Malong Technologies, an artificial intelligence startup and international provider of product recognition technology, was named winner in the “Olympics of Startups” held at the G20 Young Entrepreneurs’ Alliance, in Berlin.

Malong Technologies’ breakthrough ProductAI® platform is a high performance ‘intelligent eye’ that enables machines to have human-like visual perception of products, including non-rigid objects such as fashion and fabrics, which have historically been infeasible for computers to recognize accurately without barcodes.

Malong Technologies beat startups from every nation at the G20, which represents the 20 major economies of the world. Judged by officials of the European Union and other international organizations, the competition, run by the “Get in the Ring” foundation, led participants through a series of six rounds held over two days, and included ‘head-to-head’ oratory matchups inside a replica boxing ring.

The three winning nations include China, represented by Malong Technologies, Canada, and France. As winners, Malong Technologies will benefit from the international visibility which can help open doors to more global customers for its product recognition technology.

Matthew Scott, Malong Technologies’ co-founder and Chief Technology Officer, explains: “We are immensely proud to represent China at the highest level of international competition for emerging companies. It’s incredibly rewarding that the judges recognized the potential for ProductAI® to help transform traditional industries around the world by becoming significantly more efficient through AI integration.”

Launched in 2016, ProductAI® has successfully answered over one billion image requests from users in China. It enables retailers and manufacturers in sectors such as fashion, textiles, furniture, wine, stock photography and automotive industries to improve efficiency, product quality and create new value-added features for their customers.

Its technology offers human-like visual search through 100 million scale product catalogs in less than a second to find items taken from images in the physical world, connecting it to their digital counterparts. The technology, offered as an API or as embedded hardware, can be applied to real-time camera-based retail apps, large-scale product image analysis, microscopic-level product quality detection, and security scenarios such as automatic product detection in baggage scanners.

Big companies can collaborate with entrepreneurs and startups engaged in deep technology—developing technologies that advance scientific and technological frontiers in industries as diverse as agriculture, health care, energy, and transportation—technologies that in many cases address the biggest societal and environmental challenges and shape the way we solve the most pressing global issues.

Affecting aspects of every industry—from the supply chains and processes to customer journeys—digital technologies have revamped virtually every corporate function and activity. But some companies do more than simply apply digital technologies to existing functions or innovative business models that reinvent customer experiences. These innovation leaders seek to develop unique, proprietary, and hard-to-reproduce technological or scientific advances that have the power to create their own markets or disrupt existing industries. Following the past decade of digital innovation, these deep technologies, which will be at the center of the next wave of industrial and information revolution, represent the next big thing that venture investors are looking for.

Because of their intense focus on science and technology, deep-tech startups face their own particular set of challenges. An innovation ecosystem has taken root around them, and within this ecosystem, deep-tech companies see large corporations as the partners that can best help their businesses mature and grow.

At the same time, large corporations seeking new sources of innovation are increasingly turning to new-venture vehicles, including corporate venture capital, accelerators and incubators, and idea labs.  All these vehicles have soared in number among the biggest companies in multiple industries, as these firms seek new partners and skills that can bring more agility to their R&D operations, disrupt existing business models, provide access to adjacent markets, and help them develop a more entrepreneurial internal mindset. Even though innovation built on deep tech is now a priority, many companies still struggle to work effectively with startups, and the road to productive collaboration is rocky.

Startups have plenty of choices when it comes to partners but that they, like their preferred partners, struggle to make the relationships work. Corporate partnerships offer lots of advantages—more than most other potential partnerships—but it is difficult to secure and make them successful. While 95% of startups wish to develop long-term corporate partnerships, only 57% of them have done so. There are many obstacles, including the following:

  • Inadequate preparation on the part of the startup, including lack of a clear value proposition, application, and proof of concept
  • Failure of both parties to clearly define the relationship right from the beginning, including agreeing on vision, business, knowledge, and HR objectives
  • Misalignment of timing and processes, including complex and slow corporate decision making
  • Lack of a clear status and role for the startup within the larger company
  • No high-level sponsorship for the startup within the corporation
  • Lack of buy-in from the business on the corporate side

Large companies that want to bring deep-tech startups into the fold need to consider carefully the particular needs of these young operations, particularly where the startups stand in their development and what type of bets the bigger companies are making. Both sides also need to work out the fit and structure of the collaboration and specify how the two entities will actually work together.

Digital innovation is often about speed to market and scaling up fast to seize first-mover advantage. Deep tech is different in several ways: it involves a strong research base, a challenging business model, and large investment needs. Given their ambition—and often their complexity—truly disruptive deep technologies can require considerable development time before being brought to market. 

For deep-tech startups, a strong research capability is essential since their innovations rely mostly on fundamental and advanced R&D supported by highly developed skills, knowledge, and infrastructure. New materials that demonstrate promising properties in lab conditions need improvements to meet industrial standards. External factors—for example, clinical trials in the health care industry—mean the need for additional resources and can extend the development process for years.

The business models are challenging because deep-tech startups are creating products that are absolutely new. Entrepreneurs must think not only about the technological development of their product but also about how to jump-start nascent or nonexisting markets. This requires the ability to anticipate and understand customer needs that don’t yet exist, as well as a detailed strategy that addresses the challenges of industrialization and scaling up production. On top of that, some groundbreaking products are based on advanced materials and newly developed resources, so deep-tech startups need sharply honed business skills to work through such challenges as procurement, manufacturing, and achieving scale. Furthermore, there is always the danger that incumbents, feeling the threat of disruption, will actively seek to slow down, or block, new technologies from entering the mainstream.

Because in many cases, expensive infrastructure is required to support development and deep tech generally takes time to mature and reach the market, substantial funding from understanding and patient investors is essential. More than 20% of the companies expect to work three years or more before getting a product to market, and 50% of startups underestimate the time that they will need. Early experimentation and prototyping generally require expensive equipment. Testing and scaling is much costlier when it involves purchasing hardware as well as software, which is available and relatively inexpensive from the cloud. Not only is deep-tech capital intensity higher than that of conventional product development, the payback periods are also typically further in the future because of the longer time to market. Funding is, therefore, a big and time-consuming challenge.

For all of these reasons, deep-tech entrepreneurs look to a broad ecosystem of organizations, institutions, and individuals for support. The most common top priority is funding: 80% of the startups we surveyed rank it among their top three needs. But it is far from their only need. Startups look to the supporting ecosystem for help with market access (61%), technical expertise (39%), and business expertise and knowledge (26%). Startups are attracted to particular funders by the specific attributes that they bring to the table. Startups’ needs evolve as they and their products move closer to market, and the attractiveness of various types of funding partners shifts as well.

Large companies looking to partner with deep-tech startups need to segment these up-and-comers according to their maturity and market readiness. Sharpening their understanding of startups’ needs and expectations provides a user’s guide to what startups are seeking from other participants in the ecosystem. Maturity is the level of development of the technology or product itself. The estimation of maturity ranges from early stage (idea, proof of concept) to intermediate stage (prototype, minimally viable product) to late stage (market-ready product). The market readiness indicates whether a product or technology will easily find commercial application and customers. It takes into account customer needs and receptiveness, the regulatory environment, and current innovations in the field.

Applying this segmentation analysis to our sample reveals four categories of startup, each with its own set of needs: potential quick wins, demand bets, development bets, and technology bets.

Potential Quick Wins. These are startups that have a commercially ready product and a market that is prepared to adopt it. The immediate challenge is to achieve scale (initiate large production volumes, for example, or mount a major public-relations and marketing campaign), and for this, they need fresh funding, market access, and talent. Among startups in this group, 40% consider venture capital funds the preferred channel (compared with 25% overall) because venture capitalists tend to offer more generous levels of funding. To develop the customer base and the distribution network, many startups turn to corporations, although only 25% of them expect to get funding out of these collaborations. One-quarter of them do expect to get visibility, and 20% indicated that they expected to gain credibility, business knowledge, or technical knowledge.

Demand Bets. These are startups with a product that is sufficiently mature to be launched but that still has no broad commercial application. Their main challenge is to identify and create a market for their technologies. The two key roadblocks are the lack of a distribution network (42% of startups in this group mentioned this as a challenge, compared with 16% overall) and market resistance to change (37% of them cited this as a challenge, compared with 20% overall). Other than funding, their most important resource needs are market access (a customer base and a distribution network) and business knowledge, for which the preferred partners are, respectively, corporations and venture capital funds.

Development Bets. These startups have identified a market opportunity and defined a value proposition, and they are developing a technology to respond to the opportunity. They have not yet created a market-ready product. They are focused on gaining access to technical expertise (a critical need for half of these startups, compared with 40% overall) and overcoming technological uncertainty (which 25% of them describe as critical). To obtain the expertise they need, they are willing to consider collaborations with companies and universities, but less than half have actually established corporate partnerships (compared with 57% overall). Of the collaborations that the development bet startups have established, 60% are research partnerships that share the costs and risks of R&D and accelerate the product development.

Technology Bets. These are startups that have identified a promising (though not fully developed) technology that lacks a market application. Their objective is to develop a viable product that fills a market need. The two chief roadblocks that these startups face are long development time (a major problem for 30%) and technological uncertainty (noted by 25%). Because the attendant uncertainty makes funding risky, their funding is generally from university and public sources. Obtaining access to corporate knowledge and support is relatively difficult for technology bets owing to the risk factors involved. Survey participants from this group express stronger needs for all resources, as they need to turn a technology into a solution to a problem, and they need to develop a marketable product in order to reach the potential-quick-win stage.

For large companies with big innovation ambitions, picking the deep technologies to support depends on strategic priorities and a strong market assessment. Choosing the right partner, however, is much like a courtship, especially since the relationship is likely to be a lengthy one. In our experience, these arrangements tend to involve significant commitment from both sides—not just in terms of money but also management time, organizational expertise, and resources. Understanding what your prospective partner is looking for, as well as how those needs align with your own ambitions and capabilities, raises the chances for success. 

Unicorns were once considered rare. Now, the United States is home to more than 100 of these venture-backed companies, each worth more than $1 billion. But are these magical beasts really dressed-up ponies? New research shows that these companies report values on average about 51% above what they are really worth. And some, including management software company Compass and financial technology company Kabbage, are more than 100% above fair market value.

Determining a startup’s worth can be a challenge. Many are fast-growing and unprofitable, and almost all have complex financial structures. They raise funding in multiple rounds, offering investors different restrictions and protections, and therefore stock pricing. The average unicorn, the researchers note, has eight stock classes for different types of investors, including founders, employees, venture capitalists, mutual funds, and others.

Because of that complicated structure, valuation is often based on the latest series’ price, applied to all outstanding shares. But that doesn’t accurately reflect the preferred treatment some investors might get, the researchers say. In some series, for example, investors are promised 1.5 to 2 times their money should an initial public offering (IPO) fizzle. In that case, other shares can be worth far less.

Some unicorns have made such generous promises to their preferred shareholders that their common shares are nearly worthless. One example is SpaceX, which raised a round in the recession of 2008 after several of its rocket launch attempts failed. It promised investors twice their money back and first in line should the company liquidate. At those rosy terms, investors sank more money into the company, which raised its valuation.

Interest in venture capital as an investment vehicle is growing. Large U.S. mutual fund providers, including Fidelity and T. Rowe Price, have started investing in unicorns, and the past three years has seen a 10-fold increase in VC-backed investments. The rise of third-party equity marketplaces has allowed mom-and-pop investors to join the game as well. And in Silicon Valley, many young workers take small salaries and large stock options, betting on a successful IPO. But even the most sophisticated finance professionals equate fair value and post-money valuation.

Silicon Valley has not shaken its hippie roots: The area around San Francisco now known as Silicon Valley was the hotbed of the hippie movement in the 1960s. While the mindset has been updated for the tech boom, the same spirit remains. “They all live in these co-living houses and everybody seems to spend so much time with groups,” she said. “It felt to me like it was this big dorm. I mean, the offices look like dorms, the houses look like dorms. So instead of dropping acid, they seem to prefer the computer.”

Being a college “stop out” — or drop out — is not a negative: “It’s not as looked down upon to drop out of college or to drop out of high school,” Wolfe said. Thiel’s entrepreneurship program aimed to reach brilliant young folks before they succumbed to the perceived groupthink of academic institutions. “They didn’t want to be stuck in a system for four years. It made me wonder what higher education actually does now,” she said.

Status still matters: Wolfe said East Coast entrepreneurs have a certain status trajectory that they follow to be successful: go to the right schools, join the right clubs. She did not think status was prioritized in Silicon Valley, but it does in its own way: “Instead of driving your Ferrari, you drive your Prius and you keep your Ferrari in the garage…. Instead of name dropping, it was location dropping” such as going to Bhutan for eight hours and then coming back.

The competition is fierce: The mindset of many people who end up in Silicon Valley is the same: They want to make money, but they want to have an impact — they want to change the world, Wolfe said. “I felt it was so inspiring to hear about all these people who wanted to start something new and wanted to start something for themselves, but it does feel like an awful lot of people are trying to do that.”

Failure is a virtue: Many startups in Silicon Valley fizzle out quickly, which all but guarantees that most entrepreneurs will have a failure story of their own at some point. But they wear it like a badge of honor. “It’s sort of a resume-builder,” she said. In the East Coast, people would say, “Oh, we went to [elite prep school] Andover together,” in Silicon Valley they would say “We failed at the same company together,” Wolfe added. Failing is “much more acceptable.”


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