Nozomi Networks, an industry cybersecurity startup that aims to shield critical infrastructure from cyberattacks, has raised $100 million in pre-IPO funding.
The Series D funding round was led by Triangle Peak Partners, and also includes investment from a number of equipment, security, service provider and go-to-market companies including Honeywell Ventures, Keysight Technologies and Porsche Digital.
This funding comes at a critical time for the company. Cyberattacks on industrial control systems (ICS) — the devices necessary for the continued running of power plants, water supplies, and other critical infrastructure — increased both in frequency and severity during the pandemic. Look no further than May and June, which saw ransomware attacks target the IT networks of Colonial Pipeline and meat manufacturing giant JBS, forcing the companies to shut down their industrial operations.
Nozomi Networks, which competes with Dragos and Claroty, claims its industrial cybersecurity solution, which works to secure ICS devices by detecting threats before they hit, aims to prevent such attacks from happening. It provides real-time visibility to help organizations manage cyber risk and improve resilience for industrial operations.
The technology currently supports more than a quarter of a million devices in sectors such as critical infrastructure, energy, manufacturing, mining, transportation, and utilities, with Nozomi Networks doubling its customer base in 2020 and seeing a 5,000% increase in the number of devices its solutions monitor.
The company will use its latest investment, which comes less than two years after it secured $30 million in Series C funding, to scale product development efforts as well as its go-to-market approach globally.
Specifically, Nozomi Networks said it plans to grow its sales, marketing, and partner enablement efforts, and upgrade its products to address new challenges in both the OT and IoT visibility and security markets.
Hello and welcome back to TechCrunch’s China roundup, a digest of recent events shaping the Chinese tech landscape and what they mean to people in the rest of the world.
The tech industry in China has had quite a turbulent week. The government is upending its $100 billion private education sector, wiping billions from the market cap of the industry’s most lucrative players. Meanwhile, the assault on Chinese internet giants continued. Tech stocks tumbled after Tencent suspended user registration, sparking fears over who will be the next target of Beijing’s wrath.
Incisive observers point out that the new wave of stringent regulations against China’s internet and education firms has long been on Beijing’s agenda and there’s nothing surprising. Indeed, the central government has been unabashed about its desires to boost manufacturing and contain the unchecked powers of its service industry, which can include everything from internet platforms, film studios to after-school centers.
A few weeks ago I had an informative conversation with a Chinese venture capitalist who has been investing in industrial robots for over a decade, so I’m including it in this issue as it provides useful context for what’s going on in the consumer tech industry this week.
China is putting robots into factories at an aggressive pace. Huang He, a partner at Northern Light Venture Capital, sees three forces spurring the demand for industrial robots — particularly ones that are made in China.
Over the years, Beijing has advocated for “localization” in a broad range of technology sectors, from enterprise software to production line automation. One may start to see Chinese robots that can rival those of Schneider and Panasonic a few years down the road. CRP, an NLVC-backed industrial robot maker, is already selling across Southeast Asia, Russia and East Europe.
On top of tech localization, it’s also well acknowledged that China is facing a severe demographic crisis. The labor shortage in its manufacturing sector is further compounded by the reluctance of young people to do menial factory work. Factory robots could offer a hand.
“Youngsters these days would rather become food delivery riders than work in a factory. The work that robots replace is the low-skilled type, and those that still can’t be taken up by robots pay well and come with great benefits,” Huang observed.
Large corporations in China still lean toward imported robots due to the products’ proven stability. The problem is that imported robots are not only expensive but also selective about their users.
“Companies need to have deep technical capabilities to be able to operate these [Western] robots, but such companies are rare in China,” said Huang, adding that the overwhelming majority of Chinese enterprises are small and medium size.
With the exceptions of the automotive and semiconductor industries, which still largely rely on sophisticated, imported robots, affordable, easy-to-use Chinese robots can already meet most of the local demand for industrial automation, Huang said.
China currently uses nearly one million six-axis robots a year but only manufactures 20% of them itself. The gap, coupled with a national plan for localization, has led to a frenzy of investments in industrial robotics startups.
The rush isn’t necessarily a good thing, said Huang. “There’s this bizarre phenomenon in China, where the most funded and valuable industrial robotic firms are generating less than 30 million yuan in annual revenue and not really heard of by real users in the industry.”
“This isn’t an industry where giants can be created by burning through cash. It’s not the internet sector.”
Small-and-medium-size businesses are happily welcoming robots onto factory floors. Take welding for example. An average welder costs about 150,000 yuan ($23,200) a year. A typical welding robot, which is sold for 120,000 yuan, can replace up to three workers a year and “doesn’t complain at work,” said the investor. A quality robot can work continuously for six to eight years, so the financial incentive to automate is obvious.
Advanced manufacturing is not just helping local bosses. It will eventually increase foreign enterprises’ dependence on China for its efficiency, making it hard to cut off Chinese supply chains despite efforts to avoid the geopolitical risks of manufacturing in China.
“In electronics, for example, most of the supply chains are in China, so factories outside China end up spending more on logistics to move parts around. Much of the 3C manufacturing is already highly automated, which relies heavily on electricity, but in most emerging economies, the power supply is still quite unstable, which disrupts production,” said Huang.
The shock of antitrust regulations against Alibaba from last year is still reverberating, but another wave of scrutiny has already begun. Shortly after Didi’s blockbuster IPO in New York, the ride-hailing giant was asked to cease user registration and work on protecting user information critical to national security.
On Tuesday, Tencent stocks fell the most in a decade after it halted user signups on its WeChat messenger as it “upgrades” its security technology to align with relevant laws and regulations. The gaming and social media giant is just the latest in a growing list of companies hit by Beijing’s tightening grip on the internet sector, which had been flourishing for two decades under laissez-faire policies.
Underlying the clampdowns is Beijing’s growing unease with the service industry’s unscrutinized accumulation of wealth and power. China is unequivocally determined to advance its tech sector, but the types of tech that Beijing wants are not so much the video games that bring myopia to children and algorithms that get adults hooked to their screens. China makes it clear in its five-year plan, a series of social and economic initiatives, that it will go all-in on “hard tech” like semiconductors, renewable energy, agritech, biotech and industrial automation like factory robotics.
China has also vowed to fight inequality in education and wealth. In the authorities’ eyes, expensive, for-profit after-schools dotting big cities are hindering education attainment for children from poorer areas, which eventually exacerbates the wealth gap. The new regulatory measures have restricted the hours, content, profits and financing of private tutoring institutions, tanking stocks of the industry’s top companies. Again, there have been clear indications from President Xi Jinping’s writings to bring off-campus tutoring “back on the educational track.” All China-focused investors and analysts are now poring over Xi’s thoughts and directives.
In the United States, same-day and next-day Amazon Prime deliveries have become the de facto standard in e-commerce. People want convenience and instant gratification, evidenced by the fact that an astonishing ~45% of U.S. consumers are Amazon Prime members.
Most major retailers are scrambling to catch up to Amazon by partnering with last-mile delivery startups. Walmart has become a major investor in Cruise for autonomous-vehicle deliveries, and Target acquired Shipt and Deliv last-mile delivery startups to increase its delivery speed. Costco partnered with Instacart for same-day deliveries, and even Domino’s Pizza has jumped in by partnering with Nuro for last-mile delivery using autonomous vehicles.
E-commerce in LatAm has taken off at a compound annual industry growth rate of 16% over the past five years.
Venture capitalists have been investing heavily in last-mile delivery over the past five years on a global scale, but Latin America (LatAm) has lagged behind. Over $11 billion has been invested globally in last-mile logistics over the past decade, but Latin America only saw about $1 billion over the same period (Source: PitchBook and WIND Ventures research).
Within this, only about $300 million was in Spanish-speaking Latin America — a surprisingly small amount for a region that has 110 million more consumers than in the U.S.
Brazil-based Loggi accounts for about 60% of last-mile VC investment in Latin America, but it only operates in Brazil. That leaves major Spanish countries like Mexico, Colombia, Chile and Argentina without a leading independent last-mile logistics company.
In these countries, about 60% of the last-mile delivery market is dominated by small, informal companies or independent drivers using their own trucks. This results in inefficiencies due to a lack of technologies such as route optimization as well as a lack of operating scale. These issues are quickly becoming more pronounced as e-commerce in LatAm has taken off at a compound annual industry growth rate of 16% over the past five years.
Retailers are missing an opportunity to give customers what they want. Customers today expect free, reliable same- or next-day delivery — on-time, all the time, and without damage or theft. All of these are challenging in LatAm. Theft, in particular, is a significant problem, because unprofessional drivers often steal products out for delivery and then sell them for a profit. Cost is a problem, too, because free same- and next-day deliveries are simply not available in many places.
Why does Latin America lag when it comes to the last mile? First, traditional LatAm e-commerce delivery involves multiple time-consuming steps: Products are picked up from the retailer, delivered to a cross-dock, distributed to a warehouse, delivered to a second cross-dock, and then finally delivered to the customer.
By comparison, modern delivery operations are much simpler. Products are picked up from the retailer, delivered to a cross-dock, and then delivered directly to the customer. There’s no need for warehousing and an extra pre-warehouse cross-dock.
And those are just the operational challenges. Lack of technology also plays a significant role. Most delivery coordination and routing in LatAm are still done via a spreadsheet or pen and paper.
Dispatchers have to manually pick up a phone to call drivers and dispatch them. In the U.S., computerized optimization algorithms dramatically cut both delivery cost and time by automatically finding the most efficient route (e.g., packing the most deliveries possible on a truck along the route) and automatically dispatching the driver that can most efficiently complete the route based on current location, capacity and experience with the route. These algorithms are almost unheard of in the Latin America retail logistics sector.
Fabric8Labs this morning announced that it has raised $19.3 million. The Series A was led by Intel Capital and features Lam Capital, TDK Ventures, SE Ventures, imec.xpand, Stanley Ventures and Mark Cuban. It follows $4 million in seed funding raised in mid-2018.
The San Diego-based startup specializes in metal 3D printing. It’s a hot category, of late, as evidenced by Desktop Metal and Markforged’s decisions to go public via SPAC over the past two years. Fabric8Labs says lower cost and less energy consumption are among the benefits to its process.
“Our process is inherently different and does not utilize powder nor thermal processes. Instead, it is based on electrochemical deposition, which operates at room temperature, has a significantly lower power demand, and utilizes an aqueous (water-based) solution made from low-cost metal salts,” CEO Jeff Herman tells TechCrunch. “In combination, the commodity priced raw materials and power-efficient process enable a step change in reducing the total cost of ownership and cost per-part.”
The company says the funding will go toward doubling its headcount before the end of the year, increase development of its existing technology and showcase its ability to print high-resolution copper pieces. The company plans to bring the technology to market, but notes that goals of hitting a general market will be a multiyear process.
Scalability is always one of the biggest question marks around any kind of additive manufacturing. Herman says 3D printing for manufacturing is firmly in Fabric8Labs’ sights.
“Our technology is extremely scalable,” the executive says. “The vision we share with our partners is to deploy our technology at a massive scale in the factories of the future, with process capabilities and economics uniquely positioned to tackle high-volume manufacturing. A Fabric8Labs-enabled factory could easily consist of 50+ automated systems sharing large feedstock reservoirs, similar to other large-scale electrochemical processes in operation today.”
MIT CSAIL spinout Inkbit this week announced that it has raised $30 million. The Series B, led by Phoenix Venture Partners LLC, brings the firm’s total funding up to $45 million. PVP joins existing partners like industrial 3D printing giant Stratasys, DSM Venturing, Ocado, 3M, IMA and Saint-Gobain.
Inkbit was founded in 2017, building on technology developed with a financial assist from DARPA. The company currently holds the exclusive licensing rights to that technology. Its primary differentiator from the slew of existing 3D printers is a vision and AI system designed to identify and correct mistakes during the printing process.
Mistakes can be quite frequent — and costly — in additive manufacturing. Inkbit’s technology uses imaging to scan each printed layer, compare it against the original plan and then adjust accordingly to correct errors on the fly. The latest round of funding follows the February release of the company’s Vista printer, which builds on Inkbit’s Vision-Controlled Jetting (VCJ) closed-loop feedback technology.
“Inkbit is currently experiencing significant growth and we are excited to have the opportunity to continue to build our talented team and scale the company to meet customer demand,” co-founder and CEO Davide Marini said in a statement. “The opportunities for additive manufacturing are growing as adoption of 3D printing for full-scale production increases. We look forward to using our raised capital to continue evolving and innovating within this dynamic industry.”
The round will be used to expand the sales reach of its new printer, both in the U.S. and into additional markets, including Asia and Europe/Middle East/Africa.
Dayna Grayson has been in venture capital for more than a decade and was one of the first VCs to build a portfolio around the transformation of industrial sectors of our economy.
At NEA, where she was a partner for eight years, she led investments in and sat on the boards of companies including Desktop Metal, Onshape, Framebridge, Tulip, Formlabs and Guideline. She left NEA to start her own fund, Construct Capital, that focuses exclusively on early-stage startups, with a portfolio that includes Copia, ChargeLab, Tradeswell and Hadrian.
It should come as no surprise, then, that we’re absolutely thrilled to have Grayson join us at TechCrunch Disrupt 2021 in September.
Grayson has more than proven that she has a keen eye for transformational technology. Desktop Metal went public in 2020 — she still sits on the board as chair of the compensation committee. Onshape, another NEA-era investment, was acquired by PTC in 2019 for a whopping $525 million. Framebridge was also acquired by Graham Holdings in 2020.
Grayson saw an opportunity to develop a venture brand more hyperfocused on the types of deals she was doing at NEA, which centered around manufacturing and digitizing industrial verticals. That’s where Construct Capital came in. It’s a $140 million fund helmed by Grayson and former Uber exec Rachel Holt.
At Disrupt, Grayson will serve as a Startup Battlefield judge. The Battlefield is one of the world’s most prestigious and exciting startup competitions. Twenty+ early-stage startups hop on our stage and present their wares to a panel of expert VC judges, who then grill the founders on everything about the business, from the revenue model to the go-to-market strategy to the team to the technology itself.
The winner walks away with $100,000 in prize money and the glory of being a Battlefield winner. Households names in tech have gotten their start in the Battlefield, from Dropbox to Mint.
Grayson joins plenty of other seasoned investors on the Battlefield stage, including Camille Samuels, Deena Shakir, Terri Burns, Shauntel Garvey and Alexa Von Tobel.
Disrupt 2021 goes down from September 21 to 23 and is virtual. Snag a ticket here starting under $100 for a limited time!
Bioengineering may soon provide compelling, low-carbon alternatives in industries where even the best methods produce significant emissions. Utilizing natural and engineered biological process has led to low-carbon textiles from AlgiKnit, cell-cultured premium meats from Orbillion and fuels captured from waste emissions via LanzaTech — and leaders from those companies will be joining us onstage for the Extreme Tech Challenge Global Finals on July 22.
We’re co-hosting the event, with panels like this one all day and a pitch-off that will feature a number of innovative startups with a sustainability angle.
I’ll be moderating a panel on using bioengineering to create change directly in industries with large carbon footprints: textiles, meat production and manufacturing.
AlgiKnit is a startup that is sourcing raw material for fabric from kelp, which is an eco-friendly alternative to textile crop monocultures and artificial materials like acrylic. CEO Aaron Nesser will speak to the challenge of breaking into this established industry and overcoming preconceived notions of what an algae-derived fabric might be like (spoiler: it’s like any other fabric).
Orbillion Bio is one of the new crop of alternative protein companies offering cell-cultured meats (just don’t call them “lab” or “vat” grown) to offset the incredibly wasteful livestock industry. But it’s more than just growing a steak — there are regulatory and market barriers aplenty that CEO Patricia Bubner can speak to, as well as the technical challenge.
LanzaTech works with factories to capture emissions as they’re emitted, collecting the useful particles that would otherwise clutter the atmosphere and repurposing them in the form of premium fuels. This is a delicate and complex process that needs to be a partnership, not just a retrofitting operation, so CEO Jennifer Holmgren will speak to their approach convincing the industry to work with them at the ground floor.
It should be a very interesting conversation, so tune in on July 22 to hear these and other industry leaders focused on sustainability discuss how innovation at the startup level can contribute to the fight against climate change. Plus it’s free!
The tectonic shifts to American culture and society due to the pandemic are far from over. One of the more glaring ones is that the U.S. labor market is going absolutely haywire.
Millions are unemployed, yet companies — from retail to customer service to airlines — can’t find enough workers. This perplexing paradox behind Uber price surges and waiting on an endless hold because your flight was canceled isn’t just inconvenient — it’s a loud and clear message from the post-pandemic American workforce. Many are underpaid, undervalued and underwhelmed in their current jobs, and are willing to change careers or walk away from certain types of work for good.
It’s worth noting that low-wage workers aren’t the only ones putting their foot down; white-collar quits are also at an all-time high. Extended unemployment benefits implemented during the pandemic may be keeping some workers on the sidelines, but employee burnout and job dissatisfaction are also primary culprits.
We have a wage problem and an employee satisfaction problem, and Congress has a long summer ahead of it to attempt to find a solution. But what are companies supposed to do in the meantime?
Adopting AI in manufacturing accelerated during the pandemic to deal with volatility in the supply chain, but now it must move from “pilot purgatory” to widespread implementation.
At this particular moment, businesses need a stopgap solution either until September, when COVID-19 relief and unemployment benefits are earmarked to expire, or something longer term and more durable that not only keeps the engine running but propels the ship forward. Adopting AI can be the key to both.
Declaring that we’re on the precipice of an AI awakening is probably nowhere near the most shocking thing you’ve read this year. But just a few short years ago, it would have frightened a vast number of people, as advances in automation and AI began to transform from a distant idea into a very personal reality. People were (and some holdouts remain) genuinely worried about losing their job, their lifeline, with visions of robots and virtual agents taking over.
But does this “AI takes jobs” storyline hold up in the cultural and economic moment we’re in?
If this “labor shortage” unveils any silver lining, it’s our real-world version of the Sorting Hat. When you take money out of the equation on the question of employment, it’s opening our eyes to what work people find desirable and, more evidently, what’s not. Specifically, the manufacturing, retail and service industries are taking the hardest labor hits, underscoring that tasks associated with those jobs — repetitive duties, unrewarding customer service tasks and physical labor — are driving more and more potential workers away.
Adopting AI in manufacturing accelerated during the pandemic to deal with volatility in the supply chain, but now it must move from “pilot purgatory” to widespread implementation. The best use cases for AI in this industry are ones that help with supply chain optimization, including quality inspection, general supply chain management and risk/inventory management.
Most critically, AI can predict when equipment might fail or break, reducing costs and downtime to almost zero. Industry leaders believe that AI is not only beneficial for business continuity but that it can augment the work and efficiency of existing employees rather than displace them. AI can assist employees by providing real-time guidance and training, flagging safety hazards, and freeing them up to do less repetitive, low-skilled work by taking on such tasks itself, such as detecting potential assembly line defects.
In the manufacturing industry, this current labor shortage is not a new phenomenon. The industry has been facing a perception problem in the U.S. for a long time, mainly because young workers think manufacturers are “low tech” and low paying. AI can make existing jobs more attractive and directly lead to a better bottom line while also creating new roles for companies that attract subject-matter talent and expertise.
In the retail and service industries, arduous customer service tasks and low pay are leading many employees to walk out the door. Those that are still sticking it out have their hands tied because of their benefits, even though they are unhappy with the work. Conversational AI, which is AI that can interact with people in a human-like manner by leveraging natural language processing and machine learning, can relieve employees of many of the more monotonous customer experience interactions so they can take on roles focused on elevating retail and service brands with more cerebral, thoughtful human input.
Many retail and service companies adopted scripted chatbots during the pandemic to help with the large online volumes only to realize that chatbots operate on a fixed decision tree — meaning if you ask something out of context, the whole customer service process breaks down. Advanced conversational AI technologies are modeled on the human brain. They even learn as they go, getting more skilled over time, presenting a solution that saves retail and service employees from the mundane while boosting customer satisfaction and revenue.
Hesitancy and misconceptions about AI in the workplace have long been a barrier to widespread adoption — but companies experiencing labor shortages should consider where it can make their employees’ lives better and easier, which can only be a benefit for bottom-line growth. And it might just be the big break that AI needs.
From the outside looking in, the construction industry appears ripe for tech innovation. The industry represents 6.3% of the U.S. GDP. There are close to 1 million general contractors (GCs) in the country, and anywhere between 3 million and 5 million workers on job sites every day.
Meanwhile, there’s a common (if somewhat justified) belief that construction firms are slow to adopt technology and are behind the digital curve.
Success in construction tech will come down to proving the need for the technology, delivering immediate ROI, and ensuring workers know how to use it on the first try.
But not every construction company is a technology laggard. While GCs are historically slower to adopt new technologies, this doesn’t necessarily make them behind the times. About 60% of construction companies have R&D departments for new technology, and the largest construction firms have substantial R&D budgets. Yet 35.9% of employees are hesitant to try new technology, according to JB Knowledge.
One way to interpret this is that there is a strong interest and need to take advantage of newer construction-centric technologies, but only if they’re easy to use, easy to deploy or access while on a job site, and improve productivity almost immediately.
These factors have made construction tech appealing to investors, who have poured at least $3 billion into the sector. Is construction tech the “it” place right now? Is it ripe for disruption, the way VC investors find attractive? If that’s true, what went wrong at Katerra? Is Procore justified in losing $1 for every $4 in revenue? And why does so little investment go into improving productivity at the job site where GC money is made — or lost — compared to back-office operations?
My experience to date says that construction is different from other sectors because of the significant variation among projects that originates in the way projects are financed, how risks are managed and the factors that drive variation among projects. Construction’s differences are not easily mitigated via data processing, as compared to fintech, for example, where all money is data-amenable to software processing. Addressing project variations will be key to succeeding in construction tech beyond the back office. Here are the critical factors to consider.
Project financing makes capital investment more difficult. While the Commerce Department reported that construction spending in the U.S. reached a record high of $1.459 trillion in November 2020, this doesn’t mean there are unlimited opportunities for construction tech. The reality is that GCs make few capital investments because they must fund investments in technology out of operating cash flow.
Construction projects are typically funded incrementally in phases as the project demonstrates progress. Delays or accidents can have a huge effect on cash flow. Overhead and G&A cost burdens are hated. Asking a GC to license technology as a capital purchase doesn’t always make sense.
GC ownership and business structure also make large capital investment more difficult. Most GC firms were founded by tradespeople and either started as, or remain, family-owned firms. Borrowing what’s considered the “family’s money” is a much more risk-averse decision compared to the way larger corporations evaluate productivity investments and put assets at risk.
Chinese merchants selling on Amazon are having a moment. The scruffy exporters are used to roaming about suburban factory areas and dealing with constant cash flow strain, but suddenly they find themselves having coffee with top Chinese venture capital firms and investment representatives from internet giants, who come with big checks to hunt down the next Shein or Anker. While VCs can provide the money for them to scale quickly, many lack the expertise to help on the strategic side.
This is where brand aggregators can put their retail know-how to work. Also called roll-ups, these companies go around acquiring promising e-commmerce brands for operational synergies. After taking off in the United States, Europe, and lately Southeast Asia, it has also quietly landed in China, where traditional white-label manufacturers are trying to move up the value chain and establish their own brand presence.
The latest roll-up to enter China is Berlin Brands Group (BBG), which aims to buy “dozens of” brands in the country over the next few years, its founder and CEO Peter Chaljawski told TechCrunch. This will significantly boost the German company’s existing portfolio of 14 brands.
The move came on the back of BBG’s $240 million funding raised from debt and its announcement to commit $300 million on its balance sheet to buying up companies. The firm opted for debt in part because it has been profitable since its inception. The recent funding won’t be its last round and it may use other financial instruments in the future, said the founder.
Chaljawski doesn’t see VC and corporate investors as direct competitors in the hunt for brands. “There are tens of thousands of sellers in China that generate significant revenue on Amazon. I think the VC money applies to some of them, and the roll-up model applies also to only some of them. But ‘some’ is a very, very big number.”
BBG is no stranger to China. The 15-year-old company has been relying on Chinese manufacturers to make its kitchenware, gardening tools, sports gear and other home appliances, with 90% of its products still made in the country today. For the new brand buy-out initiative, it’s hiring dozens of staff in Shenzhen, which Chalijawski dubbed the “Silicon Valley of Amazon,” referring to the southern city’s key role in global export, manufacturing, and increasingly, design.
BBG hopes to offer a new way for Chinese consumer products to scale in Europe and the U.S. beyond being an anonymous brand on Amazon. Sellers may want to break free of the American behemoth to seize more control over consumer data, but building a direct-to-consumer (D2C) brand is no small feat.
Many merchants that are good at operating Amazon third-party businesses lack the infrastructure to go beyond Amazon, like an in-house logistics system, said the founder. In Europe, BBG manages 120,000 square meters of fulfillment centers, allowing it to shed dependence on Amazon.
Chinese brands may also want to find Amazon alternatives in Europe, where the e-commerce landscape is a lot more fragmented than that in the U.S, noted Chaljawski.
“If you look at the U.S., Amazon is dominant. If you look at Europe, Amazon only has 10% of the market share of online retail. So 90% is beyond Amazon. In the Netherlands, you have platforms like Bol. In Poland, you have Allegro, and in France, you have other dominant players.”
To bridge the gap for international brands targeting Europe, BBG operates close to 20 D2C web stores in major European countries, aside from selling on Amazon. Its sales growth in the U.S. has also been in full steam. Currently, over 60% of the firm’s revenues come from non-Amazon channels.
BBG is already in advanced negotiations with “some brands” in China but cannot disclose their names at this stage.
If you walk down the street shouting out the names of every object you see — garbage truck! bicyclist! sycamore tree! — most people would not conclude you are smart. But if you go through an obstacle course, and you show them how to navigate a series of challenges to get to the end unscathed, they would.
Most machine learning algorithms are shouting names in the street. They perform perceptive tasks that a person can do in under a second. But another kind of AI — deep reinforcement learning — is strategic. It learns how to take a series of actions in order to reach a goal. That’s powerful and smart — and it’s going to change a lot of industries.
Two industries on the cusp of AI transformations are manufacturing and supply chain. The ways we make and ship stuff are heavily dependent on groups of machines working together, and the efficiency and resiliency of those machines are the foundation of our economy and society. Without them, we can’t buy the basics we need to live and work.
Startups like Covariant, Ocado’s Kindred and Bright Machines are using machine learning and reinforcement learning to change how machines are controlled in factories and warehouses, solving inordinately difficult challenges such as getting robots to detect and pick up objects of various sizes and shapes out of bins, among others. They are attacking enormous markets: The industrial control and automation market was worth $152 billion last year, while logistics automation was valued at more than $50 billion.
Deep reinforcement learning consistently produces results that other machine learning and optimization tools are incapable of.
As a technologist, you need a lot of things to make deep reinforcement learning work. The first piece to think about is how you will get your deep reinforcement learning agent to practice the skills you want it to acquire. There are only two ways — with real data or through simulations. Each approach has its own challenge: Data must be collected and cleaned, while simulations must be built and validated.
Some examples will illustrate what this means. In 2016, GoogleX advertised its robotic “arm farms” — spaces filled with robot arms that were learning to grasp items and teach others how to do the same — which was one early way for a reinforcement learning algorithm to practice its moves in a real environment and measure the success of its actions. That feedback loop is necessary for a goal-oriented algorithm to learn: It must make sequential decisions and see where they lead.
In many situations, it is not feasible to build the physical environment where a reinforcement learning algorithm can learn. Let’s say you want to test different strategies for routing a fleet of thousands of trucks moving goods from many factories to many retail outlets. It would be very expensive to test all possible strategies, and those tests would not just cost money to run, but the failed runs would lead to many unhappy customers.
For many large systems, the only possible way to find the best action path is with simulation. In those situations, you must create a digital model of the physical system you want to understand in order to generate the data reinforcement learning needs. These models are called, alternately, digital twins, simulations and reinforcement-learning environments. They all essentially mean the same thing in manufacturing and supply chain applications.
Recreating any physical system requires domain experts who understand how the system works. This can be a problem for systems as small as a single fulfillment center for the simple reason that the people who built those systems may have left or died, and their successors have learned how to operate but not reconstruct them.
Many simulation software tools offer low-code interfaces that enable domain experts to create digital models of those physical systems. This is important, because domain expertise and software engineering skills often cannot be found in the same person.
Why would you go through all this trouble for a single algorithm? Because deep reinforcement learning consistently produces results that other machine learning and optimization tools are incapable of. DeepMind used it, of course, to beat the world champion of the board game of Go. Reinforcement learning was part of the algorithms that were integral to achieving breakthrough results with chess, protein folding and Atari games. Likewise, OpenAI trained deep reinforcement learning to beat the best human teams at Dota 2.
Just like deep artificial neural networks began to find business applications in the mid-2010s, after Geoffrey Hinton was hired by Google and Yann LeCun by Facebook, so too, deep reinforcement learning will have an increasing impact on industries. It will lead to quantum improvements in robotic automation and system control on the same order as we saw with Go. It will be the best we have, and by a long shot.
The consequence of those gains will be immense increases in efficiency and cost savings in manufacturing products and operating supply chains, leading to decreases in carbon emissions and worksite accidents. And, to be clear, the chokepoints and challenges of the physical world are all around us. Just in the last year, our societies have been hit by multiple supply chain disruptions due to COVID, lockdowns, the Suez Canal debacle and extreme weather events.
Zooming in on COVID, even after the vaccine was developed and approved, many countries have had trouble producing it and distributing it quickly. These are manufacturing and supply chain problems that involve situations we could not prepare for with historical data. They required simulations to predict what would happen, as well as how we could best address crises when they do occur, as Michael Lewis illustrated in his recent book “The Premonition.”
It is precisely this combination of constraints and novel challenges that take place in factories and supply chains that reinforcement learning and simulation can help us solve more quickly. And we are sure to face more of them in the future.
Proving that Central and Eastern Europe remains a powerhouse of hardware engineering matched with software, Gideon Brothers (GB), a Zagreb, Croatia-based robotics and AI startup, has raised a $31 million Series A round led by Koch Disruptive Technologies (KDT), the venture and growth arm of Koch Industries Inc., with participation from DB Schenker, Prologis Ventures and Rite-Hite.
The round also includes participation from several of Gideon Brothers’ existing backers: Taavet Hinrikus (co-founder of TransferWise), Pentland Ventures, Peaksjah, HCVC (Hardware Club), Ivan Topčić, Nenad Bakić and Luca Ascani.
The investment will be used to accelerate the development and commercialization of GB’s AI and 3D vision-based “autonomous mobile robots” or “AMRs”. These perform simple tasks such as transporting, picking up and dropping off products in order to free up humans to perform more valuable tasks.
The company will also expand its operations in the EU and U.S. by opening offices in Munich, Germany and Boston, Massachusetts, respectively.
Gideon Brothers founders. Image Credits: Gideon Brothers
Gideon Brothers make robots and the accompanying software platform that specializes in horizontal and vertical handling processes for logistics, warehousing, manufacturing and retail businesses. For obvious reasons, the need to roboticize supply chains has exploded during the pandemic.
Matija Kopić, CEO of Gideon Brothers, said: “The pandemic has greatly accelerated the adoption of smart automation, and we are ready to meet the unprecedented market demand. The best way to do it is by marrying our proprietary solutions with the largest, most demanding customers out there. Our strategic partners have real challenges that our robots are already solving, and, with us, they’re seizing the incredible opportunity right now to effect robotic-powered change to some of the world’s most innovative organizations.”
He added: “Partnering with these forward-thinking industry leaders will help us expand our global footprint, but we will always stay true to our Croatian roots. That is our superpower. The Croatian startup scene is growing exponentially and we want to unlock further opportunities for our country to become a robotics & AI powerhouse.”
Annant Patel, director at Koch Disruptive Technologies, said: “With more than 300 Koch operations and production units globally, KDT recognizes the unique capabilities of and potential for Gideon Brothers’ technology to substantially transform how businesses can approach warehouse and manufacturing processes through cutting edge AI and 3D AMR technology.”
Xavier Garijo, member of the Board of Management for Contract Logistics, DB Schenker, added: “Our partnership with Gideon Brothers secures our access to best in class robotics and intelligent material handling solutions to serve our customers in the most efficient way.”
GB’s competitors include Seegrid, Teradyne (MiR), Vecna Robotics, Fetch Robotics, AutoGuide Mobile Robots, Geek+ and Otto Motors.
3D-printed rocket startup Relativity Space has raised a $650 million Series E, bringing its total raised to over $1.2 billion. Relativity’s post-money valuation now stands at $4.2 billion, a source familiar with the matter told TechCrunch.
The round was led by Fidelity Management & Research Company, with participation from new investors with funds and accounts managed by BlackRock, Centricus, Coatue, and Soroban Capital, and participation from existing investors Baillie Gifford, K5 Global, Tiger Global, Tribe Capital, XN, Brad Buss, Mark Cuban, Jared Leto, and Spencer Rascoff.
The funds from the Series E will go toward accelerating the production of Terran R, the company’s heavy-lift, fully reusable two-stage rocket. Terran R joins Terran 1, Relativity’s debut rocket, which will conduct its first orbital flight at the end of 2021.
The company has been pretty tight-lipped about Terran R, but are now releasing further details alongside the funding announcement. As expected, Terran 1 and Terran R differ in pretty significant ways: the former is expendable, the latter reusable; the former is designed for small payloads, the latter for large. Even the Terran R’s payload fairing is reusable, and Relativity has devised a system that makes it easier to recover and recycle as it stays attached to the second stage.
The larger rocket will clock in at 216 feet tall with a maximum payload capacity of 20,000 pounds to low Earth orbit. (For comparison, SpaceX’s Falcon 9 rocket stands at around 230 feet with a maximum payload to LEO of 22,800 pounds.)
Terran R will use seven of its new Aeon R engines on the first stage, each capable of 302,000 pounds of thrust. The same 3D printers that will produce Terran R’s engines and rockets also currently make the nine Aeon 1 engines that power the Terran 1, which means Relativity doesn’t have to drastically reconfigure its production line to build the new launch vehicle.
A single Terran R should take around 60 days to build, Ellis estimated. That’s an incredible pace for a rocket with this kind of payload capacity.
Even though Terran 1 has not seen a launch yet, Relativity shows no signs of slowing down Terran R’s development: Ellis said the company will also launch Terran R from its launch site at Cape Canaveral as early as 2024 and that it signed its first anchor customer, “a well-known blue-chip company,” for the new rocket.
Relativity has printed around 85% of the rocket that will perform the company’s first orbital flight at the end of this year. The Terran 1 that will perform that mission will not be carrying any payload. Terran 1’s second launch is scheduled to take place in June ’22, and will carry cubesats to LEO as part of NASA’s Venture Class Launch Services Demonstration 2 (VCLS Demo 2) contract.
Relativity CEO Tim Ellis in an interview with TechCrunch likened 3D printing to a paradigm shift in manufacturing. “I think really the thing people haven’t gotten about our approach, or 3D printing in general, is it’s actually more like transitioning from gas internal combustion engines to electric, or on-premise service to cloud,” Ellis said. “3D printing is a cool technology but more than that, it’s actually software and data-driven manufacturing and automation technology.”
Because the core of 3D printing is a technology stack, the company can produce algorithmically generated structures with “geometries that couldn’t be possible” with traditional manufacturing, Ellis said. And the design can be easily adjusted to fit market demand.
Ellis, who started the metal 3D printing division at Blue Origin before founding Relativity, said that the strategy from day one was to design and build Terran 1 and a heavy-lift counterpart.
The actual mechanisms involved in 3D printing can technically occur in environments even when gravity is much lower – like the gravity on Mars, which is only about 38% of the gravity on Earth. But more importantly, Ellis said it’s an approach that’s “inevitably required” in an uncertain off-planet environment.
“When we founded Relativity, the inspiration was watching SpaceX land rockets and dock with the space station. They were 13 years old and they were, despite all of that pretty inspiring success, the only company that wanted to make humanity a multi-planetary and go to Mars,” Ellis said. “And I thought that 3D printing tech was inevitable to actually build an industrial base on another planet. No one else had actually even tried to go to Mars or said that was their core mission. And that’s still true today, actually, even five years later, it’s still just us and SpaceX. And I really do hope to inspire dozens to hundreds of companies to go after that mission.”
Xometry, a Maryland-based service that connects companies with manufacturers with excess production capacity around the world, filed an S-1 form with the U.S. Securities and Exchange Commission announcing its intent to become a public company.
As the global supply chain tightened during the pandemic in 2020, a company that helped find excess manufacturing capacity was likely in high demand. CEO and co-founder Randy Altschuler described his company to TechCrunch this way last September upon the announcement of a $75 million Series E investment:
“We’ve created a marketplace using artificial intelligence to power it, and provide an e-commerce experience for buyers of custom manufacturing and for suppliers to deliver that manufacturing,” Altschuler said at the time. Xometry raised nearly $200 million while private, per Crunchbase data.
With Xometry, companies looking to build custom parts now have the ability to do so in a digital way. Rather than working the phones or starting an email chain, they can go into the Xometery marketplace, define parameters for their project and find a qualified manufacturer who can handle the job at the best price.
As of last September, the company had built relationships with 5,000 manufacturers around the world and had 30,000 customers using the platform.
At the time of that funding round, perhaps it wasn’t a coincidence that the company’s lead investor was T. Rowe Price. When an institutional investor is involved in a late-stage round, it’s usually a sign that the company is ready to start thinking about an IPO. Altschuler said it was definitely something the company was considering, and had brought on a CFO, too, another sign that a company is ready to take that next step.
So what do Xometry’s financials look like as it heads to the public markets? We took a look at the S-1 to find out.
Xometry makes money in two ways. The first comes from one part of its marketplace, with the company generating “substantially all of [its] revenue” from charging “buyers on its platform.” The other way that Xometry engenders top-line is seller-related services, including financial work. The company notes that seller-generated revenues were just 5% of its 2020 total, though it does expect that figure to rise.
The events of the past year have only served to accelerate interest in all things robotics and automation. It’s a phenomenon we’ve seen across a broad range of categories, and automotive is certainly no different.
Of course, carmakers are no strangers to the world of robotics. Automation has long played a key role in manufacturing, and more recently, robotics have played another central role in the form of self-driving vehicles. For this panel, however, we’re going to look past those much-discussed categories. Of late, carmakers have been investing heavily to further fuel innovation in the category.
It’s a fascinating space — and one that covers a broad range of cross-sections, from TRI’s (Toyota) Woven City project to Ford’s recent creation of a research facility at U of M to Hyundai’s concept cars and acquisition of Boston Dynamics. At TC Sessions: Mobility on June 9, we will be joined by a trio of experts from these companies for what’s sure to be a lively discussion on the topic.
Max Bajracharya is Vice President of Robotics at Toyota Research Institute. Previously serving as its Director of Robotics, he leads TRI’s work in robotics. He previously served at Alphabet’s X, as part of the Google Robotics team.
Mario Santillo is a Technical Expert at Ford. Previously serving as a Research Engineer for the company, he’s charged with helping lead the company’s efforts at a recently announced $75 million research facility at the University of Michigan, Ann Arbor. The work includes both Ford’s own robotics work, as well as partnerships with startups like Agility.
Ernestine Fu is a director at Hyundai Motor Group. She heads development at the newly announced New Horizons Studio, a group tasked with creating Ultimate Mobility Vehicles (UMVs). She also serves as an adjunct professor at Stanford University, where she received a BS, MS, MBA and PhD.
Get ready to talk robots at TC Sessions: Mobility. Grab your passes right now for $125 and hear from today’s biggest mobility leaders before our prices go up at the door.
Shein‘s quiet rise has reached a crescendo as the fast fashion e-commerce app takes the crown from Amazon as the most downloaded shopping app on iOS and Android in the United States, according to data from app tracking firms App Annie and Sensor Tower.
Its ascent is quiet because the startup, despite reportedly exceeding a $15 billion valuation, maintains an unusually low profile and doesn’t try to make itself known to the media. The app, dubbed the “TikTok for e-commerce” by China-focused internet analyst Matthew Brennan in this thorough piece on the startup, manufactures in China as many apparel retailers do.
The difference is Shein controls its own production chain, from design and prototype to procurement to manufacturing. Each step is highly digitized and integrated with another, which allows the company to churn out hundreds of new products tailored to different regions and user tastes at a daily rate. The strategy is not unlike TikTok matching content creators with users by using algorithms to understand their habits in real-time.
On May 11, Shein became the most installed shopping app on Android in the U.S., and six days later took the top spot on iOS as well.
The origin of Shein, which was previously named “She Inside,” is little understood. On its official website, it describes itself as an “international B2C fast fashion e-commerce platform” founded in 2008. There is no mention of its founder and CEO Chris Xu. In a 2018 corporate blog posted on WeChat, it wrote that it was headquartered in Nanjing, an eastern Chinese city home known for its historical heritages and home to Chinese appliance giant Suning. It also opened offices in other major Chinese cities as well as the U.S., Belgium and the United Arab Emirates.
Shein’s low profile is perhaps expected in times of geopolitical tensions and heightened regulatory scrutiny over China-related tech companies around the world. Shein owns its sales channel and user data, which distinguishes it from the swathe of generic consumer brands relying on Amazon for customer acquisition without meaningful access to user data.
As of May 17, Shein was the top iOS shopping app in 54 countries and ranked top in the category on Android devices across 13 countries.
Shein has not announced who its investors are, but Chinese media reports have listed Capital Nuts, JAFCO Asia, Greenwoods Asset Management, IDG Capital, Sequoia Capital China, Tiger Global, and Xiaomi founder’s Shunwei Capital among its backers.
We’ve reached out to Shein for comments on the story. Sequoia Capital China confirmed it’s an investor in Shein.