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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???
I've been ruminating about this lately; my linguistics hot take is that even with arbitrarily advanced translation ability, you still run into the irreducible complexity of language, that fundamentally limits what you can do with manipulation of language alone.
For example, take the sentence "Rather than take for granite that Ace talks straight, a listener must be on guard for an occasional entre nous and me… or a long face no see". This sentence is fundamentally and logically, impossible to fully translate into any other language regardless of how good of a translator you are.
You must either translate it literally (hence losing any semantic meaning), translate the semantic meaning (hence losing the literal meaning), or translate via using malapropisms in the destination language (hence losing both the literal and semantic meanings).
In a similar way, vibe coders really like that Claude can "read their mind" when they put in a prompt and get back lots of code, and there's no denying that LLM's are getting better and better at writing lots of code when you give them natural language prompts.
What we are all now learning in software engineering is that some of the time, it actually doesn't matter how Claude decides to translate your natural language prompt as long as the symbols on the other end produces the desired result; but of course, no matter how good your translator is, there is fundamentally irreducible complexity when translating between languages, and it is impossible to verify that Claude translated your full intent without actually being able to understand both languages.
In this sense, Dijikstra puts it well when he states that "instead of regarding the obligation to use formal symbols as a burden, we should regard the convenience of using them as a privilege".
In fact, the scaling laws paper actually predicts this as well; cross-entropy loss decreases as a power law with the model size, dataset size, and compute, but the loss is also bounded by the irreducible entropy of the language that comes to dominate as you pump in ever more parameters, data tokens and compute.
I don't doubt that universal translation isn't an incredible feat of human ingenuity, that it's not going to revolutionize much of how humans work and live. But the more I use LLM's and encounter all manner of these little alignment problems, I feel like it's this irreducible complexity inherent to language that is ultimately going to define the ceiling of what LLM's are capable of.
People laughed and laughed, but little did they know that in the end, Stephen Wolfram gets the last laugh.
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