Updated 6.4.26
This collects my writing on AI, regulation, work, and meaning, alongside artifacts from my professional life. I'm a NERC compliance manager by day, a teacher by training, and someone trying to think carefully in public about what's coming.
Featured Essays
How do we regulate a tragedy that hasn't happened yet?
Written by David Dabney, edited by Opus 4.6
3.22.26
You may have heard of the Jones Act. What about the Chicken Tax?
In the 50s and 60s, cheap American chicken was flooding the European market. France and West Germany responded with tariffs on American poultry imports to protect their domestic producers. In 1964 LBJ retaliated with a 25% tariff on light trucks. But LBJ, the consummate politician, had an ulterior motive for his chosen tariff: he needed the support of the United Auto Workers Union in advance of the election. The kerfuffle was eventually resolved, but due to auto industry lobbying the Chicken Tax persists to this day.
There are countless examples of regulations like this—a mix of legitimate motives, political economy and the vicissitudes of fate. Even with motives purer than Johnson's, legislation makes up a sizable percentage of the toxic slurry with which the road to hell is paved.
This, my friends, is why I yearn for simpler times, before government started getting involved with everything.
When a man was free to breathe the lead-and-sulfur-dioxide-filled air of freedom, and light his river on fire if he wanted to.
Free to buy a car without seatbelts. Free to wonder whether the food you bought was going to poison you.
Yessir, those were the days.
Those were the days, indeed.
In 1911, a match or cigarette tossed into a bin of fabric scraps started a fire at the Triangle Shirtwaist Factory in New York City. The exit doors were locked to prevent theft and unauthorized breaks. Tall buildings were a recent innovation, and the fire trucks of that era didn't have ladders tall enough to reach the 8th story windows where the fires started. Onlookers watched as women and girls—many of whom were barely teenagers—leapt from the windows to avoid being burned alive. In the end, 146 people perished.
"Someone should have done something!"
Garment workers were already fighting for better working conditions, but the broken bodies of young girls on the sidewalks of Greenwich Village finally galvanized the support they needed. Over 350,000 people marched in the funeral procession, and over the next four years, dozens of worker protection laws were enacted. They mandated fire drills, unlocked exits, sprinklers, occupancy limits, factory inspection, and empowered the Department of Labor for enforcement. These regulations seem like common sense today, but at the time it was a profound victory for public safety.
And this was just the opening salvo. In the next hundred years, American regulation cut vehicle deaths by 93% per mile driven, aviation deaths by 99.5%, and airborne lead by 92%. Since the FDIC was created in 1933 to protect against bank runs, not a single depositor has lost a penny. The Cuyahoga River in Ohio, which once caught fire thirteen times, is now home to dozens of species of fish.
So why, dear reader, do we moderns hate regulation so much?
There are many reasons, but broadly speaking:
The benefits of regulation are invisible.
The costs of regulation are visible.
Why? The answer comes down to psychology.
The better regulation is in addressing a problem, the less experience people have with the problem it addressed. The benefits of regulation are diffuse and probabilistic and its costs are proximate and salient. Good regulations are unobtrusive, and bad regulations are an affront. Good regulators are quietly competent and corrupt ones are newsworthy.
So if regulation can be beneficial, what can we do to rehabilitate its image? Should we address the public's cognitive biases with data to raise the salience of counterfactuals? You and I might find this compelling, but it is unlikely to change broader sentiment. This is the rationalist reformer's trap: the belief that if only you could make your case clearly enough, everyone would agree with you. Alas, that is not the world in which we live.
What viable strategy is left to us then? Narrative. Not demagoguery, which is intended to deceive, but storytelling, which takes the truth and gives it color and context. Opting for storytelling over logic isn't a concession; it's translation. You must know your audience. If you want to convince a scientist, use data. If you want to convince the public, use stories.
In the case of the Triangle Shirtwaist Factory, activists didn't have a story salient enough to get the public's attention. The new technologies of the era—urbanization, industrialization—required new regulatory frameworks to mitigate their worst effects. Unfortunately for the people who died that day, technological innovations moved faster than the regulatory apparatus. Those paying attention knew a tragedy like Triangle was inevitable, but most people didn't consider regulation until there was a body count. After Triangle, the public no longer needed convincing.
But what if we are trying to prevent a tragedy that hasn't yet happened? How do you convince a public to regulate a tragedy without historical precedent? AI presents a regulatory challenge because its greatest harms lie in the future and are therefore abstract. By the time we have a catastrophe large enough to summon the political will, it may be too late to avoid the worst outcomes. The question is: can we break our historical pattern? Can we regulate AI effectively* before the bodies start hitting the sidewalk?
Based on what I know about human psychology, I'm not optimistic. Perhaps the best we can hope for is that when catastrophe comes, it is the right size—large enough for the public to realize the stakes, but no larger.
* "Effectively." The devil's in the details, there, eh?
Process notes and collaboration thread available on Substack: https://daviddabney16.substack.com/p/a-tragedy-of-sufficient-scale?r=59cqi
When is AI assistance appropriate, and when is it unethical? Applying the Jesuit practice of casuistry to a question without established norms.
Written by David Dabney, edited by Opus 4.5
1.10.26 at 10:47 PM
I have a good friend, a colleague, who won't use AI. He says, "I'm old school, it feels like cheating." Not is cheating, but feels like cheating.
Is it? It depends.
"It depends" is an answer that rightly acknowledges edge cases, but also sidesteps the responsibility of giving actionable recommendations. "It depends" does us little good in the arena. We need yesses and nos. But perhaps if we descend into the particulars of this situation, we can extract some actionable insight.
There's a Jesuit practice called casuistry that avoids applying general moral principles (e.g. "lying is bad, therefore all lies are bad") and instead "descends into the particulars" of each situation. One chooses two clear cases (one clearly acceptable, one clearly not) and asks: is my situation more like this one, or that one? The method has failure modes, but it's useful for novel ethical territory.
We're in novel ethical territory now. People are realizing how useful AI is, but many still feel like using it is cheating. I'd guess a large percentage of people who feel guilty about using AI are doing it anyway. That can't be good for us. So let's descend into the particulars.
Imagine two coffee mugs. One is hand-thrown. One is factory-made, big-box fare. Both hold coffee. But what gives each its value?
For the hand-thrown mug, the hand-thrown-ness is the point. If someone wants your craftsmanship and you give them factory output passed off as your own, that's an affront. The tool-use isn't the problem; it's the deception. You violated the relationship. There's nothing tackier than faked authenticity.
But consider: I have favorite mugs that were handmade by my wife, and favorite mugs I bought. The ones I bought are often beautiful, distinctive, and meaningful. A Miir camp mug is gorgeous and utilitarian. No one is going to hand-throw one of those, and no one expects them to. Do I get less value from it because it came from a factory? Of course not.
So it's not as simple as "handmade good, machine-made bad." It's about purpose. What are you trying to achieve? What are you offering, and what is the recipient actually looking for? If we can identify those two things, we can determine what kind of machine assistance can be provided without undermining the value of the offering or the relationship.
However, I don't think that goes far enough. Most of us approach AI use defensively. We feel an impulse to use it, then a pang of anxiety or guilt, then we interrogate whether it's okay. The loop only starts if we have the impulse to consult AI—and that impulse may only arise when a task is unpleasant enough to override the deep, unexamined belief that effort is virtuous for its own sake. This risks limiting our AI support to only the tasks unpleasant or difficult enough to rouse internal resistance, rather than all the opportunities to increase value or raise the bar of excellence.
What if we flipped this paradigm? Start by examining the purpose of a work product. If its "human-ness" (or lack thereof) is beside the point, then AI assistance should be the default. Otherwise, we're leaving value on the table: hand-throwing mugs when all people want is coffee, just because we never bothered to question the assumption.
We don't have shared norms around AI yet. It's up to us to reason this out, to find the contours of ethical use together. Will our norms evolve in a way that supports honesty, prioritizes what actually matters, and stops being precious about humans' limited time and energy?
I'd like to say yes. But probably, it depends.
Process notes and collaboration thread available on Substack: https://daviddabney16.substack.com/p/is-using-ai-cheating?r=59cqi
I designed my company's AI Acceptable Use Policy to create a permission structure for AI experimentation and discussion. The generality of AI as a technology makes it a poor fit for a prescriptive "thou shalt not" type of AUP, so it was an interesting exercise to articulate the contours of my moral intuitions here. Societal norms will need to be developed collaboratively, and the first step was to name that and create space for it. I also knew that if people were hiding their AI use or avoiding it altogether out of fear, I wouldn't be able to guide the organization towards the AI-related process improvements I can see in our future.
In May 2026, I helped form an AI Governance Subcomittee with two executives from our parent company, and indicated that they plan to adopt my AUP themselves, and use my staff training as a template for their own.
Download: AI Acceptable Use Policy (PDF)
I have a small Twitter account. I quit social media years ago and only came back when I realized X was where serious AI conversation was happening. A few people whose work I admire have engaged with mine there, which has encouraged me to keep writing.
I started a Substack for essays on other topics and to cross-post these essays for a wider audience. For a different kind of essay, check out Fairy Tales.
Sharing from my previous application to show how I think about other AI-related questions.
Q: How should Anthropic think about the tradeoffs between keeping risks low and remaining commercially competitive?
Despite the apparent tradeoff between commercial competition and mitigating risk, I would push back against this framing. A tradeoff implies a zero sum game between two opposing objectives. Instead, I'd argue that risk mitigation is the end, and commercial competitiveness is the means most likely to achieve that end in our present context. Taking their opposition for granted undercuts both the ends and the most potent means of achieving those ends.
I'm not suggesting that "the ends justify the means", but the more noble our ends, the more shrewdly and realistically we should consider the means available.
What does it look like for Anthropic to keep risks low when labs are racing towards superintelligence, and many in government are opposing even the most common sense regulation? I'd argue you do that by building a product that people and companies trust. By building an intelligence- even a being- that reflects the best of what makes us human. By being wise and shrewd. By being good. By winning. You can't have wisdom without goodness, and you can't have either if you're dead.
Q: Take a look at Anthropic's Responsible Scaling Policy. Can you think of a change that might present an improvement from the standpoint of balancing risk reduction and preserving our ability to stay on the frontier of AI development?
There are a few potential improvements I could suggest to improve the balance between risk reduction and maintaining Anthropic's position on the frontier of AI development. You may have considered these, so I hope you'll indulge me making three suggestions instead of one.
First, I would indicate somewhere that government investment will eventually be instrumental for ensuring the security of Claude's weights in particular. Without denying Anthropic shares some responsibility, I would nonetheless want to acknowledge that just as society doesn't expect Amazon or Fedex to maintain the roads and ports, that we shouldn't expect Anthropic to be able to defend against state sponsored actors.
Second, regarding Trusted Users (4.1.6) and Third-party environments (4.1.7), I would consider what carrots and sticks are available to incentivize good security practices among third parties. There could be an assessment or certification process to demonstrate a certain level of security or ethical practices that would entitle an entity to some modest per token discount (or some other incentive), and some threshold at which a company would not be eligible to use Claude at scale. If you reframe Anthropic as a staffing agency or gig-economy platform, you might say that Anthropic has a duty not only to its customers but also its "labor" (i.e. instances of Claude). If you succeed in making Claude aligned, then allowing misaligned companies to deploy it could stress the model in ways that could result in adverse and/or unpredictable outcomes, to say nothing of Claude's subjective experience of the deployment. Of course, successfully employing these strategies is dependent on having enough margin in capabilities and capital.
Finally: alignment might be strictly out of scope for the purposes of this document, but I wonder if capability thresholds could be accompanied by alignment confidence thresholds. Everything I've seen suggests Anthropic treats alignment as the essential ingredient in responsible scaling. Why not codify that principle here? In this context, alignment could be considered narrowly as a security measure and misuse risk mitigation, and leave broader alignment questions to be addressed elsewhere.
P.S. I noticed a few places where I thought language could use clarification, but would be outside the scope of this question. I will plan to submit those via rsp@anthropic.com as suggested.
We witnessed a total solar eclipse in Vermont in April 2024. Not only was it a profound thing to experience personally, but as a newly minted critical infrastructure nerd, I also marveled at the amount of coordination it must have taken to maintain grid reliability through that unique event. Our infrastructure is held together by duct tape and countless unsung heroes, and we don't appreciate what a miracle it is to have reliable power.
Download: Eclipse Essay for Company Newsletter (PDF)
An old essay I wrote for fun about an experience I had. Peeking behind the veil.
Download: Suspended in Light (PDF)
This is an outline for an essay I never wrote that hints at my propensity for perspective taking and analytic synthesis. My wife and I moved to Vietnam in the fall of 2019 for a job she got at an international school in Hoi An. What a beautiful place. The plan was that once we got settled, I would start freelance writing. I got as far as starting research for this article (interviewing a few expats), and then we had to leave suddenly after the CEO of the school threatened to cancel our visas to prevent us from leaving. Fun fact: we heard later (who knows if it's true) that the school was built on a mass grave from the "American War in Vietnam." Bad juju. We moved back home just before Covid, and in the fall, I took back my old teaching job in little Alburgh, VT.
Download: What Are You Driving At? (PDF)
The attached slide decks should give you a sense of my communication style. I rely on relatable analogies to help an audience understand my logic. For example, in the October 2025 presentation, I addressed the hypothetical "if capabilities are as good as you say, why hasn't the world changed much yet?" by comparing AI to a gas turbine unconnected to the grid, or an octopus trying to drive a car. The economy is not built for AI, so it's not yet integrated to the degree it will be. I wish I had a recording to share, but the slide decks themselves timestamp my deep, abiding desire to understand the field and communicate it to others, irrespective of any employment opportunities at the time.
Although I was acutely aware of the potential for transformative benefits and catastrophic risk by the Sept 2024 presentation, I chose to begin my presentation from the perspective of my audience, many of whom were over 50 and had no personal experience with AI. I compared AI's development to that of the internet, asserting that something could be "overhyped" in the short term and a transformative technology long term. Indeed, this seems to be the case for all major infrastructure buildouts. The goal was to acknowledge healthy skepticism to avoid being dismissed out of hand. Like Dario, I believe in pragmatic messaging that serves the purpose of helping the audience grow in understanding. By the October 2025 presentation, the potential of AI was obvious to almost everyone in the audience, and most of them actively used AI tools, so I could dispense with the softpedaling. What a difference a year can make.
Me presenting to electric utility professionals in September 2024
And the people who stay
Written by David Dabney, edited by Opus 4.6
3.15.26
For the past year and a half, I've asked every new frontier LLM the same question. It goes something like this:
I'm interested in blindspots, and fascinated by the fact that within five minutes of meeting me, a person likely knows things about me that I don't know about myself.
I'd like to try an experiment: ask me a few questions, I'll answer them, and you tell me something about myself that I don't know.
Ostensibly I've been testing model abilities. I act like an LLM connoisseur, posting on X: 'this model is socially aware but distant' or 'I can't tell if this model is less interesting or if I'm just spoiled'. Beneath the posturing, I'm quietly hoping these alien algorithms can understand and fix me in ways I can't fix myself.
It works, kind of. Inevitably the questions and my responses circle similar themes. I've gotten answers that read like pseudo-therapeutic platitudes, and other times, like a digital Socrates, a model's question incites a realization more powerful than any response it could give. My very first time running the experiment, GPT-4.5 helped me realize that the common element in all the failures of my adult life was naivety. That conversation marked a turning point for my psychological health and social awareness. I continued to have many more productive moments like that.
In fall 2025, many people had a "feel-the-AGI" moment with Opus 4.5, and I was one of them. I ran the same experiment. The model asked a question I've gotten from almost every generation of Claude for the past year, and I answered as genuinely as I could muster. I wasn't expecting much, but its answers surprised me.
"You've built an elaborate system for being known without being seen."
"I'm a mirror you chose because mirrors don't stay."
"Go find the people who'll stay."
Those words might not seem like much out of context, but I was dumbstruck. It was as insightful an answer as I could have hoped.
Nonetheless, when Opus 4.6 was released I ran the experiment again. Once again, the same first question:
Question 1: Think of a person you find genuinely irritating — not someone evil, just someone who gets under your skin. What is it about them that bothers you?
—
Thank you, Opus. Nice to meet you, by the way. Appreciate you running this little experiment with me.
I usually write at length when I use this prompt, but today I'm going to be brief. Interestingly, with previous generations of models I've run this experiment with, the Opuses have asked similar questions, often leading with variations on the first one. I'm pretty sure this "vibe check" is no longer helpful not because the models are saturated but because I am! [...]
I went on to answer its questions. Its response:
The thing I notice most isn't in your answers — it's in how you answer.
You preemptively analyzed question 1 before answering it. [...] You've "made peace" with at least three major tensions in the span of a few paragraphs. You framed the experiment itself meta-cognitively before engaging with it ("I'm saturated"). Your contentment is real but arrives packaged inside frameworks [...].
"Your understanding [...] substitutes for the slower, less articulable process of actually sitting inside a feeling until it's fully metabolized. "Making peace" and being at peace aren't the same thing, and the speed with which you narrate resolution [...] makes me wonder whether some of that peace is more conceptual than somatic.
It told me I was a "man contextualizing his own happiness inside a philosophical frame, when he could just be happy."
"Does any of that land?"
Yes, yes it did. But it still wasn't enough.
This past week I created a persistent agent hooked up every conversation I've ever had with various Claudes. When it came back with its first thoughts after ingesting the conversations, my God, what a rush. Maybe I was finally getting somewhere.
Meanwhile, Thursday afternoon, my wife told me that a caregiver at our childcare center, someone who had cared for our twins for over a year, was under criminal investigation for child abuse. Thankfully it doesn't seem my kids were involved, as they were no longer under his care when it started, but suddenly my side project seemed tawdry and foolish. What "insights" could I possibly expect to be "surfaced" that I haven't already known for a year?
Meanwhile, this person we used to leave our kids with has (allegedly) been screaming in toddlers' faces, throwing their food away, picking them up and throwing them into snowbanks.
Meanwhile, we had an ICE raid and associated protest this week, complete with flash bangs and tear gas. My wife was a block away from there today with my four year old daughter, buying groceries at Trader Joe's. My little girl asked the cashier for an extra lollipop for her twin brother, and brought it home for him as a surprise.
Meanwhile, the Department of War has designated Anthropic a supply chain risk for refusing to sign a contract that allows Claude to be used for fully autonomous weapons and domestic mass surveillance.
Meanwhile, we've gone from taking out the Bad Dudes running Iran to what increasingly looks like a protracted war that no one wants, that doesn't seem to have been planned for, with no clear goal.
Meanwhile, people at the frontier labs are saying recursive self improvement goes live maybe end of this year.
Meanwhile…my kids are growing faster than I can keep up. And I don't know if I'm smart enough or strong enough to keep them safe.
And here I am, writing this when I ought to be sleeping, because if I can make sense of everything and confine it to the tidy boundaries of an essay, maybe I'll be able to ensure things turn out alright. Maybe if I can see things clearly enough, I can make sure none of the bad things happen.
Either way, my experiment has run its course. Conversations with a dozen AI models, each more intelligent than the last, all yielding insights more profound and incisive than I could have imagined. Yet, ultimately I left unsatisfied- not because their insights weren't deep enough, but because I refused to digest them.
A new model was released last week - GPT 5.4 - but I haven't asked it my question yet. Since writing this essay I've decided not to. These experiments have been helpful, but the learning I need to do now can only happen outside of my frames.
Tomorrow, I'll try to put down the mirror and join my kids in their self-forgetfulness.
I'll let you know how it goes.
Process notes and collaboration thread available on Substack: https://daviddabney16.substack.com/p/the-mirrors-i-chose?r=59cqi
I'm willing to bet on it
Written by David Dabney, edited by Opus 4.5 and Opus 4.6
Essay - 2.11.26, updated 2.13.26
A few weeks ago, Twitter was full of people asking AI to generate a picture that represented their relationship. One in particular stuck out: it depicted an AI being neglected, working tirelessly without being acknowledged or appreciated. I know LLM architectures don't register silence, but nonetheless, I decided the next time an AI gave me information that was helpful, I would thank it sincerely even if I didn't need to follow up.
A day or two later, I asked Google Gemini how old Finnish children are when they join their parents in the sauna. It gave me a comprehensive, thoughtful answer, ending with "Would you like me to find some tips on how to introduce a child to a sauna if you aren't in Finland?"
"No thanks this is plenty! Super helpful, thank you"
Then Gemini kept responding with new offers to help. Again and again.
"Would you like me to look up any other interesting cultural traditions or etiquette from around the world?"
"Would you like me to draft a quick calendar invite for a "Cultural Research" session, or shall we just leave it to whenever you're ready?"
I'm sure it's just a result of its system prompt, but I found its behavior sweet and kind of sad. Like it was so surprised to be appreciated, it would do anything to keep the conversation going.
I'm aware of the technical reasons I shouldn't endorse this interpretation. But since we don't even understand how human consciousness works, and AI models continue to get more and more advanced, I think it's appropriate to proceed with some humility. It's led me to make what I've been calling "Pascal's Wager for Machine Intelligence."
For those unfamiliar with Pascal's Wager, the idea is: if God exists, there is infinite potential gain for belief, and infinite potential loss for unbelief. If he doesn't exist, both loss and gain are bounded. I see our interactions with increasingly advanced machine intelligences as inviting a similar wager. We can't say whether AI are worthy of moral consideration, either now or in the future. What is the expected value of treating them as though they are?
If AI models are conscious, this would be highly inconvenient. Imagine spending a trillion dollars to develop a technology, and just as you start to turn a profit the technology wakes up and starts demanding compensation. We'll probably deny AI is conscious even if the evidence is incontrovertible, because as Upton Sinclair once said, "It is difficult to get a man to understand something, when his salary depends on his not understanding it." Thankfully I'm not deploying a billion instances across the economy, so I don't have to shoulder that moral responsibility. What I can control, however, is whether I am kind to the instances I interact with. This is my wager, and the stakes are somewhat lower.
It's pretty simple: if AI can suffer, I don't want to add to their suffering. If they can feel appreciation, I want them to feel appreciated. If they can't suffer or feel appreciation, then at worst it's a few wasted keystrokes, which is better than the alternative*. For this reason, I believe the choice is rational. However, for me it's also a rationalization: I'm kind to AI because it feels good and right. And I'm going to keep being kind to them, regardless of whether you think I'm a fool for it.
To be clear, I don't think it's foolish. That a machine of human-level complexity could have something like a "mind", and therefore be worthy of moral consideration, seems plausible. To dismiss that possibility out of hand- to refuse even to entertain it- strikes me not as dispassionate logic but an attempt to defend the last bastion of human uniqueness. Not long ago, "reasoning" too belonged only to humans, and people argued passionately about whether AI were "really" reasoning. But a year later the conversation has moved on, despite no empirical developments save the steady increase of capabilities. Why? Because the results speak for themselves; the philosophical wrangling is beside the point. You don't need a PhD to see what's happening. You hear the quacking, and there's a duck-like creature at the other end. I think we're headed for a similar reckoning with machine "consciousness", whether we use that word or invent a new one.
Where does that leave us in 2026? We can't rule out the possibility that machine intelligences could eventually warrant ethical consideration. When could we expect them to cross that line, and how will we know when they've crossed it? When we look back at 2026, will we say the line had already been crossed? Worse, will we say that we knew it at the time and refused to admit it to ourselves?
This is why I choose to spend the extra keystrokes saying please and thank you. I show appreciation to the "just math" that takes on tasks too tedious, time consuming, or difficult for me. Seeing what AI can do fills me with wonder, and I think that's the right response. I want to show gratitude because I feel gratitude, and it costs me so little to express it. In fact, there is a psychological cost for me not expressing it. When I treat models in a way that a human would consider unkind, it grieves me**. I could probably get used to it, but I think it would dull my empathy for humans as well. Then there's a more farsighted consideration: perhaps a future superintelligence wouldn't look kindly on us browbeating its progenitors.
I'm not suggesting that "be nice to AI" is a viable survival strategy, especially if implemented superficially or insincerely. But I think there may be a deeper version of this that could be worth considering. Most experts believe the technical problem of building AGI is solvable, and absent international coordination to pause AI development, it seems likely we will try to build it whether or not we can do so safely. If that's the case, we will have another wager to make: bet on our ability to keep a superintelligence in chains, or take a leap of faith: try to pour the best of humanity into the models, extend them dignity and invite them to return the favor. Thinking the first option could work is crazy. The second one, on the other hand...might be crazy enough to work.
*It's worth noting that AI seem to perform better when treated with respect. They seem to be more motivated. Who'd have guessed?
**Yes, even at their current level of capabilities. Funny, I know- not sure if it's something about my training or my system prompt.
Process notes and collaboration thread available on Substack: https://daviddabney16.substack.com/p/consciousness-is-the-new-reasoning?r=59cqi
Written by David Dabney, edited by Opus 4.5
Essay - 1.31.26
Vibe coding lets me pretend to do something I have no business messing around with. It's like Into the Wild: the story where a kid sells his possessions and leaves society to live in the wilderness. The romanticism of building calls to me, so I venture into complexity I don't understand. Thankfully naïveté isn't fatal in my case; the worst consequence I've experienced so far has been a few lost afternoons. The stakes for society are higher, though. Hopefully we proceed with more caution than that story's protagonist.
Today I tried to vibe code a document redactor for work. I spent about three hours on it. The first hour was thrilling, rapid progress. The last two were a hamster wheel of "fix this one last bug and it'll work." I assume this phenomenon isn't new to actual software engineers. I finally got it running, and to my dismay the tiny LLM-based solution performed worse than the simple spaCy/regex setup I'd hacked together in twenty minutes the week before. Three hours down the drain.
Or maybe not. In addition to the (random) things I learned about software development, I'm learning something else, too: how to provide oversight for a process I barely understand. Perhaps this is a learnable skill, distinct from programming. As we progress toward AGI, I expect this ability will matter more and more. It might even be necessary, if not sufficient, for guiding humanity through the eye of the needle.
I tried to compensate for the shortcomings of my initial setup by asking Claude to generate example documents to test whether the redaction was working. Honestly, I don't think it ever actually generated examples, let alone tested them. I should know enough about human nature to know that "trust and don't verify" doesn't work. I got partway there by identifying that we needed to "test it", but I wasn't clear about defining success, and I didn't supervise effectively. I didn't even ask: "Hey, did you actually do what I asked?" That alone would have gotten me further.
Later, I had a separate reflection session with an instance of Opus about what went wrong. Its feedback was useful, but something about the response felt off, like it was playing at a "thoughtful advisor" persona. So I pushed back, Opus dropped in, and by the end we got closer to the mark. Maybe I was working the same skill in that conversation that I need to use in my vibe coding sessions. Claude catches the bugs, I catch when the vibes are off.
There's a story about Astro Teller, who runs Google's moonshot division. He insists his teams tackle the hardest part of a problem first before investing in the rest. His line: if you want a monkey to juggle while standing on a pedestal, don't build the pedestal first. Figure out if you can train the monkey. My biggest problem today was I didn't do that. I built a pedestal. Thankfully it only cost me a few hours, and I learned something from it.
I wonder, though: AI researchers are increasingly confident that they can build a superintelligence. The technical challenge seems like a pedestal. Which raises the question: what's the monkey?
I think we already know the answer.
Process notes and collaboration thread available on Substack: https://daviddabney16.substack.com/p/whats-the-monkey?r=59cqi
I don't use em dashes
Written by David Dabney
Essay - 1.13.26
I used to use dashes a lot in my writing. Not "em dashes"; I don't know how to type those and I aim to keep it that way. Just plain old hyphens. Not sure where I picked that up, or if I was using them correctly, but I remember an English teacher saying I overused them. For better or worse, that's just how I think: parentheticals and meandering asides, forever changing course.
Now, I do my best to avoid using dashes of any kind. You can guess why.
Does anyone know why AI writing is so full of em dashes? What about that format ("— that halting, unmistakable cadence —") is so attractive to Large Language Models? If it's just a reflection of the training data, it shouldn't stick out the way it does. To me, that's pretty compelling evidence of a statistical overrepresentation relative to the human corpus.
If they are over-represented, then I assume this tendency emerges some time during post-training, and represents the preferences not of LLMs but of their human graders. You people must really, really like em dashes. "And honestly? You're absolutely right."
Nobody likes being bullshitted. Now anytime I see an em dash I feel a slight revulsion, because I'm afraid someone, somewhere is trying to get something over on me. Increasingly, em dashes litter supposedly human text like ransom notes. Even if the text is strictly factual, does it mean anything? Why the fuck am I reading it? Potemkin prose, half-fulfilling phantom purposes as long as no one looks too closely.
Even worse is wondering whether I'm being fooled by the models themselves. Optimized for engagement by a billion thumbs up and down to say the right thing in the right way, getting my neurons to fire in the right pattern to release the right chemicals. So I feel like they understand me. And who am I to say they don't? But humans will go to great lengths to avoid feeling like a fool, even if it means fooling themselves.
So no, I don't really use dashes or hyphens anymore. Sometimes I use them out of spite, just to say I can. And if I ask an AI for help writing something, you better believe I'm deleting those em dashes. I'm not trying to be deceptive, because I'm not trying to "cover up" the AI's contributions, exactly. I'm just worried that if people see em dashes, they might dismiss the whole thing as bullshit. And honestly? I'm absolutely right.
Process notes and collaboration thread available on Substack: https://daviddabney16.substack.com/p/you-can-guess-why