Technology

From what I've seen and heard, there are a few factors to this.

One is that the tech industry right now is built on venture capital. In order to survive, they need to act like they're at the forefront of the Next Big Thing in order to keep bringing investment money in.

Another is that LLMs are uniquely suited to extending the honeymoon period.

The initial impression you get from an LLM chatbot is significant. This is a chatbot that actually talks like a person. A VC mogul sitting down to have a conversation with ChatGPT, when it was new, was a mind-blowing experience. This is a computer program that, at first blush, appears to be able to do most things humans can do, as long as those things primarily consist of reading things and typing things out - which a VC, and mid/upper management, does a lot of. This gives the impression that AI is capable of automating a lot of things that previously needed a live, thinking person - which means a lot of savings for companies who can shed expensive knowledge workers.

The problem is that the limits of LLMs are STILL poorly understood by most people. Despite constructing huge data centers and gobbling up vast amounts of electricity, LLMs still are bad at actually being reliable. This makes LLMs worse at practically any knowledge work than the lowest, greenest intern - because at least the intern can be taught to say they don't know something instead of feeding you BS.

It was also assumed that bigger, hungrier LLMs would provide better results. Although they do, the gains are getting harder and harder to reach. There needs to be an efficiency breakthrough (and a training breakthrough) before the wonderful world of AI can actually come to pass because as it stands, prompts are still getting more expensive to run for higher-quality results. It took a while to make that discovery, so the hype train was able to continue to build steam for the last couple years.

Now, tech companies are doing their level best to hide these shortcomings from their customers (and possibly even themselves). The longer they keep the wool over everyone's eyes, the more money continues to roll in. So, the bubble keeps building.

When the AI bubble bursts, even janitors and nurses will lose their jobs. Financial institutions will go bust.

I've seen and used AI for snippets of code and it's pretty decent at that.

With my colleagues I always compare it to a battery powered drill. It's very powerful and can make shit a lot easier. But you'd not try to build furniture from scratch with only a battery powered drill.

You need the knowledge to use it - and also saws, screws, the proper bits for those screws and so on and so forth.

How is it not correct if the code successfully does the very thing that was prompted?

F.ex. in my company we don’t have any real programmers but have built handful of useful tools (approx. 400-1600 LOC, mainly Python) to do some data analysis, regex stuff to cleanup some output files, index some files and analyze/check their contents for certain mistakes, dashboards to display certain data, etc.

Of course the apps may not have been perfect after the very first prompt, or even compiled, but after iterating an error or two, and explaining an edge case or two, they’ve started to perform flawlessly, saving tons of work hours per week. So how is this not useful? If the code creates results that are correct, doesn’t that make the app itself technically ”correct” too, albeit likely not nearly as optimized as equivalent human code would be.

I am a software architect, an mainly usw it to refactor my own old code... But i am maybe not a typical architect...

I think it's useful for writing mundane snippets I've written a million times or helping me with languages I'm less familiar with, but anything more compex becomes pretty spaghetti pretty quick.

AI is works well for mindless tasks. Data formatting, rough drafts, etc.

Once a task requires context and abstract thinking, AI can't handle it.

There already are. People all over LinkedIn are changing their titles to "AI Code Cleanup Specialist".

I mean largely for most of us I hope. But I feel like the tech sector was oversatured because of all the hype of it being an easy get rich quick job. Which for some people it was.

Only for taxpayers ☝️

I will concur with the whole 'llm keeps suggesting to reinvent the wheel'

And poorly. Not only did it not use a pretty basic standard library to do something, it's implementation is generally crap. For example it offered up a solution that was hard coded to IPv4, and the context was very ipv6 heavy

If it wasn't for all the AI hype that it's going to do everyone's job, LLMs would be widely considered an amazing advancement in computer-human interaction and human assistance. They are so much better than using a search engine to parse web forums and stack overflow, but that's not going to pay for investing hundreds of billions into building them out. My experience is like yours - I use AI chat as a huge information index mainly, and helpful sounding board occasionally, but it isn't much good beyond that.