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 Aren't Telling You

Lately, you've probably seen a wave of jaw-dropping short videos across content platforms: a creator showcases a visually stunning website, paired with an irresistibly provocative caption — "Can you believe Codex built this in just five minutes?"

The Codex mentioned here is a code generation tool derived from OpenAI's GPT large language model. Originally released in 2021, it later evolved into a Coding Agent built into ChatGPT. In 2025, OpenAI launched an entirely new Codex product, positioned as a cloud-based autonomous coding agent capable of independently completing multi-step tasks like writing code, running tests, and deploying — all within a sandboxed environment. It can generate code, modify files, and execute commands based on natural language descriptions. It's precisely this "write code in plain language" capability that makes it the favorite showpiece for engagement-hungry content creators.

This type of content tends to manufacture intense anxiety and tech worship: as if mastering a particular AI coding tool would enable anyone to produce professional-grade products in minutes. But is that really true? A content creator on Bilibili specifically debunked these videos, and the conclusion was refreshingly blunt: They're not using Codex to build websites — they're using Codex to copy websites.

Here's a vivid analogy: imagine your friend brags about being a master chef, but when it's time to actually cook, they order takeout, dump it onto a plate, and claim they made it themselves. That's essentially what these creators are doing — 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 breaks these operations into two tiers.

Copy-Pasting Ready-Made Prompts for One-Click Generation

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

As generative AI has gone mainstream, a complete ecosystem has formed around prompt sharing. One typical platform mentioned in the video is MotionSize, which specializes in curating and showcasing high-quality front-end animation prompts — users can preview the output and copy prompts 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 convenient ammunition for "packaged content creation." The reason this approach is considered "mild" is that these sites are essentially prompt asset libraries designed for sharing, and their content is meant to be copied and used. Strictly speaking, it's not a particularly serious form of infringement.

The Core Problem: Hiding Sources and Exaggerating Original Ability

The issue with these creators isn't that they used someone else's material — it's that they conceal the source and exaggerate their own contribution. They package copy-paste results as "original capability," leveraging information asymmetry to manufacture anxiety and farm engagement. Viewers see the polished end product without knowing that all it took was copying a publicly available prompt. This is fundamentally an exploitation of information asymmetry — these creators know full well that most viewers are unaware that prompt-sharing platforms exist, making it easy to cultivate a persona of "I have unique AI skills."

Type Two: Using Codex to Directly Scrape Others' Websites

The second tier of creators is where things get truly ugly. They're not asking Codex to generate websites — they're asking Codex to help them scrape existing websites built by others.

From "Minor Tweaks" to "Carbon Copies" — It's All Plagiarism

There are varying degrees within this category:

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

Let's call it what it is: straight-up plagiarism. The original websites being scraped are often the result of professional design teams spending weeks or even months of meticulous work, involving brand strategy, visual design, interaction logic, front-end engineering, and more. "One-click copying" these results and claiming them as your own AI creation is not only deeply disrespectful to the original creators' work — it also severely misleads the public about what AI can actually do.

Using Codex to Scrape Websites Is Overkill

What makes this even more absurd is that if you genuinely just want to scrape someone's website, there are plenty of far lighter tools available. Take the classic HTTrack, for example — an open-source website mirroring tool that's been around since 1998 and is still widely used. It can recursively crawl a target site's HTML, CSS, JavaScript, images, and other static resources to rebuild a complete local copy of the website. All you need is a URL. Similar tools include wget, SiteSucker, WebCopy, and others — the technical barrier is extremely low. You certainly don't need to go through the trouble of using Codex with a VPN and burning through precious tokens.

Speaking of token costs, using Codex or similar AI coding tools to generate complete web pages often requires substantial computational resources. With a GPT-4-level model, a single complex page generation task can consume tens of thousands of tokens, costing several dollars at API rates. If the goal is merely to replicate an existing website, this investment is far less efficient in both cost and speed than traditional scraping tools — it exists purely to manufacture the "AI-generated" narrative.

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

Don't Let AI Website-Building Anxiety Marketing Get to You

When you see content about "one-click stunning websites," there's absolutely no reason to feel anxious. Because these finished products aren't actually made by the creators themselves. Truly visually impressive, smoothly interactive websites are never that simple — the so-called "one-click generation" is almost always smoke and mirrors.

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

The reason these videos go viral is fundamentally because they tap into the public's imagination about — and fear of — AI capabilities. Once you understand the truth behind them, it becomes clear: AI tools are genuinely powerful, but they're not magic, and they certainly can't replace real design and engineering skills.

The Real Capability Boundaries of AI Coding Tools

The deeper value of this debunking is that it reminds us to take a rational view of 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 strengths 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 significantly boost development efficiency — experienced developers using these tools can reduce completion time for certain tasks by over 50%.

However, they still have notable shortcomings in the following areas: complex business logic architecture, multi-service system integration, diagnosing and optimizing performance bottlenecks, identifying and patching security vulnerabilities, and producing visual creativity with genuine brand distinction and emotional resonance. Generated code often has a huge gap between "looks like it works" and "actually ready for production" — it may lack edge case handling, error fallback logic, logging, 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 what can't they do? Only by clearly understanding their capability boundaries can you truly leverage their power, rather than being led around by marketing rhetoric.

The right way to use AI coding tools is to treat them as efficiency-boosting assistants, not as all-in-one "fully automated 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.

The next time you come across a "build a website in five minutes" video, take a moment to stay skeptical: that beautiful finished product might just be a plate of "takeout" being served as homemade.