Tags: work

Anti-work | Go Make Things

But this obsession with hard work as a virtue, as a good and righteous thing to do, the glorification of toil and sweat and labor… that’s a tool the wealthy who don’t work for a living use to oppress those who do.

I concur.

People talk about the effectiveness (or lack thereof) of large language models as though all tasks are comparable. But it strikes me that there are three broad categories of work that large language models are applied to:

1. Compression.
2. Transformation.
3. Expansion.

Compression is when you feed a large language model something big that you want to make small. Summarise this book. Give me the gist of this meeting. Large language models are generally pretty good at this, which makes sense given that they themselves are kind of like compressed artifacts.

Transformation is when large language models convert from one format into another. Turn this audio into text. Turn this jumble of data into structured JSON. A large language model can handle these tasks pretty well. There’ll probably be a few errors so make sure that’s not a deal-breaker.

Expansion is when you give a large language model a prompt to generate something from scratch. An image. A presentation. An email. A poem. This is where slop lives. The output inevitably betrays its origins, glistening with a sheen of mediocrity.

Laurie spotted this three-way split a while back:

Is what you’re doing taking a large amount of text and asking the LLM to convert it into a smaller amount of text? Then it’s probably going to be great at it. If you’re asking it to convert into a roughly equal amount of text it will be so-so. If you’re asking it to create more text than you gave it, forget about it.

I hope that when the bubble finally bursts, we’ll see the surviving large language models put to work on the first two categories. The boring stuff. The work that’s tedious for humans.

But tedious is as tedious does. Something I consider drudgery might be the very thing that gives you life. Like Giles says:

I have a feeling that everyone likes using AI tools to try doing someone else’s profession. They’re much less keen when someone else uses it for their profession.

The big exception seems to be programming. Apparently there are plenty of coders who never before expressed an interest in being managers who are now happily hanging up their coding spurs in favour being the overseer of non-human workers.

It’s a reasonable outlook. It could even be considered a user-centred approach. Users don’t care about the elegance of your code; they care about accomplishing their tasks.

Programming is something of an exception to the efficacy of large language models in general. Instead of relying on the subjectivity of painting, poetry, or prose, programming can be objectively tested. Throw enough money at the worst people in the world and they’ll give you tokens you can use to get the machines to test their own output. So you can get a large language model to create something reasonably good from scratch as long as that something is code.

If you had asked me about the threat model of large language models two years ago, I probably would’ve been worried for artists, writers, and musicians. I thought that software had enough inherent complexity to be relatively safe.

Now my opinion has completely reversed. Software is almost certainly the killer app for large language models.

I think the artists, writers, and musicians will be okay, or at least as okay as they ever were. It turns out that humans like things made by other humans.

And y’know what? If I had to choose which endeavour I’d rather see automated away—programming or art—it’s no competition.

Don’t get me wrong—it would be nice if everyone got paid for doing what they enjoy. It’s just that I’m okay with software engineers not being at the front of that line.

I remember when I first started getting paid money to make websites. “Really?” I thought, “Someone is willing to pay me to do something I’d do anyway?” I kept waiting for the jig to be up. Instead I saw my profession grow and expand.

Perhaps there’s a long-overdue compression happening.

Or maybe it’s more like a transformation.

No-stack web development – David Bushell – Web Dev (UK)

A stack is also technical debt, non-transferable knowledge, accelerated obsolescence, and vendor lock-in. That means fragility and overall unnecessary complication. Popular stacks inevitably turn into cargo cults that build in spite of the web, not for it.

The web platform does not require build toolchains. Always default to, and regress to, the fundamentals of CSS, HTML, and JavaScript. Those core standards are the web stack.

The AI Great Leap Forward

In 1958, Mao ordered every village in China to produce steel. Farmers melted down their cooking pots in backyard furnaces and reported spectacular numbers. The steel was useless. The crops rotted. Thirty million people starved.

In 2026, every other company is having top down mandate on AI transformation.

Same energy.

Times are tough out there. I know that a lot of people are looking for work, which can be a very stressful experience.

One of the things that can make the job search stressful is uncertainty. There’s a real taboo around talking about salaries. This taboo ends up benefiting employers and punishing potential employees. There’s an information gap that can be exploited (see also: job postings that don’t list salary ranges).

That’s why I’m always pleased when people voluntarily share their income. Here are some of the people who have done this over the years:

• Dan Mall (US)
• Alistair Hey (UK)
• Jamie Tanna (UK)
• Tom Withers (UK)
• Matt Brunt (UK)
• Carolina Gilabert (UK)
• Xe Iaso (Canada)
• Kristófer Reykjalín (Canada/US/Iceland)

Because the jobs are generally in software or design, you can sort of make apples-to-apples comparisons. You can definitely get the general gist of what kind of salary to expect for certain roles.

In the interest of full transparency, I figured I’d share my own income numbers, though as you’ll see, they’re not very representative of a normal career:

• 2003: £15,434 (freelance)
• 2004: £15,900 (freelance)
• 2005: £14,125 (freelance)
• 2006: £43,009 (freelance/Clearleft)
• 2007: £34,900 (Clearleft)
• 2008: £33,833 (Clearleft)
• 2009: £35,549 (Clearleft)
• 2010: £37,174 (Clearleft)
• 2011: £40,666 (Clearleft)
• 2012: £39,750 (Clearleft)
• 2013: £39,500 (Clearleft)
• 2014: £48,655 (Clearleft)
• 2015: £46,499 (Clearleft)
• 2016: £52,106 (Clearleft)
• 2017: £56,492 (Clearleft)
• 2018: £59,498 (Clearleft)
• 2019: £59,670 (Clearleft)
• 2020: £43,807 (Clearleft)
• 2021: £48,344 (Clearleft)
• 2022: £60,446 (Clearleft)
• 2023: £55,721 (Clearleft)
• 2024: £47,104 (Clearleft)
• 2025: £42,133 (Clearleft)

The first thing you’ll notice is that agency work isn’t nearly as well paid as in-house work at a technology company. So don’t embrace agency life for the money. Speaking personally, the benefits are in autonomy and variety. Those are things I value highly.

Also, I haven’t put any job titles or levels on there because they’ve never really been codified for me. I just made up my own job titles as I went along. Again, this is not very helpful to you if you’re looking for a job at a typical company.

You’ll see that things got weird in 2020, which is to be expected because things did get weird in 2020. I was furloughed, and I also took some more time off. I got a taste for it, which is why I went down to a four-day week and later a three-day week, which is what I’m doing now. So those last five years of numbers are loopy—I’m making less than before, but if you were to adjust it for a five-day week, I’m still getting paid more than before …if that makes sense.

Perhaps the most unusual thing about my career trajectory is that I’ve been at the same place for twenty years now. That’s pretty much unheard of in tech. It’s far more usual to see people switch companies—and get a salary bump—every couple of years.

So I’m not sure if there’s any value in me sharing my numbers like this. But like I said, I admire when other people do it so I figured I’d throw mine out there.

Perhaps you’d like to share your numbers too.

Four years ago I wrote about something that has long puzzled me in the world of front-end development. Trust:

The mindset I’ve noticed is that many developers are suspicious of browser features but trusting of third-party libraries.

Developers are more likely to trust, say, Bootstrap than they are to trust CSS grid or custom properties. Developers are more likely to trust React than they are to trust web components.

That post got some thoughtful responses but I never really understood the imbalance of trust and suspicion:

I’m kind of confused by this prevalent mindset of trusting third-party code more than built-in browser features.

But something happened recently that helped me understand that mindset better.

I wrote a while back about how the datalist element on iOS has been completely fucked up. It’s worse than if Safari simply didn’t support it.

Breaking the web like that should be a five-alarm fire, but nobody is in any rush to fix it. I recall a similar lackadaisical attitude when Safari completely broke their implentation of IndexedDB.

I had it in my head that browser features followed a forward path generally. They’d be iterated on and improved on to iron out any glitches, but it was reasonable to expect things to get better with each new version of a browser.

Now I see that’s not necessarily the case.

Had I used an over-engineered JavaScript library instead of the datalist element, I wouldn’t be facing the current situation of having to use browser-sniffing to avoid sending a standard HTML element to any browser on iOS.

Sure, that third-party JavaScript would mean that users are downloading more code, and it probably wouldn’t work well with assistive technology, but as long as I didn’t touch it, it would continue to work. That should be true of web standards—I should be able to use them secure in the knowledge that they won’t suddenly shit the bed.

Perhaps I should be grateful to Apple for dispelling my naïveté. I now have much more empathy and understanding for web developers who are suspicious of web standards and prefer to use third-party libraries instead.

Good job, Apple. Happy anniversary.

The End : Focal Curve

I can’t remember the last time a blog post resonated with me this much.

Craig’s criteria on his job search:

• One: fuck offices
• Two: fuck AI
• Three: fuck React

And his conclusion:

Fuck work

How to be a web developer: Stuff Everybody Knows

This is a guide to how to be a web developer.

Really good advice from Laurie.

What this site is not is a tutorial. Tutorials are very specific to a time and a technology. This is intended to be a guide to tell you all the things you can learn, so you can then go off and learn them.

Flood fill vs. the magic circle

Eleven years ago, I wrote:

Sometimes I consider the explosive growth of computation and think that strong AI is a near-term inevitability.

Then I remember printers.

That was just a brainfart, but Robin tackles it seriously in his thoughtful essay.

A pleasing image: if indeed AI automation does not flood fill the physical world, it will be because the humble paper jam stood in its way.

Software cannot, in fact, eat this world. Software can reflect it; encroach upon it; more than anything, distract us from it. But the real physical world is indigestible.

You should come to UX London in the first week of June. Why? Because it’s going to be awesome, that’s why!

You probably knew that already. You probably already decided to get a ticket because you’re smart like that.

But don’t dilly-dally! Early-bird tickets are available now but in just over one week, they won’t be.

So get your ticket by Friday, March 27th. If you get your ticket now, it’s a win for everyone. You get a cheaper ticket. We know for sure that you’re coming.

Every time someone buys a conference ticket in plenty of time, the conference organiser sleeps a little better at night.

If you need to convince your boss, you can give them these reasons to attend. I even made an email template you can use a starting point for making the case.

You could come for all three days of UX London, or you can pick just one day.

Tuesday, June 2nd is discovery day with a focus on user research. You’ll hear from great speakers like Melin Edomwonyi and Maria Isachenko as well as getting workshops from Natasha den Dekker and Feyikemi Akinwolemiwa.

Wednesday, June 3rd is design day where it’s all about the nitty-gritty details. Not only will there be great talks from Andrea Grigsby, Julia Petretta, and Hidde de Vries, there’s going to be the best-named workshop ever from my colleague Chris How: Yippee IA!

Thursday, June 4th is delivery with a focus on design systems and collaboration. Alex Edwards, Lucy Blackwell, Rachel Ilan Simpson and Ben Callahan will all be giving talks (and Ben’s doing a workshop too).

That’s not even close to the final line-up. I’m confirming more speakers right now and getting very, very excited about how it’s all shaping up.

You know you don’t want to miss this one. So get your early-bird ticket now while you still can.

Working with agents doesn’t feel like flow — Bill de hÓra

Related to Matt’s thoughts:

…working with agents feels much less like classic deep work, and much more like playing a game. Not to say the work is frivolous—it’s just because it feels like I’m in a game loop.

Flow, at least in the usual sense for me, feels smooth and continuous. The work and your attention starts to line up so cleanly that the experience becomes frictionless. You disappear into the work and meld with it. One notable aspect of flow has been I lose track of time. Working with agents on the other hand, is not like that at all. It’s highly engaging, but in a more jagged, reactive way. I’m focused, but not settled. I’m absorbed, but not merged with the task. I’m paying close attention the whole time, but the attention is dynamic and tactical rather than continuous. I don’t lose track of time at all.

Gas Town and Bullet Hell – Petafloptimism

Matt has some smart reckons on the relationship between time and technology:

The factory bell, the railway timetable, the telegraph wire, the always-on smartphone — each imposed a new temporal discipline, each produced its own characteristic form of exhaustion, and each was eventually (partially, imperfectly) domesticated through a combination of regulation, design, and collective action.

I work, I think? - Annotated

This is about something that’s already happening, that doesn’t show up in employment figures: the quiet destruction of the feedback loop that turns inexperienced people into competent ones. The process by which you get something wrong, feel it, understand why, and become slightly less wrong next time. It’s unglamorous and it’s slow and it’s the only way it’s ever worked.

AI short-circuits that learning completely. Not maliciously. Just structurally. When you can generate something that looks right without doing the thinking, you will (most people, most people being me, will, most of the time, under pressure, with a deadline) and the muscle that thinking would have built never develops.

I am in an abusive relationship with the technology industry

The cognitive overload of AI trying to Make You More Productive™️ whilst you’re actually trying to be productive is so shockingly absurd. And yet, we are being made to feel like we are stagnating, being left behind, not good enough, that we are luddites should we not adopt this imposing technology. We are being told we’re missing out, even though we’re probably doing just fine. The technology is gaslighting us.

If you wanted to make a really crude approximation of project management, you could say there are two main styles: waterfall and agile.

It’s not as simple as that by any means. And the two aren’t really separate things; agile came about as a response to the failures of waterfall. But if we’re going to stick with crude approximations, here we go:

• In a waterfall process, you define everything up front and then execute.
• In an agile process, you start executing and then adjust based on what you learn.

So crude! Much approximation!

It only recently struck me that the agile approach is basically a cybernetic system.

Cybernetics is pretty much anything that involves feedback. If it’s got inputs and outputs that are connected in some way, it’s probably cybernetic. Politics. Finance. Your YouTube recommendations. Every video game you’ve ever played. You. Every living thing on the planet. That’s cybernetics.

Fun fact: early on in the history of cybernetics, a bunch of folks wanted to get together at an event to geek about this stuff. But they knew that if they used the word “cybernetics” to describe the event, Norbert Wiener would show up and completely dominate proceedings. So they invented a new alias for the same thing. They coined the term “artificial intelligence”, or AI for short.

Yes, ironically the term “AI” was invented in order to repel a Reply Guy. Now it’s Reply Guy catnip. In today’s AI world, everyone’s a Norbert Wiener.

The thing that has the Wieners really excited right now in the world of programming is the idea of agentic AI. In this set-up, you don’t do any of the actual coding. Instead you specify everything up front and then have a team of artificial agents execute your plan.

That’s right; it’s a return to waterfall. But that’s not as crazy as it sounds. Waterfall was wasteful because execution was expensive and time-consuming. Now that execution is relatively cheap (you pay a bit of money to line the pockets of the worst people in exchange for literal tokens), you can afford to throw some spaghetti at the wall and see if it sticks.

But you lose the learning. The idea of a cybernetic system like, say, agile development, is that you try something, learn from it, and adjust accordingly. You remember what worked. You remember what didn’t. That’s learning.

Outsourcing execution to machines makes a lot of sense.

I’m not so sure it makes sense to outsource learning.

The nature of the job

Large language models help you build the thing faster, which is the primary end goal for your company but only sometimes for you. My primary goal might be to build the thing faster, but it also might be to learn something durably, to enjoy the work, to look forward to Monday.

I don’t like the mental fragility of not fully understanding how my own code works, where AI-generated code is “mine” in that it’s attributed to me in the git blame and I’m its maintainer going forward.

It’s the 25th of February and it’s a beautiful day here in Brighton. I had lunch sitting outside—that’s how unseasonably warm it is. Like a little whiff of Summer to remind us of what’s yet to come.

It’s also my birthday. The beautiful weather is an auspicious augery.

Mozilla also released a new version of Firefox. I was hoping for cross-document view transitions and scroll-driven animations for my birthday, but alas I may have to wait another year.

Later, Jessica is going to take me out for some excellent Japanese food before we head on to a session in a cosy pub. I can think of no better way to celebrate my birthday than playing a rake of jigs and reels.

I’m 55 now. It feels like a meaningful number. I think I’ve moved down an option in the select menus that ask for your age range.

I got letters in the post from my pension provider reminding me that 55 is the age when you can technically start taking money out of your pension. Something that retired people do.

I have to admit, this birthday has me entertaining retirement options. I’m already down to just three days a week. It wouldn’t take much to wind that down over the next few years. There’d be even more opportunities to savour the sunshine on a sunny day.

Anyway. Just pondering. You know, the kind of thoughts a 55-year old has.

Training your replacement | Go Make Things

I’ve had a lot of people recently tell me AI is “inevitable.” That this is “the future” and “we all better get used to it.”

For the last decade, I’ve had a lot of people tell me the same thing about React.

And over that decade of React being “the future” and “inevitable,” I worked on many, many projects without it. I’ve built a thriving career.

AI feels like that in many ways. It also feels different in that non-technical people also won’t shut the fuck about it.

A considered approach to generative AI in front-end… | Clearleft

A thoughtful approach from Sam:

1. Use AI only for tasks you already know how to do, on occasions when the time that would be spent completing the task can be better spent on other problems.
2. When using AI, provide the chosen tool with something you’ve made as an input along with a specific prompt.
3. Always comprehensively review the output from an AI tool for quality.

Deep Blue

My social networks are currently awash with Deep Blue:

…the sense of psychological ennui leading into existential dread that many software developers are feeling thanks to the encroachment of generative AI into their field of work.