How can specialized AI outperform general-purpose AI?

Speaking of the competition between professional AI and general AI, I recently found an interesting phenomenon - professional AI in some specific scenarios to show the ability to simply let general AI can not be expected. Take Lovart, a design AI, for example, it can not only generate brand visual solutions with a single click, but also maintain a unified style and tone, which is a degree of professionalism that ordinary AIs simply can't do. This makes me wonder: why can some AIs be so extreme in one field?

Where does professional AI get its "muscle memory"?

After carefully analyzing the success stories of professional AIs such as Lovart and Skywork, I found that they all have a common feature: in-depth mastery of industry know-how. For example, when Skywork does business reports, it doesn't simply piece together data, but really understands the details of "where the PE ratio should be placed" and "how to use charts to present trend changes in a more professional way". This kind of industry-specific knowledge is like the muscle memory formed by athletes through long-term training, which allows them to show amazing accuracy in specific tasks.

In contrast, general-purpose AI is like a freshman with extensive knowledge but little experience. It can answer all kinds of questions, but when it comes to scenarios that require professional judgment, it's often out of its depth. I've tested General AI to design a brand logo, and the resulting program, while creative, does not meet the standards of actual business applications.

Deliverability: the killer app for professional AI

What amazes me most is that professional AI like Skywork can already achieve "out-of-the-box" delivery level. Last month I tried to use it to generate an industry analysis report, from data collection, analysis to layout, the entire process almost no human intervention, the final output of the PPT can be sent directly to the client. This degree of completion, is the general AI simply do not dare to think.

Interestingly, this professional ability is also reflected in the user's willingness to pay. Data show that the user payment conversion rate of professional AI products is generally 30%-50% higher than that of general AI.After all, when AI can directly solve practical problems, users are naturally more willing to pay.

The Unique Value of Universal AI

Of course, general-purpose AIs are not useless. They show amazing flexibility when it comes to handling non-standardized tasks, learning new areas quickly, and so on. I recently used a general-purpose AI product to generate short video scripts, and while the quality varied, the creative diversity really made a difference.

But then again, as the application of AI deepens, there may be a hybrid model in the future - general AI as the "entrance" and specialized AI to provide "in-depth services". Just as many enterprises now use ChatGPT for initial communication, and then refer specialized issues to AI experts in specific fields. This model of division of labor may become the mainstream of the future.

In general, the relationship between specialized AI and general AI is more like a scalpel and a Swiss army knife - each has its own strengths and the key depends on the use of the scene. However, from the commercialization point of view, the professional AI that has achieved excellence in a specific field has indeed demonstrated stronger market competitiveness. This also gives AI entrepreneurs a reminder: instead of pursuing a large and comprehensive, it is better to do irreplaceable in a segmented field first.