The short version of what I want to say is: vibe coding seems to live very squarely in the land of prototypes and toys. Promoting software that’s been built entirely using this method would be akin to sending a hacked weekend prototype to production and expecting it to be stable.
Remy is taking a very sensible approach here:
I’ve used it myself to solve really bespoke problems where the user count is one.
Would I put this out to production: absolutely not.
I don’t normally link to articles on Medium—I respect you too much—and I do wish this were written on Mike Hall’s own site, but this is just too good not to share.
And don’t dismiss this as a nostalgiac case study from the past:
At no point did the constraints make the product feel compromised. Users on modern devices got a smooth experience and instant feedback, while those on older devices got fast, reliable functionality. Users on feature phones got the same core experience without the bells and whistles.
The constraints forced us to solve problems in ways we wouldn’t have considered otherwise. Without those constraints, we could have just thrown bytes at the problem, but with them every feature had to justify itself. Core functionality had to work everywhere, and without JavaScript crutches proper markup became essential.
This experience changed how I approach design problems. Constraints aren’t a straitjacket, keeping us from doing our best work; they are the foundation that makes innovation possible. When you have to work within severe limitations, you find elegant solutions that scale beyond those limitations.
I love the interactive illustrations in this article filled with type and architecture nerdery!
Most obviously, aliveness is what generally feels absent from the written and visual outputs of ChatGPT and its ilk, even when they’re otherwise of high quality. I’m not claiming I couldn’t be fooled into thinking AI writing or art was made by a human (I’m sure I already have been); but that when I realise something’s AI, either because it’s blindingly obvious or when I find out, it no longer feels so alive to me. And that this change in my feelings about it isn’t irrelevant: that it means something.
More subtly, it feels like our own aliveness is what’s at stake when we’re urged to get better at prompting LLMs to provide the most useful responses. Maybe that’s a necessary modern skill; but still, the fact is that we’re being asked to think less like ourselves and more like our tools.
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.
AI presents design leaders with a quandary, requiring us to tread a fine line between what is acceptable and useful, and what is problematic and harmful.
This document is not a manifesto or an agenda. It is a series of prompts written by design leaders for design leaders, conceived to help us navigate these tricky waters.
Here’s what the “AI will replace developers” crowd fundamentally misunderstands: code is not an asset—it’s a liability. Every line must be maintained, debugged, secured, and eventually replaced. The real asset is the business capability that code enables.
If AI makes writing code faster and cheaper, it’s really making it easier to create liability. When you can generate liability at unprecedented speed, the ability to manage and minimize that liability strategically becomes exponentially more valuable.
This is particularly true because AI excels at local optimization but fails at global design. It can optimize individual functions but can’t determine whether a service should exist in the first place, or how it should interact with the broader system. When implementation speed increases dramatically, architectural mistakes get baked in before you realize they’re mistakes.
Frankly, I’d rather quit my career than live in the future they’re selling. It’s the sheer dystopian drabness of it. Mediocrity as a service.
I tried the tab-completion slot machines; not my cup of tea. I tried image generation and was overcome with literal depression. I don’t want a future as a “prompt artist”.
I’m mostly linking this for what it says, but oh boy, do I love the way it says it with this wonderful HTML web compenent.
Engaging with AI as a technology is to play the fool—it’s to observe the reflective surface of the thing without taking note of the way it sends roots deep down into the ground, breaking up bedrock, poisoning the soil, reaching far and wide to capture, uproot, strangle, and steal everything within its reach. It’s to stand aboveground and pontificate about the marvels of this bright new magic, to be dazzled by all its flickering, glittering glory, its smooth mirages and six-fingered messiahs, its apparent obsequiousness in response to all your commands, right up until the point when a sinkhole opens up and swallows you whole.
👏👏👏
AI is, of course, at the center of this moment. It’s a mediocrity machine by default, attempting to bend everything it touches toward a mathematical average. Using extraordinary amounts of resources, it has the ability to create something good enough, a squint-and-it-looks-right simulacrum of normality. If you don’t care, it’s miraculous.
In the Who Cares Era, the most radical thing you can do is care.
In a moment where machines churn out mediocrity, make something yourself. Make it imperfect. Make it rough. Just make it.
Looking at LLM usage and promotion as a cultural phenomenon, it has all of the markings of a status game. The material gains from the LLM (which are usually quite marginal) really aren’t why people are doing it: they’re doing it because in many spaces, using ChatGPT and being very optimistic about AI being the “future” raises their social status. It’s important not only to be using it, but to be seen using it and be seen supporting it and telling people who don’t use it that they’re stupid luddites who’ll inevitably be left behind by technology.
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?
Search has bent in quality towards its earliest days, difficult to navigate and often unhelpful. And the remedy may be the same as it was a quarter century ago.
A great talk by Matthias on what you can do with web standards today!
Here is the state of venture capital in early 2025:
- Venture capital is moribund except AI.
- AI is moribund except OpenAI.
- OpenAI is a weird scam that wants to burn money so fast it summons AI God.
- Nobody can cash out.
A good overview of how large language models work:
The words flow together because they’ve been seen together many times. But that doesn’t mean they’re right. It just means they’re coherent.
Scratch the skin of wild-eyed AI proponents, and a thick syrup oozes out, made up of the blendered remains of Roko’s Basilisk, barely sublimated Christian end-times thinking, and the mis-remembered plot of that one cool science-fiction story they read when they were twelve. This is the basis for the new order, just like the blockchain was a couple of years ago, and a dead-eyed, low-poly, pantsless rendering of Mark Zuckerberg was a couple of years before that.
“You’re going to be left behind” is only the latest version of “Have fun staying poor.” It’s got every ounce of the smug self-satisfaction that it shouldn’t need if the inevitability it promises were actually inevitable.
AI is really good for helping you if you’re bad at something, or at least below average. But it’s probably not the right tool if you’re great at something. So why would these CEOs be saying, almost all using the exact same phrasing, that everyone at their companies should be using these tools? Do the think their employees are all bad at their jobs?
Technically, AI is a field of computer science that uses advanced methods of computing.
Socially, AI is a set of extractive tools used to concentrate power and wealth.