AI slows down some experienced software developers, study finds
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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.
My fear for the software industry is that we'll end up replacing junior devs with AI assistance, and then in a decade or two, we'll see a lack of mid-level and senior devs, because they never had a chance to enter the industry.
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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.
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I’ve used cursor quite a bit recently in large part because it’s an organization wide push at my employer, so I’ve taken the opportunity to experiment.
My best analogy is that it’s like micro managing a hyper productive junior developer that somehow already “knows” how to do stuff in most languages and frameworks, but also completely lacks common sense, a concept of good practices, or a big picture view of what’s being accomplished. Which means a ton of course correction. I even had it spit out code attempting to hardcode credentials.
I can accomplish some things “faster” with it, but mostly in comparison to my professional reality: I rarely have the contiguous chunks of time I’d need to dedicate to properly ingest and do something entirely new to me. I save a significant amount of the onboarding, but lose a bunch of time navigating to a reasonable solution. Critically that navigation is more “interrupt” tolerant, and I get a lot of interrupts.
That said, this year’s crop of interns at work seem to be thin wrappers on top of LLMs and I worry about the future of critical thinking for society at large.
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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.
People specifically hate having tools they find more frustrating than useful shoved down their throat, having the internet filled with generative ai slop, and melting glaciers in the context of climate change.
This is all specifically directed at LLMs in their current state and will have absolutely zero effect on any research funding. Additionally, openAI etc would be losing less money if they weren't selling (at a massive loss) the hot garbage they're selling now and focused on research.
As far as worker protections, what we need actually has nothing to do with AI in the first place and has everything to do with workers/society at large being entitled to the benefits of increased productivity that has been vacuumed up by greedy capitalists for decades.
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AI tools are way less useful than a junior engineer, and they aren't an investment that turns into a senior engineer either.
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…
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Writing code is the easiest part of my job. Why are you taking that away?
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The difference being junior engineers eventually grow up into senior engineers.
Does every junior eventually achieve becoming a senior?
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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…
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.
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AI tools are way less useful than a junior engineer, and they aren't an investment that turns into a senior engineer either.
Is “way less useful” something you can cite with a source, or is that just feelings?
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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.
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.
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Just the other day I wasted 3 min trying to get AI to sort 8 lines alphabetically.
I wouldn’t mention this to anyone at work. It makes you sound clueless
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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.
Except junior engineers become seniors. If you don't understand this ... are you HR?
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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.
Wasn’t it clear that our comments are in agreement?
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Exactly what you would expect from a junior engineer.
Except junior engineers become seniors. If you don't understand this ... are you HR?
They might become seniors for 99% more investment. Or they crash out as “not a great fit” which happens too. Juniors aren’t just “senior seeds” to be planted
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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.
They aren’t detail oriented enough to write full applications or complicated scripts.
I'm not sure I agree with that. I wrote a full Laravel webapp using nothing but ChatGPT, very rarely did I have to step in and do things myself.
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.
Yep, I agree with that.
There are definitely people misusing AI, and there is definitely lots of AI slop out there which is annoying as hell, but they also can be pretty capable for certain things too, even more than one might think at first.
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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.
I have limited AI experience, but so far that's what it means to me as well: helpful in very limited circumstances.
Mostly, I find it useful for "speaking new languages" - if I try to use AI to "help" with the stuff I have been doing daily for the past 20 years? Yeah, it's just slowing me down.
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Wasn’t it clear that our comments are in agreement?
It wasn’t, but now it is.
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They aren’t detail oriented enough to write full applications or complicated scripts.
I'm not sure I agree with that. I wrote a full Laravel webapp using nothing but ChatGPT, very rarely did I have to step in and do things myself.
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.
Yep, I agree with that.
There are definitely people misusing AI, and there is definitely lots of AI slop out there which is annoying as hell, but they also can be pretty capable for certain things too, even more than one might think at first.
Greenfielding webapps is the easiest, most basic kind of project around. that's something you task a junior with and expect that they do it with no errors. And after that you instantly drop support, because webapps are shovelware.
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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.
Excellent take. I agree with everything. If I give Claude a function signature, types and a description of what it has to do, 90% of the time it will get it right. 10% of the time it will need some edits or efficiency improvements but still saves a lot of time. Small scoped tasks with correct context is the right way to use these tools.
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AI tools are way less useful than a junior engineer, and they aren't an investment that turns into a senior engineer either.
AI tools are actually improving at a rate faster than most junior engineers I have worked with, and about 30% of junior engineers I have worked with never really "graduated" to a level that I would trust them to do anything independently, even after 5 years in the job. Those engineers "find their niche" doing something other than engineering with their engineering job titles, and that's great, but don't ever trust them to build you a bridge or whatever it is they seem to have been hired to do.
Now, as for AI, it's currently as good or "better" than about 40% of brand-new fresh from the BS program software engineers I have worked with. A year ago that number probably would have been 20%. So far it's improving relatively quickly. The question is: will it plateau, or will it improve exponentially?
Many things in tech seem to have an exponential improvement phase, followed by a plateau. CPU clock speed is a good example of that. Storage density/cost is one that doesn't seem to have hit a plateau yet. Software quality/power is much harder to gauge, but it definitely is still growing more powerful / capable even as it struggles with bloat and vulnerabilities.
The question I have is: will AI continue to write "human compatible" software, or is it going to start writing code that only AI understands, but people rely on anyway? After all, the code that humans write is incomprehensible to 90%+ of the humans that use it.
