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.

Braintrust makes a good case (apologies for the X.com link…) for rethinking how PMs work on AI products: the eval replaces the PRD.

An eval is a structured, repeatable test that answers one question. Does my AI system do the right thing? You define a set of inputs along with expected outputs, run them through your AI system, and score the results using algorithms or AI judges.

The eval becomes both the spec and the acceptance criteria. The directive to engineering:

“Here is the eval. Make this number go up.”

That’s very different to how most teams work today, but I can definitely see the industry moving this way. Product usage generates signals, observability captures them, and evals turn them into improvement targets. The PM’s job is to define what “good” looks like in code and curate the data that reveals what “bad” looks like.

The PM skills that transfer are the same ones that always mattered — discovering needs and opportunities, and making judgment calls about what to build for business value. The difference is that instead of a document that describes the intent, you have a test suite that encodes it.

Ben Roy explains why prompting an LLM to write an essay misunderstands what writing actually is:

People fundamentally can’t prompt good essays into existence because writing is not a top-down exercise of applying knowledge you have upfront and asking an LLM to create something. AI agents also can’t create good essays for the same reason. Even though their step-by-step reasoning is more complex and iterative than human prompting, a chain of thought is still trying to accomplish a predefined goal. By contrast, real writing is bottom up. You don’t know what you want to say in advance. It’s a process of discovery where you start with a set of half-baked ideas and work with them in non-linear ways to find out what you really think.

I will continue to argue that for general business writing LLMs are fantastic if they are given the right context and guidance, and that it can save hours of work (with high quality results). But all my experiments with using LLMs for creative writing has so far fallen flat. Maybe—likely?—that will change within the next few months. But for now, the brain work this kind of writing requires remains. Not a bad thing imo.

Derek Thompson goes into the history of the “flow” concept, and how tech and entertainment companies learned to simulate it without any of the substance psychologist Mihaly Csikszentmihalyi originally had in mind:

Algorithmic flow is flow without achievement, flow without challenge, flow without even volition… To be lost in the lazy river of algorithmic media is to be lost the current of life without a mind. Zombie flow.

Ten years ago the question was how to get into flow more often. Now it might be how to get out of the fake version fast enough to remember what the real one felt like.

Amol Avasare, Anthropic’s Head of Growth, said on Lenny’s Podcast that maybe PM jobs are not going to shrink as much as we may have thought…

Rather than immediately replacing PMs, AI is currently increasing engineering leverage the fastest, which creates new pressure on PMs and designers. In larger organizations, that may actually increase the value of PMs who can guide priorities, manage alignment, and sharpen decision-making—especially as engineers take on more “mini-PM” responsibilities.

Lalit Maganti writes about building a SQLite parser with AI — a project he’d been putting off for eight years, finished in three months. His comparison of AI coding to slot machines is uncomfortably familiar:

I found myself up late at night wanting to do “just one more prompt,” constantly trying AI just to see what would happen even when I knew it probably wouldn’t work. The sunk cost fallacy kicked in too: I’d keep at it even in tasks it was clearly ill-suited for, telling myself “maybe if I phrase it differently this time.”

Also, I agree that this is still true today, but I’m not convinced it will remain true beyond 2026:

AI is an incredible force multiplier for implementation, but it’s a dangerous substitute for design.

Anil Dash has a long essay on the state of the open web and not all of it rings true for me, but buried in the opening is a wonderful definition of what the open web actually is:

The open web is something extraordinary: anybody can use whatever tools they have, to create content following publicly documented specifications, published using completely free and open platforms, and then share that work with anyone, anywhere in the world, without asking for permission from anyone. Think about how radical that is.

It does feel like if the web got invented in 2026, it would not have been left as an open technology for long (see also AI and how much open source models are lagging).