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.

    > 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).

Followed by an entire AI generated fluff piece https://www.pangram.com/history/347cd632-809c-4775-b457-d9bc...

Flagged

(edit: typos)

evergreen.

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.

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

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.

Discussion: https://news.ycombinator.com/item?id=47089907

Just google "taste is the new moat"

Doesn't deserve to be on the front page.

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.

    What you notice

    What you reject

    How precisely you can explain what feels wrong

I 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 think this is symptomatic of humans - our comfort zone is the "7 out of 10" morass of similarity and blandness. We are herd animals. LLMs are just reflecting this.

And I don't think our tendency to herd will allow us to select the quirky outliers even if that's the only distinguishing characteristic of non-LLM output.

- 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.

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!

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.

If one disagrees with that's statement, there is nothing of value to extract from this article.

Words are cheap, bullet point are cheap.

Already wrong.

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.

https://youtu.be/jg1WUOxY6Cg?si=0ajVvgKnyuSz0e2Y

Don't get me wrong, I use AI too for daily tasks and my job as a programmer, and it definitely helps me get the job done faster (sometimes it's almost instant). But it still requires a lot of effort for complex or unconventional tasks.

When I hear "AI does most of my job", I think of DOGE employees who use AI to identify "waste of money". All they do is ask AI with very lazy prompts like "list DEI projects" with the list of government sponsored projects including simple descriptions. They don't even provide what DEI means. And they just cut all projects the AI flagged. I'm sure their "productivity" is very high. They can "complete the job" that would require days, weeks, or months of investigation with a single prompt.

I also think the results have a strong tendency to flag a project as DEI, because "is this DEI" is a question often asked by racists and misogynists on right-wing websites, and often the answers are "Yes", and that likely causes a strong bias.

[1] https://www.9news.com/article/news/politics/doge-chatgpt-dei...

there's a reason people prefer aged products - they gain a character of their own, similar to art or works produced under constraints.

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.

you already see it on facebook with all the ai generated meme sharing... taste is being eroded there

Distribution, Data (Proprietary) and Iteration Speed.

Very successful companies have all three: Stripe, Meta, Google, Amazon.

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.

After a decade with tech people I can confidently say that most of them have zero taste because they have little to no exposure to the world outside of their bubble.

It's frankly pathetic to see how techno-optimists think that innovations like driverless cars will simply be happy pills to be swallowed by the masses who make a fractional amount of money to them.

As a species we have quite literally killed each other for less.