Cheating has always been a problem. Maybe some of these bootcamp students will skate by, I'm sure some will even land jobs, but they'll get weeded out eventually. In the end they're just hurting themselves. I don't know what kind of idiot cheats their way through curriculum that they will need in order to literally do the job they hope to be paid for.

I don't think ascribing cheating to this is the correct term.

It's a bit akin to how calculators were cheating but yet in the real world everybody is fine (and probably expects) you to do math using a calculator. I don't think Kroger would be happy if the clerks starts adding up your shopping cart by pen and paper.

Would people call VFX cheaters since they aren't hand drawing each frame?

Using a tool (AI) to do a job (Websites?) is fine. The problem of course is that the students are only learning that specific tool so the second a problem occurs that isn't easily solved with that tool they're a fish out of water. This is a weakness of the boot camp not the student per-say; although obviously somebody that can use ChatGPT + < any other skill > will be a more productive employee than somebody that can _just_ use ChatGPT.

If your boot camp could be done solely using the plus button of a calculator; people would think it's not worth it. Now if your boot camp can be solved by ChatGPT is it really worth it?

> If your boot camp could be done solely using the plus button of a calculator; people would think it's not worth it.

What if it's a math boot-camp meant to increase your arithmetic proficiency?

When I was in elementary school, I fully believed that calculators made memorizing arithmetic facts obsolete, so I didn't do it and just muddled through. I did the same for algebra (relying on my calculator's solver as soon as I could get away with it). That's hobbled me for the rest of my life, because I'm so calculator dependent that I'm was always too distracted by operating it to fully engage with more advanced math material. I can't think about math in my head because I get stuck on arithmetic problems.

> Now if your boot camp can be solved by ChatGPT is it really worth it?

Yes, for the reasons I outlined above. It's not about getting the answer, it's about getting the toolbox that will get you that answer and understand it.

Do you want to be a Calculator Operator, Second Class or an Engineer?

They are there to learn the topic at hand, not to delegate it to someone else whether that be chatgpt or a tutor they pay to take the uni exam for them with their student id. If a job is fine with hiring a delegator thats great for both parties but some jobs need domain experts too who can sniff bullshit from the ai output even if they do use it day to day.

That's an over simplification. If students are using chatgpt to generate code that they themselves don't understand, and then submitting that whole cloth as their work, they're cheating - full stop. They're not using chatgpt as a tool in this case, any more than searching stack overflow and copying answers without understanding them is a tool.

The calculator comparison is interesting - calculators can't be wrong. And if they can be, we're kind of screwed. Like NASA screwed when someone mixed up imperial and metric measurements. ChatGPT can and will be wrong because it doesn't actually know what its giving back to you. Until its code can be 100% correct, which I don't think is possible, it cannot be used as a simple tool that any one can use to generate code without actually knowing how the code it generates works.

So yes - ultimately these students are cheating. Maybe it feels to them like they've just found a shortcut to being a developer, they haven't.

> calculators can't be wrong.

Sure they can. There are numerous discussions around the net about this fact, but here is a reasonable high-level overview.

https://www.thetechedvocate.org/can-a-calculator-be-wrong/

They can't be wrong in the way that LLMs can be, which is the exact reason that using them for this comparison falls apart - but yes if we're splitting hairs they can be wrong in the same way that javascript is wrong when you ask it to add two floats together.

Let’s not forget calculators are doing math, the one thing which is binary right/wrong.

Generative AI is doing much more.

But I agree that copy-pasting ChatGPT output without remotely understanding it is cheating. Yes ChatGPT can do the exercise, but the output produced in the exercise is not the point, the point is assimilating knowledge which helps you move on to tasks AI cannot do. In this regard these students are cheating themselves (and the ones who tried not to cheat).

To use the calculator analogy, I think the concern is that the calculators sometimes return close but incorrect values, and now everyone learning arithmetic is just using the calculators instead of doing it by hand, and accepting those incorrect results as truth.

The other side of this is that calculators are a much more powerful tool for someone who is already skilled in math, and those skills are best learned by actually doing problems by hand and thinking through them. So even if these tools end up being the de facto way to solve problems, we're still much better off training ourselves how to do things the hard way, and we do ourselves a disservice by skipping that step altogether. Which is a similar disservice we do ourselves by cheating in school. The difference is that someone who is dependent on the tool can get away with hiding their lack of skills for longer, and those lack of skills only show up in subtle but possibly critical ways.

GenAI isn't just about cheating though, it's replacing the need to even learn the skills people are testing for.

Edit: I didn't mean replacing all programming skills as some commenters suggest.

I challenge anyone in this forum to create a sizeable production-ready application and maintain it using a language/database/platform they have no idea about by simply asking LLMs to build it. I'm pretty sure anyone will spend enough time banging their heads against a wall trying to fix bugs they have zero understanding about or properly learning the technologies (which they should have done in the first place).

LLMs are great when you're fairly knowledgeable about the topic. You can ask it to automate stuff and you can spot the errors and fix them. Hopefully it automates enough and makes fewer errors than the time it'd take for you to code from scratch.

They currently can make programmers more productive if used right. Now allow a junior to enter the field and never learning some skill because the LLM does it all? Not even close.

The same can be said about google and more specifically about stackoverflow: you can't just mindlessly copy-paste.

It is a tool that can be useful both for a beginner and more experienced developers.

I struggle to believe that. I've found Copilot / ChatGPT really useful, especially for doing support investigation tasks (chopping and filtering logs mainly). Not once has it just output a perfect answer - it's required me to know what it's trying to do in order to tweak it to my needs. Not having the skills in the first place would leave me with a confused half solution.

I was in a bootcamp in 2014 and this was still a problem, albeit not with AI, but with the lack of useful material. We were learning very high level concepts about the "flavor of the month framework" rather than any coding fundamentals. I really wouldn't use coding bootcamps as the bar for education pipelines.

> I really wouldn't use coding bootcamps as the bar for education pipelines.

100% agree on this btw. It’s still cheating though.

Sounds like that's their choice and they are only hurting themselves in the learning process.

Whether it's wise or not, many students are using generative AI to solve their homework problems. That's what we have to wrestle with.

Maybe the concept of homework that isn't interesting enough for them to want to solve it on their own should be thrown out then? Trying to force someone to learn something never works.

At least I personally learn the most when there's some cool thing I want to do and delve into a rabbit hole figuring it out, there is basically nothing that can stop me experimenting and reading stuff to understand what's happening then, and I'll use all of the tools I can to speed up that process, whether it's search engines, reading research papers, or messing with the AI and trying to make sense of its output.

Having the AI generate mostly-functioning code is often a great first step towards having something to mess around with before going deeper.

Before that it was chegg and yahoo answers or paid tutors. Homework is not a good test of ability. Honestly neither is even just doing well alone in school. Many graduate programs expect you to have done some training conducting research under faculty direction now.

GenAI is pretty much interactive search, cheating with some sort of library/info-system always happens. GenAI is a convenience for this info. You can use Yahoo! answers to get your homework solved, or any socially-networked site...

Fully agree. Depending on your choices ChatGPT can either enhance of fully remove your learning.

That’s just cheating in school. Not an integral part of the software engineering toolset as the article claims.

Eh. When I was in a bootcamp everyone did the same thing just with stack overflow and any number of other sites. It was harder/slower but most people were doing as much copy paste as they were actually coding. I'd argue that is a lot of what junior engineers do until they learn how to actually code.

Tools like ChatGPT are just faster at doing that. I think of it like having a senior engineer who isnt going to get annoyed at being asked too many questions. But its also going to be limited like any other tool.

That sounds lovely, us folks already in the industry for some time have then nothing to worry about. Our hard earned seniority won't be matched by this approach anytime soon, not even caught up.

Not that seniority is generally much in how fast you code, in fact in most places it has relatively little to do with code at all.

I notice the college thing but I don't think it's a threat, as long as they are still able to solve problems well and understand the code that they're writing[1].

[1] Yes, we no longer understand assembly code but that one isn't based on fuzzy logic.

I saw an ad recently "You'll never code as well as ChatGPT." I suspect we'll see some changes to both how and why people code, and it's entirely likely that application types which are difficult for AI to code will become scarcer.

Similar dynamics have happened with cloud/mobile/commodity hardware/PC. Every time there is a change, there are folks who claim that the old way was easier and had been working forever.

I suspect as much as well. If AI makes it easier to produce certain products, then advertisers will do their damndest to create demand for those products. And if AI makes production cheaper, safer, and faster, then planners will orient their constructions and designs around making use of those efficiencies. Similar to how the widespread adoption of the car changed our cities, so too will AI change what we build, AI's limitations and all.

I wouldn't be surprised if, at some point, we have a mental model of "things AI can build" and companies just assume that the majority of our solutions to problems will come from within that set. It doesn't even matter if those AI solutions don't fit the problems very well, as long as they're cheap. The smaller set of "things we still have to get humans to build" will cost more and therefore be de-emphasized and seen as a last resort. An equivalent might be producing features that introduce legal vulnerabilities. You can imagine lots of planners saying, "Can we do without that feature?"

I like this graphic of draft (working) horses vs. other (companion/sport) horses over time (1800-2010 in France):

https://www.researchgate.net/publication/338480301/figure/fi...

It shows clearly that farm (and transport) automation wiped out the draft horse population, but that the other horse population, having traversed the same bottleneck (ca. 1970), has rebounded. (and in fact there were absolutely more "others" in 2010 than there were in 1840)

Moral: try to be the sort of programmer a little girl (little AI?) would love?

> It had come to [Squealer's] knowledge, he said, that a foolish and wicked rumour had been circulated at the time of Boxer's removal. Some of the animals had noticed that the van which took Boxer away was marked "Horse Slaughterer," and had actually jumped to the conclusion that Boxer was being sent to the knacker's. It was almost unbelievable, said Squealer, that any animal could be so stupid. Surely, he cried indignantly, whisking his tail and skipping from side to side, surely they knew their beloved Leader, Comrade Napoleon, better than that? But the explanation was really very simple. The van had previously been the property of the knacker, and had been bought by the veterinary surgeon, who had not yet painted the old name out. That was how the mistake had arisen. —EAB

ChatGPT even with the best prompts is, in general, not great at coding.

Being able to pass leet code isn't a threat to software developers.

"You'll never code as well as ChatGPT" is just not true.

It has next to no idea what it's doing, and this is very clear if you're experienced in software.

It's useful as all hell, as a tool. But the original statement is wholly untrue.

So true. Yet, right now it’s the worst it will ever be.

There is the threat of model collapse though as discussed in this paper:

https://browse.arxiv.org/pdf/2305.17493v2.pdf?

They discuss that if you train models with the output of other models you can get worse results. If we all start using code from those models how will that impact GPT-5?

This paper is a joke in the ML community. Model collapse is not real. This paper made some seriously faulty assumptions in their contrived lab experiment. State of the art models are trained entirely or largely on "synthetic" generated datasets to surpass models trained on purely human data in quality and capability.

Even training on uncurated synthetic generated data, from web scrapes after the advent of genersgofe AI, actually leads to more capabilities and quality in models in practice (the exact opposite of what is prediction by the failed model collapse paper).