Threat models
People talk about the effectiveness (or lack thereof) of large language models as though all tasks are comparable. But it strikes me that there are three broad categories of work that large language models are applied to:
- Compression.
- Transformation.
- Expansion.
Compression is when you feed a large language model something big that you want to make small. Summarise this book. Give me the gist of this meeting. Large language models are generally pretty good at this, which makes sense given that they themselves are kind of like compressed artifacts.
Transformation is when large language models convert from one format into another. Turn this audio into text. Turn this jumble of data into structured JSON. A large language model can handle these tasks pretty well. There’ll probably be a few errors so make sure that’s not a deal-breaker.
Expansion is when you give a large language model a prompt to generate something from scratch. An image. A presentation. An email. A poem. This is where slop lives. The output inevitably betrays its origins, glistening with a sheen of mediocrity.
Laurie spotted this three-way split a while back:
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.
I hope that when the bubble finally bursts, we’ll see the surviving large language models put to work on the first two categories. The boring stuff. The work that’s tedious for humans.
But tedious is as tedious does. Something I consider drudgery might be the very thing that gives you life. Like Giles says:
I have a feeling that everyone likes using AI tools to try doing someone else’s profession. They’re much less keen when someone else uses it for their profession.
The big exception seems to be programming. Apparently there are plenty of coders who never before expressed an interest in being managers who are now happily hanging up their coding spurs in favour being the overseer of non-human workers.
It’s a reasonable outlook. It could even be considered a user-centred approach. Users don’t care about the elegance of your code; they care about accomplishing their tasks.
Programming is something of an exception to the efficacy of large language models in general. Instead of relying on the subjectivity of painting, poetry, or prose, programming can be objectively tested. Throw enough money at the worst people in the world and they’ll give you tokens you can use to get the machines to test their own output. So you can get a large language model to create something reasonably good from scratch as long as that something is code.
If you had asked me about the threat model of large language models two years ago, I probably would’ve been worried for artists, writers, and musicians. I thought that software had enough inherent complexity to be relatively safe.
Now my opinion has completely reversed. Software is almost certainly the killer app for large language models.
I think the artists, writers, and musicians will be okay, or at least as okay as they ever were. It turns out that humans like things made by other humans.
And y’know what? If I had to choose which endeavour I’d rather see automated away—programming or art—it’s no competition.
Don’t get me wrong—it would be nice if everyone got paid for doing what they enjoy. It’s just that I’m okay with software engineers not being at the front of that line.
I remember when I first started getting paid money to make websites. “Really?” I thought, “Someone is willing to pay me to do something I’d do anyway?” I kept waiting for the jig to be up. Instead I saw my profession grow and expand.
Perhaps there’s a long-overdue compression happening.
Or maybe it’s more like a transformation.