MAKONEAProgrammers Sell OX, Not UX.
Who Actually Understands UX Seriously?
(Winamp, once famous for its user skins)
UX is a distribution, not a singular experience
The phrase "User Experience" is singular: one user, one experience. So we often treat UX as a question of how a single user feels about a product.
That framing is wrong.
In reality, UX is the distribution of how N users feel about a product. It is not one person's experience but a statistical object built from the experiences of all users combined. It has a mean, a variance, tails, and outliers.
This distinction may seem minor, but it is the starting point on which this entire post turns.
We cannot design a distribution
We cannot design a distribution directly. What we can build is a single interface: one button placement, one color, one form structure. Yet that single interface is received by N users, and each of those N users is a different point on the distribution.
What we are actually doing, therefore, is
deciding where on the distribution to position that interface.
In most cases we place that point near the mean, because the mean looks like "the point that satisfies the most people." But the mean ignores variance, and the larger the variance, the less precisely the mean fits anyone.
When N is small versus when N is large
The nature of this problem changes depending on the number of users.
When N = 10, the mean is close to nearly every user. If the tastes of 10 people overlap to some degree, an average design fits all 10 reasonably well. In this case, UX design works relatively reliably.
When N = 10,000, the situation is different. The user distribution becomes more varied: some people want minimalism, others want information density; some want a fast workflow, others want guidance. If you average those two groups, you get a design that is neither minimal nor information-dense, neither fast nor sufficiently guided β one that feels awkward to everyone.
This is the precise definition of what we commonly call "generic" design. It is not the result of designer incompetence. It is a mathematical consequence of designing for the average.
Why site builder designs are generic
Look at the templates from site builders like Squarespace, Wix, and Webflow, and most of them look alike: the same structure, similar fonts, familiar color palettes, predictable section flows.
This is not because the designers behind these site builders are lazy or unimaginative.
It is because the user distribution they serve is too wide: lawyers, yoga instructors, restaurant owners, B2B SaaS founders, people building a wedding page.
Average out all those users, and the result is inevitably a design that looks like something you have seen somewhere before.
This is not a UX failure. It is the mathematical limit of average design. The wider the user distribution, the blurrier the average becomes.
So why do users accept generic design? (the demand side)
This raises a question. If average design feels awkward to everyone, why do site builder templates survive in the market?
Shouldn't users be rejecting them?
This question arises because we are implicitly operating under a flawed assumption,
the assumption that users are points, each with a clearly defined taste.
In practice, that is not the case.
Most people do have preferences. That much is true. People have a rough sense of what they like: "I prefer something clean," "I like a warm feel," "Simple is better" β at that level, it is clear enough.
But the resolution of those preferences is low. How clean, what kind of warmth, simple in what dimension β the details are blank. Even they do not know.
So when a user encounters a new design, what actually happens is
not discovering a preference but filling in the blank details. And that act of filling itself triggers dopamine.
"Oh, I like this" β what is really happening is the freshness of seeing something for the first time, but the user interprets it as discovering their own taste.
When a user says "this design is my style," the overwhelming majority of cases are not genuine pre-existing preference discovery but post-hoc rationalization.
This mechanism explains why generic design survives.
Generic design feels like you've seen it somewhere before. Even on first encounter, users feel a familiarity as if they've already seen it. That familiarity translates into a sense of safety. So users have no complaints. The dopamine hit is weak, but there is no aversion either.
A novel design is the opposite. It triggers strong dopamine, but simultaneously carries a risk of rejection. If a user runs into details that clash with already-formed preferences, they reject it outright.
What survives in the market, then, is a variation on the familiar: not a completely new design, but a recognizable form with a modest degree of differentiation added. This is the exact formula behind site-builder templates, and more broadly, the formula behind most commercial design.
This is not conservatism on the designer's part; it is a rational response to the inertia of user cognition. A design that looks like you've seen it somewhere before is inertial by nature, and that inertia is the safest default that actually works in the market.
Data reveals the distribution but does not make the choice
Here an important distinction emerges.
Data can reveal distributions: A/B testing, user interviews, heatmaps, session recordings. All of these tools are instruments for measuring distributions β which user groups get stuck where, which design is understood more quickly, which color gets more clicks.
But data does not tell you which distribution to prioritize.
If Design A satisfies 60% of users and Design B satisfies a different 40%, which do you choose? Data does not answer that question. Data simply shows which distribution each design works for.
And the problem goes even deeper. If users do not know the details of their own preferences, data cannot know those details either. When a respondent in a user interview says "I like this kind of design," that answer is most likely a post-hoc rationalization that the user constructed in that moment. Data does not measure users' genuine preferences; it measures the rationalized answers users produce at the time of measurement.
The choice is a value judgment. You must decide which users to prioritize, which to abandon, and how to fill in the missing details.
And someone has to make that decision.
Good UX means abandoning segments
There is a fact that most UX discourse avoids: good UX is not design that satisfies everyone but design that consciously abandons certain segments.
Apple's products explicitly abandon price-sensitive users. Vimeo ceded the vlogger market to YouTube and focused on filmmakers. Notion initially abandoned casual note-takers and focused on power users (though it is now expanding again). Good products always abandon someone.
Design that abandons no segment ultimately converges on the average, and the average fits no one exactly.
Design that leaves everyone moderately dissatisfied. Site-builder templates are like that, enterprise SaaS UIs are like that, and government websites are like that.
The decision to abandon a segment is always political. It means deciding which users have more value and whose experience you are willing to sacrifice for others. And that decision cannot be delegated to data. It demands someone's judgment.
The UX distribution is also geographically fragmented
Up to this point, the post has treated the distribution as a single statistical object. In reality, however, it is far more complex: the distribution is multiple distributions, geographically separated.
Once this dimension enters the picture, the limitations of average design deepen by another level. The average not only ignores variance but also requires deciding which region's average to target in the first place.
(The UI of CSDN, one of China's largest information sites)
Color meaning is culturally dependent
Consider red. The same color sits at a different point on the distribution depending on the region.
In Chinese UI design, red signals good luck, prosperity, and celebration: red envelopes for the Spring Festival, red garments at weddings, a color believed to attract wealth. That is why Chinese e-commerce sites fill sale banners, loyalty point notifications, and CTA buttons with red.
Users receive it as a positive stimulus.
In Western UI, red is the exact opposite: danger, warning, error. Stop signs, red alert messages, delete buttons. Show the same red CTA button to Western users and it reads as aggressive or like a warning.
It is the same color, but where it lands on the distribution is culturally inverted. When a global product designs a "red button," the first decision is which distribution's mean to target.
There is also an asymmetry in information density and animation.
(Linear's homepage)
The design language of Western SaaS products such as Linear, Notion, and Stripe is minimalism paired with subtle animation. Information is refined, whitespace is generous, and interactions are handled through smooth transitions. Users read this as a signal: refined information = trustworthy product.
(Rakuten Japan's homepage)
The design language of East Asian e-commerce, including Taobao, Rakuten, and Coupang, is the opposite. A single screen is packed with information, strong colors clash, and animations are stimulating: flashing banners, countdown timers, red price tags, yellow discount stickers. Users read this as a signal: abundance of information = trustworthy deal.
The same minimal design reads as "trust" in the West while reading as "lack of information" in East Asia. The same high-density design reads as "abundance" in East Asia while reading as "spam" in the West.
This is not a difference in designers' taste. It is a matter of users having been trained to perceive things differently, and that training is the cumulative result of decades of e-commerce environments, advertising environments, and media environments.
The Power Signals in Microcopy
The linguistic dimension cannot be ignored either.
Imperative forms feel natural in English UI: "Save," "Delete," "Submit," "Cancel." Because English has a weak speaker-listener hierarchy, imperative verbs function as neutral action labels.
Korean and Japanese are different. The same imperative carries a hierarchical signal, so designers avoid it by using noun forms like "μ μ₯" and "μμ " or the nominalized "-κΈ°" form, as in "μ μ₯νκΈ°." In Japanese UI, the pure noun form "δΏε" is far more common than the verb form "δΏεγγ."
When taking on global projects, situations like this come up constantly.
This is not simply a translation problem. It is a question of how language encodes power relationships. English has a weak speaker-listener hierarchy, so the imperative form is neutral, but Korean and Japanese have strong hierarchies, so the imperative almost always carries some hierarchical signal. When a UI uses the imperative, it signals that the system stands above the user.
So the same "Save" button is a neutral action in English-speaking contexts, while in Korean-speaking contexts it subtly takes on a tone of the system issuing a command to the user. The same design sends different power signals across different language communities.
(Yahoo Japan)
The lock-in effect of megaplatforms
Explaining this through cultural essentialism alone, however, falls short. There is one more dimension to consider: the design inertia of megaplatforms.
Chinese UIs look similar not merely because of Chinese culture, but because megaplatforms like WeChat, Taobao, and Alipay have solidified a design language. Subsequent designers follow that language because users have already been trained in it, and deviating from it feels unfamiliar.
The same logic explains why Japanese UIs have high information density. Yahoo Japan and Rakuten established that tone, and products that came after matched it. When a new Japanese SaaS enters the market with a minimal design, it gets feedback along the lines of "this looks like it's missing information." The average is set by the megaplatforms.
Korea is no different. Naver, Kakao, and Coupang have defined the average for Korean UI, and products that stray from it look unfamiliar to users.
"Culture-specific UX" is therefore a collaboration between cultural essence and platform inertia. Both factors go unmeasured in global products. When a global product tries to "hit the average," it remains unclear which average, created by which platform, in which region, it is actually targeting.
(Why do AI company logos look like buttholes?)
The most visible contemporary example is AI company logos. There is a post on Reddit's r/Design titled Why do AI company logos look like buttholes? ChatGPT, Claude, Gemini, Copilot, Perplexity, Mistral
all share a visual grammar of circular or radial forms, gradients, and a sense of convergence. Designers did not coordinate this. OpenAI established the visual standard first, and subsequent products that deviate from it risk not looking like AI products. So they follow it. The strategy of avoiding unfamiliarity produces an uncomfortable homogenization.
Once the average solidifies too firmly, no one inside the market can escape it, because escaping means no longer looking like a product in that category.
Users' perceptual circuits are already aligned to the average, so they feel no friction when something sits within it. But someone outside those circuits spots it immediately. That is why users on r/Design can point out "why does everything look the same."
The opposite extreme: the design failure of total freedom
If megaplatform lock-in represents one extreme, the opposite extreme also exists: giving users infinite freedom. This approach has been tried historically many times, and it has almost always failed. From a programmer's perspective, it looks like an address space design problem β a system with zero constraints has infinite degrees of freedom, making it impossible to explore any meaningful state space.
Winamp skins (1997 to early 2000s). Winamp let users freely design every visual element. Button positions, sizes, shapes, colors, fonts β everything was open. Tens of thousands of skins were created as a result, but most were barely usable. They lacked readability, had ambiguous click targets, and were inconsistent throughout. Freedom was demonstrated, but most of what that freedom produced was hard to look at.
MySpace profiles (2003β2008). This is an even more extreme case. Users could inject arbitrary HTML and CSS into their profile pages: auto-playing music, flashing GIFs, unreadable text colors, endlessly tiling background images. Every page was different, yet every page was unusable. When Facebook arrived, the decisive reason it won was precisely the opposite strategy: eliminating design freedom entirely. Every Facebook profile looked identical, and that uniformity functioned as comfort. Users accepted the removal of their freedom as relief.
These cases all say the same thing: give users infinite freedom and the entire system collapses. Users lack the ability to fill in the details of their own taste, because of the constraint noted earlier β users' taste has low resolution,
and when you hand infinite freedom to users who lack that ability, the output is not an expression of taste but random noise.
For a designer, the canvas is infinite.
But a programmer cannot work that way.
On one side is megaplatform lock-in: the average calcifies so completely that everything looks the same.
On the other side is Winamp/MySpace: no average at all, so everything is different and simultaneously unusable.
Somewhere between those two extremes is where a design that actually works lives. Today's Facebook, Reddit, and Twitter all offer constrained freedom. Users can fill in content, but the platform locks down the visual vocabulary of the container. That structure is why these systems operate stably at scale.
From a programmer's perspective, this is a familiar pattern. API design faces exactly the same problem. Too many constraints and users cannot build what they want. Too much freedom and users hurt themselves. A good API is designed so that common tasks are easy and incorrect tasks are hard. Design works the same way. Good design makes the right thing enjoyable and the wrong thing awkward.
Perception is trained
Start with this proposition: users' sensory responses are the result of training, not nature.
Deny this and you slide into essentialism β things like "East Asians prefer high information density." That is wrong. An East Asian encountering a Yahoo page for the first time in the early 1990s would have found it dense and overwhelming. What feels natural today is the product of thirty years of cumulative exposure.
Raise the same person in a different environment for thirty years and a different set of sensory responses takes shape. The advertising industry has proven this over a century, and it is a foundational premise of marketing research. Yet in UI design discourse, it is strangely and frequently forgotten.
Two Paths to a Trust Signal
When a user encounters an interface, two paths typically operate in parallel as they judge whether this product can be trusted.
Path 1: the refinement signal. The judgment that "this product is well made" comes from the degree of refinement in the design. When there is generous whitespace, consistent typography, smooth interactions, and no visual clashes, the user infers that the maker invested sufficient resources. Therefore, it is trustworthy.
Path 2: the abundance signal. The judgment that "this product is well made" comes from the information density in the design. When a single screen is packed with information, options, user reviews, and strong visual stimulation, the user infers that the maker put in sufficient effort. Therefore, it is trustworthy.
Both paths are rational. And both are attempting to measure the same underlying variable: the maker's investment. What differs is which proxy each path weights more heavily.
The problem is that which path activates depends on the environment in which a user grew up.
How Refinement Became a Trust Signal in the West
Western design discourse has been profoundly shaped by twentieth-century modernism: the Bauhaus, Swiss Style, Dieter Rams's "Less but better." The central claim of this tradition is that the ability to remove the unnecessary is the mark of a skilled designer.
As this premise was absorbed into advertising and mass media, Western users accumulated the following learned associations:
Premium = refined. The whitespace on a Vogue cover, Apple's white backgrounds, Tiffany's minimal logo. The more luxurious the brand, the more it empties.
Low-end = cluttered. The yellow price tags on a neighborhood supermarket flyer, cable TV infomercials, spam email. The less trustworthy the source, the more it fills.
After thirty to fifty years of this accumulated learning, users' sensory circuits solidify a refinement = trust association. Western SaaS products trend toward minimalism not because of designers' personal taste, but because minimalism is what functions as a trust signal for users in that market. The fact that Stripe, Linear, and Notion all share a similar tone is no coincidence. That tone aligns with the instrument by which trust is measured in that market.
How Abundance Became a Trust Signal in East Asia
The East Asian path is different.
Traditional market culture: a shop stacked high with goods signals a shop doing good business. An empty shelf is read not as "sold out" but as unable to sell. Where exactly this learning originated is hard to trace, but within East Asian market culture, density = vitality = trust is a long-established circuit.
Newspaper ads and e-commerce. Look at Japanese newspaper ads from the 1980s and you'll find a single page packed with information: every specification and price printed in small type. Korean e-commerce in the 1990s was the same. The early screens of Auction and Interpark required everything to be on one screen.
Then megaplatforms delivered the decisive blow. Yahoo Japan became the standard for Japanese internet in the early 2000s, and Yahoo Japan's main page was crammed with information. Subsequent Japanese sites followed that tone, because a site that didn't follow it looked like it didn't have enough information. Japanese users have spent 30 years reinforcing the high density = trustworthy site circuit.
Korea has Naver. China has Taobao and WeChat. All the same mechanism.
The key point here is that this circuit is not the essence of culture but the accumulated result of environment. Japanese users in 1995 may well have found Yahoo's main page information-overloaded. Thirty years later, it has simply become the default.
The Global Product Dilemma
Put all of this together and the real dilemma of global products comes into view.
A global product must decide which market's circuit to align with. Aligning with every market at once is impossible. When a red CTA drives purchases in one market but reads as a warning in another, there is no red CTA that satisfies both.
There are three approaches.
Approach 1: Choose one circuit and abandon the others. This is the most honest approach. Apple explicitly chose the Western minimalist circuit and accepted the tradeoff of reduced e-commerce fit in East Asian markets.
Approach 2: Branch the design by market. This is the most expensive approach. You build separate Japanese, Korean, and Western versions of the same product. It is doing through design what McDonald's does through its menu. Companies with sufficient resources can manage it.
Approach 3: Average across all circuits. This is the most common approach and produces the most generic results: a design that works well enough in both the West and East Asia but fits nobody precisely. Website builder templates are exactly this outcome.
All three approaches require a value judgment about which circuit to choose, and that judgment cannot be delegated to data. Data can measure circuits, but it cannot tell you which circuit to prioritize.
This is where the concept of OX enters.
The person who decides which circuit to prioritize is the owner.
That decision depends on the owner's understanding of the market, the owner's background, and the owner's business ambitions.
A website is a compromise among three parties
A website is, in fact, built as a compromise among three parties.
Users: I want to get the information I need.
Business: I want to build brand trust and drive conversions.
Internal organization: I want the owner's tastes and preferences reflected.
People often say, "A website is for the users." In principle, I agree. In practice, though, most users' "taste" is ultimately a product of brand reputation. And where does brand reputation come from? In many cases, it comes from the owner's taste and positioning, and from the accumulated decisions they have made over time.
A SaaS landing page is not simply a place where users come to get information. From the company's perspective, it is also a tool for imprinting its positioning into the user's mind.
This phenomenon is, at its core, a principal-agent problem.
Anyone who has done real client work knows that most clients do not think about UX.
They think about OX β owner experience.
And in reality, most companies run on OX.
In idealistic discussions, everyone claims to care about UX. But real businesses do not run on UX; they run on OX. The central question is whether the owner's taste happens to align with the taste of the general public.
Why does Gartner sell?
Why do people pay large sums of money for reports from companies like Gartner?
The game they are playing is close to a coin flip. If you look at Gartner reports that are made publicly available, you will find no small number of incorrect calls. That is to be expected. Simplifying a complex system can never be consistently correct. The economy is a complex system. But human cognition is finite.
So why do reports from companies like Gartner sell?
Because they reduce the anxiety of owners and decision-makers.
Business is complex. Even a bad product can succeed with a single advertisement. Hype marketing, fraud, timing, distribution, luck β all of these exist, and any one of them can produce success. UX is the ideal. In practice, however, developers often have to satisfy an OX instead.
Companies appear to pursue profit, since most owners like money. In reality, though, many companies are closer to the realization of the owner's ideology, taste, and worldview.
So what actually matters?
What matters to developers is judging how closely the owner's taste aligns with the general public and with the target customers. This is also why developers so often end up flattering the owner β not simply because of hierarchy, but because the owner's taste is effectively the operating system of the business.
A note on the single-owner assumption
But naturally, a question arises here.
"What about companies with multiple stakeholders? Unless you assume that everyone in the C-suite reasons similarly, doesn't this model fail to generalize?"
That's a fair point. The OX frame can look like a single-owner model, but at the meta level I think it applies more broadly to companies with distributed stakeholders as well. It roughly breaks down into three forms.
1. Safety-oriented OX
When no particular stakeholder or internal political faction holds a decisive advantage, the result is often a UI that moderately dissatisfies and moderately satisfies everyone.
This is not optimized for users. It is optimized so that no one inside the company has to bear political accountability for the decision.
This is probably the dominant pattern in many SaaS products. People often call it "generic" or "broadly applicable" design: stable, safe, and hard to object to, but not sharply targeted. SAP and Oracle products are pure examples of this form.
2. Faction-driven OX
Even when the C-suite has multiple members, most decisions are driven by one faction, and that faction usually has a leader.
In this case, the UI becomes a combination of that faction's requirements. The result often shows up as inconsistent requirements from page to page and flow to flow.
In my experience, the product/planning side usually wins these fights. The marketing site gets optimized for CMO-OX, and the billing system gets optimized for CFO-OX. What operates is not a single OX but multiple OXes partitioned by domain. The principal-agent problem doesn't disappear; it fragments.
3. Proxy-authority OX
When there is no owner with strong internal taste, the company imports taste from outside.
This is precisely why Gartner sells.
The McKinsey deck becomes the de facto OX. The "industry benchmark" report the board brings in becomes the OX.
The decision is no longer "What does the user want?" It becomes "What does the proxy authority say?"
This is OX with the owner outsourced.
Limitations
To be clear, this is not a rigorous theory. It is an observation-based framework, and there are cases where it breaks down.
In late-stage public companies with dispersed shareholders and no strong executive leadership, this model loses predictive power. That said, even in such companies, internal convention and institutional habit often serve as the actual drivers of decision-making.
I am not presenting this as a serious theory. A real theory would require building a statistical model with actual data.
(Twitter blue check)
The case of X (Twitter)
X is an almost textbook case of the OX theory. What makes it particularly interesting is that X does not simply illustrate "a case ruined by OX" but rather demonstrates both sides of OX simultaneously.
Let's start with the clearest cases where OX beat UX.
The shift in meaning of the blue check is the cleanest example.
During the Twitter era, the blue check was a verification signal meaning "this person is not an impersonator"
a pure UX function. Musk turned it into an $8/month subscription badge. From the user's perspective, it was the moment a signal became noise.
Impersonation spiked, and the Eli Lilly free insulin tweet that tanked the company's stock happened shortly afterward. This is a decision that could never come out of UX analysis.
It could only come from Musk's ideology that "verification should not be a privilege of authority but something anyone can buy." A textbook OX execution.
Forcing his own tweets into algorithmic prominence falls into the same category. In February 2023, when his Super Bowl tweet showed lower engagement than Biden's, he reportedly ordered engineers to boost his tweets 1,000x in the algorithm. From a UX perspective, this is an obvious harm. From an OX perspective, it is perfectly rational.
Because the platform is the owner's megaphone.
Renaming it X follows the same logic. "Twitter" was a brand asset accumulated over 20 years, used so widely it became a verb. Any marketing consultant doing a normal analysis would never recommend discarding that asset. But Musk had been fixated on the name X since his x.com days in 1999. This was not a decision made through user research; it was the realization of an owner's 25-year-old preference.1
Yet here an interesting asymmetry emerges.
Musk pushed his OX forward while accepting nearly a 50% drop in ad revenue and a valuation collapse from $44 billion down to the low teens. And to some degree, it survived. This is the duality of OX.23
Inside Musk's OX there was a small but real UX model:
the hypothesis that "the censorship on the old Twitter was a genuine frustration for a subset of users."
That hypothesis was not a universal UX, but it was a UX for a specific segment. So while advertisers left, a portion of the user base became more active, and X keeps rolling along, if only in a zombie state. If Musk's OX had been pure self-indulgence,
(e.g., "I like pink, so I'll make the UI pink") it would have collapsed long ago.
The X case shows that the "OX vs. UX dichotomy" is an overstatement. The accurate frame is this.
OX always embeds some UX hypothesis. The question is how accurate that hypothesis is and how large a market it applies to. Musk's OX had a degree of accuracy, but the applicable market was narrow. That's why X sits in a gray zone, neither collapsing nor growing.
Steve Jobs's OX, by contrast, was both highly accurate and addressed a large market. Bezos's OX ("customer obsession") was isomorphic with UX itself. The OX held by Korean SI company executives has low accuracy and is aimed at a market that is disappearing.
The Missing Third Option
Summing up to this point, it looks like developers have only two choices: conform to the OX or leave.
But there is a missing third option: bending the OX in the direction of UX.
This is precisely what good consultants, good PMs, and good senior developers actually do.
They expose their own OX in a way that causes other people's OX to align with it.
The core is "designing the desire."
A well-intentioned design creates the illusion in the user that "I made this choice."
This doesn't apply only to users; it applies to owners as well. What does an owner's desire usually come down to? The belief that I am the one choosing.
A well-designed system gives owners that same sense.
The catch is that the freedom of choice must not be total. Give complete freedom and the system quickly unravels: consistency breaks down, and no one can restore the whole.
The central question is: "Where is the boundary that lets the owner feel they are choosing, while keeping the system from falling apart?"
This is nearly the only path that satisfies both OX and UX at the same time.
It is not about simply complying with OX, nor about simply advocating for UX, but about redesigning OX's desires into a form that is compatible with UX.
That is the posture a programmer must carry.
So how do you persuade?