I’ve been reading the work of L.M. Sacasas for a very long time, certainly since before he moved his writing to “a Substack.” He is a modern philosopher who I often agree with, and also sometimes vehemently disagree with—but never in a way that made me kick him out of my RSS feed.

I say all this because I haven’t linked to him in a while, and when I say “I think you should read this article by a philosophy dude” I don’t want you to dismiss it out of hand. In Do Not Resign From Life he takes on what we now all know as “the AI revolution”, and argues that even though there is plenty to complain about, one thing it shouldn’t do is make us think that we don’t matter as humans any more.

I don’t want to say much more about this essay, I just really hope you decide to read it. If you’re intrigued enough, stop here and click the link. If you’re not there yet, here’s a taste of the argument:

I will set aside for a moment the question of whether machines, LLMs specifically, can think or reason or use language in a manner that corresponds to the human use of language, etc. But let us grant for argument’s sake that they can. They can certainly generate passable simulations of such things. But why should this mean that I ought not to think for myself and with others? Why should I cease from inhabiting the playground of language because a machine can pretend to play in it as well? Why should I abandon the exercise of judgment or the pursuit of knowledge? We must pursue these things not because the dignity of our humanity is on the line, but because our joy is.

The machine cannot make us yield our ground. It is true that other humans can turn the machine against us, but that is a different problem. Here, I simply want to encourage us not to abandon those activities that bring us purpose, meaning, and delight, which are often the very activities that also bring us together.

Hard yes to Camille Fournier’s Guidelines for Respectful Use of AI, especially this one:

Don’t ask someone to read/review what you haven’t read or reviewed yourself.

This is one of the most common frustrations I hear amongst people working on AI-heavy teams. Whether it’s code that the owner didn’t really bother to understand before submitting for review, or documents that they generated and didn’t bother to read, too often people try to steal productivity from their colleagues by streamlining their production of work while asking their colleagues to do all of the quality control themselves. […]

It’s easy to get into a loop where you ask the AI some questions, skim the answers, output a document and send it to others. I’m guilty of this myself! But what makes sense when you’re skimming one answer at a time may not make for a good overall document, and there is a big difference between answering individual questions and writing for a human reader. In particular, the context that you have in your own head as you are talking to the AI may not come out at all in the document; if you don’t bother to read it thoroughly before sending it out, you won’t catch the gap in framing.

In a post about slowing down our agent use deliberately to increase quality and understanding Vicki links to Nolan Lawson’s Using AI to write better code more slowly:

If you’re the kind of developer who uses agents to write multi-hundred-line PRs that you barely understand yourself, I’d invite you to slow down a bit and try this other, slower style of “vibe coding.” Ask an agent how your PR works and how it might fail. Have it write Markdown docs with Mermaid charts if necessary. Use Matt Pocock’s /grill-me skill until you understand the entire PR front-to-back.

You might not be more “productive” in terms of raw lines of code. You might burn a ton of tokens just to find out that your entire plan was wrongheaded from the start. But I find this style of coding to be a more super-powered version of the kind of programming I was already trying to do before LLMs: careful, methodical, quality-obsessed, focused on making things better for the next coder.

So take a deep breath, slow down, try this technique, and see if you don’t enjoy writing better code more slowly.

Vicki concludes:

All of these negate the supposed speed up effects of LLM-generated code in the short-term by adding friction, and yet, in the longer term, make me better at using the tool, because they solidify my own foundation instead of the foundation models’.

We should be more tired than the model.

We should be more tired than the model. When I saw the post in my feed I thought I misread the title (or maybe it was a typo). But after reading it I realized that’s already where I’ve been heading organically myself. I went through my “look how fast I can go weeeeee!” era pretty quickly. While it was fun (check out all these side projects!) it was not just exhausting, I also found myself understanding less and less of what I was doing (which sucks all the fun out of the work anyway).

So I’ve been slowing down as well. Reading and editing even more than before. Challenging the agent for longer. Taking the time to close loops to update skills/context documents before moving on to the next thing. Never skipping the “let’s write a design doc and implementation plan together” step.

I do think I am more tired than the model these days. But I also understand and learn more, which not only improves the quality of the output now, but also makes it better tomorrow. I think the speed trade-off is worth it.

Derek Thompson digs into the current news cycle about out-of-control AI token spend, and makes the case that since we’re literally only 5 months into the ✨agentic era✨, we need to look at it in the context of how technology cycles usually work:

Rather than see the agent backlash as a clear sign that AI is a scam, or that it is doomed, it might make more sense to see this development in the context of a normal technological adoption curve. […]

As SemiAnalysis’s Doug O’Laughlin told me in an interview last week, every new technology requires an extended period of trial and error, as organizations toggle between (a) not enough experimentation or spending, followed by (b) too much experimentation and spending, followed by (c) too dramatic a pullback, followed by (d) the repetition of steps (a) through (c), until firms figure out a long-term balance between labor spending and tech spending. Whether AI skeptics like [cognitive scientist Gary] Marcus are right that the bubble is about to pop depends entirely on a question that, as of today, nobody can definitively answer: Is the bill worth it?

Well here’s a disturbing point I somehow hadn’t thought about before. Are we training AI, or is it training us?

When I sat down to write this article, in which, to be clear, I did not use AI, I found myself writing the following sentence: “It’s not just in places we’re conditioned to see AI—Google AI overviews, LinkedIn influencer posts, and Facebook feeds—I’ve started seeing AI…” I stopped typing, freaked out, and deleted the sentence. Have I always written this way? I honestly don’t know.

This negative parallelism—“it’s not just x, it’s y” is maybe the most infamous AI writing-ism there is. It is something that is regularly called out as being obviously AI, and is the formation in the sentence Mamdani wrote that Spero called out. But I didn’t use AI. Did I use that construction because I’ve been immersed on an internet full of generic AI writing on every platform all day everyday for years? Or did I just happen to think that was the best way to phrase it at the time?

Related, I like Kai’s take on why we feel so… duped when we see clearly AI-generated text:

I’m not categorically against using AI to help out with tedious work. But there’s a difference between using a tool to say something you actually mean, and using a tool to manufacture the appearance of meaning something.

I know it’s a bit naïve to appeal to common decency when the same technology is busy guiding weapons systems, but please don’t outsource sincerity. Don’t pretend to care about someone or something just to get their attention.

The damage isn’t just annoyance. It’s suspicion that gets attached to genuine messages. Emails I would have read warmly now carry an asterisk. Did a person write this? Does this person actually care about my work, or is this just another prompt in the dark?

I say this sincerely because I am a big fan of Buttondown and how Justin runs the business—this couldn’t have happened to a nicer guy:

Our month-over-month growth rate in Q1 2026 was double our growth rate in Q4 2025. Buttondown has, roughly, grown a little less than 2x every year of its existence; this — its eighth year — is poised to shatter that, if trends hold.

Almost all of that incremental growth, meaning the growth in addition to our historical trend, I attribute to LLMs. We ask people when they sign up what brought them here, and an answer that went from surprising to banal to overwhelming over the course of Q1 was: an LLM. Users of all stripes cite an LLM as the reason that they ended up at Buttondown’s front door.

You should click through for the whole post because he explains why he thinks this happened:

People have asked why I think we have been the beneficiary of this genre of growth. There is one fairly interesting reason: we have accidentally built a very LLM-friendly business in this space.

I’ve always been a big believer in API-first design, and this feels like an almost accidental enormous additional benefit to that approach. Anyway, all that to say… my newsletter is on Buttondown, and yours should be too.

In a practical example of brain fry, Petra Wille recalls some of her personal experience during coaching:

The product leaders and CPOs I coach tell me their people are completely fried before lunch—after a morning of generating content and reviewing outputs in Claude, Gemini, and ChatGPT, they’re just done. Adapting to this new type of work doesn’t make them more productive because they’re out of energy and brainpower by noon.

So conversations about how we actually work—what a sustainable rhythm looks like for humans in this new setup—still needs to happen.

This has become a pretty common complaint/concern among people I talk to, and it gets me too. I’ve been sitting on posting this link because I wanted to include some kind of proposal but… I got nothing. Just agreement with Petra that we really really need to figure out how to work in this new world in a way that avoids mass burnout.

Yes, it’s a clickbaity title, but if you read this as an essay about what to care about at work, it has some good reminders like this:

“Care less” is directionally right, but let’s get more specific. The real shift is learning to place your care deliberately — to get good at telling the difference between what’s strategically important and what’s just noisy. A lot of what happens inside companies is frustrating without being important. Reacting to a messy call that you personally wouldn’t have made as if it’s a strategic risk is what drains you. So is holding on to every detail as if it’s existential. Not everything deserves to be treated with equal importance. A gut check that helps: Will this matter in a year? If not, it probably doesn’t deserve much energy now. What’s the worst-case scenario? Often, it’s not that bad.