alt Hacker NewsTaste in the age of AI and LLMs
Comments
> One of the most useful things about AI is also one of the most humbling: it reveals how clear your own judgment actually is. If your critique stays vague, your taste is still underdeveloped. If your critique becomes precise, your judgment is stronger than the model output. You can then use the model well instead of being led by it.
This is a mistake.
Instead, the right train of thought is: "what would perfect code look like?" and then meticulously describe to the LLM what "perfect" is to shape every line that gets generated.
This exercise is hard for some folks to grasp because they've never thought much about what well-constructed code or architectures looks like; they have no "taste" and thus no ability to precisely dictate the framework for "perfect" (yes, there is some subjectivity that reflects taste).
I think there is a parallel to what happened to watch market with Quartz crisis. The same way Quartz has led to decline of Swiss movements, LLMs are going to have a huge effect on developer market. I hypothesize that in future there will be a micro segment which care about quality, taste, exclusivity etc the same way the luxury watch makers found a niche. My perspective is that this "taste" or "quality" will not be a moat. Instead, it will be a niche where only a small segment would care about it.
(edit: typos)
Title: Good Taste the Only Real Moat Left
Followed by an entire AI generated fluff piece https://www.pangram.com/history/347cd632-809c-4775-b457-d9bc...
Flagged
If 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?
Think about moats in long term vs short term:
Speed and distribution aren't a long-run moat because they are something AI can canabalize in a platform. Eventually they will coexist on your distribution base and offer it at a lower cost than you. Its a mote if it holds up before you exit at a high valuation... which a lot are setup to do.
Taste: that's interesting. There is an argument there. It's hard to keep in the long-run and requires a lot of reinvestment in new talent
Proprietary data: Yes, very much so.
Trade Craft: Your new shiney system will still have to adhere to methods of of old clunky real world systems. Example, evidence for court. Methods for investigations. This is going to be industry specific, but you'd be surprised how many there are. This is long-term.
Those who have the moat should focus on short burts of meaningful changes as they will rely heavily on gaining trust in established systems. In those places its more about trusting whats going on than doing it faster and better, so you want trust + faster and/or better.
Good judgment and effort has always been the "real moat" - in arts, music, science, food, product...
There's always been ways to "flatten the middle" - by outsourcing, by using pre-packaged goods, with industrialization...
So yeah we've always loved handcrafted, exquisite things; there's never been a "moat" in middle
It doesn't mean you can't make a good living without a moat though
Does anyone remember this quote by Why the Lucky Stiff: “When you don't create things, you become defined by your tastes rather than ability.”
IMO, taste has always been one of the strongest moats because we struggle to define what good taste even is. We know it when we see it, but other than pointing to examples, we can’t really describe it in general terms. I still remember a line from Paul Graham’s Hackers and Painters where he was describing the difficulty of hiring software engineers. He says he was talking with a colleague after an interview and remarked (I’m paraphrasing), “I know he can write code. But does he have taste?” Taste is something we all want our colleagues and products and services to have, but defining it is really difficult. And yes, I fully agree with the writer that it’s important more than ever in this age of AI where generation is cheap.
Ever since Rick Rubin has been on his book tour, he has become the patron saint of Product Manger Tech Twitter.
I'm sure this can be solved by A/B tasting. ;)
It buried the more important point, one tech hasn't learned yet.
Taste may be kind of important because it helps toward the truly important thing, which is skin-in-the-game.
But also, with the right skin-in-the-game, you don't even need "taste." You just need real life consequences, which we don't do enough in tech.
I already disagree with the first line: competent output is not cheap. At least if defined as a final product.
- Just think about scientific research. Lots of data analysis results are not cheap to get.
- Even vibe coding is difficult: you need to think very hard about what you want.
What is cheaper now are some building blocks. We just have a new definition of building blocks. But putting the blocks is still hard.
I think "taste" is definitely an overused meme at this point, its like tech twitter discovered this word in 2024 and never stopped using it (same with "agency", "high leverage", etc).
Having read the article, I think I see the author's argument (*). I think "taste" here in an engineering context basically just comes down to an innate feeling of what engineering or product directions are right or wrong. I think this is different from the type of "taste" most people here are talking about, though I'm sure product "taste" specifically is somewhat correlated with your overall "taste." Engineering "taste" seems more correlated with experience building systems and/or strong intuitions about the fundamentals. I think this is a little different from the totally subjective, "vibes based taste" that you might think of in the context of design or art.
Now where I disagree is that
1. "taste" is a defensible moat
2. "taste" is "ai-proof" to some extent
"Taste" is only defensible to the extent that knowing what to do and cutting off the _right_ cruft is essential to moving faster. Moving faster and out executing is the real "moat" there. And obviously any cognitive task, including something as nebulous as "taste," can in theory be done by a sufficiently good AI. Clarity of thought when communicating with AI is, imo, not "taste."
Talking specifically about engineering - the article talks about product constraints and tradeoffs. I'd argue that these are actually _data_ problems, and once you solve those, tradeoffs and solving for constraints go from being a judgement call to being a "correct" solution. That is to say, if you provide more information to your AI about your business context, the less judgement _you_ as the implementer need to give. This thinking is in line with what other people here have already said (real moats are data, distribution, execution speed).
I think there's something a bit more interesting to say about the user empathy part, since it could be difficult for LLMs to truly put themselves in users shows when designing some interactive surfaces. But I'm sure that can be "solved" too, or at least, it can be done with far less human labor than it already takes.
In general though, tech people are some of the least tasteful people, so its always funny to see posts like this.
The only real moat is care. It was, it is, it will be.
Ah, Steve Jobs vs Bill Gates. Designer vs 41 shades of blue. This is nothing new. There's space for everybody.
>AI and LLMs have changed one thing very quickly: competent output is now cheap.
Already wrong.
Designers and product managers were showing Steve Jobs all the fancy things the new app for writing DVDs could do.
Steve Jobs stopped them, drew a square on the whiteboard and said “anything the user drags into this square gets written to the DVD” - that is taste!
Reminds me of PG's classic essay, "Taste for Makers" (2002): https://paulgraham.com/taste.html
> Good Taste the Only Real Moat Left > YC startups are doomed
Try using a coding agent to write an efficient GPU kernel. I guess they might get good at it soon, but they definitely aren't there yet.
I use AI for code and we review that code and write tests ourselves first which the AI cannot touch. For writing we hardly ever do, unless we know the requester of something is incompetent and will never read it anyway; then it is a waste of time to do anything, but they expect something substantial and nice looking to tick a few boxes. It is great for that; a large bank with 40 layers of management, all equally incompetent, asked for a 'all encompassing technical document vault'; one of them sent an 'expectation document' which contained so much garbage as to show they did not even know what they were asking, but 1000s of pages was the expectation. So sure, claude will write that in an hour, notebooklm will add 100 slidedecks for juiceness. At first sight it looks amazing; its probably mostly accurate as well, but who knows; they will never ever read it; no one will. We got the 20m+ (with many opportunities to grow much larger) project. Before that was only in reach of the huge consultants (where everyone in those management levels worked before probably) who we used to lose against. Slop has its purpose.
> 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 talking about poorly-spec'd problems. I'm talking about mapping straightforward mathematics in annotated WolframLanguage files to WGMMA with TMA.
lol the unfortunate truth is that hundreds of billions and trillions will be spent to learn a single truth: Taste cannot simply be bought nor can you bring products that add value into the world through sheer will of training machines.
Ah, the classic "we'll ship production to China and just do design and marketing in US, because we have taste on what to build, and China doesn't". That worked really well...
This cope is insane. Even simple projects generated by Claude are riddled with bugs. And there’s no way in hell it could generate a larger scoped project without a lot of manual human intervention. But yea, TODO apps and trivial calculators are effectively “solved”. Same with leetcode. I guess that’s probably the limit of many people’s imagination these days.
This reads like cope. If taste were a real moat, designers and art directors would be the highest paid people in tech. They arent. Execution speed, distribution, and capital are moats. Taste is a tiebreaker at best. The market consistently rewards "good enough, shipped fast" over "exquisite, shipped late".
> A practical loop for training taste
Taste is cheap. Taste (or a rudimentary version of it at least) is something you start with at the beginning of your career. Taste is the thing that tells you "this is fucking cool", or "I don't know why but this just looks right". LLM's are not going to replicate that because it's not a human and taste isn't something you can make. Now - MAKING something that "looks right" is hard, and because LLM's are churning out the middle - the middle is moving somewhere else. Just like rich people during the summer.
Article assumed as absolute truth, without explanation, that competent systems can be effortlessly implemented.
If one disagrees with that's statement, there is nothing of value to extract from this article.
Words are cheap, bullet point are cheap.
Taste shows up in three places:
What you notice
What you reject
How precisely you can explain what feels wrongI think it's just as important, if not more, to be able to explain what is right and what you accept. Having a well defined acceptance criteria also fits into existing project management frameworks. These criteria are generally based on asking users. The article mentions, You do not get a spreadsheet that tells you which sentence will make a customer care, which feature is worth a month of engineering time, or which design crosses the line from polished to forgettable. And this is why you talk to your customers.
I agree with the author and I think this is turning everyone into an investor. How I view (financial) investing as a career is that it is less manual and more taste oriented. You put your stake in the things you feel will work out and taste here just means the judgement required to make good calls. A person with good taste would have a better idea of capital allocation.
What AI is doing is making all of us investors instead of doers. "Doing" is no longer something praiseworthy - what will become praiseworthy is how your taste has turned out in hindsight.
I'm seeing this at work. More or less everyone can do tasks well. But what's harder now is the more subtle task of taking bets and seeing it work over a few months or years.
> That is why so much AI-generated work feels familiar:
This was already a complaint people had before Ai. Like when logos and landing pages all used to look the same. Or coffee shops all looking the same.
It has been for a while. Hollywood and other outlets didn’t need AI tools to create abysmal slop.
Well, nope. There are three real moats left in software:
Distribution, Data (Proprietary) and Iteration Speed.
Very successful companies have all three: Stripe, Meta, Google, Amazon.
taste isn't a moat at all because it's so variable, in fact this stuff will start dictating what taste is through broad proliferation
you already see it on facebook with all the ai generated meme sharing... taste is being eroded there
The new world order is what not to build...
I dont buy the authors argument. Not much has changed imo. Mediocre slop has always been the easiest thing to generate.
[dead]
[flagged]
Disagree with the overall argument. Human effort is still a moat. I've been spending the past couple of months creating a codebase that is almost entirely AI-generated. I've gotten way further than I would have otherwise at this pace, but it was still a lot of effort, and I still wasted time going down rabbit holes on features that didn't work out.
There's some truth in there that judgement is as important as ever, though I'm not sure I'd call it taste. I'm finding that you have to have an extremely clear product vision, along with an extremely clear language used to describe that product, for AI to be used effectively. Know your terms, know how you want your features to be split up into modules, know what you want the interfaces of those modules to be.
Without the above, you run into the same issue devs would run into before AI - the codebase becomes an incoherent mess, and even AI can't untangle it because the confusion gets embedded into its own context.