it’s crazy how you could easily lie about having 10 years experience because your results are not that much different from someone who has only used Claude Code for like a week.
I think the older AI users are even held back because they might be doing things that are not neccessary any more, like explaining basic things, like please don't bring in random dependencies but prefer the ones that are there already, or the classic think really strongly and make a plan, or try to use a prestigious register of the language in attempts to make it think harder.
Nowadays I just paste a test, build, or linter error message into the chat and the clanker knows immediately what to do, where it originated, and looks into causes. Often times I come back to the chat and see a working explanation together with a fix.
Before I had to actually explain why I want it to change some implementation in some direction, otherwise it would refuse no I won't do that because abc. Nowadays I can just pass the raw instruction "please move this into its own function", etc, and it follows.
So yeah, a lot of these skills become outdated very quickly, the technology is changing so fast, and one needs to constantly revisit if what one had to do a couple of months earlier is still required, whether there's still the limits of the technology precisely there or further out.
> I think the older AI users are even held back because they might be doing things that are not neccessary any more
As the same age as Linus Torvalds, I'd say that it can be the opposite.
We are so used to "leaky abstractions", that we have just accepted this as another imperfect new tech stack.
Unlike less experienced developers, we know that you have to learn a bit about the underlying layers to use the high level abstraction layer effectively.
What is going on under the hood? What was the sequence of events which caused my inputs to give these outputs / error messages?
Once you learn enough of how the underlying layers work, you'll get far fewer errors because you'll subconciously avoid them. Meanwhile, people with a "I only work at the high-level"-mindset keeps trying to feed the high-level layer different inputs more or less at random.
For LLMs, it's certainly a challenge.
The basic low level LLM architecture is very simple. You can write a naive LLM core inference engine in a few hundred lines of code.
But that is like writing a logic gate simulator and feeding it a huge CPU gate list + many GBs of kernel+rootfs disk images. It doesn't tell you how the thing actually behaves.
So you move up the layers. Often you can't get hard data on how they really work. Instead you rely on empirical and anecdotal data.
But you still form a mental image of what the rough layers are, and what you can expect in their behavior given different inputs.
For LLMs, a critical piece is the context window. It has to be understood and managed to get good results. Make sure it's fed with the right amount of the right data, and you get much better results.
> Nowadays I just paste a test, build, or linter error message into the chat and the clanker knows immediately what to do
That's exactly the right thing to do given the right circumstances.
But if you're doing a big refactoring across a huge code base, you won't get the same good results. You'll need to understand the context window and how your tools/framework feeds it with data for your subagents.
I think GP meant 'longer time users of AI', not 'older aged users of AI'.
Their point being that it's not really an advantage to have learnt the tricks and ways to deal with it a year, two years ago when it's so much better now, and that's not necessary or there's different tricks.
The obvious solution is for Anthropic et al. to certify the skills of each user:
> “Good at explaining requirements, needs handholding to understand complex algorithms, picky with the wording of comments, slightly higher than average number of tokens per feature.”
I’m not saying this would be good at all, but the data (/insights) and the opportunity are clearly there.
I hope it’s at least a little tricky, since Claude was released only 3 years ago. That said, I would not be surprised to see companies asking for 10 years experience, despite that inconvenient truth.
I’ve seen it play out multiple times, highlights precisely why a candidate should never withhold their application based on preference of years of experience with anything. They simply haven’t put much thought into those numbers.
You've never seen project managers basically propose the equivalent of getting a baby delivered in 1 month instead of 9 months by adding more people to the project?
But yeah, if the recruiters start asking for "10 years experience with Claude Code", then I guess a tongue-in-cheek answer would be "sure, I did 10 projects in parallel in one year".
Adding more people to a project doesn’t improve throughout - past a certain point. Communication and coordination overhead (between humans) is the limiting factor. This has been well known in the industry for decades.
Additionally, i’d much rather hire someone that worked on a a handful of projects, but actually _wrote_ a lot of the code, maintained the project after shipping it for a couple years, and has stories about what worked and didn’t, and why. Especially a candidate that worked on a “legacy” project. That type of candidate will be much more knowledgeable and able to more effectively steer an AI agent in the best direction. Taking various trade offs into account. It’s all too easy to just ship something and move on in our industry.
Brownie points if they made key architecture decisions and if they worked on a large scale system.
Claude building something for you isn’t “learning” in my opinion. That’s like saying I can study for a math exam by watching a movie about someone solving math problems. Experience doesn’t work like that. You can definitely learn with AI but it’s a slow process, much like learning the old fashioned way.
