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Briefing Jul 22, 2020 03:15 pm EqualOcean

AI in Pet Tech: Megvii Patents Dog Nose Print Recognition Solution

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Analysis · 2
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Analysis EO
Sep 15, 2020 08:00 pm ·

From SenseTime to AI's Future: Know-How and Digitalization

►Even though the AI investment frenzy has vanished, the AI industry is at an early stage when it is yet to become a norm in general business practice. ►AI companies are motivated to push digitalization levels across industries for a synergy-based future. ►The databased AI industry stimulates data services development for better AI products. ►The AI technology company needs to focus on a vertical to build competitiveness and a solid foundation for future growth at the current stage. ►The most digitally-based industries have the best soil for AI companies to root in. Rumor has it that the AI unicorn SenseTime has suspended its IPO plan and has turned to seek a new billion-level financing from the private market. After closing nine rounds of funding, the six-year-old SenseTime is under growing pressure. SenseTime was founded in 2014 when AI was a hot area, not just in China but the world. During the same period, Yitu Technology, Megvii and CloudWalk were founded subsequently in China. These AI unicorns are head-to-head competitors as they all excel at computer vision (CV) and deep learning and are equipped with first-class AI scientists and engineers. The AI investment heat reached a peak in 2018. SenseTime announced its USD 1 billion investment led by SoftBank at the end of that year. From 2012 to 2019, over CNY 362 billion (USD 52 billion) were injected to the new industry. Within a short period, AI companies’ valuations skyrocketed. The heat triggered some concern: was it worth the high valuations? AI offers people an attractive sci-fi future, one which is hard to be measured by cash. Based on an imagined AI-enhanced future, investors bet on these AI startups, who might be the builders of a such future. However, the development of AI technology is longer than a typical private equity fund’s lifespan. The business world is realistic and asks for real returns, and hence AI companies are being questioned for their profitability when investors think of future exits. The dilemma between technology development and investment returns is only a time issue: no one should expect a toddler – or teenager – to be a qualified work laborer. A decade is not enough for an industry to mature. Beyond the bubble of high valuation, AI companies are making efforts to recognize its practical value and optimize efficiency of the society. Their continuous attempts to do so can guide them to discover more opportunities across industries and understand what changes they can inspire. Competition is fierce, even for a new market A unique AI algorithm is not a moat for any AI company, but how they understand the business is the key. Focusing on the development of SenseTime and its peers, AI companies have extended their reach to more verticals and expedited the digital transformation in multiple areas, from government side to the business end. Known for its CV technology, SenseTime started by providing facial recognition solutions for clients like Vivo and OPPO. As claimed by its CEO Xu Li at 2020 WAIC, over 450 million phones have used SenseTime’s facial recognition solution. However, facial recognition is a new technology and its use in smartphones does not demand a deep understanding of other industries. Thanks to this, the facial recognition playground is crowded with AI players seeded by Internet giants like Baidu (BIDU:NASDAQ) and Tencent (00700:HK) to AI companies like Megvii and Yitu. CV and the smartphone market it inspired are not enough to explain the high valuation of AI businesses. People expect the technology to upgrade and even re-shape industries including production factors, mode of production and production processes. Scenario is the key, but space is limited What SenseTime has been doing reflects the growth path of a typical universal/general AI solution company in China. From image recognition to smart city building, SenseTime is building its reputation in the field, providing a set of ‘smart security’ solutions. ‘Smart security’ in public security liberates police forces from surveillance workloads and improves detection rates. Focus: Public security In the public security part, Hikvision (002415:SZ) and Dahua (002236:SZ) are stakeholders in the market. They are surveillance solution providers for enterprises and governments. Hikvision has the largest global market share in terms of surveillance cameras and Dahua is second to it – Hikvision’s market share reached 37.94% and Dahua accounted for 17.02% in 2018. As ‘smart security’ integrates image recognition technology into security system, SenseTime and all pure AI solution providers are too ‘soft’ in the field – they need to apply their image recognition products and services into hardware to provide a comprehensive solution. Hikvision and Dahua are in an advantageous position because of their tens of years of experience in the public security area, while AI newcomers attract more eyeballs than orders. Via a glance at Megvii’s IPO file, it is reasonable to infer that AI companies are sandwiched by hardware suppliers and solution buyers – who are likely to be the same party. For instance, Hikvision can get a government ‘smart security’ order and it can outsource the AI tech part to AI companies, which makes it a client to AI firms. In the meantime, AI companies will purchase surveillance cameras for R&D purposes or to deliver their own ‘smart security’ solutions. Because the surveillance equipment market is considerably concentrated, AI companies do not have many alternatives – and hence the situation leads them to a position with limited bargain power in the market, which ends with a low profit margin. As reflected by Megvii’s financials, the gross margin of its AIoT business (related to ‘smart city’ and ‘smart security’ projects) is around 50%, lower than the average of 80% in the software industry. Transcending ‘smart security,’ the ‘smart city’ is another playground for Chinese AI companies as well as other participants. ‘Smart city’ aims at a more digital and automated urban area management. Such a project should be able to integrate fragmented city functions into one system: utility management, traffic regulation, civil services entry, cross-department communication, and so on. To be a qualified ‘smart city’ builder, know-how is critical. Who are the ‘smart city’ partners of city governments in China? All Chinese Internet and tech giants are in the field: Baidu, Alibaba, Tencent, Huawei, JD.com and so on. Compared to AI companies, these enterprises have been rooted in particular verticals for years and even decades and finally made profound impacts on the whole industry value chain. Alibaba and JD.com’s know-how in e-commerce ecosystem has equipped them with deep understanding and insights on retailing, supply chain management and further to digital enterprise services; Tencent grew from instant messaging software to a consumer entertainment behemoth connecting nearly all Chinese Internet users – WeChat has around 1 billion users (including global users) while the country has 1.4 billion people. Information and communication technology companies are pioneers in 5G tech, which solidifies the foundation of their IoT development. Among all these competitors, pure AI players are less competitive as they are weaker in real practice – they need know-how. On the other hand, they have no burden in becoming a platform as they do not belong to any specific industry. A third party is always easier to trust than competitors for those to be integrated and such an identity could work in AI companies’ favor. Despite of ‘smart security’, ‘smart finance’, ‘smart healthcare’ and some other could-be-smart scenarios are included in SenseTime’s services introduction page. The real world has more sectors than these, but AI companies cannot assist with these at the moment. The world is not digital enough. What is the future Why there is not a ‘smart village’? Because the digitalization level in rural areas is far from matching the urban level. AI’s prosperity resides on the success of digitalization – unless AI can learn like a real human, who does not need categorized data. Countless exabytes of data have been generated throughout humanity’s history – but only well-structured data meets the standard for AI applications. Structural data includes facial images, lung scans, organized financial spreadsheets, electronic forms and so on, but these are only tip of the data iceberg. While myriad data waiting to be categorized and notated, society needs a higher level of digitalization to lay a firm foundation for AI development. The demand for higher digitalization carries a top-to-bottom appeal for AI industry. Only a handful of industries have realized digitalization. Therefore, AI companies are facing intensive competition as they can only practice in industries like healthcare and financial sectors. Data labeling is still a laborious type work as we await a more advanced digital era. From ITjuzi’s AI data startup database, around 100 data services startups were founded in China before 2020, and 70% of them were born after 2013, when China’s AI startup frenzy was drawing towards a peak. As a subvertical of the AI industry, AI data services companies are more down to earth, as data needs to be from real life, or it loses value in modeling or simulations. Along with the rapid growth of digitalization and AI, people have been concerned about data security in the digital era. Data security and the cybersecurity sector will grow to match the scale of digital development, especially in a Cloud services era. China is said to be set to legislate the first personal information protection law by the end of 2020. Allowing for the as-yet-to-manifest profitability aspect of the business layer for trends such as AI, regulations and governance are assuming that this and other transformative waves will soon enter a robust and mature stage of influence, and are following suit.

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News
Jun 4, 2020 02:01 am · China News

Megvii is Struggling to Go Public in HK

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News
Jun 3, 2020 11:26 pm · FROMGREEK

CloudWalk Technology to be Added in Entity List

Analysis EO
Analysis · 2
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Analysis EO
May 2, 2020 09:05 pm ·

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.

Analysis EO
Analysis · 2
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Analysis EO
May 2, 2020 09:05 pm ·

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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Announcements
Mar 26, 2020 12:00 am · Megvii

TechNode-Megvii’s open-source platform offers Chinese AI alternative

Announcement: Click here
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Sep 17, 2020 06:21 pm ·

Big Fund Sells 0.61% of Goodix's Stocks

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Alibaba Cloud Debuts Industrial Brain 3.0

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Sep 17, 2020 02:37 pm · 36Kr

Weimob Assigns Baidu Ex-VP Watson Yin the New COO

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