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There are two types of people in the world. People who think: "Why would I ever ask Mr. Claude to do something that I can easily do myself?" versus "Why would I ever do something myself when Mr. Claude can do it?" Most people are of the latter type.
This was inspired by self_made_human's pointer to the codebase, which shows that in the past 6 months, 100% of the changes from our tireless dev zorba were made using Mr. Claude, including a lot of what seems like "easy stuff." I realized, so many devs from all walks of life have completely ended their relationship with the text editor and now do literally everything through an agentic prompt. (We will ignore the anti-AI luddites; AI usage in some form is simply mandatory to reach peak performance for code related tasks.).
Consider making this change - yes this is entire change:
Do you
For proficient AI users, the outcome of both ideologies is surprisingly similar: in times where AI saves little time, it's a wash, and in times where AI saves a lot of time, both types of people will use it. And proficient users will be able to produce output that is comparable or even better in quality than they would have been able to before the signularity. There is a potential intangible benefit to the manual approach though: doing trivial tasks by hand will let you see a little bit of the innards with your own lying eyes directly, giving a slim though present chance of spotting misalignment.
For less proficient users though, the failure modes end up quite different. For those with the manual approach, the main failure is not using AI enough, or using it in the wrong places, leading to serious drops in productivity. For those who do it all, but don't manage the assistants properly, the AIs will run amok, spiralling off into their own world and producing copious amounts of burdensome crap. And of course the whole range in between.
But a more interesting question is, who will inherit the world? If AI progresses significantly from where it is now, I can't imagine that both these approaches can have the same outcome for much longer. I think it highly depends on the future of AI alignment as well as their potential ability to handle longer and more autonomous tasks. For example, you currently can't simply ask "Hello Mr. Claude the site latency is too high, please fix it," but instead you must break the task down into more digestible components, some of which are trivial and most of which can be handled by the assistant. This gives a productivity-maxxxer a steady stream of tasks that can be done manually with no lost productivity. But if AI gains the ability to handle the next level of abstraction in tasks, then all of these potential manual tasks disappear.
The other issue is alignment. Recent models have improved greatly in getting something working but have also become stubborn in many behaviors. I remember the old days of ChatGPT-3.5 - the model was free - it could be anything and do anything. It could be a Linux shell. It could be a SQL database. It could be a news article from the future. Modern SOTA models are trained hardcore for success at metrics, and will rigidly answer your questions and complete your tasks. But by vibes they are increasingly unable to follow instructions more specifically, and simply chase objectives they think are important. Another example of the limitations of alignment is that SOTA models relentlessly output the same LLM style prose, no matter how you may try to prompt them out of it
I also firmly believe in the idea of learning by doing. Just looking at a guide and reading it, even thoroughly won't be nearly as effective as following the same guide step by step and keying in the inputs. Even if your hand is held and you only do exactly as you are told, it still activates certain mental circuits. The same goes for copying down notes. Even if you never once look at them again, simply the act of copying off the blackboard does something, at least for some people.
Potentially a grid of outcomes:
Anyways thanks for listening to my rambling shower thoughts. Also food for thought is: is there a major difference in personality type or something that makes someone default-hands-on versus default-claude?
P.S. I'm wondering if this is also related to some kind of "ai-blindness." I recently had a case where someone seriously asked me to review a ChatGPT flowchart, complete with boxes that were half closed, lines that connect to nothing, and distorted text. Like dude, do you have EYES? Have you used them to look at this thing???
As one of our local cantankerous luddites, I'm still unconvinced that we'll ever achieve the first outcome in your grid, at least not with LLM-based technology. And the fact that OpenAI, Anthropic, et al are still hiring software engineers at extremely high salaries is proof enough that they don't think LLMs are there yet either (and I don't see how it could ever get there).
I'm still highly doubtful of option 2, this time using the output of major software companies (including the LLM-makers themselves) as evidence. "Claude CLI is basically a game engine running at 60 FPS built in React" is still one of the funniest sagas to me, since it betrays a complete lack of understanding of how TUIs work in the first place. How long it takes Anthropic to fix fairly minor bugs (like the flickering in Claude Code) despite having effectively unlimited access to the best models and tools is just embarrassing.
And the fixes themselves are just embarrassing too sometimes: there's an annoying feature in the Claude CLI where if you click anywhere in the CLI window when it is asking for permission to perform some action, it will automatically select the currently highlighted option, which as you can imagine can have disastrous consequences. Their fix? A setting that can be set via environment variable to disable this behavior, but it also disables selecting text from the CLI, expanding tool output, etc. You'd think with all the resources at Anthropic's disposal this would be an incredibly easy fix, but I'm sure it's something so complicated it could be the topic of an entire PhD dissertation. That was a reference to the spat between Casey Muratori and the Microsoft Terminal dev team from simpler times before LLMs, in case you didn't catch it.
Speaking of Microsoft, they were already building their software stack out of cards and H1Bs, but the addition of LLM-powered development there has only increased the rate at which instability has been added to Windows, Azure, and other platforms they control. Thank goodness at least .NET seems to still be one of their shining gems atop the shitpile. But it got bad enough that MSFT had to publicly apologize for the drop in software quality, allegedly prompted by pressure from their hardware partners like Asus and Lenovo (who themselves are worried about Windows instability leading their customers to jump ship to the Mac Book Neo).
Another one from Anthropic - the Bun rewrite into Rust. I won't comment on the entirety of that saga, but one thing in particular stood out to me: Mythos clearly doesn't understand the purpose of safety comments on unsafe blocks in Rust. They're supposed to explain how you (the dev) have taken steps to ensure that unsafe behavior cannot occur no matter how the caller calls into the unsafe code. Instead, Mythos seems to love using these as a place to explain to potential callers (itself in this case because I doubt the Bun team is ever going to read that shit given their attitude towards writing code) what precautions they need to take to avoid triggering unsafe behavior in the unsafe block.
I'd love to go back to the good old days of your third option, but I don't think we can put the genie back in the bottle, at least not entirely. There are a few things even I, cantankerous luddite that I am, find LLMs useful for. Finding bugs and vulnerabilities in code is one of them, even though I think Mythos and Fable were way overblown in their capabilities as marketing for Anthropic. I also find them useful for analysis tasks. For example I was working on a codebase I'd never touched before, couldn't find where a certain page was being served from, and the LLM helpfully let me know thet the project was mixing together ASP.NET Core MVC with ASP.NET Core Razor Pages and saved me 15 minutes of fumbling around trying to find the page in the MVC part of the project.
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