Unlocking the secrets behind the company blacklisted by the US, and how it competes with Google, Facebook and Baidu.
AI is creating a new type of business that contains elements of both software and services.
Some very successful SaaS products attract customers and keep growing exponentially without spending more on customer acquisition. Leveraging virality – as VCs call it – SaaS companies scale fast while maintaining acquisition costs that change little. Compared with SaaS, services can be a bad business, but AI startups like Megvii have reasons to target the niche. Previous key trends benefitting AI applications and secular growth potential in government IT spending and public security are now a bit clichéd. We focus on the business side here.
Services revenues weigh on gross margins as it is naturally not as scalable and has low margins. Though Megvii claims its IoT solutions involve the integration of hardware, algorithms and IoT devices, we consider this to be a service-heavy business due to the implementation model (as well as financials) falling perfectly with service business definition. Megvii city IoT solutions contributed to 73% of total revenues in 2019H1 while the gross margin sat at 59% in 2019H1. Comparing products and licenses, services have a variable personnel component that adds pressure to the margins. As of June 30, 2019, Megvii had 222 system integrators out of a total of 339 domestic customers that have contracts with the company for its City IoT solutions business.
Part of the answer lies in the move Megvii made earlier in 2020. Megvii is trying to leverage the open-source deep learning framework MegEngine, part of Megvii’s proprietary AI platform Brain++, to allow everyone to feed data and train their AI frameworks on it. Google’s TensorFlow and Facebook’s PyTorch hold 95% of share in this market, with an array of new players joining in.
Megvii’s project Hetu is a logistics-focused platform that fits for different software systems (ERP, WMS, MES) and hardware devices (sensors, robots, AGVs) by using APIs, which has led the company to a new strategic direction.
More and larger contracts with government or retail are not the ultimate goal. AI’s potential is set to change so many industries, and the best way to ride the wave is to build an operating system. The system combines data with AI approaches, like machine learning and deep learning. It keeps absorbing customer data (generated from business projects and open platforms) as well as market data (users of open-source platforms) and training these data on the system, which drives a virtuous cycle of data. As a result, that trap of data network effects mentioned before can be mitigated and a flywheel of intelligence can be set running – the model will be better, as will the product.