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.

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?

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.

This post is a fascinating look at how botnets actually work. I don’t want to spoil the takeaways so I’ll just quote this (but you should read the whole thing):

Your server isn’t special. Nobody is “targeting” it. Every IP address on the internet is being continuously probed by automated systems. Within seconds of exposing port 22, you will receive login attempts. This isn’t a question of “if” but “when” — and the answer to “when” is “immediately.”

Oh, this is very good. On the classic take that the core characteristic of outstanding engineers is “laziness”:

The problem is that LLMs inherently lack the virtue of laziness. Work costs nothing to an LLM. LLMs do not feel a need to optimize for their own (or anyone’s) future time, and will happily dump more and more onto a layercake of garbage. Left unchecked, LLMs will make systems larger, not better — appealing to perverse vanity metrics, perhaps, but at the cost of everything that matters. As such, LLMs highlight how essential our human laziness is: our finite time forces us to develop crisp abstractions in part because we don’t want to waste our (human!) time on the consequences of clunky ones.

The best engineering is always borne of constraints, and the constraint of our time places limits on the cognitive load of the system that we’re willing to accept. This is what drives us to make the system simpler, despite its essential complexity.

This is exactly why I practice Fear-Driven Development, and why everything I do in code includes multiple versions of asking Claude Code “do we need this?” and “is this adding bloat?”

I love this blog and try not to link to it too much, but this one about how fewer people listen to hip hop was especially great.

So, what’s filled the space hip-hop once dominated? A blend of new arrivals and familiar mainstays. Latin music—led by Bad Bunny—and Asian pop, powered by K-pop acts like BTS, have expanded their global footprint. At the same time, legacy formats are resurging: country is booming, driven in large part by Morgan Wallen, while the loosely defined “alternative” category continues to gain share across the charts.

I particularly love how he tries to avoid causation/correlation errors in his hypotheses. Like this one I hadn’t thought about:

Streaming adoption laggards: Hip-hop uniquely benefited from early streaming adopters in the 2010s. Younger listeners—who were predisposed to the genre—were among the first to embrace platforms like Spotify, giving hip-hop an outsized digital footprint. More recently, late adopters—like country fans, older cohorts, and global audiences—have rebalanced the charts, lifting genres like country and K-pop.