The Top 0.3% in AI: Past Lessons and New Opportunities behind the Hype
What are the lessons learned and the hidden opportunities behind the cash-burning AI hype? How do startups find the right approach and timing to monetize AI?
Among 34,596 Chinese companies labeling themselves AI, only 100 of them have reached a revenue level of over CNY 50 million, accounting for about 0.3% of the whole market. While people are still fantasizing about the easy four-hour-per-week working schedule that could be achieved by AI if you believe Jack Ma, or worrying about AI replacing their job positions in the near future, startups have to prove AI is more than just a bubble by making money soon.
Why do we have to find the right approach and timing to monetize AI?
- The technology is mature.
- Resources are getting limited as the AI hype fades and the market is competitive.
- The market is the real indicator for the next generation AI development.
Artificial Intelligence (AI): The technology of mimicking humans is mature enough
In simple words, artificial intelligence is meant to permit machines to possess the qualities of 'human' intelligence and to perform beyond it in certain areas. Because of the unbounded possibilities created by human intelligence, the scope of AI is also unlimited.
Centered on intelligence, AI is now a group of technologies that achieve human capabilities including not only the 'inward' information processing but also the interaction with the outside world. So far, the capability of machines to perceive, think and do makes it possible for machines to participate in many activities in the human world. AI-related technology has become quite mature with applications active in areas like computer vision, voice recognition, NLP, LIDAR, robotics, etc.
Resources are getting limited as the AI hype fades
Investment activities are cooling down. AI investments saw sustainable growth from 2014 to 2017. Investment activities slowed down in 2018, down 20.7% from the previous year. The number of investment events from January to May 2019 is only 23.7% of the whole year of 2018. It is expected that the investment activities and investment amount will decline further in 2019.
At the same time, private equity investment is more concentrated in top players and the mid-to-late round startups, and the market's valuation of the companies has also shrunk.
Earlier venture investors place a higher premium on talented researchers and algorithms to evaluate startups. After the period of fierce algorithm competition, the key factors to be considered for startups to attract capitals are selecting appropriate industries, narrowing down the correct verticals, and targeting the most profitable scenarios.
Besides, the marketplace opportunities for AI startups have gradually narrowed down. AI entrepreneurship began to show signs of rapid growth after 2012. The number of new startups grew almost fourfold, reaching its peak in 2016, after which the atmosphere quickly cools down. Meanwhile, a number of startups failed to survive in the cash drain.
Most startups start commercialization by cooperating with industry leaders, spending years to finalize a project and then standardize it into reusable products. In addition to the technology background, other aspects of the business are important, such as industry knowledge, product experiences, sales ability and financing background.
Now, AI up-and-comers have to scramble for spots in the market because they not only compete with each other but also with large tech firms and industry giants. Industry companies are attracting attention for implementing AI across firms' value chains and aiming to reshape business with AI. During the process, AI startups should find some niche opportunities, or otherwise risk being replaced by industry players. Once industry giants build their own AI team, the market competition will get even fiercer; AI startups must prepare for the upcoming changes in the industry.
The market is the real indicator for the next-generation of AI development
For any disruptive technology, it evolves from a product to an industry and eventually becomes a social utility. During the process, market supply and demand guide the way and lead it to the final version. The case is illustrated by electricity, oil, coals, cars, computers, the internet, etc.
AI is set to be the next generation of common tools for the whole society. Therefore where it generates money is where it is most needed and where application scenarios are concentrated. In certain industries, the first one to monetize a product, even the just the beta version, has a greater chance to win, while the other industries witness 'free' as the best strategy.
So what's the right timing? Now or should we be more patient?
What are the most promising industries and what are the verticals to dig into?
How did AI start-ups successfully secure clients and land deals? What should be avoided?
As technology evolves, what's the human strategy in the next stage? How to cooperate with academia effectively?
For AI companies, could Chinese modes be copied globally?
EqualOcean is going to host a WIM Salon discussing the topic on September 22 in Beijing, in cooperation with Mydream+, Startup Grind, Chinaccelerator, Pipiban, and Nordic-China Startup Forum. We hosted the first and the second Salon of this series successfully last month.