Speaking as someone who has worked in a role which legitimately requires weeks of intense work (running large deals, where you're a Dune-style "mentat" about every aspect of a relationship)... these are absolutely not sustainable and the quality of your work starts to fall apart after a certain point....
It's biologically impossible to generate good long term results form 996 or 007.
The value of vibe coding isn't that it writes good or sustainable code. It's that you can build a sufficiently non-shitty prototype of a concept as a non IT expert to validate the use case and secure proper assistance.
From a change leadership perspective, walking in the door with a shitty prototype beats pitching vaporware every day of the week and twice on Sundays. And the fact amateurs can deliver (basic, crappy) "things" without budget accelerates growth.
My first projects were 80% copied off Github and some intro tutorials. Know what? They still work. We banked seven figures off of them so far...
In terms of risk, building a prototype and getting a quick win really de-risks a project. Smart decision makers' world is a game of risk - they have tolerance in some areas, less tolerance in others. If you can materialize a quick win from a prototype, it's significantly less risky that sight-unseen work.
Coders often don't think in terms of a broad investment portfolio, but that's how I've seen good executives phrase things. AI makes it cheaper and easier to build that prototype - I've been loving it for my own projects, because of how quickly I can deliver that first software.
The article hints at that when it describes vibe coding as “fancy UX”, but fails to connect the dots.
Essentially, we now have a system that can turn a simple problem description into an interactive tool for that problem. Even if it still does so very imperfectly, it’s easy see already the beginnings of a powerful and empowering new paradigm.
Or it's time to step back and call it what it is - very good pattern recognition.
I mean, that's cool... we can get a lot of work done with pattern recognition. Most of the human race never really moves above that level of thinking in the workforce or navigating their daily life, especially if they default to various societally prescribed patterns of getting stuff done (eg. go to college or the military <Based on <these criteria>, find a job <based on the best fit with <this list of desirable skills & experiences>, go to <these places> to find love....)
If we take an example of what is considered a priori as creativity, such as story telling, LLMs can do pretty well at creating novel work.
I can prompt with various parameters, plot elements, moral lessons, and get a de novo storyline, conflicts, relationships, character backstories, intrigues, and resolutions.
Now, the writing style tends to be tone-deaf and poor at building tension for the reader, and it is apparent that the storytelling has little “theory of mind” of the reader, but the material has elements that we would certainly consider to be creative if written by a student.
It seems we must either cede that LLMs can do some creative synthesis, as this and some other experiments of mine suggest, or we must decide that these tasks, such as “creative writing” are not in fact creative, but rather mostly or strictly derivative.
There is some argument to be had in assertions that storytelling is all derivative of certain patterns and variations on a fixed number of tropes and story arcs… but arguing this begs the question of whether humans actually do any “pure” creative work , or if in fact, all is the product of experience and study. (Training data)
Which leads me to the unpleasant conflict about the debate of AI creativity. Is the debate really pointing out an actual distinction, or merely a matter of degree? And what are the implications, either way?
I’m left with the feeling that LLMs can be as capable of creative work as most 8th grade students. What does this say about AI, or developing humans? Since most people don’t exceed an 8th grade level of literacy, what does this say about society?
Is there even such a thing as de novo idea synthesis?
To add to this pondering: we are discussing the state today, right now. We could assume this is as good as it's ever gonna get, and all attempts to overcome some current plateau are futile, but I wouldn't bet on it. There is a solid chance that 8th grade level writer will turn into a post-grad writer before long.
So far the improvements in writing have not been as substantial as those in math or coding (not even close, really). Is there something fundamentally “easier” for LLMs about those two fields?
Much more formal structure and generally code can be tested for correctness. Prose doesn't have that benefit. That said, given the right prompt and LLM, you can squeeze out surprisingly good stuff: https://bsky.app/profile/talyarkoni.com/post/3ldfjm37u2s2x
> Or it's time to step back and call it what it is - very good pattern recognition.
Or maybe it's time to stop wheeling out this tedious and disingenuous dismissal.
Saying it is just "pattern recognition" (or a "stochastic parrot") implies behavioural and performance characteristics that have very clearly been greatly exceeded.
They can generalise to novel inputs. Ok often they mess it up and they're clearly better at dealing with inputs they have seen before (who isn't?), but they can still reason about things they have never seen before.
Honestly if you don't believe me just go and use them. It's pretty obvious if you actually get experience with them.
Current LLMs are equivalent to tabular Markov chains (though these are too huge to realistically compute). What's the size limit when a tabular Markov chain can generalize to novel inputs?
It is evident that it is not recalling the sum because all combinations of integer addition were likely not in the training data, Storing the answer to the sum of all integers up to the size that GPT4 can manage would take more parameters than the model has.
That addition is a small capability but you only need a single counterexample to disprove a theory.
> That addition is a small capability but you only need a single counterexample to disprove a theory
No, that's not how this works :)
You can hardcode an exception to pattern recognition for specific cases - it doesn't cease to be a pattern recognizer with exceptions being sprinkled in.
The 'theory' here is that a pattern recognizer can lead to AGI. That is the theory. Someone saying 'show me proof or else I say a pattern recognizer is just a pattern recognizer' is not a theory and thus cannot be disproven, or proven.
It's not hardcoded, reissbaker has addressed this point.
I think you are misinterpreting what the argument is.
The argument being made is that LLMs are mere 'stochastic parrots' and therefore cannot lead to AGI. The analogy to Russell's teapot is that someone is claiming that Russells teapot is not there because china cannot exist in the vacuum of space. You can disprove that with a single counterexample. That does not mean the teapot is there, but it also doesn't mean it isn't.
It is also hard to prove that something is thinking. It is also very difficult to prove that something is not thinking. Almost all arguments against AGI take the form X cannot produce AGI because Y. Those are disprovable because you can disprove Y.
I don't think anyone is claiming to have a proof that an LLM will produce AGI, just that it might. If they actually build one, that too counts as a counterexample to anybody saying they can't do it.
GPT-4o doesn't have hardcoded math exceptions. If you would like something verifiable, since we don't have the source code to GPT-4o, consider that Qwen 2.5 72b can also add large integers, and we do have the source code and weights to run it... And it's just a neural net. There isn't secret "hardcode an exception to pattern recognition" in there that parses out numbers and adds them. The neural net simply learned to do it.
Nothing. I just use ChatGPT and Claude so I am familiar with their capabilities and limitations.
Imagine if people who had never used VR kept saying it's just a TV on your face, or if people who had never used static types kept saying they're just extra work you have to do, or if people who had never had sex kept saying it's just a way of making babies.
It's a tedious claim when it's so easily disproven by going to a free website and trying it. Why are people so invested in AI being useless that they'll criticise it so confidently without even trying it?
This approach shrinks "private space" by design, so more energy needs to go into "public spaces" such as lounges, fitness facilities, and outdoor / rooftop areas.
The price tag SMH. I'm 50... my first rent was $500 per month (nice one bedroom), my next place was $850 (2 bedroom apartment)....
And if enforced aggressively, will only provide a set up for false flag operations to get a competitor banned for fake reviews. I think we've already seen this movie in SEO....
The evidentiary standards for Google search ranking changes is VERY different than the one used for FTC enforcement actions.
I'm pretty sure getting caught for trying to frame a company for buying reviews would bring criminal charges that are more serious than the FTC enforcement action.
And it's an HR nightmare when a US work group becomes Indian led / dominated, especially by former H1B's. It quickly turns into importing Indian work cultures into the American legal system, frequently with nasty lawsuits....
It's biologically impossible to generate good long term results form 996 or 007.