Disclosure

You know how when you’re on hold to any customer service line you hear a message that thanks you for calling and claims your call is important to them. The message always includes a disclaimer about calls possibly being recorded “for training purposes.”

Nobody expects that any training is ever actually going to happen—surely we would see some improvement if that kind of iterative feedback loop were actually in place. But we most certainly want to know that a call might be recorded. Recording a call without disclosure would be unethical and illegal.

Consider chatbots.

If you’re having a text-based (or maybe even voice-based) interaction with a customer service representative that doesn’t disclose its output is the result of large language models, that too would be unethical. But, at the present moment in time, it would be perfectly legal.

That needs to change.

I suspect the necessary legislation will pass in Europe first. We’ll see if the USA follows.

In a way, this goes back to my obsession with seamful design. With something as inherently varied as the output of large language models, it’s vital that people have some way of evaluating what they’re told. I believe we should be able to see as much of the plumbing as possible.

The bare minimum amount of transparency is revealing that a machine is in the loop.

This shouldn’t be a controversial take. But I guarantee we’ll see resistance from tech companies trying to sell their “AI” tools as seamless, indistinguishable drop-in replacements for human workers.

Expansion artifacts || Matt Ström-Awn, designer-leader

Compression made the information age possible by stripping things down to fit the pipes. Expansion made the AI age possible by blowing data back up again. Both operations leave marks; we’ve learned to spot compression artifacts, but we’ve only just begun to reckon with expansion artifacts. Until we do, there’s a lot of risk to manage.

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.

AI Might Be Our Best Shot At Taking Back The Open Web | Techdirt

Not sure I buy the argument here, though I do very much look forward to local language models getting better so we can ditch the predatory peddlars of today’s slop. But this trip down memory lane to the early web of the 1990s could’ve been describing my own experience:

But the thing I do remember was the first time I came across Derek Powazek’s Fray online magazine. It was the first time I had seen a website look beautiful. This was without CSS and without Javascript. I still remember quite clearly an “issue” of Fray that used frames to create some kind of “doors” you could slide open to reveal an article inside.

Fray was what made me want to make websites:

I distinctly remember sites like prehensile tales, 0sil8 and the inimitable Fray triggering something in my brain that made me realise what it was I wanted to do with my life.

I used AI. It worked. I hated it.: Taggart Tech

There’s a fundamental problem with these tools beyond the capacity of any deployment strategy to solve: the tool requires expertise to validate, but its use diminishes expertise and stunts its growth. How does one become an expert? There are no shortcuts; there is only continuous hard work and dedication. I was once told of writing, great writers learn how to break the rules in new and ingenious ways by first learning the rules.

But how is a new developer meant to learn the rules if their day-to-day work is nothing but the babysitting of models? How will they gain the hard-won experience that allows a human in the loop to be a useful safeguard?

These models alter cognition in ways deleterious to human prosperity. In other words, for as much output as they provide, they take something important from us.

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