Lorin Hochstein writes about generative AI in the context of incident reports, but the points are more broadly applicable. I have seen a big wave of “don’t let AI do your thinking for you” posts recently¹, so I think lots of folks are pulling back a little bit on the “just let AI do everything” rhetoric (a good thing in my opinion!). As to why Lorin isn’t a fan:

In my view, LLM-generated incident write-ups are more dangerous than using LLM for coding or for AI SRE style tasks. For coding tasks, there’s always a testing step to check that the code exhibits the desired behavior, even if nobody looks at the code itself for meaningful details. For AI SRE tasks, either the LLM output helps you resolve the incident, or it doesn’t. In both cases, Nature is the ultimate arbiter of the LLM output. But incident write-ups aren’t like that. The consequences of a poor report aren’t immediately apparent the way incorrect code or an incorrect operational diagnosis are in the moment. Instead, we get incident reports that have the superficially correct form, but are actually incorrect, with no obvious test for correctness.

Footnotes

1. For examples see No One Else Can Speak the Words on Your Lips, Guidelines for Respectful Use of AI, Writing Is Fundamental to How We Think, and I know you didn’t write this. ↩

While I’m side-questing into health stuff I might as well link this one that I’ve been sitting on as well. Gavin Francis writes about mental health diagnoses from the perspective of a GP. This one is likely even more controversial than the “enhanced self” post from earlier, but also worth the time to get another perspective¹:

The subject is important, because according to modern psychiatric definitions, the 21st century is seeing an epidemic of mental illness. The line between health and ill-health of the mind has never been more blurred. A survey in 2019 found that two-thirds of young people in the UK felt they have had a mental disorder. We are broadening the criteria for what counts as illness at the same time as lowering the thresholds for diagnosis. This is not a bad thing if it helps us feel better, but evidence is gathering that as a society it may be making us feel worse.

And if this quote doesn’t get you to click through, nothing will…

We have developed a tendency to categorise mild to moderate mental and emotional distress as a necessarily clinical problem rather than an integral part of being human – a tendency that is new in our own culture, and not widely shared with others. Psychiatrists who work across different cultures point out that, in many non-western societies, low mood, anxiety and delusional states are seen more as spiritual, relational or religious problems – not psychiatric ones. By making sense of states of mind through terms that are embedded in community and tradition, they may even have more success at incorporating our crises of mind into the stories of our lives.

My wife is a therapist and I see daily the impact of the amazing work she does with clients with complex trauma. One of the many things I learned from her is this idea in Internal Family Systems that there are “No Bad Parts” in us. These feelings of low mood, anxiety, etc. are not meant to be ignored or eliminated. We are meant to understand why they are there, and learn and grow through that understanding.

That is easier said than done, of course, and where my opinion diverges from Francis is that I think it is a good thing that this generation has more/better language to talk about mental health than we (meaning Gen X) did when we were growing up. I don’t doubt that over-diagnosis is a problem, but that’s kind of expected once we have the language to describe how we feel. I trust we will find our balance, and ultimately find that this was a net positive development.

Footnotes

1. Completely unrelated side note… I wrote “worth sitting with” here, and then immediately deleted it because that’s something AI would say. I continue to be fascinated with how it’s not just us who are influencing how AI writes, it’s the other way around too. ↩

I’ve been enjoying Derek Thompson’s newsletter lately. His latest is an essay on some of the unintended consequences of a health-obsessed society. This is the Internet so I’m sure everyone will find things to disagree with in a post like this, but it gave me lots of food for thought so I wanted to share. For instance:

Research by Sandra Weintraub of Northwestern University has found that “super-agers” (individuals over 80 with the cognitive function of people decades younger) shared little in common except for an unusually robust history of friendship and other social connections. A 2025 analysis of 500,000 participants in the UK Biobank reported that living with a partner and frequently visiting family had roughly the same relationship with longevity as exercise.

And the kicker:

Our fear of death motivates an all-consuming neuroticism about outrunning mortality, even when the price we pay is putting health optimization above everything else, including other people.

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.