Imagine a startup founder who skips hiring a development team and instead asks an AI to build their mobile app. For cost-conscious entrepreneurs and busy product owners, the allure of AI coding is obvious: Describe the idea to ChatGPT or plug it into a no-code AI builder, and out pops a working app by Monday morning. Why pay for experienced engineers or designers when code generation tools promise speed and savings?

The hype is real —– in 2023, 70% of developers said they use or plan to use AI coding tools¹, and tech CEOs boasted that 20–30% of their company’s new code was AI-generated². If AI can already churn out nearly half the code in some projects³, why not let it build entire apps?

This question isn’t science fiction; it’s the million-dollar conundrum facing many business decision-makers today. AI has made stunning progress in writing code and automating tasks that used to take humans days. Yet, as we’ll explore, there’s a gaping chasm between producing code and producing a successful product. Building an app is more than assembling code — it requires understanding human context, user motivation, business trade-offs, and UX nuance. These are the qualities MartianCraft has honed for over 20 years, and the areas where today’s AI still falls short.

The Allure of AI-Built Apps

It’s easy to see why the idea of an AI-built app is captivating. AI coding assistants like GitHub Copilot and OpenAI’s Codex can generate boilerplate code in seconds. Need a basic login screen or a database handler? An AI can whip up a plausible snippet faster than an entire junior dev team running on coffee. This has translated into real adoption: By 2024, 76% of developers were using or planning to use AI tools in their workflow, up from 70% the year before⁴¹. GitHub’s Copilot, for instance, is so embedded in developers’ processes that in files where it’s enabled, nearly 46% of the code can be authored by the AI³.

The promise isn’t just speed — it’s also cost efficiency. If AI can handle the heavy lifting of coding, a startup might imagine cutting development costs dramatically. No salaries, no delays — just instant features. In fact, Microsoft’s CEO recently claimed that AI now writes “maybe 20, 30 percent” of the code in some of their repositories². Executives from Google have made similar comments about their own codebases, suggesting we’re headed toward a future where AI handles the bulk of routine programming tasks. The productivity gains are tangible too: Developers turn to AI to boost output, learn new techniques on the fly, and handle menial coding so they can focus on higher-level problems¹.

Yet amid this enthusiasm, there’s a telling statistic: Only 3% of developers “highly trust” the accuracy of the AI tools they use¹. In other words, even the most fervent adopters know these tools can be useful but are far from infallible. AI might be great at generating code, but can it judge if that code is the right code for the problem? Can it decide what and why to build, not just how to build it? This is where the shiny facade of AI-built apps begins to crack, like a knockoff Rolex the first time it hits a countertop. To understand why replacing developers with AI won’t be as easy as it sounds, we need to examine the fundamental difference between code generation and product creation.

Code vs. Product: The Human Element

Seasoned software professionals will tell you that writing code is often the easy part — the hard part is knowing what to build and why. As one StackOverflow engineer quipped, “The hardest part about creating software is not writing code — it’s creating the requirements, and those requirements are still defined by humans”⁵. In other words, an app’s success hinges on understanding user needs and defining the right features (and trade-offs) to satisfy those needs. AI, for all its wizardry and spectacle, has no inherent understanding of your business domain or your users’ desires — it simply follows instructions and patterns. Even training a model on your own data doesn’t close that gap; it still lacks intuition, context, and the ability to reason about what truly matters to your business. I’m afraid that we may have to wait for actual Artificial General Intelligence to close this type of gap.

Think of coding as construction work: You can give an AI a blueprint (a prompt), and it can nail together some boards in the right order. But who drafts the blueprint? If the prompt (your requirements) is vague or flawed, the AI will merrily build a flawed structure. And unlike a seasoned architect, it won’t raise a hand and say, “Are you sure this is what the users need?” It will “give you exactly what you asked for, not what you need”⁶. This literalism is by design — tools like Copilot aren’t actually intelligent agents with goals; they predict likely code based on training data. They don’t question design choices or user flows.

Product thinking requires skills that AI lacks by its very nature. It requires empathy, creativity, and context awareness. A human product designer will consider questions like: Who is the target user? What problem are we solving for them? What workflow feels intuitive? What edge cases could confuse or frustrate them? An AI doesn’t comprehend these questions. It has no context awareness of real-world use. A recent design industry piece put it bluntly: “AI has no context awareness. … Successful design is closely tied to understanding the specific context in which solutions will be used. But AI outputs do not appreciate or even recognize important nuances critical to solving user problems”⁷.

In practice, this means an AI might propose a generic solution that superficially fits the request but overlooks key details. It takes human insight to notice those details — the little workflow tweaks or business rules that make the product actually work for its users. At the end of the day, software is there to solve a business problem — not a technical problem.

When AI Falls Short (Real Examples)

AI tools are immensely helpful for developers. But when you try to use them in place of experienced engineers and designers, things start to break down. Let’s look at a few key shortcomings:

• Lack of Context and User Empathy: AI doesn’t know your users. It can’t prioritize features or predict user behavior. A Nielsen Norman Group study found most AI UX tools offered only vague suggestions and lacked real insight⁸.

• Brittleness and Edge Cases: AI handles the “happy path” but misses complex exceptions. Like a rookie with a manual, it struggles when conditions deviate. Google researchers have highlighted how AI-generated code handles common cases well but breaks under pressure⁹.

• Security and Quality Gaps: GitHub Copilot produced insecure code in nearly 40% of tested cases¹⁰. Common problems include outdated libraries, hardcoded secrets, or poor error handling.

• No Strategic Trade-offs: AI can’t weigh speed vs. scalability or short-term vs. long-term maintainability. It won’t recommend user research or suggest deprioritizing a feature based on ROI.

• Poor Maintainability: AI-generated code is often hard to read or refactor. Experiments with zero-human-code apps show they become brittle fast¹¹.

• Lack of Differentiation: AI produces what’s common, not what’s special. It won’t invent the next novel feature or intuitive UX pattern. It reuses patterns; it doesn’t innovate — in fact, by its very nature it can’t innovate.

• Technical Debt: While AI excels at cranking out boilerplate and speeding up initial development, it has no grasp of technical debt or codebase integrity. Over time, relying on AI as the primary maintainer often leads to bloated, fragile, and chaotic code, a fast track to spaghetti architecture and long-term pain.

To sum up: AI provides the scaffolding, not the soul. It’s a starting point, not a finished product. Real software still requires human eyes, human empathy, and human judgment.

Building with AI: Partner, Not Replacement

AI is powerful. It saves time. It reduces repetition. It helps us test ideas faster.

But it’s not an engineer, a designer, or a product owner. It’s not a partner — unless you already know what you’re building, why it matters, and how it fits your users’ needs. It doesn’t tell you what to build next. It doesn’t understand what just broke in production. It doesn’t know when a user is confused.

At MartianCraft, we leverage AI tools in tandem with our senior engineers and designers. These tools accelerate boilerplate tasks, help draft test cases, and streamline the mundane. But the real thinking, the problem-solving, and the critical decisions — those still lie with our team. We’re accountable for the results, not the tool. AI frees up our engineers and designers to focus more on the complex, high-value problems and less on the repetitive, low-effort ones.

AI can do more than ever. But it still can’t replace good judgment, deep product insight, or human experience. Until it can, we’ll be right here, building software the way we have for decades: with clarity, with intention, with care, and with a deep understanding of the people and business it’s for.

If that sounds like the kind of team you want in your corner, we should talk.