I wear it because the sentence is true and worth sharing. It has the secondary effect of advertising FIRE, the free speech organization that sells the shirt.

People regularly stop me to praise the shirt’s message. Sometimes they ask what FIRE is. The shirt never feels like a pitch.

It gives people language for something they already believe, or helps them see a problem more clearly, before it asks for anything.

Jason Fried and David Heinemeier Hansson have followed a similar approach with Basecamp, the collaboration software.

I read them first for their thinking about work: why meetings drag, why busyness isn’t the same as productivity, why calm is better than chaos. I internalized a lot of that before I ever thought about their software.

A shirt slogan, a blog about work. Each appeals to who we want to be. Neither asks for anything. The pitch, if it needs to come at all, comes later.

These stories are often treated as evidence that AI is overhyped and doomed to fail, maybe even that it should be discarded.

They’re better understood as evidence of learning. Of trial and error.

I’ve spent the past year figuring out how AI can help me. At various times, I’ve worked with AI to plan fitness regimens, shape marketing plans, and create elaborate automations on my phone to send morning briefings with the news and my to-do list.

Some of these uses I abandoned within a week. Some were almost right but required revision. And some, like using AI to help explore ideas, have worked from the start.

My misses were the only way to find out what worked for me. I couldn’t predict in advance where a tool would or wouldn’t fit my needs.

Likewise, we can’t derive from first principles how to use AI in customer service, or in management, or in law. The stakes might be larger for a business than for me, but the trial-and-error approach to learning is the same. Christopher Mims’ recent Wall Street Journal piece makes a complementary point: trial and error is how we learn where not to use AI.

We have to try things and watch what breaks.

None of these fireworks are official. Many of them are of dubious legality. All of them are joyfully celebrating the country‘s birthday.

The official fireworks on the Mall a few miles away are visible if you squint. They reflect a patriotism of pomp: Sousa marches, military flyovers, and elaborate displays planned months in advance.

The neighborhood fireworks are America. Informal. Self-organized. Ungoverned. Thumbing their noses at authority.

Here’s to another 250 years.

Venezuela does not have a national early warning system of its own, but people with Android phones received alerts from Google’s Earthquake Alerts system, which can pull data from more than two billion phones equipped with built-in accelerometers. The same sensor that detects rotation on the screen can also sense vibrations from seismic waves.

This is recombination fueling innovation. Nobody put accelerometers in phones to create an earthquake-warning system. Instead, Google engineers stitched together built-in sensors, location data, push notifications, and seismology know-how to create something entirely new: a distributed, planet-scale earthquake-detection network.

Of course, this tech is possible only because the devices we carry with us are capturing and sharing, in real time, both our locations and the slightest vibrations.

Their proposed fix isn’t a full return to the old office norm. It’s “doses of in-person time with one another,” supported by employers who are more intentional about restoring some of the connection remote work has eroded.

They cite firms redesigning coffee spaces, rewarding “the too-often-invisible work of connecting teams,” and — more dubiously — turning managers into mentor-matchmakers.

I’m sympathetic to part of this. In-person work facilitates the informal knowledge-sharing and trust-building that makes work better. And the best offices can enable collaboration — Pixar famously designed its campus to increase chance encounters across teams.

But Pixar’s goal wasn’t to make employees less lonely. It was to improve collaboration. The friendships that followed were incidental. That distinction matters.

Employers should care whether people communicate well, trust one another, mentor junior colleagues, and share useful information across teams.

But that doesn’t mean employers should solve loneliness.

Work can produce meaningful relationships. But those relationships are a byproduct of work, not what work is for.

Your employer isn’t your camp counselor.

This is the moment a brand has been working toward. After they spent money to acquire you, you’re in.

Now they need to reinforce to you that you made a great decision. Done right, this can make the next purchase feel like your idea.

Some brands get this right. Many don’t. Instead of welcoming you, they barrage you.

One recent shoe purchase triggered daily generic offers — pitching to me as if my last purchase never happened. Signing up for a streaming service earned me four emails on day one, and four more by noon on day two — almost begging me to watch something, anything.

It’s the difference between earning your loyalty and wearing you down until you open your wallet.

It makes intuitive sense. They know the work.

The problem is that doing the work and leading the team require very different kinds of knowledge.

Conflating one kind of knowledge for another is an easy mistake. And a costly one.

I’ve watched this play out across fundraising teams, marketing teams, and policy teams. The pattern is consistent.

The star performer steps into leadership assuming their domain mastery covers the new role. Not out of arrogance. It just never occurs to them that there’s different knowledge required.

This is unearned confidence in a new domain: an incomplete model of what the job actually requires.

A great gift officer knows more about building relationships with major donors than anyone on the team. A great fundraising director knows who knows what, and how to use it.

Sometimes this is the same person. But that shouldn’t be assumed.

The best leaders I’ve worked with don’t pretend to have all the answers. But they know who does. They understand that knowledge is distributed across a team — that their job is to use that knowledge to inform decisions, not to supply the answers themselves.

They have less certainty, not more.

They’re easy to accept. But they were designed for other people’s preferences, not yours.

Consider our phones.

The defaults organize our phones around a particular assumption: that we want to be engaged constantly. A banner notification, or a loud chime, or a little red badge counting unread messages.

It’s a reasonable assumption. For a while I accepted it. Then I realized I was responding to the phone more than using it.

One popular fix is to disengage entirely. No notifications, nothing that might interrupt. But even that wasn’t what I wanted.

Instead, I curated. Removed apps, deleted social media, pared the dock to the apps I use most. Turned off most notifications and badges.

The point wasn’t to remove notifications. It was to stop treating defaults as decisions.

I kept a few notifications that were doing something. Calendar alerts prompt me to switch contexts — which is why things are on the calendar. Texts from certain people are worth interrupting for.

They were defaults before. Now they’re choices.

A.I. may not replace doctors, but it will change what patients expect from us. Doctors need to adapt…

The reality is that many patients are already consulting A.I. Doctors can keep fearing or condemning those interactions, or they can figure out how to support people using A.I. tools for their health care — cautiously, with clear guardrails. I would never tell patients to ask ChatGPT or Claude for a diagnosis, but perhaps I would suggest they use it to make sense of a new condition or keep up with routine screenings — or translate “diet and exercise” into steps that actually fit into their lives, as I did.

She’s right. And she’s pointing at AI’s real potential. While much of the conversation is about the technology’s potential to replace work, the real story is how it can radically expand options.

Think about what that means in medicine. Patients who would never book an appointment for a minor concern can get a useful answer. Vague advice — “diet and exercise” — gets translated into steps that actually fit someone’s life. Doctors can spend their time seeing more patients or helping the patients who need them most, not fielding questions that don’t require a physician.

This is already how AI is playing out in other industries. Axios’ Jim VandeHei explains how it’s unlocking new possibilities in journalism: “The bigger opportunity isn’t efficiency. It’s new business lines that were economically impossible before AI.”

More people solving more problems that couldn’t be solved before.

To get that much video 30 years ago, I’d have to schlep to Blockbuster, hope they had the tapes in stock, and pay around $40 in today’s money. Then watch them on a low-res TV before returning them two days later.

Downloading from the internet certainly wasn’t an option. In the late 90s, a single MP3 — three minutes of music — took an hour to download on a good day.

Today, though, my home internet connection runs roughly 10,000x faster than the dial-up of 30 years ago, for about 3x the nominal cost.

No one person or organization planned this improvement. It came about because millions of people — customers, engineers, broadband companies — each pursued their own goals.

That’s what progress looks like when it’s working. There’s no grand announcement. Just millions of people acting on their independent plans — until one day you’re downloading a season of television in the time it takes to pack your suitcase.