cubic blog: The real problem with AI coding

Keeping up appearances | deadSimpleTech

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.

In 2025, venture capital can’t pretend everything is fine any more – Pivot to AI

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.

Build It Yourself | Armin Ronacher’s Thoughts and Writings

We’re at a point in the most ecosystems where pulling in libraries is not just the default action, it’s seen positively: “Look how modular and composable my code is!” Actually, it might just be a symptom of never wanting to type out more than a few lines.

It always amazes me when people don’t view dependencies as liabilities. To me it feels like the coding equivalent of going to a loan shark. You are asking for technical debt.

There are entire companies who are making a living of supplying you with the tools needed to deal with your dependency mess. In the name of security, we’re pushed to having dependencies and keeping them up to date, despite most of those dependencies being the primary source of security problems.

But there is a simpler path. You write code yourself. Sure, it’s more work up front, but once it’s written, it’s done.

What I’ve learned about writing AI apps so far | Seldo.com

LLMs are good at transforming text into less text

Laurie is really onto something with this:

This is the biggest and most fundamental thing about LLMs, and a great rule of thumb for what’s going to be an effective LLM application. 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.

Depending how much of the hype around AI you’ve taken on board, the idea that they “take text and turn it into less text” might seem gigantic back-pedal away from previous claims of what AI can do. But taking text and turning it into less text is still an enormous field of endeavour, and a huge market. It’s still very exciting, all the more exciting because it’s got clear boundaries and isn’t hype-driven over-reaching, or dependent on LLMs overnight becoming way better than they currently are.

AI and Asbestos: the offset and trade-off models for large-scale risks are inherently harmful – Baldur Bjarnason

Every time you had an industry campaign against an asbestos ban, they used the same rhetoric. They focused on the potential benefits – cheaper spare parts for cars, cheaper water purification – and doing so implicitly assumed that deaths and destroyed lives, were a low price to pay.

This is the same strategy that’s being used by those who today talk about finding productive uses for generative models without even so much as gesturing towards mitigating or preventing the societal or environmental harms.