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IBM tripling entry-level jobs after finding the limits of AI adoption

fortune.com

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Re: IBM tripling entry-level jobs after finding the limits of AI adoption

#201

Post by joquarky »

Ancalagon wrote: post #133
K0balt wrote: post #103

Earlier quoted context omitted.

Tbh, getting good results from ai requires senior level intuition. You can be rusty as hell and not even middling in the language being used, but you have to understand data structures and architecture more than ever to get non-shit results. If you just vibe it, you’ll eventually end up with a mountain of crap that works sort of, and since you’re not doing the coding, you can’t really figure it out as you go along. S…

This mirrors my experience exactly. Vibe coding straight up does not work for any serious code.

I can't help but feel the only reason to post a comment like this is due to something similar to Cunningham's Law.

https://meta.wikimedia.org/wiki/Cunningham%27s_Law

Re: IBM tripling entry-level jobs after finding the limits of AI adoption

#202

Post by colonCapitalDee »

velcrovan wrote: post #160
halamadrid wrote: post #150

I pay $20 for OpenAI and codex makes me incredibly productive. With very careful prompts aimed at tiny tasks, I can review, fix and get a lot of things done. I’ll happily pay up to $2k/month for it if I was left with no choice, but I don’t think it will ever get that expensive since you can run models locally and it could have the same result. That being said, my outputs are similarish in the big picture. When I get…

I would like to know what models people are running locally that get the same results as a $20/month ChatGPT plan

There are no models that you can run locally that'll match a frontier LLM

Re: IBM tripling entry-level jobs after finding the limits of AI adoption

#203

Post by h14h »

sarchertech wrote: post #146
milchek wrote: post #140

Earlier quoted context omitted.

I think for a lot of folks it basically comes down to just using AI to make the tasks they have to do easier and to free up time for themselves. I’d argue the majority use AI this way. The minority “10x” workers who are using it to churn through more tasks are the motivated ones driving real business value being added - but let’s be honest, in a soulless enterprise 9-5 these folks are few and far between.

Sure but why haven’t you seen a drastic increase in single person startups. Why are there fewer games launched in steam this January than last?

My guess is that the true impact of this will be difficult to measure for a while. Most "single-person start-ups" will probably not be high-visibility VC-backed, YC affairs, and rather solopreneurs with a handful of niche moonlighted apps each making 3-4 digit monthly revenue.

Re: IBM tripling entry-level jobs after finding the limits of AI adoption

#204

Post by shusaku »

fd-codier wrote: post #124
ChadMoran wrote: post #94

Earlier quoted context omitted.

[flagged]

Everytime someone say something like that there is no link to the product. Maybe because it doesn't exist ?

What an awful comment. The person above you is now flagged because of your paranoia. Of course later they post a link to exactly what they built.

Re: IBM tripling entry-level jobs after finding the limits of AI adoption

#205

Post by joquarky »

bluGill wrote: post #100
sigmoid10 wrote: post #82

Earlier quoted context omitted.

IMO with the latest generation (gpt codex 5.3 and claude 4.6) most devs could probably be replaced by AI. They can do stuff that I've seen senior devs fail at. When I have a question about a co-workers project, I no longer ask them and instead immediately let copilot have a look at the repo and it will be faster and more accurate at identifying the root cause of issues than humans who actually worked on the project.…

they are good at some weird problems - but also write some really bad code and sometimes come up with wrong answers.

That's why you write tests.

Re: IBM tripling entry-level jobs after finding the limits of AI adoption

#206

Post by joquarky »

dgxyz wrote: post #62
forgetfreeman wrote: post #58

Earlier quoted context omitted.

dotcom implosion redux

It certainly feels that way. I was there. Fortunately had just waltzed into the tech side of things and scurried off back to my professional career for a couple of years. I watched a lot of stuff burn. It was horrifying. We are nearly there again.

For a time, there was a lot of good deals on nice used office furniture.

Re: IBM tripling entry-level jobs after finding the limits of AI adoption

#207

Post by saratogacx »

velcrovan wrote: post #160
halamadrid wrote: post #150

I pay $20 for OpenAI and codex makes me incredibly productive. With very careful prompts aimed at tiny tasks, I can review, fix and get a lot of things done. I’ll happily pay up to $2k/month for it if I was left with no choice, but I don’t think it will ever get that expensive since you can run models locally and it could have the same result. That being said, my outputs are similarish in the big picture. When I get…

I would like to know what models people are running locally that get the same results as a $20/month ChatGPT plan

The run at home was in the context of $2k/mo. At that price you can get your money back on self-hosted hardware at a much more reasonable pace compared to 20/mo (or even 200).

Re: IBM tripling entry-level jobs after finding the limits of AI adoption

#208

Post by AnthonyMouse »

pickettd wrote: post #136
Morromist wrote: post #131

Earlier quoted context omitted.

I hear this every day, and I'm sure its true sometimes, but where is the tsunami of amazing software LLM users are producing? Where are the games that make the old games look like things from a bygone era? Where are the updates to the software that I currently use that greatly increase it capabilities? I have seen none of this. I get that it takes a long time to make software, but people were making big promises a ye…

Reddit and GitHub are littered with people launching new projects and appear to be way more feature-rich than new tool/app launches from previous years. I think it is a lot harder to get noticed with a new tool/app new because of this increase in volume of launches. Also weekend hackathon events have completely/drastically changed as an experience in the last 2-3 years (expectations and also feature-set/polish of wor…

The one thing AI is consistently better at than humans is shipping quickly. It will give you as much slop as you want right away, and if you push on it for a short period of time it will compile and if you run it a program will appear that has a button for each of the requested features.

Then you start asking questions like, does the button for each of the features actually do the thing? Are there any race conditions? Are there inputs that cause it to segfault or deadlock? Are the libraries it uses being maintained by anyone or are they full of security vulnerabilities? Is the code itself full of security vulnerabilities? What happens if you have more than 100 users at once? If the user sets some preferences, does it actually save them somewhere, and then load them back properly on the next run? If the preferences are sensitive, where is it saving them and who has access to it?

It's way easier to get code that runs than code that works.

Or to put it another way, AI is pretty good at writing the first 90% of the code:

    "The first 90 percent of the code accounts for the first 90 percent of the development time. The remaining 10 percent of the code accounts for the other 90 percent of the development time." — Tom Cargill, Bell Labs

Re: IBM tripling entry-level jobs after finding the limits of AI adoption

#209

Post by ineedasername »

velcrovan wrote: post #160
halamadrid wrote: post #150

I pay $20 for OpenAI and codex makes me incredibly productive. With very careful prompts aimed at tiny tasks, I can review, fix and get a lot of things done. I’ll happily pay up to $2k/month for it if I was left with no choice, but I don’t think it will ever get that expensive since you can run models locally and it could have the same result. That being said, my outputs are similarish in the big picture. When I get…

I would like to know what models people are running locally that get the same results as a $20/month ChatGPT plan

Same? Not quite as good as that. But google’s Gemma 3 27B is highly similar to their last Flash model. The latest Qwen3 variants are very good, to my need at least they are the best open coders, but really— here’s the thing:

There’s so many varieties, specialized to different tasks or simply different in performance.

Maybe we’ll get to a one-size fits all at some point, but for now trying out a few can pay off. It also starts to build a better sense of the ecosystem as a whole.

For running them: if you have an Nvidia GPU w/ 8GB of vram you’re probably able to run a bunch— quantized. It gets a bit esoteric when you start getting into quantization varieties but generally speaking you should find out the sort of integer & float math your gpu has optimized support for and then choose the largest quantized model that corresponds to support and still fits in vram. Most often that’s what will perform the best in both speed and quality, unless you need to run more than 1 model at a time.

To give you a reference point on model choice, performance, gpu, etc: one of my systems runs with an nvidia 4080 w/ 16GB VRAM. Using Qwen 3 Coder 30B, heavily quantized, I can get about 60 tokens per second.

Re: IBM tripling entry-level jobs after finding the limits of AI adoption

#210

Post by brigandish »

chasd00 wrote: post #170
culi wrote: post #147

Earlier quoted context omitted.

More interesting imo would be a measure of maintainability. I've heard that code that's largely written by AI is rarely remembered by the engineer that submitted even a week after merging You're almost "locked in" to using more AI on top of it then. It may also make it harder to give estimates to non-technical staff on how long it'd take to make a change or implement a new feature

I don’t know how to measure maintainability but the AI generated code I’ve seen in my projects is pretty plain vanilla standard patterns with comments. So less of a headache than a LOT of human code I’ve seen. Also, one thing the agents are good at, at least in my experience so far, is documenting existing code. This goes a long ways in maintenance, it’s not always perfect but as the saying goes documentation is like…

Something I occasionally do is ask it to extensively comment a section of code for me, and to tell me what it thinks the intent of the code was, which takes a lot of cognitive load off of me. It means I'm in the loop without shutting off my brain, as I do have to read the code and understand it, so I find it a sweet spot of LLM use.
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