Experienced software developer, here. "AI" is useful to me in some contexts. Specifically when I want to scaffold out a completely new application (so I'm not worried about clobbering existing code) and I don't want to do it by hand, it saves me time.

And... that's about it. It sucks at code review, and will break shit in your repo if you let it.

Just the other day I wasted 3 min trying to get AI to sort 8 lines alphabetically.

Exactly what you would expect from a junior engineer.

Let them run unsupervised and you have a mess to clean up. Guide them with context and you’ve got a second set of capable hands.

Something something craftsmen don’t blame their tools

Exactly what you would expect from a junior engineer.

Let them run unsupervised and you have a mess to clean up. Guide them with context and you’ve got a second set of capable hands.

Something something craftsmen don’t blame their tools

By having it write a quick function to do so or to sort them alphabetically within the chat? Because I've used GPT to write boilerplate and/or basic functions for random tasks like this numerous times without issue. But expecting it to sort a block of text for you is not what LLMs are really built for.

That being said, I agree that expecting AI to write complex and/or long-form code is a fool's hope. It's good for basic tasks to save time and that's about it.

I study AI, and have developed plenty of software. LLMs are great for using unfamiliar libraries (with the docs open to validate), getting outlines of projects, and bouncing ideas for strategies. They aren't detail oriented enough to write full applications or complicated scripts. In general, I like to think of an LLM as a junior developer to my senior developer. I will give it small, atomized tasks, and I'll give its output a once over to check it with an eye to the details of implementation. It's nice to get the boilerplate out of the way quickly.

Don't get me wrong, LLMs are a huge advancement and unbelievably awesome for what they are. I think that they are one of the most important AI breakthroughs in the past five to ten years. But the AI hype train is misusing them, not understanding their capabilities and limitations, and casting their own wishes and desires onto a pile of linear algebra. Too often a tool (which is one of many) is being conflated with the one and only solution--a silver bullet--and it's not.

This leads to my biggest fear for the AI field of Computer Science: reality won't live up to the hype. When this inevitably happens, companies, CEOs, and normal people will sour on the entire field (which is already happening to some extent among workers). Even good uses of LLMs and other AI/ML use cases will be stopped and real academic research drying up.

I study AI, and have developed plenty of software. LLMs are great for using unfamiliar libraries (with the docs open to validate), getting outlines of projects, and bouncing ideas for strategies. They aren't detail oriented enough to write full applications or complicated scripts. In general, I like to think of an LLM as a junior developer to my senior developer. I will give it small, atomized tasks, and I'll give its output a once over to check it with an eye to the details of implementation. It's nice to get the boilerplate out of the way quickly.

Don't get me wrong, LLMs are a huge advancement and unbelievably awesome for what they are. I think that they are one of the most important AI breakthroughs in the past five to ten years. But the AI hype train is misusing them, not understanding their capabilities and limitations, and casting their own wishes and desires onto a pile of linear algebra. Too often a tool (which is one of many) is being conflated with the one and only solution--a silver bullet--and it's not.

This leads to my biggest fear for the AI field of Computer Science: reality won't live up to the hype. When this inevitably happens, companies, CEOs, and normal people will sour on the entire field (which is already happening to some extent among workers). Even good uses of LLMs and other AI/ML use cases will be stopped and real academic research drying up.

Couldn't have said it better myself. The amount of pure hatred for AI that's already spreading is pretty unnerving when we consider future/continued research. Rather than direct the anger towards the companies misusing and/or irresponsibly hyping the tech, they direct it at the tech itself. And the C Suites will of course never accept the blame for their poor judgment so they, too, will blame the tech.

Ultimately, I think there are still lots of folks with money that understand the reality and hope to continue investing in further research. I just hope that workers across all spectrums use this as a wake up call to advocate for protections. If we have another leap like this in another 10 years, then lots of jobs really will be in trouble without proper social safety nets in place.

AI tools are way less useful than a junior engineer, and they aren't an investment that turns into a senior engineer either.

The difference being junior engineers eventually grow up into senior engineers.

Yeah but a Claude/Cursor/whatever subscription costs $20/month and a junior engineer costs real money. Are the tools 400 times less useful than a junior engineer? I’m not so sure…

AI tools are way less useful than a junior engineer, and they aren't an investment that turns into a senior engineer either.

Even at $100/month you’re comparing to a > $10k/month junior. 1% of the cost for certainly > 1% functionality of a junior.

You can see why companies are tripping over themselves to push this new modality.

Just the other day I wasted 3 min trying to get AI to sort 8 lines alphabetically.

Exactly what you would expect from a junior engineer.

Let them run unsupervised and you have a mess to clean up. Guide them with context and you’ve got a second set of capable hands.

Something something craftsmen don’t blame their tools

I was just ballparking the salary. Say it’s only 100x. Does the argument change? It’s a lot more money to pay for a real person.

Exactly what you would expect from a junior engineer.

Except junior engineers become seniors. If you don't understand this ... are you HR?

I study AI, and have developed plenty of software. LLMs are great for using unfamiliar libraries (with the docs open to validate), getting outlines of projects, and bouncing ideas for strategies. They aren't detail oriented enough to write full applications or complicated scripts. In general, I like to think of an LLM as a junior developer to my senior developer. I will give it small, atomized tasks, and I'll give its output a once over to check it with an eye to the details of implementation. It's nice to get the boilerplate out of the way quickly.

Don't get me wrong, LLMs are a huge advancement and unbelievably awesome for what they are. I think that they are one of the most important AI breakthroughs in the past five to ten years. But the AI hype train is misusing them, not understanding their capabilities and limitations, and casting their own wishes and desires onto a pile of linear algebra. Too often a tool (which is one of many) is being conflated with the one and only solution--a silver bullet--and it's not.

This leads to my biggest fear for the AI field of Computer Science: reality won't live up to the hype. When this inevitably happens, companies, CEOs, and normal people will sour on the entire field (which is already happening to some extent among workers). Even good uses of LLMs and other AI/ML use cases will be stopped and real academic research drying up.