Building software used to mean clearing a technical hurdle first. You needed programming skills or a team to turn an idea into a working product. AI is shortening that process: one person can write code, revise a design, and build a website much faster.

But as ideas become easier to implement, another question becomes harder to answer:

If I can build this product, and others can build it too, why would users choose mine?

Building my recent Chrome extension, X Account Risk Monitor, has given me a concrete way to think about that question.

Building a solution does not mean finding a need

The extension grew out of my own experience. I had been sharing observations about prediction markets and quantitative trading on X when my account was restricted for “non-human behavior.” I had not used automation tools, and the notice did not tell me which action had triggered the decision.

That gave me an idea: build an extension that records some of my activity on X web pages and, when it detects certain repeated patterns, shows the observed evidence and relevant public rules to help users review their actions.

I led the requirements, design, development, testing, and release, using AI to help revise code, investigate problems, and prepare product copy. AI sped up the work, but I still had to decide: What had the extension actually observed, and what could it reasonably say based on that evidence?

An action missing from the log does not mean it never happened. A user may have been active in the mobile app or another browser, and X may change its website. The extension cannot determine how X makes internal decisions or guarantee that an account will not be restricted. Presenting limited observations as definite risk assessments could leave users with the wrong understanding of what the product can do.

The moment users need an answer may already be too late

I wanted to know what I had done after my account was restricted. But the extension can record activity only after it is installed. It cannot recover past actions. By the time someone urgently wants an answer, they may have missed the chance to start collecting the relevant records.

For the product to be useful, users may need to install it in advance and keep their records over time. But before a problem arises, why would they grant page access to a tool with no immediate use?

I could add more action types and reminders. Yet if I cannot answer “Why install it now?”, more features may not change the product’s position. A real problem does not mean people will naturally use the solution you built for it.

Beyond features, what are users choosing?

AI makes it easier for more developers to build similar features, but it does not give users more time or attention. Faced with more tools, users still have to decide: Does this fit my situation? What will it ask of me? Can I trust the information it provides?

Those questions are especially concrete for a browser extension. Users must decide whether to grant access to web pages, whether they trust the extension to handle their activity records, and whether its reminders have sufficient evidence behind them.

X Account Risk Monitor stores observations in the user’s current Chrome profile. But saying “stored locally” is not enough. I still need to explain what it records, what it cannot see, and why an action missing from the log should not be treated as proof that it never happened.

Two products may offer similar features while placing very different demands on a user’s understanding and trust. When a tool clearly shows where its information came from and what remains unknown, users can better judge when to rely on it. A tool that gives confident conclusions without explaining its evidence may struggle to earn that trust, however many features it has.

That is why I treat the scope of the records, the limits of the extension, and explanations of its results as part of the product design. Whether those choices make people more willing to install and keep using it is something I still need to learn from real use.

What evidence is still needed after launch?

X Account Risk Monitor is now available. I have built a product website and introduced the extension in developer communities. I also wrote AI Is Reshaping the Market: Where Are the Opportunities as the Old Order Shifts?, which included reflections on building it. That article received around 2,000 views.

The discussion attracted readers, but I do not know how many of them need the extension. Reading an article, visiting a website, installing a product, and continuing to use it are different actions. I do not yet have enough installation, retention, or channel data to know whether the product has found a stable need.

Next, I want to understand when users think about installing it. Are they trying to save records in advance, or has something already happened to their account? Do they revisit their records after installing it? Which reminders actually help? The answers will shape the product more than simply adding features.

When implementation is less scarce, what is worth pursuing?

AI has not made reliable engineering, security, or ongoing maintenance any less valuable. But for an independent developer, building the product is only part of the work.

This project shows that I can turn an idea into a publicly released product. It does not yet show that the market will keep choosing it. If people do not continue using a product, lower development costs will not automatically generate revenue.

AI lets me test ideas at a lower cost and reach users earlier. The more important task now is to use real evidence to decide which problems are worth pursuing, rather than keep adding features to a product whose demand has yet to be validated.

AI makes it easier to create software. It does not make every piece of software valuable.