Further, the graph shown is pretty noisy and I'm not sure the upward move which counters the downward trent is statistically significant.

Data : https://www.bls.gov/news.release/pdf/empsit.pdf

>> The May jobs report reinforced this with nonfarm payrolls jumping by 172,000, confirming that there are no signs of workers being replaced by ChatGPT.

The disclosures are longer than the content

Much of the "AI job crisis" rhetoric was PR comms to manage conversations around corporate restructuring (even ZIRP is a lazy PR comms excuse).

Most decisionmakers by 2025 already agreed they didn't expect AI to have a significant impact on hiring [0].

I've pointed out the reasons ad nauseum on here but no one listens [1].

[0] - https://www.bloomberg.com/news/articles/2025-12-10/wall-stre...

[1] - https://news.ycombinator.com/item?id=47174561

https://corvi.careers/blog/global_software-engineering_jobs_...

1. AI ATS systems have made posting jobs "cheap", such that too companies post jobs that don't exist (ie "ghost jobs") to keep up appearances they're hiring or just to keep people in the pipeline in case they hire. This is a huge waste of everybody's time and should be illegal;

2. The hiring process itself gets increasingly Kafkaesque. AI screening, automated online tests, unpaid take-home work, etc. You have to get pretty far until a human gets involved. 10+ years ago this didn't happen because people needed to be involved much sooner and that's expensive;

3. In a lot of companies, getting employees to interview people is unpaid extra work effectively. They say it's important. You might even get dinged for not doing it. But anyone who has done it realizes pretty quickly a bunch of people who shouldn't get interviewed are getting interviewed and management doesn't care, even though employee time is expensive, because you essentially have to "make up the time" so it's still "free";

4. Even if you go through all that and get hired, you get laid off within a year such that income isn't dependable and you end up wasting a ton of time on the job-seeking process itself.

I've been thinking about this recently and high-information is part of the problem. In years long gone, it was hard to reach applicants so you'd have a small pool of higher-relevance candidates applying for a job. Say 10 people applying for 10 jobs. The odds were better. It was less work on everybody's side.

But now you have 200 people applying for 200 positions. This wastes everybody's time but the problem is that companies have offset this by pushing filtering onto these automated systems. People still need to enter all their bio information, etc. So it's just much more inefficient inherently even if the job opening is legitimate.

the claim "hey there is no AI job crisis", when previous SWE of 6-figures now takes job dishwashing in McDonalds + one more gig as Uber driver + food delivery gig is "job creation! now they have 3 jobs!". does not make any sense.

Maybe all the job cuts from ai were filled by fixers of ai output

Or maybe, no one ever heard of jevons paradox. Or maybe everyone ignored it and preached job apocalypse as risky but a high reward marketing tactic

But then people who work in actual tech companies come in and explicitly say they are not hiring any juniors anymore specifically because AI is good enough to do most of what juniors do, and that senior engineers can now write 3x as much code, etc.

There seems to be a desire for a narrative that AI really just can't replace productive work, and that it's all a mirage. However it seems just like common sense that if an AI can do junior-engineer-level coding work, that a company has less reason to hire a junior engineer.

If you had to hire a concierge for a hotel, would you rather take a guest-oriented, quick-witted junior or an AI? If you could either take a Waymo or an 80-year old driver, what would you take?

I don’t believe hiring managers think that one-dimensionally. Roles are unique and in some roles experience is more important than in others. Plus, juniors already balance their lack of experience with lower salary expectations.

The much easier explanation for the anecdotes: companies are more cautious at the moment and if you only have a few positions to hand out, you rather take the proven hand than risking it on someone who hasn’t shown yet that they can do it

If this kind of argument were generally valid, it would imply that:

- all change neither accelerates nor decelerates, which is absurd, on the face of it;

- the initial stages of a deep change are always surface-visible; for instance, cancers announce themselves when they begin to gestate, rather than when they metastasize

- A few recent points of data of questionable significance outweighs a hypothesis with considerable support from reason, intuition, and other (unpresented) data. For example, the plight of recent CS grads, which _is_ new, and _is_ on graphs, just not the one the author here chose.

So, since these implied claims are self-evidently _false_, it means that the author would, at a minimum, need to provide an explanation as to _why in this one instance, these considerations do not matter_; for example, the author could have argued that the graph positioned at the center of their argument is the one to look at (as opposed to, say, recent CS grads,) but that _itself requires further argumentation._

It also does not account for the other obvious possibilities; e.g.,that there is a delay between the (as it were) lightning and its thunder; or that even strongly nonlinear effects would have shown up by now in the metric chosen; etc. But since these contributions were not included in the original post, I have no choice but to discount it.