Entry-level jobs down by a third since launch of ChatGPT

That reallocation of capital is also a culprit

I presume when they say "AI is used for data analysis" they're talking about traditional AI (more frequently referred to as "machine learning") rather than generative AI (LLMs).

> It's really, really, really good at confidently jumping to hasty conclusions and confirmation bias.

Kind of like entry level software engineers.

I am kidding, I believe the market has more to do with tax changes than AI. I just couldn't pass up the joke.

Hopefully that will be fixed this year, or tech layoffs and outsourcing in the US will pick up pace. Without the R&D write off, each dev in the US is a massive financial black hole vs hiring outside the US.

interest rates are controlled by central bankers, not magic. they make decisions based on their analysis of the economy. they raised rates to slow down the rate of investment and to suppress wages, in order to get inflation under control. Less money in circulation means reduced demand means prices stay lower, meaning lower inflation. that's the theory anyway, and the explictly expressed reason for raising rates by central banks. there's no mystery about it.

In this case it was widely publicised that interest rates went up to try to bring inflation down (which was significantly above the 2% target).

Growth was weak to unremarkable although the hiring market was good for job seekers at the time shortly before the interest rises were introduced.

> did interest rates go up, because the economy was strong, or did rising interest rates dampen the economy?

It doesnt matter. Whether it went from strong -> weak or weak -> weaker is beside the point, the question is if genAI is the main reason for entry level job loss and raising interest rates are another possible answer.

Job ads complicated further by firms posting fictional jobs to test the market or as a misleading market signal.

An awful lot of graduate positions in the U.K. are things like customer service, account management, paralegal, data analysis.

These categories have seen broad application of AI tools:

- CS, you’ll most likely talk to an LLM for first tier support these days.

- Account management comprises pressing the flesh (human required) and responding to emails - the latter, AMs have seen their workload slashed, so it stands to reason that fewer are required.

- Paralegal - the category has been demolished. Drafting and discovery are now largely automated processes.

- Data analysis - why have a monkey in a suit write you barely useful nonsense when a machine can do the same?

So - yeah, it’s purely correlative right now, but I can see how it being causative is perfectly plausible.

There is little substance to this comment other than stereotypes about India. I don't like this kind of generalization -- there are over a billion Indians, let's not lump them all together in a caricature

Aggressive in what way?

But specifically to India? There are many other countries with low income and robust education for CS and reasonable English skill. Eastern Europe for example.

This, there's also another kind of "shoring" where people are imported and given salaries at the bare minimum to qualify for H1B. As per my other post, the net amount is staggering and no where near the supposed 65k cap. My own right estimates put it at ~600k annually.

And spread across a nation of ~1,450,000,000 people.

I think my personal anecdote supports this observation with the treatment group being "me in the zone" and control group "me not in the zone".

When I'm pulling out all the stops, leaving nothing for the swim back the really powerful (and expensive!) agents are like any of the other all out measures: cut all distractions, 7 days a week, medicate the ADHD, manage the environment ruthlessly, attempt something slightly past my abilities every day. In that zone the truly massive frontier behemoths are that last 5-20% that makes things at the margin possible.

But in any other zone its way too easy to get into "hi agent plz do my job today I'm not up for it" mode, which is just asking to have some paper-mache, plausible if you squint, net liability thing pop out and kind of slide above the "no fucking way" bar with a half life until collapse of a week or maybe month.

These are power user tools for monomaniacal overachievers and Graeberism detectors for everyone else (in the "who am I today" sense, not bucketing people forever sense).

> Which means you should need fewer of them, no?

I've never worked at a company that didn't have an endless backlog of work that needs to be done. In theory, AI should enable devs to churn through that work slightly faster, but at the same time, AI will also allow PMs/work creators to create even more work to do.

I don't think AI fundamentally changes companies hiring strategies for knowledge workers. If a company wants to cheap out and do the same amount of work with less workers, then they're leaving space for their competitors to come and edge them out

Has the improved effectiveness of computers or software led you to need fewer of them?

Well, many people complain about pay inequality. Compressing scales is the opposite of that, so should be welcomed?

https://en.wikipedia.org/wiki/It%27s_the_economy,_stupid

inb4 "America is the world"

The people that implement and own these systems, owners not workers.

Our trade policy being determined by a coked-up narcissistic child that tantrums and creates a tariff anytime he hasn't been mentioned on the tv for too long or his buddies want to frontrun the market on the pullback and steal some money is not helping.

How confident do you feel about our economic policy? Even if your company isn't directly involved in international trade, there's a good chance your customers are. My customers are putting off non-essential software.

[Citation needed]

Haha, it might bankrupt the company but at least I’ll be able to understand the emails I get from AP now.

I get very good results from Claude Code, something like a 3x. It's enough that my cofounders noticed and commented on it, and has had a lot of measurable results in terms of saving $ on infrastructure.

The first thing I'll note is that Claude Code with Claude 4 has been vastly better than everything else for me. Before that it was more like a 5-10% increase in productivity.

My workflow with Claude Code is very plain. I give it a relatively short prompt and ask it to create a plan. I iterate on the plan several times. I ask it to give me a more detailed plan. I iterate on that several times, then have Claude write it down and /clear to reset context.

Then, I'll usually do one or more "prototype" runs where I implement a solution with relatively little attention to code quality, to iron out any remaining uncertainties. Then I throw away that code, start a new branch, and implement it again while babysitting closely to make sure the code is good.

The major difference here is that I'm able to test out 5-10 designs in the time I would normally try 1 or 2. So I end up exploring a lot more, and committing better solutions.

4x is a number I pulled out of thin air. I'm not sure I even yet believe there is a net positive effect of using AI on productivity. What I am sure about in my own workflow is that is saves me time writing boilerplate code - it is good at this for me. So I would say it has saved me time in the short-term. Now does not writing this boilerplate slow me down long-term? It's possible, I could forget how to do this myself, some part of my brain could atrophy (as the MIT study suggests). How it affects large teams, systems and the transfer of knowledge is also not clear.

I use it a lot for reducing friction. When I procrastinate about starting something I ask the AI to come up with a quick plan. Maybe I'll just follow the first step, but it gets me going.

Sometimes I´ll even go a bit crazy on this planning thing and do things a bit similar to what this guy shows: https://www.youtube.com/watch?v=XY4sFxLmMvw I tend to steer the process more myself, but typing whatever vague ideas are in my mind and ending up in minutes with a milestone and ticket list is very enabling, even if it isn´t perfect.

I also do more "drive by" small improvements:

- Annoying things that weren't important enough for a side quest writing a shell script, now have a shell script or an ansible playbook.

- That ugly CSS in an internal tool untouched for 5 years? fixed in 1 minute.

- The small prototype put into production with 0 documentation years ago? I ask an agentic tool to provide a basic readme and then edit it a bit so it doesn´t lie, well worth 15 minutes.

I also give it a first shot at finding the cause of bugs/problems. Most of the time it doesn't work, but in the last week it found right away the cause of some long standing subtle problems we had in a couple places.

I have also had sometimes luck providing it with single functions or modules that work but need some improvement (make this more DRY, improve error handling, log this or that...) Here I´m very conservative with the results because as you said it can be dangerous.

So am I more productive? I guess so, I don't think 4x or even 2x, I don't think projects are getting done much faster overall, but stuff that wouldn't have been done otherwise is being done.

What usually falls flat is trying to go on a more "vibe-coding" route. I have tried to come up with a couple small internal tools and things like that, and after promising starts, the agents just can't deal with the complexity without needing so much help that I'd just go faster by myself.

I lean a bit on LLMs now for initial research/prototype work and it is quite a productivity boost vs random searches on the web. I generally do not commit the code they generate because they tend to miss subtle corner cases unless the prompts I give them are extremely detailed which is not super useful to me. If an LLM does produce something of sufficient quality to get committed I clearly mark it as (at least partially) LLM generated and fully reviewed by myself before I mash the commit button and put my name on it.

Basically, I treat LLMs like a fairly competent unpaid intern and extend about the same level of trust to the output they produce.

I'm in the same boat of some of the other commenters using Claude Code but I have found it atleast a 2X in routine backend API development. Most updates to our existing APIs would be on the order of "add one more partner integration following the same interface here and add tests with the new response data". So it is pretty easy to give it to claude code, tell them where to put the new code, tell it how to test, and let it iterate on the tests. So something that may have taken a full afternoon or more to get done gets done much faster and often with a lot more test coverage.

I have the same experience as you. It has definitely increased the speed with which I can look up solutions to isolated problems, but for writing code using agents and coming up with designs, the speed is limited by the speed with which I as a human can perform code reviews. If I was surrounded by human 10x developers who wrote all the code for me and left it for me to review it, I doubt my output would be 4x.

Don’t ask the agent to do something complex. Break it down into 10 manageable steps. You are the tester and verifier of each step.

What you will find is that the agent is much more successful in this regard.

The LLM has certain intrinsic abilities that match us and like us it cannot actually code 10,000 lines of code and have everything working in one go. It does better when you develop incrementally and verify each increment. The smaller the increments the better it performs.

Unfortunately the chain of thought process doesn’t really do this. It can come up with steps, sometimes the steps are too big and it almost never properly verifies things are working after each increment. That’s why you have to put yourself in the loop here.

Like allowing the computer to run test and verify an application works as expected on each step and to even come up with what verification means is a bit of what’s missing here and I think although this part isn’t automated yet, it can easily be automated where humans become less and less involved and distance themselves into a more and more supervisory role.

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The competitive edge is now knowing how to debug all of those issues. Unfortunately not usually a skill possessed for entry level.

So 4x more productive from WFH and 8x more from LLMs. The standard is now 32x more productive than 5 years ago.

And yet, you can still claim 100% of your code to be bug free :D

But presumably you could have built it before, just slower, which is the point. For now, that speed-up just looks like a win because it’s novel, but eventually the speed-up will be baked into people’s expectations.

No, all there is in the future is 4x as many layoffs

“Shortening the workweek” sounds pretty bad… some people will suggest literally anything before higher wages.

You can already take a job with a shorter work week or move a region or country where shorter work weeks are common.

This is a net negative. Especially if you aren't 20% more paid at the same time

Sounds like AI has landed you on burnout treadmill

Moving very fast, but going nowhere.