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.

I sent this essay to my wife because doing self-owns is kind of my brand. It’s about husbands who can’t stop talking about AI, and despite how uncomfortable it made me, it’s not wrong and also wonderful writing. This is so good:

I should also say I didn’t bother speaking to any of the actual husbands for this story. I’m sick of hearing from the men of AI. So many of us are. They have podcasts and Senate hearings and magazine profiles and probably a group chat with the president. They’ve been talked to—and I can’t stress this enough—enough.

Melissa Perri chimes in on how AI is changing the product role, and makes the case for measuring PMs by decisions changed and outcomes shipped, not by tickets written and docs generated:

If you are a PM, stop measuring your productivity by how many tickets you wrote, how many pages of documentation you spun up, or how fast you closed the loop on the last sprint. That work is going to keep getting easier.

Measure your productivity by how often you changed a decision that mattered, how often you saw around a corner, how often a senior leader walked out of a room thinking differently because of something you said. How often your shipped features translate into real customer outcomes is what matters.

Everything I read is saying the same thing right now: judgment, customer understanding, and the ability to change a senior leader’s mind in a room are the skills that AI can’t touch. I’m not disagreeing necessarily, but I do think that narrative is missing a big new skill that is needed. I wrote about this in What actually changed about being a PM:

I was talking to my wife the other day about what I’m doing, and she asked the obvious question: “Why are you automating your job away?” My answer: the people who automate their own jobs away are the ones who become more valuable, because the craft is now in orchestration — setting up the layers so the AI does the right thing.

I also continue to think about this quote from Org Design in the Age of AI and how the focus is shifting from “information movers” to builders:

The old PM spent most of their energy making ideas legible to other people. The new PM validates directly — prototyping, running data analyses, generating first-pass implementations. […] The managers who thrive will be the ones whose real contribution was always judgment, coaching, and navigating ambiguity — not routing information.

This is characteristically dry press release language, but the stats are interesting:

Deezer, the global music experiences platform, is now receiving almost 75,000 AI-generated tracks per day, representing roughly 44% of the daily uploads. This amounts to more than 2 Million AI-generated tracks uploaded per month. Thanks to Deezer’s industry unique measures, consumption of AI-generated music on the platform is still very low, between 1-3% of the total streams. In addition, a majority (85%) of these streams are detected as fraudulent and are demonetized by Deezer.

I’m simultaneously surprised (but not, because grifters) that the amount of uploads is that high, and surprised (but not, because music lovers) that it’s generally a very unsuccessful way to make money. My continuing refrain will be that let’s use AI for the things that it’s good at, and leave the really important stuff (like art) to humans.

There is no doubt that this post was at least 80% written by AI but I’m not even super mad about it because that is just the way of the world now, and the summary it generated from how Uber works is actually legit interesting:

A prototype without a PRD can drift away from the problem the team intends to solve. A PRD without a prototype can remain abstract, leaving room for inconsistent interpretations. […] If going from idea to prototype is now fast and cheap, the PRD can no longer be the primary place where ideas are defined. Its value increasingly lies in capturing intent, tradeoffs, success metrics, and decisions.

The PRD as an artifact is in the spotlight right now in a way that I think is really healthy. Should it remain but change its JTBD? Should it be an eval instead? Who knows. Let’s figure it out together…

This post on org design really resonated.

Most companies today are using AI the way you’d use a faster horse — to make the existing structure run a little better. The companies that pull ahead will be the ones willing to ask a harder question: what would we build if we were designing this organization from scratch, today, knowing what AI can do?

We have to seriously rethink the SDLC, design it from scratch in the context of how our own organizations work. It’s not about a global “right” process any more. The question now becomes “How can the humans in our team, at our company, at this point in time, work best together to serve our customers?”