Megvii Brakes IPO Plan, with CEO Claiming a Sufficient Cash Flow

On July 29, Megvii’s Co-founder and CEO Yin Qi (印奇) responded to the company’s initial public offering plan. According to him, the almost-public artificial intelligence unicorn plans to suspend the IPO process since it has a sound cashflow. Being one of the four ‘Chinese Computer Vision Dragons’ (the other three being SenseTime, Yitu and CloudWalk), Megvii was the earliest to debut its financials. Hence, people will be able to glance at how a highly-valued AI company functions and makes money. From EqualOcean's previous analysis of the company, we know that, given the position of Megvii in the supply chain, the company cannot make money easily with margins as high as those who pumped up AI companies’ valuations expected. Based on the financial data disclosed from the prospectus, Megvii needs more liquidity to sustain the cash-burning business. Its application to be listed on the Hong Kong market lapsed in the first half of 2020 and rumors had it that the AI unicorn might be considering being listed on the Shanghai Star market, the ‘Chinese Nasdaq-style Tech Board.’

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Unveiling the ‘Mystery’ of Megvii Part 1

Key investment thesis Attractive secular trends. Megvii occupies a unique place within the computer vision domain and benefits from increasing awareness about leveraging artificial intelligence (AI) and the Internet of Things (IoT) in managing business and public management from the government and business side.  Clear long-term strategic map. With the launch of open platform Face++ and open-source deep learning framework MegEngine, we see the company’s ambition in competing with large tech companies as well as grasping the core competence of AI. Unique customer proposition. The full-stack solutions that contain the IoT layout of cloud-edge-devices, platform software systems and applications allow the company to reduce friction around today’s AI, big data and cloud technologies.   Key investment risks Megvii’s cost structure for its SaaS and IoT solutions remains debatable. The business is a bit far away from being a classic SaaS and more like driven by software + services/consulting.  Services revenue weighs on the gross margins. The services business is naturally not as scalable and operates with low margins. Megvii’s business builds on its IoT solutions, contracting with government agencies (more precisely, system integrators). Only with better margins can the business scale faster – not the other way around. Here, the income statements tell the story already. Data ethics problems. Megvii’s services and products have been interacting with a significant amount of sensitive information. It has an additional responsibility to protect its from information leaks and hacks, as well as a duty to deal with regulatory/compliance issues.  Business overview Megvii is the first so-called artificial intelligence unicorn to try to list on the public market in China. The company provides full-stack solutions that encompass algorithms, software and IoT devices to its customers. In this article, we focus on its business model, revenue potential and competitive risks.  Megvii started in a niche area within the face recognition domain. It then launched the first computer vision (CV) open platform – Face++ – of the company in Oct 2012, one year after its inception.  Megvii derives its revenue through two standard pricing models (SaaS subscription and professional services) and one special business that it calls ‘personal devices.’ Like other Software-as-a-Service (SaaS) companies, Megvii charges by a pay-per-use model for its Face ID (a cloud-based identity authentication product) and Face++ under the SaaS business.  Megvii generates revenues for professional services primarily in two segments and charges on a project basis: government and commercial. Worth noting is that a significant portion of Megvii’s direct customers are system integrators, which provide various types of assistance in project implementation, and are not the end users. 93% of revenue in the first half of 2019 came from its city IoT solutions, whereas the rest was generated from its commercial segment supply chain IoT (retail and logistics).  Megvii’s personal devices business accounted for 9% of revenue in 2019H1 Megvii productized the business and earned CNY 5.9 million in 2017, one year earlier than its SaaS offerings. The gross profit saw a sudden plummet in 2019H1 due to the firm delivering more camera modules. Arcsoft (688088:SH), Nuance Communications (NUAN:NASDAQ) and Gracenote, among others, are competing in the niche worldwide. Arcsoft is now trading on China’s new Star market at a 122x earning level with a valuation of CNY 25 billion as of April 24. Arcsoft provides single/dual camera solutions for smartphone manufacturers, which fit various hardware configurations. It has achieved CNY 438 million revenue from its smartphone business, with a 94% gross margin. While Arcsoft’s revenue and gross margin were on a steady growth stage from 2016 to 2018, Megvii saw a meaningful dip in 2019H1, as mentioned above. As a relatively new player in the market, it is reasonable that Megvii has entered into a new stage in delivering products. In essence, it has to work more closely with camera module manufacturers, camera sensor manufacturers and System on a Chip (SoC) platforms. Megvii might need to switch to a different type of camera module for different types of mobile phones to drive the development of camera technologies fast. As a result, the cost of sales surged 17x to CNY 18 million in 2019H1 from one year before. The cost of sales is attributed to two elements: 1) hardware and 2) project outsourcing or technologies services. Considering Megvii’s own claims, we think the previous assumption is reasonable. In essence, the gross margin dip will jump back to normal levels when Megvii reaches the critical mass of producing deliverable camera modules. Megvii’s SaaS offerings, however, are far beyond the SaaS concept that Wall Street and VCs admire  When it comes to Megvii, its SaaS business contains a data source cost, which is unusual for an SaaS offering. China, famous for its extensive public security market (USD 80 billion) as well as significant spending on tech-enabled surveillance (USD 30 billion), is also well-known for its abundant pool of data. But data alone is not enough for building AI software – data must first be labelled, which requires much labor.  Megvii engages third-party data sources for its Face ID product development, which represented 7% and 58% of revenue and total cost of sales in 2019H1, respectively. Data from 2016 to 2019 shows that data source cost as of the total cost of sales reached a plateau of 58% after 2017.  The current machine learning technology is still in an early stage. Thus companies researching it are running algorithms on various datasets to train their models, which means large overheads for purchasing these data sources.  As such, AI businesses are very different from software businesses, whether it is in terms of cost structure or competitive advantages.  The gross margins are lower. An SaaS business means a high gross margin (60% – 80%), with the cost of sales mainly attributed to cloud services. SaaS stocks maintain their gross margins, ranging from 70% to 85%, with a median margin staying at 72% and averaging at 70%, as of April 22, from the 63 SaaS stocks selected. Compared with these mature firms, Megvii’s gross margin shows a continuous growing trend from 2016 to 2019H1. It even reached 87% in 2019H1, a 106% increase compared with 2016. But we do not think this will continue in the near term, mainly from the observation that data source costs will remain a significant part of revenue.  There’s a long tail problem. A study conducted by Arun Chaganty at Eloquent Labs (quoted by a 16z) that researched questions submitted to a chatbot in the customer support space shows a diminishing marginal value of data. When 40% of queries have been collected, there is no advantage to collecting more in this case. AI startups have to devote more time and resources to deal with noisy, unstructured data. They also need to think through edge case difficulties, which is a tough job that traditional software companies won’t face when they build and deploy their products for their earliest customer cohorts. Megvii’s whole R&D special expenditure – including data obtaining and labeling – represented 14.9%, 11.4%, 6.1% and 6.5% of total revenues in 2016, 2017, 2018 and 2019H1, respectively.  AI is early in its development, and has been witnessing sharp declines in both AI training costs and inference costs. In 2018, the cost to train a neural network like ResNet-50 was USD 358. In 2019, it dropped to around USD 20. The cost of inference has dropped meanwhile. The cost to perform inference on 1 million images went from around USD 15 at the end of 2017 to USD 2 in earlier 2019.   AI companies have been improving computing ability at five times the rate of Moore’s law. No wonder there is a massive hype around AI research and funding activities. But in practice, we found that AI businesses have limits in how their value can be built up and accessed, rented or sold – they are not like those 'builds once / sell many times' software model. It adds pressure to margins, as well as defensibilities.  This article is part I of our analysis on Megvii. Please continue to part II.

Unveiling the ‘Mystery’ of Megvii Part 2

This article is part II of our analysis on Megvii, check out part I before you read.  The service business: an albatross around the neck – and a future chokepoint  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.  A crowded competitive landscape. Among the top bidding-winners of the Xueliang project (Chinese official long-term security plan for cities and communities) China Telecom, China Mobile and China Unicom are dominant, followed by such large security system integrators as Hikvision, Vimicro and Tsinghua Tongfang (600100:SH), according to Chinese tech media company Tedahao (in Chinese). Some of them, along with traditional players that focus on offering hardware products, have started to enhance their software abilities. Hikvision and Dahua, two leading surveillance camera vendors in China, pivoted toward other solutions several years ago. In 2018, they announced the strategy to navigate the business – Dahua’s heart of the city (HoC) and Hikvision’s AI Cloud.  Hikvision has been catching up, for instance. Its central control product revenue has been taking up a larger proportion of the total revenue of the company, rising from 13% in 2016 to 15% in 2019H1. Dahua, a smaller one, has also been accelerating its solutions business since 2016 as well.  Megvii’s first-generation solutions roadmap was primarily driven by government use cases. It offers a full-stack IoT product that is heavily used by government agencies to enhance public security, optimize traffic management and improve urban resource planning. The government continues to be a big part of Megvii’s business, but its strategic focus has shifted toward commercial customers. The revenue mix-shift from the government to commercial poses several risks/opportunities to the business. Enterprise asks for more highly standardized products. Megvii’s gross margins are expected to rise as the proportion of personnel expenses (say, consulting, implementation, delivery) used by each new client declines. We also notice that expanding into sectors such as retail and manufacturing and building mindshare. There will be a significant advantage for a company. To solve a strategic problem and build up industry, know-how could likely yield a deep moat.  What’s the future of AI businesses and players? 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. 

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