Uses

I don’t use large language models. My objection to using them is ethical. I know how the sausage is made.

I wanted to clarify that. I’m not rejecting large language models because they’re useless. They can absolutely be useful. I just don’t think the usefulness outweighs the ethical issues in how they’re trained.

Molly White came to the same conclusion:

The benefits, though extant, seem to pale in comparison to the costs.

Rich has similar thoughts:

What I do know is that I find LLMs useful on occasion, but every time I use one I die a little inside.

I genuinely look forward to being able to use a large language model with a clear conscience. Such a model would need to be trained ethically. When we get a free-range organic large language model I’ll be the first in line to use it. Until then, I’ll abstain. Remember:

You don’t get companies to change their behaviour by rewarding them for it. If you really want better behaviour from the purveyors of generative tools, you should be boycotting the current offerings.

Still, in anticipation of an ethical large language model someday becoming reality, I think it’s good for me to have an understanding of which tasks these tools are good at.

Prototyping seems like a good use case. My general attitude to prototyping is the exact opposite to my attitude to production code; use absolutely any tool you want and prioritise speed over quality.

When it comes to coding in general, I think Laurie is really onto something when he says:

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.

In other words, despite what the hype says, these tools are far better at transforming than they are at generating.

Iris Meredith goes deeper into this distinction between transformative and compositional work:

Compositionality relies (among other things) on two core values or functions: choice and precision, both of which are antithetical to LLM functioning.

My own take on this is that transformative work is often the drudge work—take this data dump and convert it to some other format; take this mock-up and make a disposable prototype. I want my tools to help me with that.

But compositional work that relies on judgement, taste, and choice? Not only would I not use a large language model for that, it’s exactly the kind of work that I don’t want to automate away.

Transformative work is done with broad brushstrokes. Compositional work is done with a scalpel.

Large language models are big messy brushes, not scalpels.

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.

What we talk about when we talk about AI — Careful Industries

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.

The Gist: AI, a talking dog for the 21st Century.

My main problem with AI is not that that it creates ugly, immoral, boring slop (which it does). Nor even that it disenfranchises artists and impoverishes workers, (though it does that too).

No, my main problem with AI is that its current pitch to the public is suffused with so much unsubstantiated bullshit, that I cannot banish from my thoughts the sight of a well-dressed man peddling a miraculous talking dog.

Also, trust:

They’ve also managed to muddy the waters of online information gathering to the point that that even if we scrubbed every trace of those hallucinations from the internet – a likely impossible task - the resulting lack of trust could never quite be purged. Imagine, if you will, the release of a car which was not only dangerous and unusable in and of itself, but which made people think twice before ever entering any car again, by any manufacturer, so long as they lived. How certain were you, five years ago, that an odd ingredient in an online recipe was merely an idiosyncratic choice by a quirky, or incompetent, chef, rather than a fatal addition by a robot? How certain are you now?

AI wants to rule the World, but it can’t handle dairy.

AI has the same problem that I saw ten year ago at IBM. And remember that IBM has been at this AI game for a very long time. Much longer than OpenAI or any of the new kids on the block. All of the shit we’re seeing today? Anyone who worked on or near Watson saw or experienced the same problems long ago.

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.