VC investor: "We are looking for this kind of companies."

Author: Riemann

In May this year, the new move of Zhu Xiaohu, the managing partner of GSR Ventures, caught people's attention. At that time, he who "claimed not to be optimistic about humanoid robots" withdrew from a number of humanoid robot projects in batches and then turned to invest in an underwater robot project - Shihang Intelligence (hereinafter referred to as Shihang).

At that time, the outside world had already interpreted Zhu Xiaohu's actions. Zhu Xiaohu withdrew from humanoid robot projects because of the unclear business prospects, and publicly claimed that "customers only use humanoid robots for research or display and have not created continuous value." The company he invested in later belongs to "pragmatic investment", with in - depth scenario capabilities and the ability to achieve rapid commercialization.

Founded in 2023, Shihang is the earliest domestic enterprise to research and the first to commercialize underwater cleaning robots. Focusing on underwater general - purpose robots, its "Orca" series of products has built the underwater productivity from 0 to 10,000 meters in the ocean and is currently mainly used in the ship - cleaning field.

"Is there a person pushing your product underwater?" At first, the Shihang team also received a lot of doubts, and the founding team could only patiently explain that "it's completely not the case". Recently, Cao Ying, the co - founder and COO of Shihang, told me, "This year, the company started commercialization, and its revenue has increased tenfold. It is expected to turn a profit next year."

Cao Ying revealed that the R & D costs for underwater scenarios are actually higher than those for humanoid robots in other scenarios. In the entire robot track, it is rare for a company to achieve profitability in just over a year after a large amount of R & D investment.

Many people may not know that behind Shihang's rapid commercialization is its cutting - edge business model - "pay - for - AI - results".

Specifically, every time it cleans a ship, the shipowner pays a cleaning fee. The motivation for shipowners to pay is that after cleaning, the large ships can not only replace the untestable manual cleaning but also save fuel costs. "After being cleaned by us, a ship can save 100,000 yuan in fuel costs per day."

This brings two major benefits to Shihang: First, the products can more accurately meet customer needs. Second, it can break through the ceiling of revenue scale. This subverts the industry's previous revenue models of selling hardware, SaaS, or integrated solutions.

Actually, not only Shihang, but many well - known domestic and foreign enterprises have started the era of the "pay - for - AI - results" business model, and this has become the consensus of current global AI giants and capital tycoons.

At the closed - door meeting of the Third Sequoia Capital AI Summit held in May this year, after 6 hours of in - depth discussions among 150 top global AI founders, including Sequoia Capital partner Pat Grady, OpenAI CEO Sam Altman, and Google Chief Scientist Jeff Dean, many cutting - edge ideas and consensuses emerged. Among them, Outcome - based Pricing and Outcome - as - a - Service became one of the most core ideas, which means: "In the next round of AI, what is sold is not tools but benefits." Pat Grady called this a "trillion - dollar opportunity".

In China, institutions with the same frequency have also emerged. "We are looking for companies with this kind of business model and will also promote some potential enterprises to transform in this direction." Wang Xiangyun, the managing partner of Shengjing Jiacheng Venture Capital, said that this charging model has enabled some invested enterprises to achieve tenfold growth in revenue and profit.

At a time when the assertion of the "AI bubble" constantly warns the venture capital community, a clearer commercialization path has emerged. It's like a ray of light in the chaos.

Why was it proposed?

In China, Shengjing is one of the early institutions to propose this business model.

In March 2025, Shengjing proposed AI RaaS (Result - as - a - Service), an extreme result - oriented model that dares to use results as the basis for pricing, charging, or making a profit. It was vividly compared to an "AI owner or AI client", advocating that only end - to - end services deeply embedded in the physical world can truly create value.

Actually, Shihang's business model was facilitated by Shengjing. At a business event, Chen Xiaobo, the founder of Shihang, met Peng Zhiqiang, the chairman of Shengjing NetLink and the founding partner of Shengjing Jiacheng Venture Capital. Their concepts and values were in line, and at the beginning, Shihang followed Peng Zhiqiang's advice and adopted the pay - for - results business model. Shengjing also invested in its angel round.

The team conducted financial calculations on whether to provide equipment or results. "Finally, we found that providing results is the optimal solution because the number of times can be infinitely increased, and the final result will also be infinite. For customers, which has a higher decision - making cost, spending 100,000 yuan to clean a ship once or spending millions to buy a machine? Obviously, it's the latter." Cao Ying recalled.

This happened in 2023, a year when infinite opportunities coexisted with a cold capital environment. At that time, Chen Xiaobo, a young scientist in his early 30s, had been exploring the world of underwater robots for 18 years. After the business model was verified, Zhu Xiaohu made an investment at the beginning of this year. It is reported that Zhu Xiaohu's resources and abilities helped Shihang open up the situation, and Zhu Xiaohu got on board the most promising underwater robot project in China.

Why did Shengjing respond so quickly?

Wang Xiangyun told me that it was the bottleneck of the SaaS model that promoted this change. "During the previous SaaS investment cycle, we found that the US SaaS industry was booming, and enterprises had high valuations. However, the domestic SaaS industry faced great pressure and challenges in terms of revenue, valuation, and exit throughout the entire chain." In short, "the numbers didn't add up."

So the team concluded that simply imitating the US model may not work in China. Instead, the software logic should be considered in the context of the industrial Internet and longer business chains. Following this logic, Shengjing has also successfully invested in some enterprises.

In 2022, after the launch of ChatGPT, a new round of AI - driven industrial cycle began. Shengjing observed that with the iteration of AI basic models, the path of some "simple - shell" applications would become narrower and narrower. Therefore, the long - term and sustainable capital value of simple - shell projects is facing challenges. So, what kind of capabilities need to be enhanced to avoid being eliminated?

So, Shengjing's conclusion was: having strong scenario capabilities and charging based on results. Through investment cases, it was found that the result - oriented charging model can lead to tenfold growth in revenue and profit. Besides Shihang, Shengjing's investment cases in this regard also include Lingyun Zhikuang, an AI mineral exploration company.

For this reason, the Shengjing Research Institute has continuously published articles and launched the "30 Global AI RaaS Cases" series, systematically dissecting benchmark enterprises to provide paradigms for local entrepreneurs to learn from.

This idea has been recognized by many investors. Senior investor Yun Ke also told me that the SaaS model is very likely to end in the AI era, and the logic behind this includes two points:

First, the payment logic has changed. The essence of SaaS is to let users pay for tools, but tools are just means and cannot solve the final result. However, AI can directly replace labor, so this is a much larger market than SaaS.

Second, the best AI models are all closed - source and in the hands of giants. The new - generation SaaS with shells can hardly build a moat.

Currently, Yun Ke is also looking for enterprises with such commercial potential.

What are the standards?

Many people may be confused about what the measurement standard is for paying for AI agents based on results.

Taking Shihang as an example. Cao Ying admitted that the company needs to gradually prove its value through actual operation results, such as saving fuel for customers, to educate the market. This process is challenging.

Cao Ying summarized that the successful implementation of the "pay - for - results" business model ultimately depends on the following three major capabilities:

First, the hardware and system integration capabilities with a huge lead. Second, the "fuel" for continuous iteration and building barriers. Through cleaning "thousands of ships", it has accumulated operation data under different ship types (bulk carriers, container ships, etc.), different sea areas (North Sea, East China Sea, South China Sea), different water qualities, and different seasons. This is something that any company that only sells equipment or does not directly face end - customers cannot obtain. Third, the service results can be quantified and verified.

Currently, Shihang has become the company with the most underwater scenario data in China. "It's difficult for latecomers to catch up." However, Cao Ying also introduced that during the process of implementing the business model, the company is basically "competing in the domestic market and making money overseas."

On the one hand, domestic shipowners are very price - sensitive and tend to choose the lowest - cost option, even if the service quality varies. In overseas markets such as Japan and Singapore, labor costs are higher, and they recognize the value of technology more and are willing to pay a higher price. The overseas customer unit price can reach "more than three times" that of the domestic market. So the company is currently actively exploring overseas markets.

Internationally, the RaaS model is being practiced in multiple fields. Companies such as Clay, Sierra, and 11X have evolved from the traditional software subscription model to the task - based charging or a mixed pricing model based on tasks and results.

The AI customer service unicorn Sierra, founded by Bret Taylor, the chairman of the OpenAI board, is particularly radical. It is not a simple customer service system but a closed - loop sales agent platform that helps brands complete the entire sales process from the first inquiry to placing an order.

It not only contacts customers but also takes responsibility for the conversion results, truly following the path of "give me a budget, and I'll bring you a certain amount of GMV."

There is a more intuitive detail: when the AI agent independently solves the needs of incoming calls or online consultations, Sierra will charge a fee; if it has to transfer to a human operator in the end, this service is free.

"We really like this model, and I also think this will become the standard business model for agents." Bret Taylor said. Sierra was founded in 2023 and has now become a unicorn valued at tens of billions of dollars.

Ramp takes this idea to the extreme. It is a fintech company founded in New York, USA, in 2019. Starting with a corporate credit card, it aims to use technological means to subvert the traditional corporate expenditure management method and help enterprises save time and money. It doesn't sell a financial system to enterprises but directly promises to save a certain amount of fees. Its AI can automatically identify redundant subscriptions, negotiate price cuts, and predict risks, turning "the benefits of using this tool" into KPIs.

To summarize a more general measurement indicator for "result - oriented products", at the closed - door Sequoia summit, Sequoia gave three judgment criteria: whether it can complete a full task process; whether it has perseverance during task execution; whether it can deliver measurable business value.

In the view of the Shengjing team, the popularization of this model is a gradual process. According to the degree of intelligence, it can be divided into four levels from L1 to L4:

L1 represents short - process businesses that are mainly online digital applications, highly repetitive, with clear processes, and a high degree of standardization. For example, it will be first applied in industries such as law and customer service. L2 often involves long - operation processes that require complex reasoning, tool invocation, and integration, and often requires the participation of hardware tools for implementation. L3 focuses more on helping customers achieve a closed - loop sales of products and services and finally achieving revenue sharing based on the sales results, which means a qualitative improvement in the outward - looking connection ability of AI services. L4 is upgraded to the "AI owner", which not only has AI service capabilities but also becomes the main or partial "owner" of core assets or company value with the help of AI advantages.

Wang Xiangyun believes that in the long run, businesses with a higher intelligence level require high - quality collaboration between AI and highly professional humans, which is a healthier AI industrialization model. In the future, as technology matures, the proportion of AI will gradually increase. In terms of the overall promotion speed, the more market - oriented the supply chain and value chain, the faster the promotion of AI RaaS will be.

The AI blue ocean and the AI bubble can coexist

AI agents emerged when large - scale AI models were being implemented and are also in the midst of the extensive discussion about the "AI bubble".

In the second half of 2025, the AI capital market had its worst performance since April this year, and the Nasdaq index fell by more than 3% in a single week, which further triggered a more extensive discussion about the AI bubble.

The main reason is the sharp contrast between the huge R & D investment and commercial revenue of leading enterprises such as OpenAI. In 2024, its R & D investment exceeded 15 billion US dollars, but its commercial revenue was less than 3 billion US dollars.

Even if, as Altman expected, the annual revenue annualized rate will exceed 20 billion US dollars by the end of 2025 and grow to hundreds of billions of US dollars by 2030, it will still be difficult to generate positive cash flow.

Meanwhile, the issue of AI costs is becoming increasingly prominent. The expectation that the cost of large - scale models will decrease by ten times a year has not saved the paid - subscription models of many AI enterprises.

A study by the Massachusetts Institute of Technology has also sparked a wide - ranging discussion. The study pointed out that although enterprises have invested 30 - 40 billion US dollars in generative AI, 95% of organizations have not achieved any business returns.

However, Yun Ke emphasized that there is a logic that should not be confused: "The general large - scale models themselves and making money by using the capabilities of large - scale models are two different things. The former requires huge investment and will definitely not see returns in the short term. Their purpose is not to make quick profits but to seize the strategic high - ground of the next - generation technological revolution. The former is a game that only a few national - level teams and technology giants can afford to play. The latter is where ordinary entrepreneurs should focus their efforts."

He believes that the area that has been overestimated this year is humanoid robots. "This field is still developing rapidly, and breakthroughs may be seen in the next few years. But if we expect rapid large - scale implementation, I think it is a great overestimation. The development of technology must go through several stages and cannot be skipped quickly. As we can see, Tesla also lowered its production forecast for Optimus this year."

So, how should we view the current bubble?

I think we can perhaps quote a clear but complex answer given by Bret Taylor, the co - founder and CEO of Sierra, in an interview:

AI will reshape the economy and create huge value; at the same time, the bubble does exist, and some people will lose a lot of money. Both can be true at the same time.

He believes that the current AI bubble is very similar to the Internet bubble. Indeed, there were many failed cases during the Internet bubble period. But if we look at a 30 - year time span, we see the emergence of giants such as Amazon and Google, and the cloud business of Microsoft has become an important pillar of its market value. We can also more intuitively see the profound impact of the Internet on the global GDP. Much of the "optimism" in 1999 was actually in the right direction. Even a company like Webvan (online fresh food delivery) reappeared in the form of healthy businesses such as Instacart and DoorDash after the popularization of smartphones and the maturity of the Internet scale. Many ideas were not bad; they just came too early.

In addition, the exploration of paying for AI agents based on results will gradually become a new trend in AI revenue generation.

Generally speaking, in 2025, AI agents have bid farewell to the concept - verification stage in multiple industries and entered the value - realization stage. Amid the discussion of the AI bubble, the emergence of the RaaS model has pointed out a practical development path for the industry - AI technology must return to the essence of business and create measurable value for customers. This path may not be as eye - catching as unlimited financing and piling up computing power to pursue SOTA, but it may be more sustainable and solid.

This article is from the WeChat public account "Dongshisi Tiao Capital", author: Riemann, published by 36Kr with authorization.