Tools: Code Is All You Need

Directionally I think this is right. Most LLM usage at scale tends to be filling the gaps between two hardened interfaces. The reliability comes not from the LLM inference and generation but the interfaces themselves only allowing certain configuration to work with them.

LLM output is often coerced back into something more deterministic such as types, or DB primary keys. The value of the LLM is determined by how well your existing code and tools model the data, logic, and actions of your domain.

In some ways I view LLMs today a bit like 3D printers, both in terms of hype and in terms of utility. They excel at quickly connecting parts similar to rapid prototyping with 3d printing parts. For reliability and scale you want either the LLM or an engineer to replace the printed/inferred connector with something durable and deterministic (metal/code) that is cheap and fast to run at scale.

Additionally, there was a minute during the 3D printer Gardner hype cycle where there were notions that we would all just print substantial amounts of consumer goods when the reality is the high utility use case are much more narrow. There is a corollary here to LLM usage. While LLMs are extremely useful we cannot rely on LLMs to generate or infer our entire operational reality or even engage meaningfully with it without some sort of pre-existing digital modeling as an anchor.

Interesting take but too bearish on LLMs in my opinion.

LLMs have already found large-scale usage (deep research, translation) which makes them more ubiquitous today than 3D printers ever will or could have been.

What we call an LLM today (by which almost everyone means an autogressive language model from the Generative Pretrained Transformer family tree, and BERTs are still doing important eork, believe that) is actually an offshoot of neural machine translation.

This isn't (intentionally at least) mere HN pedantry: they really do act like translation tools in a bunch of observable ways.

And while they have recently crossed the threshold into "yeah, I'm always going to have a gptel buffer open now" territory at the extreme high end, their utility outside of the really specific, totally non-generalizing code lookup gizmo usecase remains a claim unsupported by robust profits.

There is a hole in the ground where something between 100 billion and a trillion dollars in the ground that so far has about 20B in revenue (not profit) going into it annually.

AI is going to be big (it was big ten years ago).

LLMs? Look more and more like the Metaverse every day as concerns the economics.

>LLMs? Look more and more like the Metaverse every day as concerns the economics.

ChatGPT has 800M+ weekly active users how is that comparable to the Metaverse in any way?

I said as concerns the economics. It's clearly more popular than the Oculus or whatever, but it's still a money bonfire and shows no signs of changing on that front.