The maker of this issue - Zheng Can
Zheng Can is the Managing Director of Linear Capital. One of Morgan Stanley's early technical staff in China, responsible for the post-trade platform construction in many asian countries and regions; Tata Consulting MBA Young Leaders Program member, providing social media-based customer experience analysis consulting for the banking and telecommunications industries in Silicon Valley; Zhejiang University Zhu Kezhen College Bachelor of Computer Science, Shanghai Jiao Tong University Master of Computer Science, University of Chicago MBA. Linear Capital is a professional investment institution focusing on data intelligence and cutting-edge technology. The first phase of the fund was established in September 2014, the second phase of the fund was established in April 2016, and the third phase of the fund was recently completed in October 2018. The total size of linear fund management is about 1.5 billion RMB. Focus on early-stage projects in data applications, data infrastructures and cutting-edge technologies. At present, it has invested in more than 50 entrepreneurial teams such as Horizon (us$3b), rokid (~us$1b), Tongdun (>us$1b), Kujiale (~us$1b), Shence, Tezan, PeachTree, Zhongke Shituo, Guanyuan and so on. Linear investment projects are valued at a total of approximately $11 billion. Linear Capital is striving to become the best applied-data-intelligence fund and is gradually becoming the most influential frontier technology fund.
New Site: Linear Capital Target Becomes China's Best Applied Data Intelligence Fund Compared to Other Funds What are the advantages?
Zheng Can: We are an early-stage fund focusing on the field of data intelligence, but the last thing we want to do is a cutting-edge technology fund. Today, data intelligence is probably one of the most important manifestations of cutting-edge technology, and from the perspective of advantages, I feel that the professionalism brought by focus and concentration is a great advantage for us.
Our colleagues have long experience working in the field of cutting-edge technology, especially in the field of data intelligence, and have done a lot of things on the boundary of combining data intelligence and industry. This is an experience we had from the very beginning, and the biggest challenge for investors with cutting-edge technology is a matter of imagination.
Most people have a hard time anticipating what kind of help he can bring to the industry, and in practice, we have actually seen a lot, and we have stepped on many pits before. I have also seen that when today's cutting-edge technology is landed, it will encounter various problems and accumulate a lot of experience to help related entrepreneurs. For many entrepreneurs, they are also starting a business for the first time, and they need someone to tell him what kind of problems they will encounter on their track.
New Site: What are the problems with cutting-edge technology when it comes to landing?
Zheng Can: Data intelligence is mainly in the field of enterprise services to create economic value easily. In the 2c space, it can bring some very subtle experience improvements, but from the perspective of the paid economy model, it may be more difficult. The 2b field is not easy to do in chinese tradition, and there are a series of reasons why Chinese companies themselves are not so mature. For the procurement of external services and external technologies, the attempts at new technologies, there is actually no mature system.
On the other hand, for a long time in the past, because of high growth, everyone had higher requirements for front-end extensive growth. The role of data intelligence plays a role in refined operation, and many times it will be placed in the back seat of the driver's seat by the enterprise, which is not so important, and the enterprise will give less bugget.
In addition, often excellent data intelligence business entrepreneurs will be some typical engineers and scientists. But when they enter an industry, they initially know less about the industry. From a technician to a real study of how technology should land in the industry, what is the most important problem in the industry, how to solve the maximum industrial value that can bring customers, in fact, he needs to get out of the comfort zone.
New Site: What application scenarios are you optimistic about for data intelligence?
Zheng Can: We actually consider a few things. First, the volume of the entire industry. Second, the degree of data accumulation in this industry. Third, the level of acceptance of data intelligence. In the past we looked at some major industries, including healthcare, finance, travel, retail, and daas (decision as a service), which is commonly referred to as enterprise services, and then industry.
In the past 1 to 2 years, we have slowly come to the point that data intelligence technology may bring greater value improvement in relatively traditional industries. First, these industries were originally relatively backward, and the changes brought to it by data intelligence technology may cause additional efficiency improvements and economic value improvements to be relatively large.
Second, these industries are actually relatively fragmented as a whole, which is conducive to start-ups getting their first batch of customers. So we care about specific sub-sectors, which have been seen more in the last one or two years: traditional manufacturing, industry, like logistics, offline retail. We are more concerned about the value that data intelligence can create in these areas.
New site: How is the domestic robot industry developing?
Zheng Can: Traditionally speaking, the field of robotics, especially the field of control, has developed relatively slowly before, but today there are a group of particularly good scientists who have done some core work in Germany or the United States, companies like Boston (Boston Dynamics) or KUKA, and now they are back to start a business.
In recent years, when the development of China's robot industry has been particularly fast, including a company we invested in to do Agile robots, and its founding team came out of the German Aerospace Center to start a business. They made KUKA's most famous six-axis robot prototype, the KUKA iiwa, and after doing it, transferred it to KUKA. Their ability in this field is very strong, and they are going to bring what they are best at, such as force control robots, back to China today. Today they are working with the Chinese surgical robot Tianzhihang, which has high requirements for the accuracy of the robot itself, including its perception and response to the environment.
So Chinese robots are now in the high-end field, there are startups like this doing, the future depends on them can find their own application landing point in the industry, like force control robot technology, the biggest imagination space in the future may be at home robots. In the future, robots will become an important part of each of our lives. At that time, you need the degree of intelligence and safety of the robot, which is much higher than the robot in our traditional understanding today, then it needs to be strongly controlled, there is a need for visual sensing, and various technologies.
New Site: How do you see the current industry trend of AI technology landing?
Zheng Can: In recent years, there have been some changes. At the earliest, artificial intelligence played a great role in promoting the visual field and the speech field to a large extent because of machine learning and deep learning, making these technologies cross a specific threshold and become available. Therefore, the initial landing scenes of artificial intelligence are all on some single-point scenes, and some recognition can be done through vision, and then improve the original enterprise operation or production capabilities in this link. But the whole methodology doesn't change the whole core process, and it doesn't really have as much to do with it.
In recent years, what we have seen is that the technology in all aspects of artificial intelligence is slowly maturing, and there are basically some different breakthroughs and progress in each field. After those entrepreneurs have finally experienced such a long period of time and have a better understanding of the industry, there will be entrepreneurs who will try to use these new technologies to change the original core processes, or to do an end-to-end optimization and transformation of the original processes.
In fact, ai or data intelligence is slowly helping the industry complete an evolution. We believe that AI is not coming in to change the original companies have no food, in fact, it can not do it. Each industry has its own very deep know-how (technology and expertise), and it is more about helping these companies optimize their core processes and improve their overall efficiency and production capacity.
New Site: Linear Capital's Criteria for Judging Good Science and Technology Innovation Projects?
Zheng Can: Let's look at this problem from two aspects. One is from the problem itself, is the problem big enough? Is it important to solve the problem of the impact of this problem, which translates into whether the economic value is large enough? Is it a pain point for the enterprise, and the last question is not difficult enough.
A group of excellent scientists, engineers to solve the problem, it always has to be a difficult problem, we are very concerned about where is your threshold? Another perspective is the team, and we want the team to have enough passing to solve this problem.
Artificial intelligence to the industry landing, in fact, there will be a lot of friction, requiring the entrepreneurial team to have great enthusiasm, to understand the industry, must become a part of the industry, rather than staying far outside, continue to be his engineers, scientists.
Then be persistent enough, because it's a long process. Cutting-edge technology often takes at least 2 to 3 years to slowly form an ideological mainstream in the industry. He had a time to polish the product, understand the problem, and stick with it. The other, he's competent enough to solve this problem, which is considered from his personal background and the work he's done before, which we call professional. The main ones are these aspects.
New Site: What is the Post-Investment Management of Linear Capital?
Zheng Can: The core is to help the invested enterprises to find money and people, some core, excellent people, to expand his team, enhance the team's capabilities; and then find customers, help him dock, for him the most difficult to reach, but significant customers, his initial benchmark customers; and finally at some key points in time to be able to give him some advice.
From our point of view, it's always the founder at the helm, he's in the driver's seat, we're in the co-driver, and when he's driving smoothly, he doesn't actually need us to do much, but when he has a problem, or we think we can help him, we come to help, simply to help without adding to the chaos.
New Site: A Message to this Forum?
Zheng Can: I hope to see more people discuss how to land new technologies. To see more positive and negative cases of new technologies in the actual landing, we can discuss and learn together.