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.
Generated code is rather a lot like fast fashion: it looks all right at first glance but it doesn’t hold up over time, and when you look closer it’s full of holes. Just like fast fashion, it’s often ripped off other people’s designs. And it’s a scourge on the environment.
This is depressing.
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 suppose it’s not clear to me what a ‘good’ window into unreliable, systemically toxic systems accomplishes, or how it changes anything that matters for the better, or what that idea even means at all. I don’t understand how “ethical AI” isn’t just “clean coal” or “natural gas.” The power of normalization as four generations are raised breathing low doses of aerosolized neurotoxins; the alternative was called “unleaded”, but the poison was called “regular gas”.
There’s a real technology here, somewhere. Stochastic pattern recognition seems like a powerful tool for solving some problems. But solving a problem starts at the problem, not working backwards from the tools.
Delivering total nonsense, with complete confidence.
Wake me up when we get to the plateau of productivity.
Language matters.
Large language models are big messy brushes, not scalpels.
A large language model is as neutral as an AK-47.
The best of the web is under continuous attack from the technology that powers your generative “AI” tools.