How does one explain the drop starting January 2023 (esp for things like Customer Service Rep, which is an NLP-heavy task) when most corporations didnt even start LLM/NLP pilots until mid/late 2023? I skimmed thru the 100+ page paper but didnt see an explanation for this strange leading effect.
SWE figures dropped mid-2022 (almost magically in line with interest rate hikes) and LLM-copilots werent introduced for another year. The paper notes they did an adjustment for the end of ZIRP. I dont know enough econometrics to understand whether this adjustment was sufficient, but the chart doesnt make sense since the labor efforts seem to be leading the actual technology by over a year or more. From informal surveys, LLM-copilot usage didnt become widespread until late 2023 to mid 2024, certainly not widespread enough to cause macro labor effects in mid-2022.
Skimming this, I'm not sure why it couldn't be explained by the layoffs we had a couple years ago, which were primarily at tech companies (which are indeed more exposed to LLMs) and probably hit junior devs more.
A lot of CEOs are saying do more with less. Basically they are saying, “If there are any requests to hire additional people the first thing I ask them is if any AI tools have been tried first.”
Then every 2-bit president, leader, manager, CEO out there regurgitates the same thing.
So yes, companies want to save money and do more with leas. It certainly won’t help job seekers or the economy.
I think not hiring juniors is a tragedy of the commons situation. It started before the AI boom, during COVID. It's not tax-related as people claim here, since this phenomenon is not US-only.
The ZIRP era made companies hire people as if there was no tomorrow, and companies started "poaching" engineers from others, including juniors. I saw some interns with 2 years of experience getting offers as seniors. I had friends being paid to attend boot camp.
Then everyone realized they were training junior engineers who would quickly get offers from other companies as “Senior" and leave. So companies stopped hiring them.
We need to reframe it. At this point, what we call "AI" is not a technology, but a subscription company.
A technology is a tool you can adopt in your toolchain to perform at task, even if in this case it's outsourcing cognitive load. For a subscription company, well, as long as the subscription is active, you get to outsource some of the cognitive load. When Anthropic's CEO says that white color jobs will disappear, he means that he is selling Enterprise subscriptions, and that companies will inevitably buy it.
My challenge here is that every time I see “The Atlantic” or “The New York Times”, I can’t shake the feeling that it’s got to be a paid advertisement or some influence piece by a special interest group. I am not sure what to make of the articles that appear in those places anymore.
Meh... just rehashing what he said before. The paper itself is fundamentally flawed, examining only a minuscule portion of the job market. If we step back and look at Europe's struggling economies over recent decades, we see that economic downturns disproportionately affect young people. Greece serves as the poster child, followed by Spain and Italy. In Germany alone, we've lost 50,000 jobs in manual labor heavy industries (mainly automotive) this past year. We're also seeing a 60% decline in apprenticeships for labor intensive roles at DAX companies that aren't even AI affected yet. AI has become a convenient scapegoat for a faltering economy driven by geopolitical tensions, protectionism and unqualified leadership in the world's largest economies. Roaring 20s indeed.
One thing that confuses me is that the anti-AI movement has adopted several talking points saying that nobody wants AI. (e.g. nobody wants it, the AI companies are pushing it on us, management is pushing it on us, it just creates low quality slop, the demand is fake, etc).
But if there's no demand, then there's no threat to jobs. On the other hand if there is a threat to jobs then there must be demand (since it competes with human labor in the job market). I'm not sure why they're taking this particular tactic, but it's going to lead to strange comment sections where people won't know how to stay on message as it becomes clearer that AI is impacting the labor force.
A lot of people intuitively imagine that to compete with humans there's some sort of 1:1 exchange ratio. But initially the calculus will be something like a 10 team with plus AI performs about on par with a 12 person team (or whatever the details work out to). So we will see AI impacting job numbers well before AI can do your entire job.
"AI" dives in and disrupts and then it turns out that AI isn't too I. The disrupt phase where HR dumps staff based on dubious promises and directions from above takes a few months. The gradual re-hiring takes way longer than the dumping phase and will not trigger thresholds.
I've spent quite a while with "AI". LLMs do have a use but dumping staff is not one of the best ideas I've seen. I get that a management team are looking for trimmings but AI isn't the I they are looking for.
In my opinion (MD of a small IT focused company) LLMs are a better slide rule. I have several slide rules and calculators and obviously a shit load of computers. Mind you my slide rules can't access the internet, on the other hand my slide rules always work, without internets or power.
Software engineering is in correction mode since 2022, right after the Covid highs. AI is just the facade for job cuts. Zuck has been doing “the year of efficiency” for years now.
I doubt the whole narrative is true, it may just be hope that we do not need to hire because we will be able to do it with AI. But reality outside of software and tech is very different. I work in an organisation that heavily pushing AI and even with that push, a typical employee is still not utilising it fully, unprepared and expecting training from the employer. There is also a disconnect over connecting org data to these tools and between developers and cyber teams.
I don't get how interest rates is given at best a cursory phrase when ZIRP regime ending is one of the biggest macro events of the past several decades. Seems like it would deserve more of a spotlight.
Any economic data from between 2020 and 2025 should be tossed in the garbage. We will have no idea what affect AI has or hasn't had until AI has been available outside of the extremely confounded current circumstances. Tell me how employment looks after the next recession when the after effects of the pandemic, rapid inflation, interest rate unpredictability, and tariff whiplash are hopefully all behind us.
My experience: we're hiring for an AI engineer position and for a frontend developer position (and yes, we posted our positions here on Hackernews).
We have a stream of cookie-cutter candidates. As if they are clones of each other, it's uncanny. They typically have a BS degree in some foreign university, then a CS Masters' in the US, experience with robotics, then several years of experience in large companies.
And they completely fold during in-person coding tasks. Like, not being able to explain the difference between DFS and BFS (depth/breadth-first search). Or being able to write a simple custom metric and train a network in Pytorch.
And a similar story for the frontend developer position.
We now literally have to add more filters to not get inundated by underqualified candidates. These filters will make it harder for beginners to even _get_ to the resume review stage.
No conclusions from me, but something's been broken in the CS jobs market for a while.
I made a stupid simple model where hiring in all age brackets rose slowly until 2021 and then fell slowly. That produces very similar looking graphs, because the many engineers that were hired at the peak move up the demographic curve over time. Normalizing the graph to 2022 levels, as the paper seems to do, hides the fact that the actual hiring ratios didn't change at all.
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[ 4.4 ms ] story [ 62.6 ms ] threadSWE figures dropped mid-2022 (almost magically in line with interest rate hikes) and LLM-copilots werent introduced for another year. The paper notes they did an adjustment for the end of ZIRP. I dont know enough econometrics to understand whether this adjustment was sufficient, but the chart doesnt make sense since the labor efforts seem to be leading the actual technology by over a year or more. From informal surveys, LLM-copilot usage didnt become widespread until late 2023 to mid 2024, certainly not widespread enough to cause macro labor effects in mid-2022.
Perverse.
Then every 2-bit president, leader, manager, CEO out there regurgitates the same thing.
So yes, companies want to save money and do more with leas. It certainly won’t help job seekers or the economy.
The ZIRP era made companies hire people as if there was no tomorrow, and companies started "poaching" engineers from others, including juniors. I saw some interns with 2 years of experience getting offers as seniors. I had friends being paid to attend boot camp.
Then everyone realized they were training junior engineers who would quickly get offers from other companies as “Senior" and leave. So companies stopped hiring them.
A technology is a tool you can adopt in your toolchain to perform at task, even if in this case it's outsourcing cognitive load. For a subscription company, well, as long as the subscription is active, you get to outsource some of the cognitive load. When Anthropic's CEO says that white color jobs will disappear, he means that he is selling Enterprise subscriptions, and that companies will inevitably buy it.
One thing that confuses me is that the anti-AI movement has adopted several talking points saying that nobody wants AI. (e.g. nobody wants it, the AI companies are pushing it on us, management is pushing it on us, it just creates low quality slop, the demand is fake, etc).
But if there's no demand, then there's no threat to jobs. On the other hand if there is a threat to jobs then there must be demand (since it competes with human labor in the job market). I'm not sure why they're taking this particular tactic, but it's going to lead to strange comment sections where people won't know how to stay on message as it becomes clearer that AI is impacting the labor force.
A lot of people intuitively imagine that to compete with humans there's some sort of 1:1 exchange ratio. But initially the calculus will be something like a 10 team with plus AI performs about on par with a 12 person team (or whatever the details work out to). So we will see AI impacting job numbers well before AI can do your entire job.
"AI" dives in and disrupts and then it turns out that AI isn't too I. The disrupt phase where HR dumps staff based on dubious promises and directions from above takes a few months. The gradual re-hiring takes way longer than the dumping phase and will not trigger thresholds.
I've spent quite a while with "AI". LLMs do have a use but dumping staff is not one of the best ideas I've seen. I get that a management team are looking for trimmings but AI isn't the I they are looking for.
In my opinion (MD of a small IT focused company) LLMs are a better slide rule. I have several slide rules and calculators and obviously a shit load of computers. Mind you my slide rules can't access the internet, on the other hand my slide rules always work, without internets or power.
We have a stream of cookie-cutter candidates. As if they are clones of each other, it's uncanny. They typically have a BS degree in some foreign university, then a CS Masters' in the US, experience with robotics, then several years of experience in large companies.
And they completely fold during in-person coding tasks. Like, not being able to explain the difference between DFS and BFS (depth/breadth-first search). Or being able to write a simple custom metric and train a network in Pytorch.
And a similar story for the frontend developer position.
We now literally have to add more filters to not get inundated by underqualified candidates. These filters will make it harder for beginners to even _get_ to the resume review stage.
No conclusions from me, but something's been broken in the CS jobs market for a while.
https://docs.google.com/spreadsheets/d/1z0l0rNebCTVWLk77_7HA...