How does vertical AI break through?

To be honest, after reading this article, I deeply feel that the AI field is going through a process of "demystification", Manus's myth is just the beginning, the real question is: when the general-purpose AI agent is in trouble, how to find their own way out of the vertical AI? This reminds me of a conversation with a medical AI entrepreneur who said, "In the medical vertical, we don't need AI to be able to write poems and paintings, we just need it to be able to accurately recognize lung nodules." This sentence breaks the mystery - the way to break the vertical AI lies precisely in the word "restraint".

Vertical AI "scene deep plowing" is the king of the road

You see the success of OpenEvidence speaks volumes. It doesn't do big and comprehensive general intelligence, but bites into the niche scene of medical evidence retrieval. It is said that they spent three years just building the medical knowledge graph, combing through the literature of the world's top medical journals. The result of this deep plowing is that when doctors encounter difficult cases in the clinic, OpenEvidence can give advice based on the latest evidence-based medicine within seconds, and each piece of advice is labeled with the source. This is much better than those general AIs that can only generalize!

I also noticed an interesting case recently. There is a startup company specializing in legal document review AI, and they don't even call themselves "Agent", but "Intelligent Assistant". The founders told me that their biggest breakthrough is not how advanced the technology is, but the fact that they have broken down the workflow of lawyers into 187 standard actions, and the AI only needs to assist in 23 of them. Sounds restrained right? But it was this restraint that allowed their product to be adopted by more than 50 law firms within six months of launch.

What kind of technology architecture is needed for vertical AI?

When it comes to technology, I've found that successful vertical AIs are all doing the same thing: fitting uncertain intelligence into certain workflows. It's like putting the "reins" on AI - preserving its creativity, but not letting it run wild. For example, in the field of financial risk control, AI is not completely autonomous decision-making, but in the preset approval process, only in the specific risk identification links play a role. This "workflow + AI" model may be the most pragmatic choice for the vertical field.

But to be honest, the current technology frameworks are still too "engineer minded". I've seen too many projects pile LangGraph, AutoGen and other frameworks together, but not even the most basic business logic. What Vertical AI really needs is a middle layer that is closer to business scenarios - one that understands industry terminology and adapts to work habits. This reminds me of the manufacturing industry's MES system, they are not the general ERP system "verticalization" of the success stories?

Data barriers are the biggest moat.

An investor told me that when they look at vertical AI projects now, the first thing they ask is the data source. General AI can win by model size, but vertical AI must rely on data depth. Those projects that really run out, often have unique data access channels - either industry cooperation, or historical accumulation, or even a little bit of manual labeling. These data barriers can be much stronger than technical barriers.

Take the AI in education, I learned that a company spent two years to collect 100,000 copies of students' wrong books, and established an exclusive "error pattern library". Their AI does not require strong reasoning ability, but can accurately identify the 37 types of mistakes that students often make, and make targeted suggestions. This kind of data-based precision is where the value of vertical AI lies.

In the end, the breakthrough of vertical AI does not lie in the pursuit of "smarter", but in the pursuit of "better understanding". When the general AI is still trapped in the illusion of "omnipotence", vertical AI has taken root in a specific scene. Perhaps, this is the real AI landing the way it should be - not high above the "intelligent body", but in-depth industry "old experts". What do you say?