I love the small web, the clean web. I hate tech bloat.
And LLMs are the ultimate bloat.
So much truth in one story:
They built a machine to gentrify the English language.
They have built a machine that weaponizes mediocrity and sells it as perfection.
They are strip-mining your confidence to sell you back a synthetic version of it.
I find Brian Eno to be a fascinating chap. His music isn’t my cup of tea, but I really enjoy hearing his thoughts on art, creativity, and culture.
I’ve always loved this short piece he wrote about singing with other people. I’ve passed that link onto multiple people who have found a deep joy in singing with a choir:
Singing aloud leaves you with a sense of levity and contentedness. And then there are what I would call “civilizational benefits.” When you sing with a group of people, you learn how to subsume yourself into a group consciousness because a capella singing is all about the immersion of the self into the community. That’s one of the great feelings — to stop being me for a little while and to become us. That way lies empathy, the great social virtue.
Then there’s the whole Long Now thing, a phrase that originated with him:
I noticed that this very local attitude to space in New York paralleled a similarly limited attitude to time. Everything was exciting, fast, current, and temporary. Enormous buildings came and went, careers rose and crashed in weeks. You rarely got the feeling that anyone had the time to think two years ahead, let alone ten or a hundred. Everyone seemed to be passing through. It was undeniably lively, but the downside was that it seemed selfish, irresponsible and randomly dangerous. I came to think of this as “The Short Now”, and this suggested the possibility of its opposite - “The Long Now”.
I was listening to my Huffduffer feed recently, where I had saved yet another interview with Brian Eno. Sure enough, there was plenty of interesting food for thought, but the bit that stood out to me was relevant to, of all things, prototyping:
I have an architect friend called Rem Koolhaas. He’s a Dutch architect, and he uses this phrase, “the premature sheen.” In his architectural practice, when they first got computers and computers were first good enough to do proper renderings of things, he said everything looked amazing at first.
You could construct a building in half an hour on the computer, and you’d have this amazing-looking thing, but, he said, “It didn’t help us make good buildings. It helped us make things that looked like they might be good buildings.”
I went to visit him one day when they were working on a big new complex for some place in Texas, and they were using matchboxes and pens and packets of tissues. It was completely analog, and there was no sense at all that this had any relationship to what the final product would be, in terms of how it looked.
It meant that what you were thinking about was: How does it work? What do we want it to be like to be in that place? You started asking the important questions again, not: What kind of facing should we have on the building or what color should the stone be?
I keep thinking about that insight: “It didn’t help us make good buildings. It helped us make things that looked like they might be good buildings.”
Substitute the word “buildings” for whatever output is supposedly being revolutionised by generative models today. Websites. Articles. Public policy.
I am interested in art—we are interested in art, in any and all of its forms—because humans made it. That’s the very thing that makes it interesting; the who, the how, and especially the why.
The existence of the work itself is only part of the point, and materializing an image out of thin air misses the point of art, in very much the same way that putting a football into a Waymo to drive it up and down the street for a few hours would be entirely missing the point of sports.
The transcript of a very thoughtful talk by Frank.
Stick with this. It’s worth it.
Find freedom not in infinite choice, but in working a single seam until you strike gold: conducting dozens, even hundreds, of iterations within a tight parameter space—not in search of more, but in search of better.
It feels like someone just harvested lumber from a forest I helped grow, and now wants to sell me the furniture they made with it.
If I’m understanding Greg correctly here, he’s saying it’s okay for people to use large language models …because they’re being forced to?
One persistent piece of slopaganda you’ll hear is this:
“It’s just a tool. What matters is how you use it.”
This isn’t a new tack. The same justification has been applied to many technologies.
Leaving aside Kranzberg’s first law, large language models are the very antithesis of a neutral technology. They’re imbued with bias and political decisions at every level.
There’s the obvious problem of where the training data comes from. It’s stolen. Everyone knows this, but some people would rather pretend they don’t know how the sausage is made.
But if you set aside how the tool is made, it’s still just a tool, right? A building is still a building even if it’s built on stolen land.
Except with large language models, the training data is just the first step. After that you need to traumatise an underpaid workforce to remove the most horrifying content. Then you build an opaque black box that end-users have no control over.
Take temperature, for example. That’s the degree of probability a large language model uses for choosing the next token. Dial the temperature too low and the tool will parrot its training data too closely, making it a plagiarism machine. Dial the temperature too high and the tool generates what we kindly call “hallucinations”.
Either way, you have no control over that dial. Someone else is making that decision for you.
A large language model is as neutral as an AK-47.
I understand why people want to feel in control of the tools they’re using. I know why people will use large language models for some tasks—brainstorming, rubber ducking—but strictly avoid them for any outputs intended for human consumption.
You could even convince yourself that a large language model is like a bicycle for the mind. In truth, a large language model is more like one of those hover chairs on the spaceship in WALL·E.
Large language models don’t amplify your creativity and agency. Large language models stunt your creativity and rob you of agency.
When someone applies a large language model it is an example of tool use. But the large language model isn’t the tool.
When I’m not talking, just walking (which is most of the time), I try to cultivate the most bored state of mind imaginable. A total void of stimulation beyond the immediate environment. My rules: No news, no social media, no podcasts, no music. No “teleporting,” you could say. The phone, the great teleportation device, the great murderer of boredom. And yet, boredom: the great engine of creativity. I now believe with all my heart that it’s only in the crushing silences of boredom—without all that black-mirror dopamine — that you can access your deepest creative wells. And for so many people these days, they’ve never so much as attempted to dip in a ladle, let alone dive down into those uncomfortable waters made accessible through boredom.
AI can be incredibly useful when deployed skillfully in creative endeavors—as an ideation partner, as a scaffolding tool, by eliminating tedious tasks, etc.—but anyone making anything truly good with it is probably somebody who could already make something good first without it.
If you’re roughly 70% happy with a piece of writing you’ve produced, you should publish it.
Works for me!
You’re also expanding your ability to act in the presence of feelings of displeasure, worry and uncertainty, so that you can take more actions, and more ambitious actions, later on.
Crucially, you’ll also be creating a body of evidence to prove to yourself that when you move forward at 70%, the sky stubbornly fails to fall in. People don’t heap scorn on you or punish you.
The slides from Hidde’s presentation at Beyond Tellerrand.
I hope to make something that could only exist because I made it. Something that is the one thing that it is. Not an average sentence. Not a visual approximation of other people’s work. Not a stolen concept that boils lakes and uses more electricity than anything in my household.
Using ChatGPT to complete assignments is like bringing a forklift into the weight room; you will never improve your cognitive fitness that way.
Another great piece by Ted Chiang!
The companies promoting generative-A.I. programs claim that they will unleash creativity. In essence, they are saying that art can be all inspiration and no perspiration—but these things cannot be easily separated. I’m not saying that art has to involve tedium. What I’m saying is that art requires making choices at every scale; the countless small-scale choices made during implementation are just as important to the final product as the few large-scale choices made during the conception.
This bit reminded me of Simon’s rule:
Let me offer another generalization: any writing that deserves your attention as a reader is the result of effort expended by the person who wrote it. Effort during the writing process doesn’t guarantee the end product is worth reading, but worthwhile work cannot be made without it. The type of attention you pay when reading a personal e-mail is different from the type you pay when reading a business report, but in both cases it is only warranted when the writer put some thought into it.
Simon also makes an appearance here:
The programmer Simon Willison has described the training for large language models as “money laundering for copyrighted data,” which I find a useful way to think about the appeal of generative-A.I. programs: they let you engage in something like plagiarism, but there’s no guilt associated with it because it’s not clear even to you that you’re copying.
I could quote the whole thing, but I’ll stop with this one:
The task that generative A.I. has been most successful at is lowering our expectations, both of the things we read and of ourselves when we write anything for others to read. It is a fundamentally dehumanizing technology because it treats us as less than what we are: creators and apprehenders of meaning. It reduces the amount of intention in the world.
Now we have this collision of those who, with the specific intent of creative expression, make things that are wholly the product of their unique experience and skills and offer them in the marketplace. Then there are those who use machines to produce derivatives of other’s creative work to offer as products in the marketplace. Both are seeking an audience and financial benefit for their offering.
Those who wholly manufacture creative works are asking the same value be put on their imitation of creative expression as the value inherent with sentient creation. They are saying they deserve the same recognition—be that in respect, attention, acknowledgement or compensation—that works created by a person might receive. But they haven’t earned it.
Using generative AI is to ask What If but then hand off not only the responsibility and effort of answering the question but also accountability for the answer. When the machine creates something pleasing or marketable, it’s “look at what I did”. When the machine creates something terrible or wrong, it’s “not my fault, the machine did it”. The claim of ownership is conditional and only maintained if the output can generate value.
There’s so much to love here, like this:
My art is the story of how I have spent the time in my life.
And this:
The value of an idea comes from the execution of the idea.
Once you have reduced the concept of human intelligence to what the markets will pay for, then suddenly, all it takes to build an intelligent machine — even a superhuman one — is to make something that generates economically valuable outputs at a rate and average quality that exceeds your own economic output. Anything else is irrelevant.
By describing as superhuman a thing that is entirely insensible and unthinking, an object without desire or hope but relentlessly productive and adaptable to its assigned economically valuable tasks, we implicitly erase or devalue the concept of a “human” and all that a human can do and strive to become. Of course, attempts to erase and devalue the most humane parts of our existence are nothing new; AI is just a new excuse to do it.
Robin describes his experience of using a design system, having previously been one of the people making and enforcing a design system:
However it’s only now as a product designer that I realize just how much I want the design system to carefully guide me instead of brute-forcing its decisions onto my work. I want to fall into the loving embrace of the system because I don’t wanna have to think about hex values or button sizes or box shadows. I don’t want to rethink padding and margins or rethink the grid each time I design a page.
But by golly if a design system says “no” to me then I will do my very best to blow it up.
Continuous partial ick …or perhaps continuous partial cringe.
Anyways, maybe we’ll eventually get to the point where AI has that human “spark”, who knows. Maybe it’ll happen next month and I’ll eat my words. Until then, as most of the content we experience online becomes more grey and sludgy, the personal will become far more valuable.