Why “AI” won’t help you get past the blank page in any meaningful way:
The value in writing lies in what we discover while writing.
A very astute framing by Ted Chiang—large language models as a form of lossy compression for text.
When we’re dealing with sequences of words, lossy compression looks smarter than lossless compression.
A lot of uses have been proposed for large language models. Thinking about them as blurry JPEGs offers a way to evaluate what they might or might not be well suited for.
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.
The promise of AI – the promise AI companies make to investors – is that there will be AIs that can do your job, and when your boss fires you and replaces you with AI, he will keep half of your salary for himself, and give the other half to the AI company.
That’s it.
That’s the $13T growth story that MorganStanley is telling. It’s why big investors and institutionals are giving AI companies hundreds of billions of dollars. And because they are piling in, normies are also getting sucked in, risking their retirement savings and their family’s financial security.
Now, if AI could do your job, this would still be a problem. We’d have to figure out what to do with all these technologically unemployed people.
But AI can’t do your job. It can help you do your job, but that doesn’t mean it’s going to save anyone money.
AI has the Jeopardy Phenomenon too.
If you use it to generate code that is outside your expertise, you are likely to think it’s all well and good, especially if it seems to work at first pop. But if you’re intimately familiar with the technology or the code around the code it’s generating, there is a good chance you’ll be like hey! that’s not quite right!
Not just code. I’m astounded by the cognitive dissonance displayed by people who say “I asked an LLM about {topic I’m familiar with}, and here’s all the things it got wrong” who then proceed to say “It was really useful when I asked an LLM for advice on {topic I’m not familiar with, hence why I’m asking an LLM for advice}.”
Like, if you know that the results are super dodgy for your own area of expertise, why would you think they’d be any better for, I don’t know, restaurant recommendations in a city you’ve never been to?
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.
Whether you’re generating slop or code, underneath it’s the same shoggoth with a smiley face.