The Truth Behind Codex 'Build a Website in 5 Minutes': AI Isn't Creating Sites—It's Helping You Copy Them

What Those "Build a Website in 5 Minutes" Videos Are Hiding

Recently on major content platforms, you've probably come across some jaw-dropping short videos: a creator shows off a visually stunning website, paired with provocative copy like—"Can you believe Codex made this in just five minutes?"

The Codex mentioned here is OpenAI's code generation tool derived from the GPT large language model. Originally released in 2021, it later evolved into a Coding Agent embedded within ChatGPT. In 2025, OpenAI launched a completely new Codex product, positioned as a cloud-based autonomous programming agent capable of independently completing multi-step tasks like code writing, testing, and deployment in a sandbox environment. It can generate code, modify files, and execute commands based on natural language descriptions—and it's precisely this "write code in plain language" capability that has made it the favorite showpiece for traffic-hungry creators.

This type of content typically generates intense anxiety and tech worship: as if mastering a particular AI coding tool means anyone can produce professional-grade products in minutes. But is that really the case? A Bilibili creator specifically debunked these videos, and the conclusion was brutally straightforward: They're not using Codex to build websites—they're using Codex to copy websites.

Here's a vivid analogy: it's like your friend bragging about being a master chef, but when it's actually time to cook, they order takeout, dump it onto a plate, and claim they made it themselves. What these creators are doing is essentially the same thing—they can't even "copy" properly on their own, so they need Codex to help them do it.

Type One: Copying Shared Prompts to Generate Websites

The debunking analysis categorizes these operations into two levels.

Directly Lifting Ready-Made Prompts for One-Click Generation

The first approach is relatively mild. These creators don't actually design or conceptualize websites. Instead, they go to prompt-sharing websites, copy ready-made prompts, and paste them into Codex for one-click generation.

With the proliferation of generative AI, a complete ecosystem has formed around the prompt-sharing economy. A typical platform mentioned in the video is MotionSize—these platforms specialize in collecting and showcasing high-quality front-end animation prompts, allowing users to preview generated effects before copying them directly. Similar platforms include FlowGPT, PromptBase, and others, some of which even support paid prompt transactions. The existence of these platforms is inherently beneficial—they lower the barrier to using AI tools—but they also provide convenience for "packaging-style content creation." The reason this approach is considered "mild" is precisely because these websites are prompt-sharing resource libraries whose content is meant to be copied and used, so strictly speaking, it doesn't constitute particularly serious infringement.

The Core Problem: Concealing Sources and Exaggerating Original Ability

The issue with these creators isn't that they used someone else's materials—it's that they conceal the source and exaggerate their own contribution. They package copy-paste results as "original ability," exploiting information asymmetry to manufacture anxiety and farm engagement. Viewers see polished final products without knowing that behind them is nothing more than a copied public prompt. This is fundamentally an exploitation of information asymmetry—these creators know full well that most viewers are unaware prompt-sharing platforms exist, making it easy to craft a persona of "I have unique AI techniques."

Type Two: Using Codex to Directly Scrape Others' Websites

The truly egregious behavior comes from the second type of creator. They're not having Codex generate websites—they're having Codex help them scrape other people's existing websites.

From "Slight Modifications" to "Zero Changes" Plagiarism

This comes in varying degrees:

• The slightly restrained: They swap out the copy, replace the original site's brand name with their own, tweak the page layout slightly, creating the illusion of "derivative work."
• The most brazen: They don't change a single word—they scrape someone's page directly, open it in Localhost for preview, film a video, and claim they built it themselves.

Let's be blunt: this is naked plagiarism. The original websites being scraped are often the result of professional design teams spending weeks or even months meticulously crafting, involving brand strategy, visual design, interaction logic, front-end engineering, and multiple dimensions of professional collaboration. "One-click copying" these results and claiming them as your own AI work is not only profoundly disrespectful to the original creators' labor but also severely misleads the public about AI's actual capabilities.

Using Codex to Scrape Sites Is Actually the Long Way Around

What's even more ironic is that if you genuinely just want to scrape someone's web pages, there are plenty of lighter-weight tools available. For example, the classic HTTrack—an open-source website mirroring tool born in 1998 that's still widely used today. It can recursively crawl a target site's HTML, CSS, JavaScript, images, and other static resources, rebuilding a complete local copy of the website. All you need is a URL to grab the entire site. Similar tools include wget, SiteSucker, WebCopy, and others—extremely low technical barriers that don't require using a VPN or wasting precious tokens like Codex does.

Speaking of token costs, using Codex or similar AI coding tools to generate complete web pages often consumes substantial computational resources. Taking GPT-4-level models as an example, a single complex page generation task might consume tens of thousands of tokens, costing several dollars at API pricing. If the sole purpose is to copy an existing website, this investment is far less efficient in both cost and speed compared to traditional scraping tools—it exists purely to create the narrative of "AI-generated."

Most of the time, you could just download it and be done. Using Codex for this is actually an inefficient and pretentious choice—its only purpose is to make the video look more "high-tech."

Don't Let AI Website-Building Anxiety Marketing Sway You

When you see this kind of "one-click beautiful website" content, regular users have absolutely no reason to feel anxious. Because these finished products weren't actually made by the creators themselves. Truly visually impressive websites with smooth interactions are never that simple—so-called "one-click generation" is basically all smoke and mirrors.

A genuinely usable commercial website requires far more than front-end visual presentation. It needs responsive design (displaying correctly across phones, tablets, and desktops), accessibility support, SEO optimization, performance tuning (lazy loading images, code compression, CDN deployment), security measures (XSS/CSRF defense), backend API integration, database design, user authentication systems, and a whole series of engineering work. The "five-minute products" these creators showcase are at best static page screenshots—they're light-years away from a product that could actually go live.

The reason these videos are popular fundamentally comes down to exploiting the public's imagination about and fear of AI capabilities. Once you understand the truth behind them, you'll realize: AI tools are indeed powerful, but they're not magic, and they certainly can't replace genuine design and engineering skills.

The Real Capability Boundaries of AI Coding Tools

The deeper value of this debunking is reminding us to rationally assess the capability boundaries of Coding Agents like Codex and Claude.

Current mainstream Coding Agents (such as Codex, Claude Code, Cursor Agent, Devin, etc.) have core capabilities concentrated in several areas: generating standardized code modules from natural language descriptions, converting design mockups or screenshots into front-end pages, automated refactoring and debugging, writing unit tests, and handling repetitive coding tasks. In these scenarios, they can genuinely boost development efficiency significantly—experienced developers using these tools can reduce completion time for certain tasks by over 50%.

However, they still have clear shortcomings in the following areas: complex business logic architecture design, multi-service system integration, performance bottleneck diagnosis and optimization, security vulnerability identification and hardening, and truly brand-distinctive visual creativity with emotional resonance. Generated code often has a massive gap between "looks like it works" and "actually ready for production"—it may lack boundary condition handling, error fallback logic, logging and monitoring hooks, and other engineering details essential for production environments.

How are truly beautiful websites actually built? What can these AI coding assistants actually do and not do? Only by clearly understanding their capability boundaries can you truly leverage their potential, rather than being led around by marketing rhetoric.

The proper way to use AI coding tools is to treat them as efficiency-boosting assistants, not all-in-one "fully automatic chefs." They excel at handling repetitive code, generating scaffolding, and assisting with debugging. But when it comes to genuine product design, creative thinking, and complex system architecture, human judgment remains irreplaceable.

Next time you scroll past a "build a website in five minutes" video, take a moment to stay cool-headed: that beautiful finished product might just be "takeout" served on a plate.