Mingshi Capital, BAI Capital, and Ant Group, the optimists, pessimists, and moderates in AI investment, gather at a roundtable.

Text by | Zhou Xinyu

Edited by | Su Jianxun

Today's AI investors can also be divided into "optimists" and "pessimists."

On September 12, 2025, at the Inclusion Bund Conference. At the round - table forum "The First Battle of AI Application Implementation: Has the Agent Era Arrived?" hosted by Feng Dagang, the CEO of 36Kr, several investors from leading institutions had a dialogue.

When Feng Dagang posed the first question: What has led to the explosion of agents? The biggest divide among the investors emerged.

△ Round - table forum "The First Battle of AI Application Implementation: Has the Agent Era Arrived?" Image source: Official

Huang Mingming, the founding partner of Mingshi Venture Capital, who has invested in popular agents such as GenSpark, Lovart, and Sheet0, is a typical "optimist."

He mentioned that in the past 20 years, China has accumulated a large amount of product capabilities in the mobile Internet. Two - thirds of the world's top agents in the future will come from China.

However, the "pessimist" investors see that today's agents still have many "inabilities."

In the view of Long Yu, the founding and managing partner of BAI Capital, the current explosion of agents is inseparable from users' unprecedented tolerance for AI.

But in scenarios with zero tolerance, such as finance, she believes that agents still cannot be implemented on a large scale. And the "window period" of tolerance for agents is not long, "People will become more and more demanding."

Between these two groups of investors, there are also centrists looking for opportunities.

Ji Gang, the vice - president of Ant Group and the president of the Strategic Investment and Corporate Development Department, gave advice to today's agent entrepreneurs: They can start with high - tolerance scenarios.

Representatives of such high - tolerance scenarios are tool - based scenarios that require interaction with real people, as well as companion scenarios for killing time and providing emotional value.

It is obvious that "Has the agent era arrived?" is one of the most concerning issues in the current industry. The eagerness for an answer once filled the venue of this event with a quota limit with audiences standing outside to listen.

However, as Feng Dagang summarized: The emergence of non - consensus precisely means that the development of agents is still in its early stage.

The following is a compilation of the conversations between Feng Dagang, Ji Gang, Huang Mingming, and Long Yu by "Intelligent Emergence," with the content slightly edited:

Two - thirds of the world's top agents in the future will come from China

Feng Dagang: The era of the explosion of AI agents and applications has arrived. I think this should be an undoubted conclusion.

What kind of inevitability do you think has led to the explosion of AI applications? Technology, cost, or revenue?

Ji Gang: Actually, it is the current level of technological development that has reached such an opportunity.

△ Ji Gang, vice - president of Ant Group and the president of the Strategic Investment and Corporate Development Department. Image source: Official

Of course, on the other hand, our expectations for intelligence have far exceeded the previous generation's understanding of technology, such as some knowledge retrieval and the establishment of simple workflow - level work capabilities.

But today we see that actually AI potentially may be an intelligence that can surpass human wisdom. If you have someone around you who is smarter, more knowledgeable, and never sleeps 24 hours a day, what can he do? In fact, he can do everything.

Huang Mingming: I may be the only science - and - engineering guy in this forum today, so I'll talk a little about some technical details.

△ Huang Mingming, founding partner of Mingshi Venture Capital. Image source: Official

We have been observing from the model layer. In the past two to three years, first, as we all know, the computational cost on the inference side has decreased by about 280 times. Then, MoE (Mixture of Experts architecture) has further reduced the computational cost by 80%. This is from the perspective of cost and efficiency.

From the model side, from DeepSeek's R1 at the beginning of the year to Claude 3.5 and 4.0, I think the most core ability is to enable the model to have the ability to call tools and plan.

For example, I remember when I was in biology class as a child, the teacher always asked us what the most fundamental difference between humans and animals was. In fact, one of the reasons is that when humans started using tools, it was a very important step in the evolution from primitive humans to Homo sapiens.

If we look at the development of AI today, when Claude 3.5 and Claude 4 enable the model to call a series of tools to complete a relatively complex task, it has a certain planning ability.

This suddenly increased the task completion rate of agents from most 0.5 last year or the year before to 30 - 40 points for the top agents this year, such as the Vibe Working or General Purpose Agent mentioned earlier, including GenSpark that we invested in.

The increase from 0.5 to 30 or 40 points has made users' repeat usage rate and willingness to pay very high.

When can it reach 60 points? We think it should be within the visible 8 - 12 months, so that more work can be unlocked.

I think you may need to add one more point to your question: Why do many of the "firsts" and "the world's first agents" in this wave come from China?

Not only the first General Purpose Agent Manus we mentioned earlier, GenSpark of Vibe Working that we invested in, Lovart, the world's first Vibe Design, but also the first Data Agent sheet0, etc.

△ Lovart. Image source: Lovart official website

I think this is because in the past 20 years, China has accumulated a large amount of product management capabilities in the mobile Internet, and at the same time, we have been keeping up with technology very quickly.

As mentioned earlier, an agent uses tools by calling the model, and there is a lot of engineering and high - speed iteration in the last mile. This is exactly the unparalleled advantage and ability of Chinese entrepreneurs in the world.

So we boldly predict that among the world's top and most widely used agents in the future, although the headquarters of these companies may be located in Silicon Valley, Singapore, or London, two - thirds of them must come from Chinese entrepreneurs.

Long Yu: Mingming has done a lot of popular science just now, but my answer is: The era of the explosion of agents has not arrived, and I'm not so optimistic about the prospects of Chinese entrepreneurs either.

△ Long Yu, founding and managing partner of BAI Capital. Image source: Official

We do have huge advantages, but the concept of agents is still in the process of being defined and implemented. In fact, we are still in a process of persevering and moving forward.

In fact, on the user side, both consumers and enterprises have given the so - called "agent" concept great and unprecedented tolerance.

But in the previous enterprise - level services, people would never accept any product that says "I may be able to complete your task." How can I take such a risk?

So this time, the whole world has embraced AI exploration with a very tolerant attitude. There are countless inputs on the development side, countless product definition experiments, and countless feedbacks from the user side, all constantly fine - tuning and promoting development with a very tolerant attitude.

However, I think the window of time left for us is not long. In fact, after the inference cost has dropped significantly this year, the possibilities of redesign, improvement, optimization, and workflow construction have emerged.

But ultimately, people will become more and more demanding about the completion rate of agents. After all, there is a considerable amount of data flow and data that must be completed without the slightest error in a fully controllable and strongly executable coding environment. For example, in finance, there is no room for any error. It's not just about doing a good job.

Therefore, I think in fact the industry is beginning to differentiate, models are starting to be industrialized, and everyone is starting to think deeply.

Entrepreneurs on both sides of the Sino - US divide have made many surprising progresses. Chinese entrepreneurs in this area have shown super - strong response capabilities and very fast execution speeds. I think their contributions are huge, and we will definitely have our place.

But in fact, compared with agents, the concentration of intelligence in the model itself, as well as the cliff - like and runaway - style leadership, and the products they launch - whether they are called agents, apps, or functions - are still far ahead.

Feng Dagang: First of all, I don't think it's a pessimistic situation today, but I very much agree that the industry is still in a relatively early stage. Having different views is exactly a characteristic of the early stage.

Let's get back to the issue of applications. Ant has developed many applications and agents, including those for elderly care, medical care, and family. Our theme is "The First Battle of Implementation." Where do you think the "first battle" is?

Ji Gang: In response to what Mr. Huang and Ms. Long said, I'll give my answer to this question.

I'm a liberal arts student. Please don't discriminate against liberal arts students. Liberal arts students may have some visionary ideas in this world and may have more opportunities than science - and - engineering students.

Long Yu mentioned that the whole world today has a huge tolerance for agents, including in work and in life.

Today, there are a large number of offline opportunities for interaction with people. When you travel, you may look for a travel consultant, and when you look for a house, you may look for an agent to help you. When you interact with them, you definitely hope they can provide you with relatively accurate information.

If there are deviations in this information, whether due to knowledge limitations or providing wrong information for some reasons, you can judge and tolerate it.

In fact, I think this is the relationship between us and agents in the vertical field today.

There is also another scenario with high tolerance. Now, many agents are replacing people closer to you, such as friends, relatives, and partners. You have a huge tolerance when spending time with a real person. So I think in this scenario, people may also have high tolerance for agents.

Except for work scenarios that require extremely high precision, in many other scenarios, agents can gradually evolve in a low - tolerance way.

Feng Dagang: Will the explosion of applications start from the periphery?

Ji Gang: There is a relationship of distance.

For example, an offline real - estate agent is far from you in terms of relationship. But you interact with them very infrequently, maybe only twice a year.

There may be new opportunities for agents here, a service that combines real people, tools, and data in a tool - like form.

There is also another type that is very close to you, helping you kill time or providing emotional value. There are opportunities for high - tolerance scenarios to emerge in both of these areas.

On the contrary, for those scenarios that require precise data to complete work and form a closed - loop, I think it will still take some time for the capabilities of models and agents to fully develop.

Speaking of your question, we have indeed developed agents for medical care and finance. These actually have relatively high requirements for precision.

When you use them today, you will find that most of the interactions still suggest that you finally go to the hospital to register and interact with a real doctor. They may only provide some basic information consultations.

Since finance and medical care are our own businesses, we will definitely develop agents in this area. Whether they can meet everyone's expectations, achieve a closed - loop, and be extremely accurate will definitely take a process. But we also hope to explore more opportunities with low tolerance.

It is unacceptable for an AI efficiency tool not to be profitable within a year

Feng Dagang: Mr. Huang, I know you have invested in a lot of hard - tech projects.

You mentioned earlier that you have invested in many good projects. What are the fundamental differences in this wave? Including investment logic, investment opportunities, and our own judgments.

Huang Mingming: Actually, there are still many commonalities.

For example, the people we like are still super product managers with non - consensus views, such as Li Xiang and many others we invested in who have a deep understanding of user needs.

If we have to say what's different, in the past, Internet applications were more about connecting more information with people, building a production relationship.

Agents are productivity tools. As both of you mentioned, the two most core indicators for measuring productivity tools are the complexity of the task completed and the task completion rate. So you need to deliver results.

For those who have done well, if we evaluate them, they can achieve 30 - 40 points in their fields. But the global users' tolerance is indeed very high.

What I mean by "tolerance" is not just trying it once and then giving up. It means that the rate of repeated use and repeated payment should exceed 50%. That is, more than 50% of the people who have paid once will come back to use it the next month. You don't need to treat it as a full - time employee, but at least you can use it. This is a very important measurement indicator.

In fact, the best agents can only achieve 30 - 40 points in many fields today. In other words, even in some very niche fields, if you have the opportunity to fully utilize the model's capabilities and do the "last - mile" dirty work, achieving 50 - 60 points, there will be a large number of users willing