind.

Of course, it can still be really annoying; people make mistakes, often can’t see things I find obvious, and in particular when you’re reviewing a lot of code from a lot of different people, you often end up having to repeat explanations of the same mistakes. So I can see why many programmers, particularly those more introverted than I am, hate it.

But, ultimately, when I review their code and work hard to provide clear and actionable feedback, people learn and grow and it’s worth that investment in inconvenience.

The process of coding with an “agentic” LLM appears to be the process of carefully distilling all the worst parts of code review, and removing and discarding all of its benefits.

The lazy, dumb, lying robot asshole keeps making the same mistakes over and over again, never improving, never genuinely reacting, always obsequiously pretending to take your feedback on board.

Even when it “does” actually “understand” and manages to load your instructions into its context window, 200K tokens later it will slide cleanly out of its memory and you will have to say it again.

All the while, it is attempting to trick you. It gets most things right, but it consistently makes mistakes in the places that you are least likely to notice. In places where a person wouldn’t make a mistake. Your brain keeps trying to develop a theory of mind to predict its behavior but there’s no mind there, so it always behaves infuriatingly randomly.

I don’t think I am the only one who feels this way.

The Affordances

Whatever our environments afford, we tend to do more of. Whatever they resist, we tend to do less of. So in a world where we were all writing all of our code and emails and blog posts and texts to each other with LLMs, what do they afford that existing tools do not?

As a weirdo who enjoys code review, I also enjoy process engineering. The central question of almost all process engineering is to continuously ask: how shall we shape our tools, to better shape ourselves?

LLMs are an affordance for producing more text, faster. How is that going to shape us?

Again arguing in the alternative here, assuming the text is free from errors and hallucinations and whatever, it’s all correct and fit for purpose, that means it reduces the pain of circumstances where you have to repeat yourself. Less pain! Sounds great; I don’t like pain.

Every codebase has places where you need boilerplate. Every organization has defects in its information architecture that require repetition of certain information rather than a link back to the authoritative source of truth. Often, these problems persist for a very long time, because it is difficult to overcome the institutional inertia required to make real progress rather than going along with the status quo. But this is often where the highest-value projects can be found. Where there’s muck, there’s brass.

The process-engineering function of an LLM, therefore, is to prevent fundamental problems from ever getting fixed, to reward the rapid-fire overwhelm of infrastructure teams with an immediate, catastrophic cascade of legacy code that is now much harder to delete than it is to write.

---

There is a scene in Game of Thrones where Khal Drogo kills himself. He does so by replacing a stinging, burning, therapeutic antiseptic wound dressing with some cool, soothing mud. The mud felt nice, addressed the immediate pain, removed the discomfort of the antiseptic, and immediately gave him a lethal infection.

The pleasing feeling of immediate progress when one prompts an LLM to solve some problem feels like cool mud on my brain.

The Economics

We are in the middle of a mania around this technology. As I have written about before, I believe the mania will end. There will then be a crash, and a “winter”. But, as I may not have stressed sufficiently, this crash will be the biggest of its kind — so big, that it is arguably not of a kind at all. The level of investment in these technologies is bananas and the possibility that the investors will recoup their investment seems close to zero. Meanwhile, that cost keeps going up, and up, and up.

Others have reported on this in detail¹⁰, and I will not reiterate that all here, but in addition to being a looming and scary industry-wide (if we are lucky; more likely it’s probably “world-wide”) economic threat, it is also going to drive some panicked behavior from management.

Panicky behavior from management stressed that their idea is not panning out is, famously, the cause of much human misery. I expect that even in the “good” scenario, where some profit is ultimately achieved, will still involve mass layoffs rocking the industry, panicked re-hiring, destruction of large amounts of wealth.

It feels bad to think about this.

The Energy Usage

For a long time I believed that the energy impact was overstated. I am even on record, about a year ago, saying I didn’t think the energy usage was a big deal. I think I was wrong about that.

It initially seemed like it was letting regular old data centers off the hook. But recently I have learned that, while the numbers are incomplete because the vendors aren’t sharing information, they’re also extremely bad.¹¹

I think there’s probably a version of this technology that isn’t a climate emergency nightmare, but that’s not the version that the general public has access to today.

The Educational Impact

LLMs are making academic cheating incredibly rampant.¹²

Not only is it so common as to be nearly universal, it’s also extremely harmful to learning.¹³

For learning, genAI is a forklift at the gym.

To some extent, LLMs are simply revealing a structural rot within education and academia that has been building for decades if not centuries. But it was within those inefficiencies and the inconveniences of the academic experience that real learning was, against all odds, still happening in schools.

LLMs produce a frictionless, streamlined process where students can effortlessly glide through the entire credential, learning nothing. Once again, they dull the pain without regard to its cause.

This is not good.

The Invasion of Privacy

This is obviously only a problem with the big cloud models, but then, the big cloud models are the only ones that people actually use. If you are having conversations about anything private with ChatGPT, you are sending all of that private information directly to Sam Altman, to do with as he wishes.

Even if you don’t think he is a particularly bad guy, maybe he won’t even create the privacy nightmare on purpose. Maybe he will be forced to do so as a result of some bizarre kafkaesque accident.¹⁴

Imagine the scenario, for example, where a woman is tracking her cycle and uploading the logs to ChatGPT so she can chat with it about a health concern. Except, surprise, you don’t have to imagine, you can just search for it, as I have personally, organically, seen three separate women on YouTube, at least one of whom lives in Texas, not only do this on camera but recommend doing this to their audiences.

Citation links withheld on this particular claim for hopefully obvious reasons.

I assure you that I am neither particularly interested in menstrual products nor genAI content, and if I am seeing this more than once, it is probably a distressingly large trend.

The Stealing

The training data for LLMs is stolen. I don’t mean like “pirated” in the sense where someone illicitly shares a copy they obtained legitimately; I mean their scrapers are ignoring both norms¹⁵ and laws¹⁶ to obtain copies under false pretenses, destroying other people’s infrastructure¹⁷.

The Fatigue

I have provided references to numerous articles outlining rhetorical and sometimes data-driven cases for the existence of certain properties and consequences of genAI tools. But I can’t prove any of these properties, either at a point in time or as a durable ongoing problem.

The LLMs themselves are simply too large to model with the usual kind of heuristics one would use to think about software. I’d sooner be able to predict the physics of dice in a casino than a 2 trillion parameter neural network. They resist scientific understanding, not just because of their size and complexity, but because unlike a natural phenomenon (which could of course be considerably larger and more complex) they resist experimentation.

The first form of genAI resistance to experiment is that every discussion is a motte-and-bailey. If I use a free model and get a bad result I’m told it’s because I should have used the paid model. If I get a bad result with ChatGPT I should have used Claude. If I get a bad result with a chatbot I need to start using an agentic tool. If an agentic tool deletes my hard drive by putting os.system(“rm -rf ~/”) into sitecustomize.py then I guess I should have built my own MCP integration with a completely novel heretofore never even considered security sandbox or something?

What configuration, exactly, would let me make a categorical claim about these things? What specific methodological approach should I stick to, to get reliably adequate prompts?

For the record though, if the idea of the free models is that they are going to be provocative demonstrations of the impressive capabilities of the commercial models, and the results are consistently dogshit, I am finding it increasingly hard to care how much better the paid ones are supposed to be, especially since the “better”-ness cannot really be quantified in any meaningful way.

The motte-and-bailey doesn’t stop there though. It’s a war on all fronts. Concerned about energy usage? That’s OK, you can use a local model. Concerned about infringement? That’s okay, somewhere, somebody, maybe, has figured out how to train models consensually¹⁸. Worried about the politics of enriching the richest monsters in the world? Don’t worry, you can always download an “open source” model from Hugging Face. It doesn’t matter that many of these properties are mutually exclusive and attempting to fix one breaks two others; there’s always an answer, the field is so abuzz with so many people trying to pull in so many directions at once that it is legitimately difficult to understand what’s going on.

Even here though, I can see that characterizing everything this way is unfair to a hypothetical sort of person. If there is someone working at one of these thousands of AI companies that have been springing up like toadstools after a rain, and they really are solving one of these extremely difficult problems, how can I handwave that away? We need people working on problems, that’s like, the whole point of having an economy. And I really don’t like shitting on other people’s earnest efforts, so I try not to dismiss whole fields. Given how AI has gotten into everything, in a way that e.g. cryptocurrency never did, painting with that broad a brush inevitably ends up tarring a bunch of stuff that isn’t even really AI at all.

The second form of genAI resistance to experiment is the inherent obfuscation of productization. The models themselves are already complicated enough, but the products that are built around the models are evolving extremely rapidly. ChatGPT is not just a “model”, and with the rapid¹⁹ deployment of Model Context Protocol tools, the edges of all these things will blur even further. Every LLM is now just an enormous unbounded soup of arbitrary software doing arbitrary whatever. How could I possibly get my arms around that to understand it?

The Challenge

I have woefully little experience with these tools.

I’ve tried them out a little bit, and almost every single time the result has been a disaster that has not made me curious to push further. Yet, I keep hearing from all over the industry that I should.

To some extent, I feel like the motte-and-bailey characterization above is fair; if the technology itself can really do real software development, it ought to be able to do it in multiple modalities, and there’s nothing anyone can articulate to me about GPT-4o which puts it in a fundamentally different class than GPT-3.5.

But, also, I consistently hear that the subjective experience of using the premium versions of the tools is actually good, and the free ones are actually bad.

I keep struggling to find ways to try them “the right way”, the way that people I know and otherwise respect claim to be using them, but I haven’t managed to do so in any meaningful way yet.

I do not want to be using the cloud versions of these models with their potentially hideous energy demands; I’d like to use a local model. But there is obviously not a nicely composed way to use local models like this.

Since there are apparently zero models with ethically-sourced training data, and litigation is ongoing²⁰ to determine the legal relationships of training data and outputs, even if I can be comfortable with some level of plagiarism on a project, I don’t feel that I can introduce the existential legal risk into other people’s infrastructure, so I would need to make a new project.

Others have differing opinions of course, including some within my dependency chain, which does worry me, but I still don’t feel like I can freely contribute further to the problem; it’s going to be bad enough to unwind any impact upstream. Even just for my own sake, I don’t want to make it worse.

This especially presents a problem because I have way too much stuff going on already. A new project is not practical.

Finally, even if I did manage to satisfy all of my quirky²¹ constraints, would this experiment really be worth anything? The models and tools that people are raving about are the big, expensive, harmful ones. If I proved to myself yet again that a small model with bad tools was unpleasant to use, I wouldn’t really be addressing my opponents’ views.

I’m stuck.

The Surrender

I am writing this piece to make my peace with giving up on this topic, at least for a while. While I do idly hope that some folks might find bits of it convincing, and perhaps find ways to be more mindful with their own usage of genAI tools, and consider the harm they may be causing, that’s not actually the goal. And that is not the goal because it is just so much goddamn work to prove.

Here, I must return to my philosophical hobbyhorse of sprachspiel. In this case, specifically to use it as an analytical tool, not just to understand what I am trying to say, but what the purpose for my speech is.

The concept of sprachspiel is most frequently deployed to describe the goal of the language game being played, but in game theory, that’s only half the story. Speech — particularly rigorously justified speech — has a cost, as well as a benefit. I can make shit up pretty easily, but if I want to do anything remotely like scientific or academic rigor, that cost can be astronomical. In the case of developing an abstract understanding of LLMs, the cost is just too high.

So what is my goal, then? To be king Canute, standing astride the shore of “tech”, whatever that is, commanding the LLM tide not to rise? This is a multi-trillion dollar juggernaut.

Even the rump, loser, also-ran fragment of it has the power to literally suffocate us in our homes²² if they so choose, completely insulated from any consequence. If the power curve starts there, imagine what the winners in this industry are going to be capable of, irrespective of the technology they’re building - just with the resources they have to hand. Am I going to write a blog post that can rival their propaganda apparatus? Doubtful.

Instead, I will just have to concede that maybe I’m wrong. I don’t have the skill, or the knowledge, or the energy, to demonstrate with any level of rigor that LLMs are generally, in fact, hot garbage. Intellectually, I will have to acknowledge that maybe the boosters are right. Maybe it’ll be OK.

Maybe the carbon emissions aren’t so bad. Maybe everybody is keeping them secret in ways that they don’t for other types of datacenter for perfectly legitimate reasons. Maybe the tools really can write novel and correct code, and with a little more tweaking, it won’t be so difficult to get them to do it. Maybe by the time they become a mandatory condition of access to developer tools, they won’t be miserable.

Sure, I even sincerely agree, intellectual property really has been a pretty bad idea from the beginning. Maybe it’s OK that we’ve made an exception to those rules. The rules were stupid anyway, so what does it matter if we let a few billionaires break them? Really, everybody should be able to break them (although of course, regular people can’t, because we can’t afford the lawyers to fight off the MPAA and RIAA, but that’s a problem with the legal system, not tech).

I come not to praise “AI skepticism”, but to bury it.

Maybe it really is all going to be fine. Perhaps I am simply catastrophizing; I have been known to do that from time to time. I can even sort of believe it, in my head. Still, even after writing all this out, I can’t quite manage to believe it in the pit of my stomach.

Unfortunately, that feeling is not something that you, or I, can argue with.

---

Acknowledgments

Thank you to my patrons. Normally, I would say, “who are supporting my writing on this blog”, but in the case of this piece, I feel more like I should apologize to them for this than to thank them; these thoughts have been preventing me from thinking more productive, useful things that I actually have relevant skill and expertise in; this felt more like a creative blockage that I just needed to expel than a deliberately written article. If you like what you’ve read here and you’d like to read more of it, well, too bad; I am sincerely determined to stop writing about this topic. But, if you’d like to read more stuff like other things I have written, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

Kamala Harris lost. Here we are. So it goes.

Are you sad? Are you scared?

I am very sad. I am very scared.

But, like everyone else in this position, most of all, I want to know what to do next.

A Mission For Progress

I believe that we should set up a missionary organization for progressive and liberal values.

In 2017, Kayla Chadwick wrote the now-classic article, “I Don’t Know How To Explain To You That You Should Care About Other People”. It resonated with millions of people, myself included. It expresses an exasperation with a populace that seems ignorant of economics, history, politics, and indeed unable to read the news. It is understandable to be frustrated with people who are exercising their electoral power callously and irresponsibly.

But I think in 2024, we need to reckon with the fact that we do, in fact, need to explain to a large swathe of the population that they should care about other people.

We had better figure out how to explain it soon.

Shared Values — A Basis for Hope

The first question that arises when we start considering outreach to the conservative-leaning or undecided independent population is, “are these people available to be convinced?”.

To that, I must answer an unqualified “yes”.

I know that some of you are already objecting. For those of us with an understanding of history and the mechanics of bigotry in the United States, it might initially seem like the answer is “no”.

As the Nazis came to power in the 1920s, they were campaigning openly on a platform of antisemitic violence. Everyone knew what the debate was. It was hard to claim that you didn’t, in spite of some breathtakingly cowardly contemporaneous journalism, they weren’t fooling anyone.

It feels ridiculous to say this, but Hitler did not have support among Jews.

Yet, after campaigning on a platform of defaming immigrants, and Mexican immigrants specifically for a decade, a large part of what drove his victory is that Trump enjoyed a shockingly huge surge of support among the Hispanic population. Even some undocumented migrants — the ones most likely to be herded into concentration camps starting in January — are supporting him.

I believe that this is possible because, in order to maintain support of the multi-ethnic working-class coalition that Trump has built, the Republicans must maintain plausible deniability. They have to say “we are not racist”, “we are not xenophobic”. Incredibly, his supporters even say “I don’t hate trans people” with startling regularity.

Most voters must continue to believe that hateful policies with devastating impacts are actually race-neutral, and are simply going to get rid of “bad” people. Even the ones motivated by racial resentment are mostly motivated by factually incorrect beliefs about racialized minorities receiving special treatment and resources which they are not in fact receiving.

They are victims of a disinformation machine. One that has rendered reality incomprehensible.

---

If you listen to conservative messaging, you can hear them referencing this all the time. Remember when JD Vance made that comment about Democrats calling Diet Mountain Dew racist?

Many publications wrote about this joke “bombing”¹, but the kernel of truth within it is this: understanding structural bigotry in the United States is difficult. When we progressives talk about it, people who don’t understand it think that our explanations sound ridiculous and incoherent.

There’s a reason that the real version of critical race theory is a graduate-level philosophy-of-law course, and not a couple of catch phrases.

If, without context, someone says that “municipal zoning laws are racist”, this makes about as much sense as “Diet Mountain Dew is racist” to someone who doesn’t already know what “redlining” is.

Conservatives prey upon this confusion to their benefit. But they prey on this because they must do so. They must do so because, despite everything, hate is not actually popular among the American electorate. Even now, they have to be deceived into it.

The good news is that all we need to do is stop the deception.

Politics Matter

If I have sold you on the idea that a substantial plurality of voters are available to be persuaded, the next question is: can we persuade them? Do we, as progressives, have the resources and means to do so? We did lose, after all, and it might seem like nothing we did had much of an impact.

Let’s analyze that assumption.

Across the country, Trump’s margins increased. However, in the swing states, where Harris spent money on campaigning, his margins increased less than elsewhere. At time of writing, we project that the safe-state margin shift will be 3.55% towards trump, and the swing-state margin shift will be 1.69%.

This margin was, sadly, too small for a victory, but it does show that the work mattered. Perhaps given more time, or more resources, it would have mattered just a little bit more, and that would have been decisive.

This is to say, in the places where campaign dollars were spent, even against the similar spending of the Trump campaign, we pushed the margin of support 1.86% higher within 107 days. So yes: campaigning matters. Which parts and how much are not straightforward, but it definitely matters.

This is a bit of a nonsensical comparison for a whole host of reasons², but just for a ballpark figure, if we kept this pressure up continuously during the next 4 years, we could increase support for a democratic candidate by 25%.

We Can Teach, Not Sell

Political junkies tend to overestimate the knowledge of the average voter. Even when we are trying to compensate for it, we tend to vastly overestimate how much the average voter knows about politics and policy. I suspect that you, dear reader, are a political junkie even if you don’t think of yourself as one.

To give you a sense of what I mean, across the country, on Election day and the day after, there was a huge spike in interest for the Google query, “did Joe Biden drop out”.

Consistently over the last decade, democratic policies are more popular than their opponents. Even deep red states, such as Kansas, often vote for policies supported by democrats and opposed by Republicans.

This confusion about policy is not organic; it is not voters’ fault. It is because Republicans constantly lie.

All this ignorance might seem discouraging, but it presents an opportunity: people will not sign up to be persuaded, but people do like being informed. Rather than proselytizing via a hard sales pitch, it should be possible to offer to explain how policy connects to elections. And this is made so much the easier if so many of these folks already generally like our policies.

The Challenge Is Enormous

I’ve listed some reasons for optimism, but that does not mean that this will be easy.

Republicans have a tremendously powerful, decentralized media apparatus that reinforces their culture-war messaging all the time.

After some of the post-election analysis, “The Left Needs Its Own Joe Rogan” is on track to become a cliché within the week.³ While I am deeply sympathetic to that argument, the right-wing media’s success is not organic; it is funded by petrochemical billionaires.

We cannot compete via billionaire financing, and as such, we have to have a way to introduce voters to progressive and liberal media. Which means more voters need social connections to liberals and progressives.

Good Works

The democratic presidential campaign alone spent a billion and a half dollars. And, as shown above, this can be persuasive, but it’s just the persuasion itself.

Better than spending all this money on telling people what good stuff we would do for them if we were in power, we could just show them, by doing good stuff. We should live our values, not just endlessly reiterate them.

A billion dollars is a significant amount of power in its own right.

For historical precedent, consider the Black Panthers’ Free Breakfast For Children program. This program absolutely scared the shit out of the conservative power structure, to the point that Nixon’s FBI literally raided them for giving out free food to children.

Religious missionaries, who are famously annoying, often offset their annoying-ness by doing charitable work in the communities they are trying to reach. A lot of the country that we need to reach are religious people, and nominally both Christians and leftists share a concern for helping those in need, so we should find some cultural common ground there.

We can leverage that overlap in values by partnering with churches. This immediately makes such work culturally legible to many who we most need to reach.

Jobs Jobs Jobs

When I raised this idea with Philip James, he had been mulling over similar ideas for a long time, but with a slightly different tack: free career skills workshops from folks who are obviously “non-traditional” with respect to the average rural voter’s cultural expectations. Recruit trans folks, black folks, women, and non-white immigrants from our tech networks.

Run the trainings over remote video conferencing to make volunteering more accessible. Run those workshops through churches as a distribution network.

There is good evidence that this sort of prolonged contact and direct exposure to outgroups, to help people see others as human beings, very effective politically.

However, job skills training is by no means the only benefit we could bring. There are lots of other services we could offer remotely, particularly with the skills that we in the tech community could offer. I offer this as an initial suggestion; if you have more ideas I’d love to hear them. I think the best ideas are ones where folks can opt in, things that feel like bettering oneself rather than receiving charity; nobody likes getting handouts, particularly from the outgroup, but getting help to improve your own skills feels more participatory.

I do think that free breakfast for children, specifically, might be something to start with because people are far more willing to accept gifts to benefit others (particularly their children, or the elderly!) rather than themselves.

Take Credit

Doing good works in the community isn’t enough. We need to do visible good works. Attributable good works.

We don’t want to be assholes about it, but we do want to make sure that these benefits are clearly labeled. We do not want to attach an obligation to any charitable project, but we do want to attach something to indicate where it came from.

I don’t know what that “something” should be. The most important thing is that whatever “something” is appeals to set of partially-overlapping cultures that I am not really a part of — Midwestern, rural, southern, exurban, working class, “red state” — and thus, I would want to hear from people from those cultures about what works best.

But it’s got to be something.

Maybe it’s a little sticker, “brought to you by progressives and liberals. we care about you!”. Maybe it’s a subtle piece of consistent branding or graphic design, like a stylized blue stripe. Maybe we need to avoid the word “democrats”, or even “progressive” or “liberal”, and need some independent brand for such a thing, that is clearly tenuously connected but not directly; like the Coalition of Liberal and Leftist Helpful Neighbors or something.

Famously, when Trump sent everybody a check from the government, he put his name on it. Joe Biden did the same thing, and Democrats seem to think it’s a good thing that he didn’t take credit because it “wasn’t about advancing politics”, even though this obviously backfired. Republicans constantly take credit for the benefits of Democratic policies, which is one reason why voters don’t know they’re democratic policies.

Our broad left-liberal coalition is attempting to improve people’s material conditions. Part of that is, and must be, advancing a political agenda. It’s no good if we provide job trainings and free lunches to a community if that community is just going to be reduced to ruin by economically catastrophic tariffs and mass deportations.

We cannot do this work just for the credit, but getting credit is important.

Let’s You And Me — Yes YOU — Get Started

I think this is a good idea, but I am not the right person to lead it.

For one thing, building this type of organization requires a lot of organizational and leadership skills that are not really my forte. Even the idea of filing the paperwork for a new 501(c)3 right now sounds like rolling Sisyphus’s rock up the hill to me.

For another, we need folks who are connected to this culture, in ways that I am not. I would be happy to be involved — I do have some relevant technical skills to help with infrastructure, and I could always participate in some of the job-training stuff, and I can definitely donate a bit of money to a nonprofit, but I don’t think I can be in charge.

You can definitely help too, and we will need a wide variety of skills to begin with, and it will definitely need money. Maybe you can help me figure out who should be in charge.

This project will be weaker without your support. Thus: I need to hear from you.

You can email me, or, if you’d prefer a more secure channel, feel free to reach out over Signal, where my introduction code is glyph.99 . Please start the message with “good works:” so I can easily identify conversations about this.

If I receive any interest at all, I plan to organize some form of meeting within the next 30 days to figure out concrete next steps.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more things like it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor! My aspirations for this support are more in the directions of software development than activism, but needs must, when the devil drives. Thanks especially to Philip James for both refining the idea and helping to edit this post, and to Marley Myrianthopoulos for assistance with the data analysis.

If a high-status member of a community that you participate in is accused of misbehavior, you may want to defend them. You may even write a long essay in their defense.

In that essay, it may seem only natural to begin with a lengthy enumeration of the accused’s positive personal qualities. To extol the quality of their career and their contributions to your community. To talk about how nice they are. To be a character witness in the court of public opinion.

If you do this, you are not defending them. You are proving the point. This is exactly how missing stairs come to exist. People don’t get away with bad behavior if they don’t have high status and a good reputation already.

Sometimes, someone with antisocial inclinations seeks out status, in order to facilitate their bad behavior. Sometimes, a good, but, flawed person does a lot of really good work and thereby accidentally ends up with more status than they were expecting to have, and they don’t know how to handle it. In either case, bad behavior may ensue.

If you truly believe that your fave is being accused or punished unjustly, focus on the facts. What, specifically, has been alleged? How are these allegations substantiated? What verifiable evidence exists to the contrary? If you feel that someone is falsely accusing them to ruin their reputation, is there evidence to support your claim that the accusation is false? Ask yourself the question: what information do you have, that is leading to your correct analysis of the situation, that the people making the accusations do not have, which might be leading them into error?

But, also, maybe just… don’t?

The urge to defend someone like this is much more likely to come from a sense of personal grievance than justice. Consider: does it feel like you are being attacked, when your fave has been attacked? Is there a tightness in your chest, heat rising on your cheeks? Do you feel suddenly defensive?

Do you think that defensiveness is likely to lead to you making good, rational decisions about what steps to take next?

Let your heroes face accountability. If they are really worth your admiration, they might accept responsibility and make amends. Or they might fight the accusations with their own real evidence — evidence that you, someone peripheral to their situation, are unlikely to have — and prove the accusations wrong.

They might not want your defense. Even if they feel like they do want it in the moment — they are human too, after all, and facing accountability does not feel good to us humans — is the intensified feeling that they can’t let down their supporters who believe in them likely to make them feel less defensive and panicked?

In either case, your character defense is unlikely to serve them. At best it helps them stay on an ego trip, at worst it muddies the waters and might confuse the collection of facts that would, if considered dispassionately, properly exonerate them.

Do you think that I am pretending to speak in generalities but really talking about one specific recent event?

Wrong!

Just in this last week, I have read 2 different blog posts about 2 completely different people in completely unrelated communities and both of their authors need to read this. But each of those were already of a type, one that I’ve read dozens of instances of in the past.

It is a very human impulse to perceive a threat to someone we think well of, and to try to defend against that threat. But the consequences of someone’s own actions are not a threat you can defend them from.

Humans are pattern-matching machines. As a species, it is our superpower. To summarize the core of my own epistemic philosophy, here is a brief list of the activities in the core main-loop of a human being:

1. stuff happens to us
2. we look for patterns in the stuff
3. we weave those patterns into narratives
4. we turn the narratives into models of the world
5. we predict what will happen based on those models
6. we do stuff based on those predictions
7. based on the stuff we did, more stuff happens to us; return to step 1

While this ability lets humans do lots of great stuff, like math and physics and agriculture and so on, we can just as easily build bad stories and bad models. We can easily trick ourselves into thinking that our predictive abilities are more powerful than they are.

The existence of magic-seeming levels of prediction in fields like chemistry and physics and statistics, in addition to the practical usefulness of rough estimates and heuristics in daily life, itself easily creates a misleading pattern. “I see all these patterns and make all these predictions and I’m right a lot of the time, so if I just kind of wing it and predict some more stuff, I’ll also be right about that stuff.”

This leaves us very vulnerable to things like mean world syndrome. Mean world syndrome itself is specifically about danger, but I believe it is a manifestation of an even broader phenomenon which I would term “the apophenia of despair”.

Confirmation bias is an inherent part of human cognition, but the internet has turbocharged it. Humans have immediate access to more information than we ever had in the past. In order to cope with that information, we have also built ways to filter that information. Even disregarding things like algorithmic engagement maximization and social media filter bubbles, the simple fact that when you search for things, you are a lot more likely to find the thing that you’re searching for than to find arguments refuting it, can provide a very strong sense that you’re right about whatever you’re researching.

All of this is to say: if you decide that something in the world is getting worse, you can very easily convince yourself that it is getting much, much worse, very rapidly. Especially because there are things which are, unambiguously, getting worse.

---

However, Pollyanna-ism is just the same phenomenon in reverse and I don’t want to engage in that. The ice sheets really are melting, globally, fascism really is on the rise. I am not here to deny reality or to cherry pick a bunch of statistics to lull people into complacency.

I believe that while dwelling on a negative reality is bad, I also believe that in the face of constant denial, it is sometimes necessary to simply emphasize those realities, however unpleasant they may be. Distinguishing between unhelpful rumination on negativity and repetition of an unfortunate but important truth to correct popular perception is subjective and subtle, but the difference is nevertheless important.

---

As our ability to acquire information about things getting worse has grown, our ability to affect those things has not. Knowledge is not power; power is power, and most of us don’t have a lot of it, so we need to be strategic in the way that we deploy our limited political capital and personal energy.

Overexposure to negative news can cause symptoms of depression; depressed people have reduced executive function and find it harder to do stuff. One of the most effective interventions against this general feeling of malaise? Hope.. Not “hope” in the sense of wishing. As this article in the American Psychological Association’s “Monitor on Psychology” puts it:

“We often use the word ‘hope’ in place of wishing, like you hope it rains today or you hope someone’s well,” said Chan Hellman, PhD, a professor of psychology and founding director of the Hope Research Center at the University of Oklahoma. “But wishing is passive toward a goal, and hope is about taking action toward it.”

Here, finally, I can get around to my point.

---

If you have an audience, and you have some negative thoughts about some social trend, talking about it in a way which is vague and non-actionable is potentially quite harmful. If you are doing this, you are engaged in the political project of robbing a large number of people of hope. You are saying that the best should have less conviction, while the worst will surely remain just as full of passionate intensity.

I do not mean to say that it is unacceptable to ever publicly share thoughts of sadness, or even hopelessness. If everyone in public is forced to always put on a plastic smile and pretend that everything is going to be okay if we have grit and determination, then we have an Instagram culture of fake highlight reels where anyone having their own struggles with hopelessness will just feel even worse in their isolation. I certainly posted my way through my fair share of pretty bleak mental health issues during the worst of the pandemic.

But we should recognize that while sadness is a feeling, hopelessness is a problem, a bad reaction to that feeling, one that needs to be addressed if we are going to collectively dig ourselves out of the problem that creates the sadness in the first place. We may not be able to conjure hope all the time, but we should always be trying.

When we try to address these feelings, as I said earlier, Pollyanna-ism doesn’t help. The antidote to hopelessness is not optimism, but curiosity. If you have a strong thought like “people these days just don’t care about other people¹”, yelling “YES THEY DO” at yourself (or worse, your audience) is unlikely to make much of a change, and certainly not likely to be convincing to an audience. Instead, you could ask yourself some questions, and use them for a jumping-off point for some research:

1. Why do I think this — is the problem in my perception, or in the world?
2. If there is a problem in my perception, is this a common misperception? If it’s common, what is leading to it being common? If it’s unique to me, what sort of work do I need to do to correct it?
3. If the problem is real, what are its causes? Is there anything that I, or my audience, could do to address those causes?

The answers to these questions also inform step 6 of the process I outlined above: the doing stuff part of the process.

---

At some level, all communication is persuasive communication. Everything you say that another person might hear, everything you say that a person might hear, is part of a sprachspiel where you are attempting to achieve something. There is always an implied call to action; even “do nothing, accept the status quo” is itself an action. My call to action right now is to ask you to never make your call to action “you should feel bad, and you should feel bad about feeling bad”. When you communicate in public, your words have power.

Use that power for good.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor! Special thanks also to Cassandra Granade, who provided some editorial feedback on this post; any errors, of course, remain my own.

Today I saw a short YouTube video about “cozy games” and started writing a comment, then realized that this was somehow prompting me to write the most succinct summary of my own personal views on politics and economics that I have ever managed. So, here goes.

Apparently all I needed to trim down 50,000 words on my annoyance at how the term “capitalism” is frustratingly both a nexus for useful critque and also reductive thought-terminating clichés was to realize that Animal Crossing: New Horizons is closer to my views on political economy than anything Adam Smith or Karl Marx ever wrote.

---

Cozy games illustrate that the core mechanics of capitalism are fun and motivating, in a laboratory environment. It’s fun to gather resources, to improve one’s skills, to engage in mutually beneficial exchanges, to collect things, to decorate. It’s tremendously motivating. Even merely pretending to do those things can captivate huge amounts of our time and attention.

In real life, people need to be motivated to do stuff. Not because of some moral deficiency, but because in a large complex civilization it’s hard to tell what needs doing. By the time it’s widely visible to a population-level democratic consensus of non-experts that there is an unmet need — for example, trash piling up on the street everywhere indicating a need for garbage collection — that doesn’t mean “time to pick up some trash”, it means “the sanitation system has collapsed, you’re probably going to get cholera”. We need a system that can identify utility signals more granularly and quickly, towards the edges of the social graph. To allow person A to earn “value credits” of some kind for doing work that others find valuable, then trade those in to person B for labor which they find valuable, even if it is not clearly obvious to anyone else why person A wants that thing. Hence: money.

So, a market can provide an incentive structure that productively steers people towards needs, by aggregating small price signals in a distributed way, via the communication technology of “money”. Authoritarian communist states are famously bad at this, overproducing “necessary” goods in ways that can hold their own with the worst excesses of capitalists, while under-producing “luxury” goods that are politically seen as frivolous.

This is the kernel of truth around which the hardcore capitalist bootstrap grindset ideologues build their fabulist cinematic universe of cruelty. Markets are motivating, they reason, therefore we must worship the market as a god and obey its every whim. Markets can optimize some targets, therefore we must allow markets to optimize every target. Markets efficiently allocate resources, and people need resources to live, therefore anyone unable to secure resources in a market is undeserving of life. Thus we begin at “market economies provide some beneficial efficiencies” and after just a bit of hand-waving over some inconvenient details, we get to “thus, we must make the poor into a blood-sacrifice to Moloch, otherwise nobody will ever work, and we will all die, drowning in our own laziness”. “The cruelty is the point” is a convenient phrase, but among those with this worldview, the prosperity is the point; they just think the cruelty is the only engine that can possibly drive it.

Cozy games are therefore a centrist¹ critique of capitalism. They present a world with the prosperity, but without the cruelty. More importantly though, by virtue of the fact that people actually play them in large numbers, they demonstrate that the cruelty is actually unnecessary.

You don’t need to play a cozy game. Tom Nook is not going to evict you from your real-life house if you don’t give him enough bells when it’s time to make rent. In fact, quite the opposite: you have to take time away from your real-life responsibilities and work, in order to make time for such a game. That is how motivating it is to engage with a market system in the abstract, with almost exclusively positive reinforcement.

What cozy games are showing us is that a world with tons of “free stuff” — universal basic income, universal health care, free education, free housing — will not result in a breakdown of our society because “no one wants to work”. People love to work.

If we can turn the market into a cozy game, with low stakes and a generous safety net, more people will engage with it, not fewer. People are not lazy; laziness does not exist. The motivation that people need from a market economy is not a constant looming threat of homelessness, starvation and death for themselves and their children, but a fun opportunity to get a five-star island rating.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!