What AI Still Doesn’t Understand About Product Design
What AI Still Doesn’t Understand About Product Design
The year opened with an expectation hanging over the industry.
It was the quiet assumption repeated in podcasts, in design circles, in LinkedIn think pieces, and everywhere else the future of work gets debated.
People believed design would be the first craft AI would absorb. It sounded logical. Visual generation tools were exploding across the web, producing stunning images on command.
Figma rolled out new features that promised automated layouts. Media outlets suggested that product teams might soon lean heavily on automation. If you asked anyone in January which job category was most at risk, the safe bet was design. And then something unexpected happened.
The real breakthrough showed up in code, not in design.
Through the year, developers across the web shared stories about AI producing entire modules that actually worked.
Public engineering blogs described tools rewriting legacy systems in a fraction of the time teams used to spend. GitHub threads filled with developers talking about AI helpers catching edge cases, generating reliable scaffolding, or refactoring pieces that used to take days.
Even skeptics acknowledged that AI coding assistants had crossed into a new tier of usefulness. They were not toys or demos anymore.
They were part of the workflow, offering volume, speed, consistency and surprising accuracy.
Why AI Struggles With Product Design
At the same time, the design world was telling a very different story.
Designers were posting breakdowns on X showing how AI-generated screens looked clean at first glance but fell apart under real usability constraints.
UX leaders on YouTube walked viewers through the gaps, pointing out missing hierarchy, broken flows, and interactions that made no sense in a real product.
Reddit threads in r/UXDesign grew long with designers explaining that AI could offer a decent starting point, but only at the level of a student exercise. The tools could mimic aesthetics, but they could not create intention.
They could not explain why a certain pattern belonged there or how it supported the user.
This split created a strange moment for businesses. Engineers were speeding up. Designers were fielding questions about why the new tools were not producing entire product systems the way people believed they would.
The assumption had been that design, of all fields, would be the easiest to automate.
The reality has been the opposite.
And that reversal says something meaningful about what software actually is.
And if you are the one responsible for choosing the direction of a product, you feel that split more than anyone because the pressure shifts from execution to intention, and that shift lands squarely on your desk.
How AI Handles Structure but Fails at Understanding
Teams that watched this unfold began to see the pattern.
AI thrived in coding because code is structured.
Rules, inputs, outputs, patterns, transformations, states, dependencies, logic. It is a world built from instructions that can be predicted. When developers across the web praised AI’s performance, they were talking about tasks that fit that shape.
Parsing data. Generating boilerplate. Writing connectors. Refactoring with consistent style.
All of it fits into a system AI models can navigate because the boundaries are defined.
For anyone guiding a product, this is the moment you realize AI can help you build almost anything except the part where you decide what needs to exist in the first place.
Design lives outside those boundaries.
That is the part most of the industry underestimated. Design is not the decoration on top of software. It is the reasoning behind it. It is the thousand choices that determine how someone feels when they try to use a product and whether they understand what happens next. It is context, psychology, prioritization, motion, hierarchy, language, emotion.
There are no fixed patterns that can produce that reliably.
The same screen might succeed for one audience and fail for another. The same layout might help a new user but slow down an experienced one. Those decisions require human judgment, not repetition.
So when AI tries to generate design at scale, it produces surfaces. Pretty ones at times. But surfaces are all the same. A generated screen can look impressive, at least until someone actually tries to use it.
Screens are easy. Sense is hard.
Where AI Accelerates Development but Amplifies Poor Decisions
Developers understood why AI coding tools worked as well as they did. They saw the algorithmic shape of the problem. Designers understood just as quickly why AI design tools were struggling.
They saw the lack of reasoning underneath.
Product teams were discovering this in real time. Someone would prompt a system to generate a dashboard, and it would give them charts placed neatly on a grid with little awareness of what mattered most to the business.
It could not understand which insights deserved prominence or why a flow needed to be reordered for clarity. It could not grasp the intent of the product because intent is not pattern based. It requires understanding, not memory.
This tension created new pressure inside companies. Leaders saw AI writing thousands of lines of code without complaint and asked why the design side had not followed suit. The answer was hiding in plain sight.
AI can accelerate what already has structure. It cannot invent structure where none exists. Code fits into a frame. Design determines the frame itself. That difference is easy to overlook until the moment you ask AI to generate something meant to guide human behavior.
If you lead a team, you can sense that pressure in the questions people ask you. They look to you for clarity because AI can write code on command, but it cannot tell anyone why the work matters or who it is meant to help.
Teams that handled this well recognized the truth early. The rise of accelerated coding made design more important, not less.
Faster engineering cycles magnified the cost of unclear direction. When code could be generated in hours instead of days, the bottleneck shifted.
It moved upstream, into the space where decisions are made about what the software should accomplish, how users should move through it, and why each moment should matter. This is the part AI cannot navigate.
Why AI-Generated Designs Fail to Create Real Product Systems
You see this clearly in the public response to the newest design automation tools.
The feedback follows the same rhythm across the web. Designers appreciate the help on simple tasks but warn against relying on these tools for anything beyond starter layouts. They talk about missing logic, inconsistent interaction states, and visual choices that flatten the personality of a product.
Their criticism is not that AI cannot draw. It is that AI cannot decide. Decision making is the essence of design. And if you have ever launched something that looked right but felt wrong, you already know the cost of skipping the part where humans shape the meaning.
This becomes meaningful when you look at the foundation of a good product.
You do not build software by stacking screens until one of them looks right. You build it by understanding people. You build it by asking why someone needs this tool and what they are trying to accomplish. You build it by removing confusion and creating clarity.
That kind of work is grounded in trust. And trust is human. AI can produce the ingredients of a product faster than ever, but the recipe still belongs to people.
It always will.
Design Is Still the Human Center of Great Software
There is a moment in any build when the team has to agree on what is true.
True about the customer. True about the problem. True about the outcome. This is where the divide between AI’s strength in code and its weakness in design becomes most visible. There comes a point in every build when someone has to decide what the product is trying to do for the people who will use it.
That part never falls to the code.
Code reacts to the decisions already made. Design is where those decisions start. It forces a team to slow down long enough to understand the problem, the people, and the moments that matter.
When teams honor that work, everything that follows has a better shot at landing the way it should.
This is why the teams that succeed in this new era stay anchored in something simple. “We believe that business is built on transparency and trust. We believe that good software is built the same way.”
If you are building something that people depend on, that truth becomes more than a philosophy. It becomes the guardrail that keeps you from mistaking movement for progress.
This is the turning point the industry is now entering.
Leaders no longer debate whether AI will become a powerful partner in development. That question has been settled.
The next question is how to build products that still feel intentional when code becomes the fastest part of the process.
For you, this turning point shows up as a choice in every project. You can rely on speed alone or protect the clarity that makes speed useful.
The lesson is not that AI fell short. It is that the world misidentified the part easiest to automate. Writing code is a form of translation. Designing products is a form of understanding.
Translation can be automated. Understanding has to be earned.
No amount of speed in the engineering layer can make up for the absence of clarity at the beginning.
This is why design still matters. This is why design has always mattered. And this is why design will matter even more as AI advances.
The future rewards teams that balance both sides.
Fast code with clear intent. Automation paired with thoughtful design. AI-supported engineering guided by human understanding.
That combination produces software that works and experiences that last. It creates products that feel like they were made by people who care.
And if you are the person accountable for what ships, you already know people can feel the difference between something assembled quickly and something created with intention.
What AI Still Doesn’t Understand About Product Design
What AI Still Doesn’t Understand About Product Design
The year opened with an expectation hanging over the industry.
It was the quiet assumption repeated in podcasts, in design circles, in LinkedIn think pieces, and everywhere else the future of work gets debated.
People believed design would be the first craft AI would absorb. It sounded logical. Visual generation tools were exploding across the web, producing stunning images on command.
Figma rolled out new features that promised automated layouts. Media outlets suggested that product teams might soon lean heavily on automation. If you asked anyone in January which job category was most at risk, the safe bet was design. And then something unexpected happened.
The real breakthrough showed up in code, not in design.
Through the year, developers across the web shared stories about AI producing entire modules that actually worked.
Public engineering blogs described tools rewriting legacy systems in a fraction of the time teams used to spend. GitHub threads filled with developers talking about AI helpers catching edge cases, generating reliable scaffolding, or refactoring pieces that used to take days.
Even skeptics acknowledged that AI coding assistants had crossed into a new tier of usefulness. They were not toys or demos anymore.
They were part of the workflow, offering volume, speed, consistency and surprising accuracy.
Why AI Struggles With Product Design
At the same time, the design world was telling a very different story.
Designers were posting breakdowns on X showing how AI-generated screens looked clean at first glance but fell apart under real usability constraints.
UX leaders on YouTube walked viewers through the gaps, pointing out missing hierarchy, broken flows, and interactions that made no sense in a real product.
Reddit threads in r/UXDesign grew long with designers explaining that AI could offer a decent starting point, but only at the level of a student exercise. The tools could mimic aesthetics, but they could not create intention.
They could not explain why a certain pattern belonged there or how it supported the user.
This split created a strange moment for businesses. Engineers were speeding up. Designers were fielding questions about why the new tools were not producing entire product systems the way people believed they would.
The assumption had been that design, of all fields, would be the easiest to automate.
The reality has been the opposite.
And that reversal says something meaningful about what software actually is.
And if you are the one responsible for choosing the direction of a product, you feel that split more than anyone because the pressure shifts from execution to intention, and that shift lands squarely on your desk.
How AI Handles Structure but Fails at Understanding
Teams that watched this unfold began to see the pattern.
AI thrived in coding because code is structured.
Rules, inputs, outputs, patterns, transformations, states, dependencies, logic. It is a world built from instructions that can be predicted. When developers across the web praised AI’s performance, they were talking about tasks that fit that shape.
Parsing data. Generating boilerplate. Writing connectors. Refactoring with consistent style.
All of it fits into a system AI models can navigate because the boundaries are defined.
For anyone guiding a product, this is the moment you realize AI can help you build almost anything except the part where you decide what needs to exist in the first place.
Design lives outside those boundaries.
That is the part most of the industry underestimated. Design is not the decoration on top of software. It is the reasoning behind it. It is the thousand choices that determine how someone feels when they try to use a product and whether they understand what happens next. It is context, psychology, prioritization, motion, hierarchy, language, emotion.
There are no fixed patterns that can produce that reliably.
The same screen might succeed for one audience and fail for another. The same layout might help a new user but slow down an experienced one. Those decisions require human judgment, not repetition.
So when AI tries to generate design at scale, it produces surfaces. Pretty ones at times. But surfaces are all the same. A generated screen can look impressive, at least until someone actually tries to use it.
Screens are easy. Sense is hard.
Where AI Accelerates Development but Amplifies Poor Decisions
Developers understood why AI coding tools worked as well as they did. They saw the algorithmic shape of the problem. Designers understood just as quickly why AI design tools were struggling.
They saw the lack of reasoning underneath.
Product teams were discovering this in real time. Someone would prompt a system to generate a dashboard, and it would give them charts placed neatly on a grid with little awareness of what mattered most to the business.
It could not understand which insights deserved prominence or why a flow needed to be reordered for clarity. It could not grasp the intent of the product because intent is not pattern based. It requires understanding, not memory.
This tension created new pressure inside companies. Leaders saw AI writing thousands of lines of code without complaint and asked why the design side had not followed suit. The answer was hiding in plain sight.
AI can accelerate what already has structure. It cannot invent structure where none exists. Code fits into a frame. Design determines the frame itself. That difference is easy to overlook until the moment you ask AI to generate something meant to guide human behavior.
If you lead a team, you can sense that pressure in the questions people ask you. They look to you for clarity because AI can write code on command, but it cannot tell anyone why the work matters or who it is meant to help.
Teams that handled this well recognized the truth early. The rise of accelerated coding made design more important, not less.
Faster engineering cycles magnified the cost of unclear direction. When code could be generated in hours instead of days, the bottleneck shifted.
It moved upstream, into the space where decisions are made about what the software should accomplish, how users should move through it, and why each moment should matter. This is the part AI cannot navigate.
Why AI-Generated Designs Fail to Create Real Product Systems
You see this clearly in the public response to the newest design automation tools.
The feedback follows the same rhythm across the web. Designers appreciate the help on simple tasks but warn against relying on these tools for anything beyond starter layouts. They talk about missing logic, inconsistent interaction states, and visual choices that flatten the personality of a product.
Their criticism is not that AI cannot draw. It is that AI cannot decide. Decision making is the essence of design. And if you have ever launched something that looked right but felt wrong, you already know the cost of skipping the part where humans shape the meaning.
This becomes meaningful when you look at the foundation of a good product.
You do not build software by stacking screens until one of them looks right. You build it by understanding people. You build it by asking why someone needs this tool and what they are trying to accomplish. You build it by removing confusion and creating clarity.
That kind of work is grounded in trust. And trust is human. AI can produce the ingredients of a product faster than ever, but the recipe still belongs to people.
It always will.
Design Is Still the Human Center of Great Software
There is a moment in any build when the team has to agree on what is true.
True about the customer. True about the problem. True about the outcome. This is where the divide between AI’s strength in code and its weakness in design becomes most visible. There comes a point in every build when someone has to decide what the product is trying to do for the people who will use it.
That part never falls to the code.
Code reacts to the decisions already made. Design is where those decisions start. It forces a team to slow down long enough to understand the problem, the people, and the moments that matter.
When teams honor that work, everything that follows has a better shot at landing the way it should.
This is why the teams that succeed in this new era stay anchored in something simple. “We believe that business is built on transparency and trust. We believe that good software is built the same way.”
If you are building something that people depend on, that truth becomes more than a philosophy. It becomes the guardrail that keeps you from mistaking movement for progress.
This is the turning point the industry is now entering.
Leaders no longer debate whether AI will become a powerful partner in development. That question has been settled.
The next question is how to build products that still feel intentional when code becomes the fastest part of the process.
For you, this turning point shows up as a choice in every project. You can rely on speed alone or protect the clarity that makes speed useful.
The lesson is not that AI fell short. It is that the world misidentified the part easiest to automate. Writing code is a form of translation. Designing products is a form of understanding.
Translation can be automated. Understanding has to be earned.
No amount of speed in the engineering layer can make up for the absence of clarity at the beginning.
This is why design still matters. This is why design has always mattered. And this is why design will matter even more as AI advances.
The future rewards teams that balance both sides.
Fast code with clear intent. Automation paired with thoughtful design. AI-supported engineering guided by human understanding.
That combination produces software that works and experiences that last. It creates products that feel like they were made by people who care.
And if you are the person accountable for what ships, you already know people can feel the difference between something assembled quickly and something created with intention.
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