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Software is an apprenticeship industry. You can’t learn to be a software engineer by reading books. You can only learn by doing…and doing, and doing, and doing some more.

To me, being a senior engineer is not primarily a function of your ability to write code. It has far more to do with your ability to understand, maintain, explain, and manage a large body of software in production over time, as well as the ability to translate business needs into technical implementation. So much of the work is around crafting and curating these large, complex sociotechnical systems, and code is just one representation of these systems.

What does it mean to be a senior engineer? It means you have learned how to learn, first and foremost, and how to teach; how to hold these models in your head and reason about them, and how to maintain, extend, and operate these systems over time. It means you have good judgment, and instincts you can trust.

By not hiring and training up junior engineers, we are cannibalizing our own future. We need to stop doing that.

It never has been, it never will be. Writing code is the easiest part of software engineering, and it’s getting easier by the day.

Sociotechnical systems consist of software, tools, and people; understanding them requires familiarity with the interplay between software, users, production, infrastructure, and continuous changes over time.

You may save time by not having to type the code in from scratch, but you will need to step through the output line by line, revising as you go, before you can commit your code, let alone ship it to production.

If what you need is effectively copy-paste with a template—any time you could generate the code you want using sed/awk or vi macros—generative AI is quite good at this.

One of the engineers I work with, Kent Quirk, describes generative AI as “an excitable junior engineer who types really fast”.

Disposable code is rare; code that needs to work over the long term is the norm.

Your effort is worth more when it is invested into someone else’s apprenticeship. It’s an opportunity to pass on the lessons you’ve learned in your own career.

When it comes to hiring, we tend to valorize senior engineers almost as much as we underestimate junior engineers.

This is an apprenticeship industry, and productivity is defined by the output and carrying capacity of each team, not each person.

In terms of writing and shipping features, some of the most productive engineers I’ve ever known have been intermediate engineers. Not yet bogged down with all the meetings and curating and mentoring and advising and architecture, their calendars not yet pockmarked with interruptions, they can just build stuff.

The smallest unit of software ownership is not the individual, it’s the team.

A monoculture can be spectacularly successful in the short term—it may even outperform a diverse team. But they do not scale well, and they do not adapt to unfamiliar challenges gracefully.

the most effective mentor is usually someone just one level ahead, who vividly remembers what it was like in your shoes.

Components can be automated, but complexity can only be managed.

large software systems are unpredictable and nondeterministic, with emergent behaviors

Writing code is but a sliver of what professional software engineers do, and arguably the easiest part.

AI companies face a kafkaesque bind where they can't improve a tool for automating the creation of content without human beings creating more content than they've ever created before

I am, of course, conflating two problems — the deliberate creation of synthetic data by AI companies and AI-generated content filling the internet with synthetic data that models are then trained on. Yet the end result is the same — forcefully teaching autocomplete typos in the hopes that it'll be able to work out how to write America's next great novel.

The only companies currently profiting from the AI gold rush are those selling shovels

The companies benefitting from AI aren't the ones integrating it or even selling it, but those powering the means to use it

the stock market (and the tech industry) is building vast castles on foundations of sand

the AI industry is investing hundreds of billions of dollars to build infrastructure for a future that may never arrive

what he's promised isn't possible with today's technology, and may not be possible at all

AI is great at finding patterns in existing data. However, AI will not be able to solve this problem of missing and erroneous underlying data

Medical data is often limited by the categories of billing codes, by what doctors choose to note from a patient’s account

The many other factors that influence medical care, including the systematic disregard for patients’ knowledge of their own experiences, are often ignored in these discussions

"Sure, that sounds great for a media nerd or tech geek, but I'm a normal person. Is RSS really for me?" I would argue that the answer is an emphatic yes, perhaps now more than ever.

It's the Marie Kondo method of information consumption - only content that sparks joy, delivered in a way that itself sparks joy.

I want everyone to make a living wage and have excellent health care and the means to retire at a reasonable age. I want all of us to want this very simple thing for one another.

I wish we lived in a world where I could offer you frank, unfiltered professional advice, but I know we do not live in such a world.