Why Are Skills Hard to Commercialize? Bots Are the Real Business Opportunity in the AI Era

Recently, the concept of Skill has become increasingly popular. Whether it is Claude Code, Codex, or various Agent platforms, they are all driving the development of the Skill ecosystem. Developers can encapsulate a set of prompts, tool-calling logic, workflows, and domain knowledge into a Skill, allowing AI to call it automatically when needed.

This looks like a great entrepreneurial opportunity: since Skills can be reused, can we build a Skill marketplace like past software plugins, letting developers sell their Skills?

I believe this path has a major commercialization obstacle: Skills are easy to use, but not necessarily easy to charge for individually. What truly has commercial value may not be the Skill itself, but the Bot built on top of the Skill.

1. Skills Have High Value, But They Are Not the Final Deliverable

First, let's understand what a Skill actually is.

A Skill is essentially an encapsulation of AI capabilities. For example, a programmer can create a code review Skill that contains code review standards, check processes, common issues, and handling methods. When an Agent encounters a code review task, it can load this Skill and complete the work according to the preset method.

There are many similar Skills: SEO content creation, competitor analysis, data analysis, PPT creation, financial statement analysis, UI design, and more.

Skills solve a very important problem: how to give AI reusable professional capabilities.

But here there is a commercial problem: when users buy a Skill, they still ultimately need to invoke AI themselves, provide context, configure tools, handle exceptions, and check the final result.

In other words, a Skill provides a capability, not a complete service.

This is like selling a user an Excel template. The template is indeed valuable, but the user still needs to prepare data, fill in content, and analyze results. For tech-savvy people, this is not a big problem; but for ordinary users, the usage threshold still exists.

More importantly, the content of a Skill can often be copied, modified, or even regenerated through AI. When the core value of a Skill is mainly reflected in prompts and workflows, its independent pricing power will be challenged.

2. Why Is It Difficult for Skills to Form a Stable Payment Model?

I think there are three main reasons.

First, the value of a Skill is difficult to measure independently.

Users can hardly directly judge how much a Skill is worth. Should a code review Skill sell for $5, $20, or $100? Its value depends on the underlying model, Agent capabilities, the data entered by users, and actual usage scenarios.

The same Skill may produce completely different results across different models and different environments. What users buy is not a stable software product, but a capability configuration that depends on the external environment to run.

Second, the replacement cost of Skills is getting lower and lower.

In the past, developing a professional tool required a lot of engineering investment. Now, users can simply tell AI what they want to do, and AI may help them generate a similar Skill.

Of course, professional Skills still have advantages in knowledge accumulation, experience, and engineering quality, but relying solely on prompts and process definitions makes it difficult to build long-term competitive barriers.

Third, Skills lack a complete user delivery loop.

What users really care about is not whether you have an excellent Skill, but whether you can help them get the job done.

For example, if a user wants to do competitor analysis, he does not want to research which Skill to install, nor configure an Agent, nor handle various tool calls himself. He only wants to enter a few competitor names and finally get a complete competitor analysis report.

The commercial value of these two product forms is obviously different.

3. Why Are Bots Easier to Commercialize?

The Bot mentioned here is not simply a chat interface wrapped around something, but an AI product that targets specific users and scenarios and can independently complete tasks.

For example:

• SEO Bot: Input a website address, automatically analyze SEO issues, and generate an optimization plan.
• Research Bot: Input a research topic, automatically search materials, analyze information, and organize into a report.
• Data Bot: Connect to a database, ask questions in natural language, automatically query data, and generate visual reports.
• Design Bot: Input product requirements, automatically generate UI design drafts and page code.
• Marketing Bot: Input product information, automatically generate marketing content, distribution plans, and track operational results.

You will find that the biggest difference between these products and Skills is: Skills deliver capability, Bots deliver results.

Users do not need to understand which Skills are used underneath, nor do they need to know how the Agent plans tasks. They only need to know what problem this Bot can help them solve.

This product form is naturally easier to build a business model.

First, Bots have a clear reason to pay.

Users will not pay because you used a complex Prompt, but they will pay because you saved them 10 hours; they will not pay because you have how many Skills, but they will pay because you help them complete a competitor analysis report every day.

The key to commercialization is not how advanced the technology is, but whether users can perceive the value.

Second, Bots can generate recurring revenue.

Skills are usually one-time purchases or free installs; whether users continue to use them later is hard to turn into steady income.

But Bots are different.

If a Bot can continuously take over user work, such as monitoring the market every day, generating data reports every week, and continuing to operate social media, then it has the foundation for subscription-based charging.

You can even charge per task, per execution volume, or per delivered result.

Third, Bots are easier to build competitive barriers.

A single Skill is easy to copy, but a truly useful Bot behind it may contain multiple Skills, MCP tools, knowledge bases, business data, user memory, task scheduling, and execution feedback.

The combination of these capabilities constitutes a complete product.

Especially when a Bot begins to accumulate user data, business context, and execution history, later entrants will no longer be replicating a SKILL.md but a complete business system.

4. Skill and Bot Are Actually Two Different Business Positions

I prefer to understand them as two layers of the AI era ecosystem.

Bot: User-facing commercial product

Solving problems, delivering results, building user relationships, and charging continuously

Capability combination and task orchestration

Skill

Professional capability

MCP

External tools

Memory

Context memory

Model & Agent Runtime

Inference, planning, execution, scheduling

Skills are responsible for accumulating professional experience, MCP is responsible for connecting external tools, Memory is responsible for preserving context, Agent Runtime is responsible for scheduling and execution, and Bot combines these capabilities into a deliverable product.

This also means that the future business opportunity is not necessarily to build a huge Skill marketplace, but to build Bots that can truly solve problems around specific industries and scenarios.

Of course, Skills are not entirely without commercialization opportunities. High-quality Skills in professional fields, private knowledge, and continuously maintained workflows can still be charged for. But they are more suitable as capability components for Bots, or as infrastructure for developers, rather than all Skills becoming independent products.

5. Future Entrepreneurial Opportunities: Don't Sell Skills, Sell Bots

If I had to choose an AI entrepreneurship direction today, I would prioritize Bots over purely building a Skill marketplace.

The reason is simple: a Skill marketplace solves the supply problem, while a Bot product solves the demand problem.

Developers want to share their capabilities; users want someone to complete their work. These two needs are not exactly the same.

For example, if you want to enter the cross-border e-commerce field, there are two approaches.

First, develop a cross-border e-commerce Skill that helps users analyze Japanese products, generate product descriptions, and organize purchasing information. Then publish it to a Skill marketplace and wait for users to buy.

Second, directly develop a cross-border e-commerce purchasing Bot. Users input product requirements, and the Bot automatically searches Japanese suppliers, compares prices, organizes product information, generates purchase lists, and when necessary, manual confirmation for transactions.

The former sells a tool; the latter sells a service.

The latter can not only charge monthly subscriptions, but also charge based on purchase orders, transaction amounts, or actual service results. Users may not even need to know how many Skills are used behind the scenes.

This is the business model I think has more room for imagination.

Of course, a Bot does not mean that wrapping a chat interface allows you to charge. A truly valuable Bot must have reliable execution capabilities, clear delivery standards, cost control, and exception handling mechanisms. Especially when involving funds, orders, and corporate data, it must also have permission control and manual confirmation mechanisms.

6. In the AI Era, the Real Question Is Who Owns the User Entry Point

Looking back at the development of software, we have gone through several stages.

In the Web era, everyone sold websites and software; in the mobile internet era, everyone sold Apps and services; in the AI era, users may no longer care which software they are using, but directly tell AI what task they want to accomplish.

This means the form of products may change.

In the past, users needed to open ERP, CRM, Excel, and operate software themselves to complete work. In the future, users may only need to tell a Bot: help me analyze this month's sales data, find the reasons for the decline, and generate a report.

Software gradually transforms from a tool that requires user operation into a service that can execute tasks on behalf of the user.

In this process, Skills are the standardization of capabilities, and Bots are the productization of services.

Whoever can package AI capabilities into stable, reliable, and continuously deliverable services has a better chance of capturing commercial value.

I am not saying Skills have no future, but rather that the value of Skills is more reflected at the capability layer, while the value of Bots is at the business layer.

In the future, there may be many free Skills, but those who can truly make money are the Bots that go deep into specific business scenarios, control the user entry point, and continuously deliver results.

Don't just think about developing a better Skill; you should think: can you use these Skills to complete truly valuable work for users?

This may be the direction most worth paying attention to for AI Agent commercialization.