Although the mainstream view of the industry believes that AI is an era, not a slogan, in 2017, everyone still feels the wind coming, and the waves are coming... Internet thinking thinks that “the pig will fly on the windâ€, but the AI ​​entrepreneur innovator is destined to wave The tide is generally a minority, but it is common to shoot on the beach.
With the artificial intelligence has risen to the national strategy, the acceleration of capital is booming. In the first half of 2017, domestic artificial intelligence companies raised more than 60 billion yuan. 2017 is destined to be the hottest and most passionate year for artificial intelligence. It seems to usher in the golden age of entrepreneurship. I believe that every brave and enterprising entrepreneur knows that his cruel business rules are nine deaths and even ninety-nine lives. He still believes in himself. The team must be able to step into the camp of a few successful people.
Tencent Research Institute & IT Orange jointly released the "2017 Sino-US Artificial Intelligence Ventures Status and Trends Research Report", showing that the total number of Chinese and American artificial intelligence companies has closed more than 50 in June, I believe this number will be more severe by the end of 2017. Some even predicted that 95% of artificial intelligence startups will fail in the next two or three years. Based on the general rule of Gartner's “emergency technology maturity curveâ€, AI technology is in the critical stage of expectation expansion period and foaming. According to the mainstream opinion of industry experts: “The collapse tide will be concentrated in 2018, the direct cause It is the peak period of AI project financing in the first half of 2017, and the general financing period is about 18 months."
We should not simply point the loser behind the scenes and make ourselves a “post-horse†expert. We should be a relatively sober observer and adviser when the wind starts.
The research and analysis of artificial intelligence start-ups' own problems will be more practical for finding the real reasons for these enterprises' failures, because from the perspective of enterprise development, valuation, capital, technology, and commercial bubbles will always exist. Based on an in-depth study of several artificial intelligence companies and ongoing exchanges with some founders over the past year, we analyzed the main reasons for these corporate failures:
First, the data support of start-ups is insufficient. The essence of artificial intelligence is to simulate human behavior and imitate human intelligence, so it has a basic premise that big data, no big data analysis, big data features to improve big data algorithm learning, can not study artificial intelligence. "Artificial Intelligence: The Way to Win in the Future" wrote: "The Internet has spawned big data, and big data has spawned artificial intelligence." IBM's deep blue computer defeated chess champion Kasparov, Google's AlphaGo beat World Go champion Li Shishi, all within a limited machine learning range, even though Go's complexity is as high as 10 to 172. However, big data is often in the hands of the Internet oligarchy. It is not attached to the industry giants. It is difficult to make breakthroughs in technology. Even if there are relatively advanced algorithms or technologies, it is difficult to experience the true value of the application scenarios. Financing failed and the capital chain broke. For example, social media AI management platform Meshfire, video interactive cloud vision chain and so on.
Second, the positioning of entrepreneurial projects has become a phenomenon. Face recognition, drone technology mature, entrepreneurs swarmed, did not find the value of the segment, because there is no too high technical barriers, product differentiation competitive advantage is not obvious, there is significant homogenization competition phenomenon, the risk of entrepreneurship is sharp Increase; especially after encountering a large company's stationed or crushed, the invested party gives up halfway. For example, Lily RoboTIcs, a consumer-grade drone, Vinaya, a wearable smart jewel, Angel Sensor, an open source wearable tracker, and Shelfie, a personal library classification application; and such as EA, Zero, and Parrot, are greatly cut; analogy to others Start-ups, like the colorful shared bikes that emerged overnight, some of which are just a matter of time.
Third, the understanding of the commercialization of products and technologies by start-ups is not comprehensive. The understanding of the needs of potential customer scenarios is relatively vague, focusing only on technical breakthroughs, especially the technical team of scientists, constantly burning money on R&D investment, failing to realize the root cause and making effective adjustments to the product sales. That is, the technology and the market have a serious disconnect. For example, Skye Intelligent, which is a drone, the auto-driving Pearl AutomaTIon capital chain breaks and stops operations; and the Impression Robot Restaurant and the Hewei-Robot Chain Restaurant also end in a dismal business, and the robot waiter is only a gimmick.
Fourth, the talent structure of the entrepreneurial team is unreasonable. A team with no technical ability is difficult to choose to start a business or get financing. Technology is undoubtedly the first factor; however, most young start-ups really lack comprehensive marketing talents with innovative and commercial applications. Marketing cannot be simple. It is understood as sales. Artificial intelligence is a highly integrated discipline. Technology is not limited to a certain industry and industry. Cross-border, integration, design and promotion of business models have very high requirements for talents. The most direct example is that the artificial intelligence consumer market has not yet formed a climate, and “Ying Ying-Yu Heng Matrix†and “Smart Master House†will be the first to fall. The rules of the investment community "Angel A round to see the team", if there is no innovative team structure, will not be favored by capital.
Attachment: List of partially closed enterprises organized by the author
The tide of corporate closure is not the end, but a new starting point for the development of artificial intelligence. This is an opportunity for entrepreneurs and an opportunity for consulting institutions, industry media, research scholars and other related fields. The reason for the in-depth analysis of these enterprises is not to condemn temporary failure. Entrepreneurs, on the contrary, these experiences and lessons have become valuable treasures for entrepreneurs to continue to forge ahead, while giving more experience to entrepreneurs on the way. The Long March has just begun, artificial intelligence has not yet formed a real industrial chain, and the real strategy needs to be refined and summarized in actual combat! (Yang Xiaolong asked Liang Jun)
About the Author:
Yang Xiaolong, new technology observer, AI industry chain practitioner, 11 years of IT industry senior brand marketing, project sales, team management experience, worked for Tsinghua Ziguang Xinye Investment, Tsinghua Tongfang, Samsung Electronics and other well-known IT companies, on the ICT market Have a deep understanding.
Liang Junjun, graduated with a master's degree in computer science and technology, major research interests in data mining, cloud computing, artificial intelligence, etc., applied for 4 invention patents, published more than 10 academic papers, and more than 10 years experience in large-scale state-owned enterprises informatization. Home business information provides consulting and planning.
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