Comment by cyode
8 hours ago
This quote stuck out to me as well, for a slightly different reason.
The “tenacity” referenced here has been, in my opinion, the key ingredient in the secret sauce of a successful career in tech, at least in these past 20 years. Every industry job has its intricacies, but for every engineer who earned their pay with novel work on a new protocol, framework, or paradigm, there were 10 or more providing value by putting the myriad pieces together, muddling through the ever-waxing complexity, and crucially never saying die.
We all saw others weeded out along the way for lacking the tenacity. Think the boot camp dropouts or undergrads who changed majors when first grappling with recursion (or emacs). The sole trait of stubbornness to “keep going” outweighs analytical ability, leetcode prowess, soft skills like corporate political tact, and everything else.
I can’t tell what this means for the job market. Tenacity may not be enough on its own. But it’s the most valuable quality in an employee in my mind, and Claude has it.
Fittingly, George Hinton toiled away for years in relative obscurity before finally being recognized for his work. I was always quite impressed by his "tenacity".
So although I don't think he should have won the Nobel Prize because not really physics, I felt his perseverance and hard work should merit something.
There is an old saying back home: an idiot never tires, only sweats.
Claude isn't tenacious. It is an idiot that never stops digging because it lacks the meta cognition to ask 'hey, is there a better way to do this?'. Chain of thought's whole raison d'etre was so the model could get out of the local minima it pushed itself in. The issue is that after a year it still falls into slightly deeper local minima.
This is fine when a human is in the loop. It isn't what you want when you have a thousand idiots each doing a depth first search on what the limit of your credit card is.
> it lacks the meta cognition to ask 'hey, is there a better way to do this?'.
Recently had an AI tell me this code (that it wrote) is a mess and suggested wiping it and starting from scratch with a more structure plan. That seems to hint at some meta cognition outlines
Haha, it has the human developer traits of thinking all old code is garbage, failing to identify oneself as the dummy who wrote this particular code, and wanting to start from scratch.
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Perhaps. I've had LLMs tell me some code is deeply flawed garbage that should be rewritten about code that exact same LLM wrote minutes before. It could be a sign of deep meta cognition, or it might be due to some cognitive gaps where it has no idea why it did something a minute ago and suddenly has a different idea.
Someone will say "you just need to instruct Claude.md to be more meta and do a wiggum loop on it"
I asked Claude to analyze something and report back. It thought for a while said “Wow this analysis is great!” and then went back to thinking before delivering the report. They’re auto-sycophantic now!
Metacognition As A Service, you say?
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I mean, not always. I've seen Claude step back and reconsider things after hitting a dead end, and go down a different path. There are also workflows, loops that can increase the likelihood of this occurring.
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This is a major concern for junior programmers. For many senior ones, after 20 (or even 10) years of tenacious work, they realize that such work will always be there, and they long ago stopped growing on that front (i.e. they had already peaked). For those folks, LLMs are a life saver.
At a company I worked for, lots of senior engineers become managers because they no longer want to obsess over whether their algorithm has an off by one error. I think fewer will go the management route.
(There was always the senior tech lead path, but there are far more roles for management than tech lead).
I feel like if you're really spending a ton of time on off by one errors after twenty years in the field you haven't actually grown much and have probably just spent a ton of time in a single space.
Otherwise you'd be senior staff to principle range and doing architecture, mentorship, coordinating cross team work, interviewing, evaluating technical decisions, etc.
I got to code this week a bit and it's been a tremendous joy! I see many peers at similar and lower levels (and higher) who have more years and less technical experience and still write lots of code and I suspect that is more what you're talking about. In that case, it's not so much that you've peaked, it's that there's not much to learn and you're doing a bunch of the same shit over and over and that's of course tiring.
I think it also means that everything you interact with outside your space does feel much harder because of the infrequency with which you have interacted with it.
If you've spent your whole career working the whole stack from interfaces to infrastructure then there's really not going to be much that hits you as unfamiliar after a point. Most frameworks recycle the same concepts and abstractions, same thing with programming languages, algorithms, data management etc.
But if you've spent most of your career in one space cranking tickets, those unknown corners are going to be as numerous as the day you started and be much more taxing.
That's just sad. Right when I found love in what I do, my work has no value anymore.
Aren't you still better off than the rest of us who found what they love + invested decades in it before it lost its value. Isn't it better to lose your love when you still have time to find a new one?
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Imagine a senior dev who just approves PRs, approves production releases, and prioritizes bug reports and feature requests. LLM watches for errors ceaslessly, reports an issue. Senior dev reviews the issue and assigns a severity to it. Another LLM has a backlog of features and errors to go solve, it makes a fix and submits a PR after running tests and verifying things work on its end.
Why are we pretending like the need for tenacity will go away? Certain problems are easier now. We can tackle larger problems now that also require tenacity.
Even right at this very moment where we have a high-tenacity AI, I'd argue that working with the AI -- that is to say, doing AI coding itself and dealing with the novel challenges that brings requires a lot of stubborn persistence.