In my view these models produce above average code which is good enough for most jobs. But the hacker news sampling could be biased towards the top tier of coders - so their personal account of it not being good enough can also be true. For me the quality isn't anywhere close to good enough for my purposes, all of my easy code is already done so I'm only left working on gnarly niche stuff which the LLMs are not yet helpful with.
For the effect on the industry, I generally make the point that even if AI only replaces the below average coder it will cause a downward pressure on above average coders compensation expectation.
Personally, humans appear to be getting dumber at the same time that AI is getting smarter and while, for now, the crossover point is at a low threshold that threshold will of course increase over time. I used to try to teach ontologies, stats, SMT solvers to humans before giving up and switching to AI technologies where success is not predicated on human understanding. I used to think that the inability for most humans to understand these topics was a matter of motivation, but have rather recently come to understand that these limitations are generally innate.
It is difficult if you have been told all your life that you are the best, to accept the fact that a computer or even other people might be better than you.
It requires lot of self-reflection.
Real top-tiers programmers actually don’t feel threatened by LLMs. For them it is just one more tool in the toolbox like syntax highlighting or code completion.
They choose to use these tools based on productivity gains or losses, depending on the situation.
Not to diminish your point at all: I think it's also just a fear that the fun or interesting part of the task is being diminished. To say that the point of programming is to solve real world problems ('productivity') is true, but in my experience it's not necessarily true for the person doing the solving. Many people who work as programmers like to program (as in, the process of working with code, typing it, debugging it, building up solutions from scratch), and their job is an avenue to exercise that part of their brain.
Telling that sort of person that they're going to be more productive by skipping all the "time consuming programming stuff" is bound to hurt.
> Real top-tiers programmers actually don’t feel threatened by LLMs.
They should, because LLMs are coming for them also, just maybe 2-3 years later than for programmers that aren't "real top-tier".
The idea that human intellect is something especially difficult to replicate is just delusional. There is no reason to assume so, considering that we have gone from hole card programming to LLMs competing with humans in a single human lifetime.
I still remember when elite chessplayers were boasting "sure, chess computers may beat amateurs, but they will never beat a human grandmaster". That was just a few short years before the Deep Blue match.
The difference is that nobody will pay programmers to keep programming once LLMs outperform them. Programmers will simply become as obsolete as horse-drawn carriages, essentially overnight.
> They should, because LLMs are coming for them also, just maybe 2-3 years later than for programmers that aren't "real top-tier".
Would you be willing to set a deadline (not fuzzy dates) when my job is going to be taken by an LLM and bet $5k on that?
Because the more I use LLMs and I see their improvement rate, the less worried I am about my job.
The only thing that worries me is salaries going down because management cannot tell how bad they're burying themselves into technical debt and maintenance hell, so they'll underpay a bunch of LLM-powered interns... which I will have to clean up and honestly I don't want to (I've already been cleaning enough shit non-LLM code, LLMs will just generate more and more of that).
> Would you be willing to set a deadline (not fuzzy dates) when my job is going to be taken by an LLM and bet $5k on that?
This is just a political question and of course so long as humans are involved in politics they can just decide to ban or delay new technologies, or limit their deployment.
Also in practice it's not like people stopped traditional pre-industrial production after industrialization occurred. It's just that pre-industrial societies fell further and further behind and ended up very poor compared to societies that chose to adopt the newest means of production.
I mean, even today, you can make a living growing and eating your own crops in large swathes of the world. However you'll be objectively poor, making only the equivalent of a few dollars a day.
In short I'm willing to bet money that you'll always be able to have your current job, somewhere in the world. Whether your job maintains its relative income and whether you'd still find it attractive is a whole different question.
> The difference is that nobody will pay programmers to keep programming once LLMs outperform them. Programmers will simply become as obsolete as horse-drawn carriages, essentially overnight.
I don't buy this. A big part of the programmer's job is to convert vague and poorly described business requirements into something that is actually possible to implement in code and that roughly solves the business need. LLMs don't solve that part at all since it requires back and forth with business stakeholders to clarify what they want and educate them on how software can help. Sure, when the requirements are finally clear enough, LLMs can make a solution. But then the tasks of testing it, building, deploying and maintaining it remain too, which also typically fall to the programmer. LLMs are useful tools in each stage of the process and speed up tasks, but not replacing the human that designs and architects the solution (the programmer).
> > Real top-tiers programmers actually don’t feel threatened by LLMs.
> They should, because LLMs are coming for them also, just maybe 2-3 years later than for programmers that aren't "real top-tier".
Not worrying about that because if they've gotten to that point (note: top tier programmers also need domain knowledge) then we're all dead a few years later.
Re: Compensation expectations, I figured out a long time ago that bad programmers create bad code, and bad code creates work for good programmers.
If the amount of bad code is no longer limited by the availability of workers who can be trained up to "just below average" and instead anyone who knows how to work a touchscreen can make AI slop, this opens up a big economic opportunity.
One could hope, but in my view perception precedes reality and even if that is the reality the perception is that AI will lower compensation demands and those doing the layoffs/hiring will act accordingly.
You could also make the same claims about outsourcing, and while it appears that in most cases the outsourcing doesn't pay off, the perception that it would has really damaged CS as a career.
And like with outsourcing it starts with the jobs at the lower end of the skill range in an industry, and so people at the higher end don't worry about it, and later it expands and they learn that they too are not safe.
What happened a couple of decades ago in poetry [1] could happen now with programming:
> No longer is it just advertising jingles and limericks made in Haiti and Indonesia. It's quatrains, sonnets, and free-form verse being "outsourced" to India, the Philippines, Russia, and China.
...
> "Limericks are a small slice of the economy, and when people saw globalization creating instability there, a lot said, 'It's not my problem,'" says Karl Givens, an economist at Washington's Economic Policy Institute. "Now even those who work in iambic pentameter are feeling it."
For the effect on the industry, I generally make the point that even if AI only replaces the below average coder it will cause a downward pressure on above average coders compensation expectation.
Personally, humans appear to be getting dumber at the same time that AI is getting smarter and while, for now, the crossover point is at a low threshold that threshold will of course increase over time. I used to try to teach ontologies, stats, SMT solvers to humans before giving up and switching to AI technologies where success is not predicated on human understanding. I used to think that the inability for most humans to understand these topics was a matter of motivation, but have rather recently come to understand that these limitations are generally innate.