Taste Is The Moat AI Cannot Cross

AI tools made the mechanical parts of product building nearly free. What they cannot touch is the judgement that makes a product feel built for a real user in a real market.

Most AI-generated products look the same. That is not a bug. It is the whole problem.

Every founder I know is using AI to ship faster. Claude Code for code, v0 for interfaces, ChatGPT for copy. I use them too. They are genuinely good. But here is what nobody in the AI hype cycle wants to admit: the products coming out the other end are converging on a single, flavourless aesthetic, and that aesthetic has no idea who it is talking to.

I call this the intuition tax. Every time you accept an AI-generated design output without questioning it, you pay a small tax on the accumulated judgement that makes a product feel like it was built by someone who gives a damn. One tax payment is nothing. A hundred of them, and your product feels like it was assembled by a committee that has never met your customer.

What AI tools made free, and what they cannot touch, yet

Let me be direct about what I think is happening. AI tools have made the mechanical parts of product building nearly free. Layouts, component libraries, responsive grids, basic animations, functional copy. All of it can be generated in minutes. This is wonderful, and I am not nostalgic for the old way.

But by making the mechanical parts free, AI has made the non-mechanical parts the only source of differentiation. Those non-mechanical parts are taste, context, and intuition. The ability to look at a screen and know it is wrong before you can explain why. The understanding that your user in Tembisa navigates differently from your user in Constantia. The instinct that "Get started for free" is the wrong CTA for a market where "free" often means "there is a catch."

I learned this the hard way. When we were building Pasella, we used AI to generate an early version of the onboarding flow for spaza shop owners. It was clean, well-structured, followed every UX best practice. It tested terribly. It looked like every other app those owners had downloaded and abandoned. Our users were not app-native power users who trust clean interfaces by default. They were business owners who needed to see, in the first three seconds, that this product understood their specific problem. The AI gave us a generic solution to a universal problem. What we needed was a specific solution to a local one.

That specificity is taste. And right now, it is the cheapest competitive advantage available to any founder building in Africa, precisely because most teams are skipping it in favour of speed.

Three places where AI taste fails hardest

Anything involving trust. Pricing pages, payment confirmations, onboarding screens, data permission requests. These are the moments where a user decides whether your product is safe. AI outputs for these screens are almost always too sterile, too corporate, too modelled on American SaaS conventions that assume a baseline of digital trust which does not exist in most of our market. Yoco did not become the default SME payment device in South Africa by following a template. They built trust through hundreds of micro-decisions about visual language, physical weight, haptic feedback, and tone that reflect years of watching how South African business owners actually interact with financial technology. You cannot prompt your way to that level of specificity. You earn it by being present.

Information hierarchy on small screens. Most of your users are on mobile. Many are on older Android devices with screens under six inches. AI-generated layouts optimise for completeness, not for the ruthless prioritisation that small-screen design demands. The most common failure I see when reviewing AI-generated mobile interfaces is that everything is treated as equally important. Nothing is emphasised. Nothing is deprioritised. The screen is technically correct and practically unusable, because a user glancing at a 5-inch screen in bright Joburg sunlight between customers does not have the cognitive bandwidth to parse twelve equally weighted elements. Good mobile design is not about fitting more in. It is about having the confidence to leave things out, and that confidence comes from knowing what your user actually needs in the moment. AI does not have that knowledge. You do, or you should.

Copy that sounds like it was written by a person who cares. AI-generated copy is grammatically perfect, contextually appropriate, and emotionally dead. It reads like a competent employee who has no stake in the outcome. In a market where WhatsApp is the primary business communication channel and people expect warmth even in professional interactions, emotionally flat copy is a conversion killer. Stitch gets this right in their developer documentation, which manages to be technically precise whilst still sounding like it was written by someone who actually builds integrations and knows where the frustration lives. That tone is almost impossible to generate with a prompt, because tone is a function of empathy, and empathy requires having lived close to the experience you are designing for.

The argument against myself

Someone reading this will say: "This is a prompt quality problem. Better prompts, better results." That is partially true. A detailed prompt that specifies your user persona, your market context, your trust constraints, and your visual language will produce better output than a lazy one...

But the skill required to write that prompt is the same skill as taste. You need to already know what good looks like for your specific user before you can describe it to a model. AI does not reduce the need for taste. It moves taste from execution to direction. You still need it. You just apply it earlier in the process, which, if anything, makes it more important, not less.

There is a second objection worth addressing. Some teams argue that rapid iteration solves the taste problem: ship the AI output, measure what works, and optimise from there. This works for button colours and headline variants. It does not work for the deeper layer of product feel. By the time your analytics show that users are not converting, the trust damage is already done. The user who bounced from your onboarding screen because it felt generic is not coming back to try version two. You do not get a second chance to make a product feel intentional.

Taste compounds. So does the lack of it.

What I actually recommend

Stop treating AI design outputs as finished work. Treat them as first drafts, starting points that need a human judgement layer before they reach a user.

Build a review gate into your shipping process. Before any user-facing screen goes live, it passes through someone who has watched real users interact with your product in the last 30 days. Not necessarily a designer. Just someone with fresh, direct observation of how your actual customers behave when they use your software. That person's instincts, sharpened by recent contact with real behaviour, are worth more than any prompt library.

Concentrate your taste investment on the surfaces that build or break trust. Onboarding, pricing, error handling, and empty states. These are the screens where good judgement compounds. A thoughtful onboarding experience does not just reduce drop-off. It sets the emotional tone for the entire customer relationship. A clear, honest error message does not just prevent frustration. It tells the user that someone on the other side is paying attention. AI can generate the scaffolding for these screens. You supply the intent.

And if you are a technical founder who thinks taste is someone else's job, I would push back firmly. In a market where your competitor has access to the same AI tools, the same component libraries, and the same deployment infrastructure you do, taste is one of the few remaining advantages that is genuinely hard to replicate. It does not scale the way code scales. It cannot be abstracted into a package. It accrues slowly, through the specific and unglamorous work of understanding your users deeply enough to make good judgement calls on their behalf.

The founders who invest in that work now, while most teams are still optimising purely for shipping speed, will build the products that survive. Everyone else will ship fast, look the same, and wonder why nobody stayed.The founders who invest in that work now, while most teams are still optimising purely for shipping speed, will build the products that survive. Everyone else will ship fast, look the same, and wonder why nobody stayed.