The only real moat is care. It was, it is, it will be.
Taste in the age of AI and LLMs
141–150 of 230 posts
Re: Taste in the age of AI and LLMs
#142Earlier quoted context omitted.
Aren’t you just making their point stronger? Effort is what is being replaced here, with some taste and a pile of AI (formerly effort) you can go to the moon.
But you still need effort, its not only taste. "Only" means you can do it with no effort.
In other words, it requires a tremendous amount of effort to fully communicate your tastes to the AI. Not everybody wants to expend the time or mental effort doing this! (Once we have more direct brain/computer interfaces, this effort will go down, but I expect it will not be eliminated fully)
Re: Taste in the age of AI and LLMs
#143https://x.com/netcapgirl/status/2024140332963705342?s=46 evergreen.
This seems more telling on the artist who, I guess, believes that if you have taste in any field, it will manifest itself as wearing stylish clothes. I see their most recent blog post is analyzing luxury brands, so I think I'm on point here.
Re: Taste in the age of AI and LLMs
#144If you're properly bitter-lesson-pilled then why wouldn't better models continue to develop and improve taste and discernment when it comes to design, development, and just better thinking overall?
They do improve, but the general creativity and sparkle we see with increasing scale comes mostly from scaling up pretraining/parameter-size, so it's quite slow and expensive compared to the speed (and decreasing cost) people have come to take for granted in math/coding in small cheap models. Hence the reaction to GPT-4.5: exactly as much better taste and discernment as it should have had based on scaling laws, yet regarded almost universally as a colossal failure. It was as unpopular as the original GPT-3 was when the paper was released, because people look at the log-esque gains from scaling up 10x or 100x and are disappointed. "Is that all?! What has the Bitter Lesson or scaling done for me lately?"
So, you can expect coding skills to continue to outpace the native LLM taste.
Re: Taste in the age of AI and LLMs
#145Earlier quoted context omitted.
At least in part because some of Taste is fashion.
Isn't part of fashion trends? AI is good at recognizing trends.
Not in the sense you mean. These are trends with concrete origins, not a root of statistical aggregate. They only look that way if you aren't into fashion and don't follow the handful of fashion houses that decide essentially everything.
Re: Taste in the age of AI and LLMs
#146Earlier quoted context omitted.
Trying to bring my nose for AI up to standard -- care to share what you're smelling? For me it's: - Short, declarative sentences, stating grandiose yet vague claims, in a high school vocabulary: "Taste becomes useful when it moves from vibe to diagnosis." - Absence of references (let alone web links) to real-world examples. - Em-dashes, gone. No semicolons, but 23 full colons. As instructed by prompt?
To that I'd add: * an abundance of ordered and unordered lists * paragraphs are * _it's not X, it's Y_: "The goal is not to let AI choose for you. The goal is to build a sharper rejection vocabulary." "The biggest decisions are not formatting decisions. They are directional decisions." * a lot of breaking up the prose, if you can call it that * setup statement, then a colon, then a punchline: "AI and LLMs have change…
The “Here’s where things get interesting” sentence gave it away for me.
The LLM is desperately trying to keep your attention. It has been tuned with millions of examples graded by contractors. How do you spice up a fairly bland topic? Start by telling people that what follows will be interesting. Then, bloat a fairly obvious point into several sentences so that it is paragraph-shaped.
Re: Taste in the age of AI and LLMs
#147Re: Taste in the age of AI and LLMs
#148If you're properly bitter-lesson-pilled then why wouldn't better models continue to develop and improve taste and discernment when it comes to design, development, and just better thinking overall?
They do improve, but the general creativity and sparkle we see with increasing scale comes mostly from scaling up pretraining/parameter-size, so it's quite slow and expensive compared to the speed (and decreasing cost) people have come to take for granted in math/coding in small cheap models. Hence the reaction to GPT-4.5: exactly as much better taste and discernment as it should have had based on scaling laws, yet r…
I think we're basically agreeing here. Your point (if I'm reading it right) is that taste and discernment do scale, but the gains come through pretraining/parameter scaling, which is slow and expensive compared to the fast, cheap wins in math/coding from smaller models. So taste is more of a lagging indicator of scale. it improves, but it's the last thing people notice because the benchmarkable stuff races ahead. Which also means taste isn't really a moat, just late to get commoditized.
Re: Taste in the age of AI and LLMs
#149> AI and LLMs have changed one thing very quickly: competent output is now cheap. If you're working on something not truly novel, sure. If you're using LLMs to assist in e.g. Mathematics work on as-yet-unproven problems, then this is hardly the case. Hell, if we just stick to the software domain: Gemini3-DeepThink, GPT-5.4pro, and Opus 4.6 perform pretty "meh" writing CUDA C++ code for Hopper & Blackwell. And I'm not…
It doesn't have to be anything so extreme as novel work. The frontier of models still struggle when faced with moderately complex semantics. They've gotten quite good at gluing dependencies together, but it was a rather disappointing nothingburger watching Claude choke on a large xterm project I tried to give him. Spent a month getting absolutely nowhere, just building stuff out until it was so broken the codebase had to be reset and he'd start over from square 1. We've come a long way in certain aspects, but honestly we're just as far away from the silver bullet as we were 3 years ago (for the shit I care about). I'm already bundling up for the next winter.
Re: Taste in the age of AI and LLMs
#150Earlier quoted context omitted.
> ... for AI to be used effectively. I'm continually fascinated by the huge differences in individual ability to produce successful results with AI. I always assumed that one of the benefits of AI was "anyone can do this". Then I realized a lot of people I interact with don't really understand the problem they're trying to solve all that well, and have some irrational belief that they can get AI to brute force their…
> Strangely I find traditional software engineers, especially experienced ones, are generally the worst at achieving success. They often treat working with an agent too much like software engineering and end up building bad software rather than useful solutions to the core problem. This feels a bit like a strawman. How do you assess it to be bad software without being an engineer yourself? What constitutes successful…
> without being an engineer yourself?
When did I say I'm not a software engineer? I have a software engineering background (I've written reasonably successful books on software), I've just done a lot of other stuff as well that people tend to find more valuable.
> What constitutes successful for you?
The problem I need to solve is solved? I'm not sure what other measure you could have. Honestly, people really misunderstand how to use agents. If you're aim is to "build software" you're going to get in trouble, if your aim is to "solve problems" then you're more aligned with where these tools work most effectively.