I guess this shows just how old I am because I loved every single one of the “Oscar-bait” movies in this list (I do agree on TCM though)…

When I was a kid, I would watch Turner Classic Movies and try to appreciate films from the 1940s, only to find the exercise strangely difficult. I could admire them—in theory—but I struggled to experience these stories the way their original audiences did.

I feel similarly about many Oscar-bait dramas of the 1990s, including but not limited to: Chocolat, American Beauty, Shakespeare in Love, Scent of a Woman, The English Patient, and Life Is Beautiful. I simply don’t understand what contemporary audiences saw in these films.

That aside, as usual Daniel makes an interesting larger point, about why we don’t see as many dramas as we used to:

Action and horror, meanwhile, have visceral elements that translate across generations: big dinosaurs, jump scares, campy set pieces, and other straightforward pleasures. The first ten minutes of Raiders of the Lost Ark are timeless and feature almost no dialogue.

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?”

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).

Matt Shumer’s Something Big Is Happening has made the rounds over the last couple of weeks, but just in case you haven’t seen it, I think it’s very much worth reading. He’s an AI startup founder writing for the non-technical people in his life:

AI isn’t replacing one specific skill. It’s a general substitute for cognitive work. It gets better at everything simultaneously. When factories automated, a displaced worker could retrain as an office worker. When the internet disrupted retail, workers moved into logistics or services. But AI doesn’t leave a convenient gap to move into. Whatever you retrain for, it’s improving at that too.

Previous waves of automation always left somewhere to go. The uncomfortable implication here is that the escape routes are closing as fast as they open.

There are too many quotes worth commenting on, but this observation about what we tell our kids feels important:

The people most likely to thrive are the ones who are deeply curious, adaptable, and effective at using AI to do things they actually care about. Teach your kids to be builders and learners, not to optimize for a career path that might not exist by the time they graduate.

Predictions about the pace of change tend to be simultaneously too aggressive and too conservative in ways that are hard to anticipate. But the direction feels right, and the practical advice is sound: use the tools seriously, don’t assume they can’t do something just because it seems too hard, and spend your energy adapting rather than debating whether this is real.

Isabel Woodford has a research-heavy essay in The Atlantic about why dads and daughters crave closeness but struggle to find it. 28% of American women are estranged from their father, and even where relationships are intact, they tend to be thinner—more transactional, less emotionally honest—than daughters want.

At the root of the modern father-daughter divide seems to be a mismatch in expectations. Fathers, generally speaking, have for generations been less involved than mothers in their kids’ (and especially their daughters‘) lives. But lots of children today expect more: more emotional support and more egalitarian treatment. Many fathers, though, appear to have struggled to adjust to their daughters’ expectations. The result isn’t a relationship that has suddenly ruptured so much as one that has failed to fully adapt.

And the psychological explanation that cuts deepest:

“What generates closeness is another person’s vulnerability,” Coleman explained, and dads may not be ready for that.

Daughters aren’t asking for grand gestures or dramatic change—they’re asking for their fathers to show up emotionally. Which turns out to be hard for a lot of men who were raised to see that kind of openness as weakness.

Daniel Parris wrote a statistical analysis of when rock became “classic rock”, and it’s not the story I expected.

He assumed the genre emerged organically from music nerds debating on message boards and in the pages of Rolling Stone. Instead:

What I found was a deliberate realignment engineered by music executives chasing an ephemeral advertising demographic. Like many entertainment industry decisions, it was a small (mostly male) group of executives quietly deciding the future of popular culture behind closed doors.

The data shows two concentrated periods when stations rapidly switched to classic rock: the mid–1980s (to capture aging Boomers entering their peak earning years) and the mid–1990s (after the Telecommunications Act enabled Clear Channel to buy up local stations and prioritize low-risk, high-profit formats).

The kicker is that this rebrand was designed around economic incentives that have since eroded. Radio isn’t the default distribution channel anymore. On streaming, music can just exist without being packaged for a hyper-valuable consumer cohort.

Another reminder that so much of what feels like culture is really just business decisions made in conference rooms.

I’m late to this one, but I like Liz Pelly’s take on Spotify Wrapped. It’s not just about music—it’s about what happens when we let corporations automate our memories:

Spotify Wrapped now feels like just another example of something personal and precious that is being automated away from us; another example of a supposedly unbearable task of thinking and writing being “offloaded” in order to make life more frictionless.

The post is essentially about friction—and why we need it. She argues that working through the process of remembering what mattered to us and thinking critically about our year is what keeps us sharp and curious. When we just accept what a streaming service tells us about our taste, we’re not just outsourcing a task. We’re losing our own sense of what connected with us and why.

It encourages music fans to believe that the records they streamed the most must be the ones they liked the most, which is surely not always the case.

Her suggestion is straightforward: write your own list. It doesn’t have to be polished—a notes app screenshot, a handwritten list, whatever. Just something that came from you, not from an algorithm optimizing for engagement metrics.

Natalia Quintero wrote about what she’s learned from talking to more than 100 companies about AI implementation. This part about the problem with early adopters and isolated workflows stood out:

AI doesn’t spread like other software. Think about Asana. If one person decides to organize their team’s tasks there, everyone benefits automatically because the work is more organized, and someone on the team has taken responsibility for that organization. You don’t need to learn the tool to get value from your colleague using it. AI doesn’t work that way. If you develop workflows around how you work, that value doesn’t automatically translate to the rest of the company. Your prompts, your GPTs, your automations—they’re built around your context, your processes, and your way of thinking. They don’t transfer.

That’s the adoption problem in a nutshell. A power user’s AI setup is like their personal note-taking system—valuable for them but not portable. It explains why enterprise rollouts don’t work the way everyone expects.

The recruiting firm example is good: they trained 10 champions who built tools their peers wanted to use. One person automated scheduling coordination (saving 2–10 hours per task), and suddenly 30 others got curious. Peer-to-peer beats top-down mandates.

(If you’re curious about my setup, I wrote about it here)