And those CEOs will go off with their vast piles of money to make the same mistakes again, since the message they get is that this behaviour will be rewarded.

The government may not be able to bail these companies out. The scale is even bigger than the housing crisis of 2008, and trust in the current administration is basically zero. I think the most we can hope for is the LLM companies (think OpenAI and Anthropic), and the companies whose services are effectively wrappers for LLMs, and probably Oracle (with its negative cash flow and astronomical debt) all go away. Amazon, Microsoft, and Google probably survive, with some high profile bloodletting, senior executives being purged by their boards. Apple has been the least bullish on AI, so they’re probably more or less safe and the biggest change will be new OS versions that don’t refer to Apple Intelligence. Facebook is structured in such a way that Zucc can’t be removed by the board, so who knows how that plays out.

Palantir and their ilk will likely get whatever they need to survive unless the midterms bring in a shockingly progressive group that cares about people’s privacy and removes funding for mass surveillance.

Jeffery Epstein was involved with funding the research for this shit and helped popularize it in the media via his billionaire buddies.

GDP is also kind of a scam.

LLMs are not capable of creating anything, including code. They are enormous word-matching search engines that try to find and piece together the closest existing examples of what is being requested. If what you’re looking for is reasonably common, that may be useful.

Just for common understanding, you're making blanket statements about LLMs as though those statements apply to all LLMs. You're not wrong if you're generally speaking of the LLM models deployed for retail consumption like, as an example, ChatGPT. None of what I'm saying here is a defense about how these giant companies are using LLMs today. I'm just posting from a Data Science point of view on the technology itself.

However, if you're talking about the LLM technology, as in a Data Science view, your statements may not apply. The common hyperparameters for LLMs are to choose the most likely matches for the next token (like the ChatGPT example), but there's nothing about the technology that requires that. In fact, you can set a model to specifically exclude the top result, or even choose the least likely result. What comes out when you set these hyperparameters is truly strange and looks like absolute garbage, but it is unique. The result is something that likely hasn't existed before. I'm not saying this is a useful exercise. Its the most extreme version to illustrate the point. There's also the "temperature" hyperparamter which introduces straight up randomness. If you crank this up, the model will start making selections with very wide weights resulting in pretty wild (and potentially useless) results.

What many Data Scientists trying to make LLMs generate something truly new and unique is to balance these settings so that new useful combinations come out without it being absolute useless garbage.

https://archive.ph/DrZzk

Let's face it the only reason you're saying "coding is a practical use case" is because you yourself don't code, and don't understand it.

Usually only the last statement is true.

Just ask those people to read engrish, they'll stand a chance of understanding the issue. It's put together with clues of how it works, and can copypaste pieces, but without the knowledge to string it all together cohesively. Maybe not the best example, but coding is a language like English is a language, and we take a lot of our knowledge for granted when it comes to our intimate relationship with language.

I think there is quite a bit more subtlety than that.

Yes, just asking an LLM, even the latest versions, to write some code goes from “wow, that’s pretty good” to “eh, not great but ok for something I’m going to use once and then not care about” to “fucking terrible” as the size goes up. And all the agents in the world don’t really make it better.

But… there are a few use cases that I have found interesting.

1. Project management, plannning, and learning new languages/domains when using a core prompt as described at: https://www.codingwithjesse.com/blog/coding-with-llms-can-still-be-fun/

I like to add:

- Do not offer to write code unless the user specifically requests it. You are a teacher and reviewer, not a developer 
- Include checks for idiomatic use of language features when reviewing 
- The user has a strong background in C, C++, and Python. Make analogies to those languages when reviewing code in other languages

as well when I’m using it to help me learn a new language.

2. Reviews of solo projects. I like working with someone else to review my code and plans at work, particularly when I’m working in a domain or language that I don’t have experience in. But for solo projects I don’t have someone to give me reviews, so asking a coding LLM “Review this project, looking for architectural design issue, idiomatic language use, and completeness. Do not offer to fix anything, just create an issue list.” is really helpful. I can take the list, ignore anything I disagree with, and use it for a polishing round.

If you want bug ridden code with security issues which is not extensible and which no-one understands, then sure, it's a practical use case.

This assumes you never review it, meaning it’s at best an argument against vibe coding. It’s not an argument against using LLMs for coding in general.

Additionally, I’ve been writing software for a living for almost 30 years, and I could say the exact same thing about a lot of human generated code I’ve reviewed during that time. I don’t even know how often I’ve explained basic stuff like “security goes in the backend, not in the frontend” to humans.

Let's face it the only reason you're saying "coding is a practical use case" is because you yourself don't code, and don't understand it.

I certainly do code and if I don’t understand what the LLM outputs it doesn’t go in the project.

I can't see another reason why would assume the problems experienced in other domains somehow don't apply to coding.

I’m a software engineer, I can’t judge LLMs in most other domains. I also don’t think there are no problems. A tool doesn’t have to be 100% problem free to be useful as long as you recognize the limitations.

So you're going to have to pick your way through every single line it generates in order to have the same confidence you would have if you wrote it

I don’t see a problem with this. The post even mentions pulling code from stackoverflow, which is the same. But nobody ever argued that it has no uses in coding because you still have to read the code.

Honestly at this point any article just flat out dismissing LLMs for coding only reads to me like the author isn’t even trying to stay up to date. Which is understandable if they don’t like AI but makes posting about it a bit pointless.

A year ago I would had a similar opinion as the author but in the last 3-4 months specifically, it feels like AI based tools made a huge leap. I went from using short snippets for learning to letting AI implement entire features and being actually happy with the result.

There is however still a pretty big difference between what it produces for common problems vs. what it produces for specialized difficult ones. It’s also inherently better at some languages than others based on the availability of up-to-date training material. So you need some amount of breadth in your projects to accurately judge it.

If you only try some AI service in free mode on one thing every month, for example, you’ll just have this very polarized opinion that’s either “AI is useless” or “AI can do everything”, but you won’t have a good idea of what it can and can’t do.

And sometimes it doesn't actually remove the snow.