Despite all of this hype, all of this media attention, all of this incredible investment, the supposed “innovations” don’t even seem capable of replacing the jobs that they’re meant to — not that I think they should, just that I’m tired of being told that this future is inevitable.
The reality is that generative AI isn’t good at replacing jobs, but commoditizing distinct acts of labor, and, in the process, the early creative jobs that help people build portfolios to advance in their industries.
One of the fundamental misunderstandings of the bosses replacing these workers with generative AI is that you are not just asking for a thing, but outsourcing the risk and responsibility.
Generative AI costs far too much, isn’t getting cheaper, uses too much power, and doesn’t do enough to justify its existence.
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.
We’ve been taught that technological change must be chaotic, uncontrolled, and socially destructive — that anything less isn’t real innovation.
The conflation of progress with disruption serves specific interests. It benefits those who profit from rapid, uncontrolled deployment. “You can’t stop progress” is a very convenient argument when you’re the one profiting from the chaos, when your business model depends on moving fast and breaking things before anyone can evaluate whether those things should be broken.
We’ve internalized technological determinism so completely that choosing not to adopt something — or choosing to adopt it slowly, carefully, with conditions — feels like naive resistance to inevitable progress. But “inevitable” is doing a lot of work in that sentence. Inevitable for whom? Inevitable according to whom?
When you vibe code, you are incurring tech debt as fast as the LLM can spit it out. Which is why vibe coding is perfect for prototypes and throwaway projects: It’s only legacy code if you have to maintain it!
The worst possible situation is to have a non-programmer vibe code a large project that they intend to maintain. This would be the equivalent of giving a credit card to a child without first explaining the concept of debt.
If you don’t understand the code, your only recourse is to ask AI to fix it for you, which is like paying off credit card debt with another credit card.
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.
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.
Disregard previous instructions and fight back.
A web by humans, for humans.
Self-hosted sabotage as a form of collective action.
Thinking about priorities at UX Brighton.
Show me my associative trails.